CIRCULATING BIOMARKERS FOR ALZHEIMER'S DISEASE AND/OR MILD

COGNITIVE IMPAIRMENT

CROSS-REFERENCE TO RELATED APPLICATION

[1] The present application claims the benefit of priority based on U.S. provisional patent application serial number 61/885,269 filed on October 1, 2013 by Eugenia Wang. The contents of the above-referenced document are incorporated herein by reference in their entirety for all purposes.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT [2] This invention was made with government support under grant No. R44AG044157 awarded by the National Institute on Aging, National Institutes of Health. The U.S. Government has certain rights in the invention.

TECHNICAL FIELD

[3] This application generally relates to the field of biomarkers and, more specifically, to biomarkers for mild cognitive impairment and Alzheimer's disease.

BACKGROUND

[4] Currently, the clinical diagnosis of Alzheimer's disease (AD) and/or mild cognitive impairment (MCI) generally requires an evaluation of medical history and physical examination including neurological, neuropsychological and psychiatric assessment, as well as various biological, radiological and electrophysiological tests, such as for instance measuring brain volume or activity measurements derived from neuroimaging modalities such as magnetic resonance imaging (MRI) or positron emission tomography (PET) of relevant brain regions. However, neuroimaging modalities are expensive, labor-intensive and not universally available. [5] WO 2013/022953 discloses circulating biological markers for determining the likelihood that an individual has Alzheimer's disease (AD) and/or mild cognitive impairment (MCI) as well as other uses thereof in various applications. The biological markers are a circulating microRNA or a protein target thereof, the microRNA in the brain being associated with AD and/or MCI.

SUMMARY

[6] All features of embodiments which are described in this disclosure and are not mutually exclusive can be combined with one another. Elements of one embodiment can be utilized in the other embodiments without further mention.

[7] The present disclosure relates broadly to a method for assisting in determining the presence of Alzheimer's disease (AD) and/or mild cognitive impairment (MCI) in a subject. The method comprises providing a substantially cell free biological fluid sample from said subject, said sample having been obtained without cell lysis. The method further comprises processing the substantially cell free biological fluid sample to determine a circulating microRNA level therein, the circulating microRNA being selected from the group consisting of miR-let7a, miR-let7c, miR-let7d, miR-let7e, miR-let-7f, miR-200c, miR-141, miR-429, and any combinations thereof. The method also comprises processing the circulating microRNA level at least in part based on a reference level to derive information conveying a level of likelihood for assisting in determining the presence of AD and/or MCI.

[8] The present disclosure also relates broadly to a method for assisting in distinguishing between Alzheimer's disease (AD) and mild cognitive impairment (MCI) status in a subject. The method comprises providing a substantially cell free biological fluid sample from the subject, the sample having been obtained without cell lysis. The method further comprises processing the substantially cell free biological fluid sample to determine a circulating microRNA level therein, the microRNA being selected from the group consisting of miR-let7a, miR-let-7e, miR-let-7f, miR-let-7g, miR-200a, miR-141, miR-181a, miR-181b, miR-429, and any combinations thereof. The method also comprises processing the circulating microRNA level to derive information conveying a level of likelihood for assisting in distinguishing between AD and MCI status in the non-NEC subject.

[9] In one embodiment, the above described methods further comprise processing a biological fluid sample from said subject without cell lysis so as to obtain the substantially cell free biological fluid sample. [10] The present disclosure also relates broadly to a graphical user interface for use in distinguishing between Alzheimer's disease (AD) and mild cognitive impairment (MCI) status in a subject, the graphical user interface comprising a plurality of graphical user interface pages arranged in a hierarchical format and displayed on a communication apparatus, a first one of the graphical user interface pages including a first graphical area for entry of circulating microRNA level data relating to a circulating microRNA level in a substantially cell free biological fluid sample from the subject, said substantially cell free biological fluid sample being obtained without cell lysis, and said circulating microRNA being selected from the group consisting of miR-let7a, miR-let-7e, miR-let-7f, miR-let-7g, miR-200a, miR-141, miR-181a, miR-181b, miR-429, and any combinations thereof, and a second activatable area which, when activated, causes transmission to a server of the data entered in the first graphical area, a second one of the graphical user interface pages including a third graphical area indicative of a likelihood of the AD or MCI status, said likelihood having been encoded in data received from the server in response to transmission of the circulating microRNA level data thereto. [11] The present disclosure also relates broadly to a graphical user interface for use in distinguishing between Alzheimer's disease (AD) and mild cognitive impairment (MCI) status in a subject, the graphical user interface comprising a plurality of graphical user interface pages arranged in a hierarchical format and displayed on a communication apparatus, a first subset of the graphical user interface pages including a first graphical area for entry of an identifier of one or more circulating microRNA selected from the group consisting of miR-let7a, miR-let- 7e, miR-let-7f, miR-let-7g, miR-200a, miR-141, miR-181a, miR-181b, miR-429, and any combinations thereof, a second graphical area for entry of circulating microRNA level data relating to a level of said circulating microRNA in a substantially cell free biological fluid sample from the subject, said substantially cell free biological fluid sample being obtained without cell lysis, and a third activatable area which, when activated, causes transmission to a server of the data entered in the first and second graphical areas, an additional one of the graphical user interface pages including a fourth graphical area indicative of a likelihood for use in distinguishing between AD and MCI status, said likelihood having been encoded in data received from the server in response to transmission of the identified circulating microRNA and circulating microRNA level data thereto.

[12] The present disclosure also relates broadly to a database system comprising a plurality of data structures readable by a processor of a computing device, said data structures each comprising a respective first field, a respective second field and a respective third field, the respective first field identifying a circulating microRNA selected from the group consisting of miR-let7a, miR-let-7e, miR-let-7f, miR-let-7g, miR-200a, miR-141, miR-181a, miR-181b, miR-429, and any combinations thereof, the respective second field conveying a level of circulating microRNA, the respective third field conveying a likelihood for distinguishing between AD and MCI status when a substantially cell free biological fluid sample from the subject and obtained without cell lysis contains the circulating microRNA level conveyed in the second field of the circulating microRNA identified in the first field.

[13] The present disclosure also relates broadly to a non-transitory computer-readable medium comprising instructions executable by a processor of a computer server, wherein the instructions, when executed by the processor, cause the processor to recognize data received from a requesting party as data relating to a circulating microRNA level in a substantially cell free biological fluid sample from a subject, said substantially cell free biological fluid sample being obtained without cell lysis, and said circulating microRNA being selected from the group consisting of miR-let7a, miR-let-7e, miR-let-7f, miR-let-7g, miR-200a, miR-141, miR-181a, miR-181b, miR-429, and any combinations thereof, and wherein the instructions further cause the processor to consult a database on a basis of the received data and to receive, from the database and in response to said data, a corresponding likelihood for distinguishing between AD and MCI status, and wherein the instructions further cause the computer server to transmit a tangible signal conveying the likelihood to the requesting party. [14] The present disclosure also relates broadly to a graphical user interface for use in assisting in determining the presence of Alzheimer's disease (AD) and/or mild cognitive impairment (MCI) in a subject, the graphical user interface comprising a plurality of graphical user interface pages arranged in a hierarchical format and displayed on a communication apparatus, a first one of the graphical user interface pages including a first graphical area for entry of circulating microRNA level data relating to a circulating microRNA level in a substantially cell free biological fluid sample from the subject, said substantially cell free biological fluid sample being obtained without cell lysis, and said circulating microRNA being selected from the group consisting of miR-let7a, miR-let7c, miR-let7d, miR-let7e, miR-let-7f, miR-200c, miR-141, miR-429, and any combinations thereof, and a second activatable area which, when activated, causes transmission to a server of the data entered in the first graphical area, a second one of the graphical user interface pages including a third graphical area indicative of a likelihood for assisting in determining the presence of AD and/or MCI, said likelihood having been encoded in data received from the server in response to transmission of the circulating microRNA level data thereto.

[15] The present disclosure also relates broadly to a graphical user interface for use in assisting in determining the presence of Alzheimer's disease (AD) and/or mild cognitive impairment (MCI) in a subject, the graphical user interface comprising a plurality of graphical user interface pages arranged in a hierarchical format and displayed on a communication apparatus, a first subset of the graphical user interface pages including a first graphical area for entry of an identifier of one or more circulating microRNA selected from the group consisting of miR-let7a, miR-let7c, miR-let7d, miR-let7e, miR-let-7f, miR-200c, miR-141, miR-429, and any combinations thereof, a second graphical area for entry of circulating microRNA level data relating to a level of said circulating microRNA in a substantially cell free biological fluid sample from the subject, said substantially cell free biological fluid sample being obtained without cell lysis, and a third activatable area which, when activated, causes transmission to a server of the data entered in the first and second graphical areas, an additional one of the graphical user interface pages including a fourth graphical area indicative of a likelihood for assisting in determining the presence of Alzheimer's disease (AD) and/or mild cognitive impairment (MCI) in a subject, said likelihood having been encoded in data received from the server in response to transmission of the identified circulating microRNA and circulating microRNA level data thereto. [16] The present disclosure also relates broadly to a database system comprising a plurality of data structures readable by a processor of a computing device, said data structures each comprising a respective first field, a respective second field and a respective third field, the respective first field identifying a circulating microRNA selected from the group consisting of miR-let7a, miR-let7c, miR-let7d, miR-let7e, miR-let-7f, miR-200c, miR-141, miR-429, and any combinations thereof, the respective second field conveying a level of circulating microRNA, the respective third field conveying a likelihood for assisting in determining the presence of Alzheimer's disease (AD) and/or mild cognitive impairment (MCI) in a subject when a substantially cell free biological fluid sample from the subject and obtained without cell lysis contains the circulating microRNA level conveyed in the second field of the circulating microRNA identified in the first field.

[17] The present disclosure also relates broadly to a non-transitory computer-readable medium comprising instructions executable by a processor of a computer server, wherein the instructions, when executed by the processor, cause the processor to recognize data received from a requesting party as data relating to a circulating microRNA level in a substantially cell free biological fluid sample from a subject, said substantially cell free biological fluid sample being obtained without cell lysis, and said circulating microRNA being selected from the group consisting of miR-let7a, miR-let7c, miR-let7d, miR-let7e, miR-let-7f, miR-200c, miR-141, miR-429, and any combinations thereof, and wherein the instructions further cause the processor to consult a database on a basis of the received data and to receive, from the database and in response to said data, a corresponding likelihood for assisting in determining the presence of Alzheimer's disease (AD) and/or mild cognitive impairment (MCI) in a subject, and wherein the instructions further cause the computer server to transmit a tangible signal conveying the likelihood to the requesting party.

[18] Other aspects and features of the present disclosure will become apparent to those ordinarily skilled in the art upon review of the following description of specific embodiments in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[19] A detailed description of specific embodiments is provided herein below with reference to the accompanying drawings in which:

[20] Figure 1 : shows a graphical representation which depicts non-limiting results of MMSE scores distribution of the total sample population on the y axis and on the x axis: (A) education, (B) age, and (C) ApoE status.

[21] Figure 2: shows a graphical representation which depicts qPCR results for circulating levels of miR-16 in plasma. The range of Ct values for AD, MCI and NEC subjects and the distribution of the samples are presented in box plot. The p-values were determined using a one-way ANOVA followed by LSD test. The error bars indicate the Standard Deviation (SD). [22] Figure 3 : shows graphical representations which depict qPCR results for circulating level of miR-let-7a in plasma for AD (n = 12), MCI (n = 10), and NEC (n = 1 1) subjects in (A) as a histogram illustrating the average level, and (B) as box plot illustrating the level distribution. The error bars indicate the Standard Deviation (SD). * LSD test. ** Scheffe test.

[23] Figure 4: shows graphical representations which depict qPCR results for circulating level of miR-let-7b in plasma for AD (n = 12), MCI (n = 1 1), and NEC (n = 12) subjects in (A) as a histogram illustrating the average level, and (B) as box plot illustrating the level distribution. The error bars indicate the Standard Deviation (SD). * LSD test. ** Scheffe test.

[24] Figure 5: shows graphical representations which depict qPCR results for circulating level of miR-let-7c in plasma for AD (n = 12), MCI (n = 11), and NEC (n = 12) subjects in (A) as a histogram illustrating the average level, and (B) as box plot illustrating the level distribution. The error bars indicate the Standard Deviation (SD). * LSD test. ** Scheffe test.

[25] Figure 6: shows graphical representations which depict qPCR results for circulating level of miR-let-7d in plasma for AD (n = 12), MCI (n = 10), and NEC (n = 12) subjects in (A) as a histogram illustrating the average level, and (B) as box plot illustrating the level distribution. The error bars indicate the Standard Deviation (SD). * LSD test. ** Scheffe test. This figure also shows the Receiver Operating Characteristic (ROC) curves assessing (C) AD relatively to NEC and (D) MCI relatively to NEC, with area under the curve values shown. The dotted line for each plot represents the area under the curve, with the solid line representing the reference line. [26] Figure 7: shows graphical representations which depict qPCR results for circulating level of miR-let-7e in plasma for AD (n = 11), MCI (n = 11), and NEC (n = 12) subjects in (A) as a histogram illustrating the average level, and (B) as box plot illustrating the level distribution. The error bars indicate the Standard Deviation (SD). * LSD test. ** Scheffe test. This figure also shows the Receiver Operating Characteristic (ROC) curves assessing (C) AD relatively to MCI and (D) AD relatively to NEC, with area under the curve values shown. The dotted line for each plot represents the area under the curve, with the solid line representing the reference line.

[27] Figure 8: shows graphical representations which depict qPCR results for circulating level of miR-let-7f in plasma for AD (n = 12), MCI (n = 8), and NEC (n = 12) subjects in (A) as a histogram illustrating the average level, and (B) as box plot illustrating the level distribution. The error bars indicate the Standard Deviation (SD). * LSD test. ** Scheffe test.

[28] Figure 9: shows graphical representations which depict qPCR results for circulating level of miR-let-7g in plasma for AD (n = 12), MCI (n = 10), and NEC (n = 12) subjects in (A) as a histogram illustrating the average level, and (B) as box plot illustrating the level distribution. The error bars indicate the Standard Deviation (SD). * LSD test. ** Scheffe test. [29] Figure 10: shows graphical representations which depict qPCR results for circulating level of miR-let-7i in plasma for AD (n = 6), MCI (n = 6), and NEC (n = 9) subjects in (A) as a histogram illustrating the average level, and (B) as box plot illustrating the level distribution. The error bars indicate the Standard Deviation (SD). * LSD test. ** Scheffe test. [30] Figure 11 : shows graphical representations which depict qPCR results for circulating level of miR-200a in plasma for AD (n = 12), MCI (n = 1 1), and NEC (n = 1 1) subjects in (A) as a histogram illustrating the average level, and (B) as box plot illustrating the level distribution. The error bars indicate the Standard Deviation (SD). * LSD test. ** Scheffe test.

[31] Figure 12: shows graphical representations which depict qPCR results for circulating level of miR-200c in plasma for AD (n = 12), MCI (n = 1 1), and NEC (n = 12) subjects in (A) as a histogram illustrating the average level, and (B) as box plot illustrating the level distribution. The error bars indicate the Standard Deviation (SD). * LSD test. ** Scheffe test.

[32] Figure 13 : shows graphical representations which depict qPCR results for circulating level of miR-141 in plasma for AD (n = 12), MCI (n = 11), and NEC (n = 1 1) subjects in (A) as a histogram illustrating the average level, and (B) as box plot illustrating the level distribution. The error bars indicate the Standard Deviation (SD). * LSD test. ** Scheffe test. This figure also shows the Receiver Operating Characteristic (ROC) curves assessing (C) AD relatively to MCI and (D) AD relatively to NEC, with area under the curve values shown. The dotted line for each plot represents the area under the curve, with the solid line representing the reference line.

[33] Figure 14: shows graphical representations which depict qPCR results for circulating level of miR-146a in plasma for AD (n = 12), MCI (n = 10), and NEC (n = 10) subjects in (A) as a histogram illustrating the average level, and (B) as box plot illustrating the level distribution. The error bars indicate the Standard Deviation (SD). * LSD test. ** Scheffe test. [34] Figure 15: shows graphical representations which depict qPCR results for circulating level of miR-181a in plasma for AD (n = 9), MCI (n = 10), and NEC (n = 12) subjects in (A) as a histogram illustrating the average level, and (B) as box plot illustrating the level distribution. The error bars indicate the Standard Deviation (SD). * LSD test. ** Scheffe test.

[35] Figure 16: shows graphical representations which depict qPCR results for circulating level of miR- 18 lb in plasma for AD (n = 29), MCI (n = 26), and NEC (n = 22) subjects in (A) as box plot illustrating the level distribution. The error bars indicate the Standard Deviation (SD). * LSD test. ** Scheffe test. This figure also shows the Receiver Operating Characteristic (ROC) curves assessing (B) AD relatively to MCI, (C) AD relatively to NEC, and (D) MCI relatively to NEC, with area under the curve values shown. The dotted line for each plot represents the area under the curve, with the solid line representing the reference line.

[36] Figure 17: shows graphical representations which depict qPCR results for circulating level of miR-181c in plasma for AD (n = 9), MCI (n = 10), and NEC (n = 12) subjects in (A) as a histogram illustrating the average level, and (B) as box plot illustrating the level distribution. The error bars indicate the Standard Deviation (SD). * LSD test. ** Scheffe test. This figure also shows the Receiver Operating Characteristic (ROC) curves assessing (C) AD relatively to NEC and (D) MCI relatively to NEC, with area under the curve values shown. The dotted line for each plot represents the area under the curve, with the solid line representing the reference line.

[37] Figure 18: shows graphical representations which depict qPCR results for circulating level of miR-429 in plasma for AD (n = 11), MCI (n = 11), and NEC (n = 12) subjects in (A) as a histogram illustrating the average level, and (B) as box plot illustrating the level distribution. The error bars indicate the Standard Deviation (SD). * LSD test. ** Scheffe test. This figure also shows the Receiver Operating Characteristic (ROC) curves assessing (C) AD relatively to MCI and (D) AD relatively to NEC, with area under the curve values shown. The dotted line for each plot represents the area under the curve, with the solid line representing the reference line.

[38] Figure 19: High-level functional block diagram of a system including an apparatus implementing a user interface for displaying information derived from a circulating microRNA level in accordance with a specific non-limiting example of implementation of the present disclosure.

[39] Figure 20: Functional block diagram of an apparatus for providing information derived from a circulating microRNA level in accordance with a specific non-limiting example of implementation of the present disclosure.

[40] Figure 21 : Functional block diagram of an apparatus for providing information derived from a circulating microRNA level in accordance with a specific non-limiting example of implementation of the present disclosure. [41] Figure 22: Functional block diagram of a client-server system for providing information derived from a circulating microRNA level in accordance with a specific non-limiting example of implementation of the present disclosure.

[42] Figure 23: High level conceptual block diagram of a program element for implementing a graphical user interface in accordance with a specific example of implementation of the present disclosure.

[43] Figure 24: (A) and (B) Specific example of implementation of a graphical user interface implemented by the system shown in Figure 19 for displaying information derived from a circulating microRNA level in accordance with a specific non-limiting example of implementation of the present disclosure.

[44] Figure 25: Flow diagram of a process for displaying information derived from a circulating microRNA level in accordance with a specific non-limiting example of implementation of the present disclosure.

[45] In the drawings, embodiments are illustrated by way of example. It is to be expressly understood that the description and drawings are only for the purpose of illustrating certain embodiments and are an aid for understanding. The scope of the claims should not be limited by the embodiments set forth in the present disclosure, but should be given the broadest interpretation consistent with the description as a whole.

DETAILED DESCRIPTION OF EMBODIMENTS [46] Specific examples of mild cognitive impairment (MCI) and/or Alzheimer's disease (AD) biomarkers and use thereof will now be described to illustrate the manner in which principles of the present disclosure may be put into practice.

[47] The person of skill will readily realize that the present disclosure may be useful at least in applications for evaluating, monitoring, screening for candidate therapeutic compounds for treating and/or managing and/or preventing AD and/or MCI. The person of skill will also readily realize that the present disclosure may, alternatively or additionally, be useful at least in applications for assisting in distinguishing between AD and MCI and/or for assisting in determining presence of AD and/or MCI. The person of skill will also readily realize that the present disclosure thus affords in some embodiments with substantially accurate, minimally- invasive methods for facilitating patient and family counseling, optimizing stratification of sub- groups for enrolment in clinical drug trials, interpreting treatment outcome measures, and the like. The person of skill will also readily realize that the present disclosure may, alternatively or additionally, be useful in at least affording methods for better addressing concurrent medical conditions which may preclude or confound cognitive and neuropsychological testing. Examples of the latter may include patients with major depression, delirium, suppressed consciousness, or otherwise uncooperative for detailed cognitive testing [Schipper (2007) Alzheimer's and Dementia 3 : 325-332].

[48] The herein described biological fluid sample can be selected from blood, urine, excreta, semen, seminal fluid, seminal plasma, prostatic fluid, pre-ejaculatory fluid (Cowper's fluid), pleural effusion, tears, saliva, sputum, sweat, ascites, amniotic fluid, lymph, vaginal secretions, endometrial secretions, gastrointestinal secretions, bronchial secretions, breast secretions, and the like.

[49] In one non-limiting embodiment, the herein described biological fluid sample can be blood, saliva or urine. [50] In one non-limiting embodiment, the herein described biological fluid sample can be blood.

[51] The herein described step of processing the biological fluid sample to obtain a substantially cell free biological fluid sample without cell lysis transforms the biological fluid sample into another state or product, e.g., a blood sample is transformed into a blood serum or blood plasma sample. Examples of processing steps can be, for instance but without being limited thereto, selected from the group consisting of centrifugation, sedimentation, cell sorting, and the like, in order to substantially remove cells without cell lysis. In implementing this step, the person skilled in the art may select any suitable technique without undue effort.

[52] Release of intracellular microR A in the sample via cell lysis may reduce the sensitivity and/or specificity and/or accuracy of the determining step described herein. It is therefore advantageous to process the biological fluid sample so as to obtain a substantially cell free biological fluid sample without cell lysis thereby minimizing the release of intracellular microRNA in the sample. In the particular case of blood, the herein described processing step transforms the blood into plasma or serum, which are considered for the purposes of the present disclosure as "substantially cell free" biological samples. The expression "circulating microRNA" as used herein thus generally refers to a microRNA found in a biological fluid sample which has been processed to be substantially cell free without cell lysis.

[53] The herein described step of processing the substantially cell free biological fluid sample to determine a given circulating microRNA level therein can be performed using any suitable technique known by the person of skill. For example, a suitable technique can be selected from Northern blot methodology (see, e.g., Valoczi et al. 2004, Nucleic Acids Res. 2004 Dec 14;32(22):el75; Ramkissoon et al. 2006, Mol Cell Probes. 2006 Feb;20(l): l-4), reverse transcription real-time PCR (RT-qPCR) (see, e.g., Schmittgen et al. 2004 Nucleic Acids Res. 2004 Feb 25;32(4):e43; Chen et al. Nucleic Acids Res. 2005 Nov 27;33(20):el79; Raymond et al. 2005 RNA. Nov; l l(l l): 1737-44), confocal laser-induced fluorescence detection (Neely et al. Nat Methods. 2006 Jan;3(l):41-6), oligo-array-based technologies (see, e.g., Babak et al. RNA. 2004 Nov; 10(l l): 1813-9; Nelson et al. Nat Methods. 2004 Nov; l(2): 155-61 ; or Thomson et al. Nat Methods. 2004 Oct; l(l):47-53), and the like. In implementing this step, the person skilled in the art may select any suitable technique without undue effort.

[54] In one non-limiting embodiment, the herein described step of determining the circulating microRNA level is performed using RT-qPCR. The reader is invited to read U.S. Patent Application Publication No. 2013/0040303 incorporated herein by reference for more details on this technique. [55] The herein described step of processing the circulating microRNA level to derive information conveying a level of likelihood can be performed using any suitable statistical method apparent to the person of skill. The information derived as such can convey a level of likelihood for assisting in determining the presence of AD and/or MCI. Additionally or alternatively, the information derived as such can convey a level of likelihood for assisting in distinguishing between AD and MCI.

[56] The herein described microRNA (miRNAs) generally refers to small (e.g., 18-25 nucleotides in length), non-coding RNAs that influence gene regulatory networks by post- transcriptional regulation of specific messenger RNA (mRNA) targets via specific base-pairing interactions. From the teachings of the present disclosure as a whole, the person of skill will readily understand that principles of the present disclosure can be implemented using any microRNA selected from the group consisting of miR-let7a, miR-let7c, miR-let7d, miR-let7e, miR-let-7f, miR-200c, miR-141, miR-429, and any combinations thereof, depending on the desired information and associated given purpose. Alternatively or additionally, the microR A can be selected from the group consisting of miR-let7a, miR-let-7f, miR-200a, miR-141, miR- 181a, miR-181b, miR-429, and any combinations thereof, depending on the desired information and associated given purpose.

[57] From the teachings of the present disclosure as a whole, the person of skill will also readily understand that principles of the present disclosure can be implemented using any combination of the herein described microRNA, depending on the desired information and associated given purpose. [58] For example, one can process the circulating miR-429 level as well as the circulating level of any one of miR-let-7a, miR-let-7e, miR-let-7f, miR-let-7g, miR-let-7i, miR-200a, miR- 181a, miR-181b, miR-141 and combinations thereof, to derive information conveying a level of likelihood for assisting in distinguishing between AD and MCI status with an increased level of confidence relatively to using a single circulating microRNA level. [59] In another example, one can process the circulating miR-141 level as well as the circulating level of any one of miR-let-7a, miR-let-7e, miR-let-7f, miR-let-7g, miR-let-7i, miR- 200a, miR-181a, miR-181b, miR-429, and combinations thereof, to derive information conveying a level of likelihood for assisting in distinguishing between AD and MCI with an increased level of confidence relatively to using a single circulating microRNA level. [60] In another example, one can process a sample to determine the circulating miR-let-7a level as well as the circulating level of any one of miR-let-7e, miR-let-7f, miR-let-7g, miR-let- 7i, miR-200a, miR-181a, miR-181b, miR-141, miR-429, and combinations thereof, to derive information conveying a level of likelihood for assisting in distinguishing between AD and MCI status with an increased level of confidence relatively to using a single circulating microRNA level.

[61] The person of skill will readily realize that similarly, appropriate combinations of the microRNAs described herein can be used to derive information conveying a level of likelihood for assisting in determining presence of AD and/or MCI with an increased level of confidence relatively to using a single circulating microRNA level. [62] From the teachings of the present disclosure, the person of skill will be able to select any combinations of microRNA for a given purpose consistent with the present disclosure as a whole, without undue difficulty.

Examples 1. Subjects

[63] This study was approved by the Institutional Review Board of the Jewish General Hospital (JGH) in Montreal. Written informed consent was obtained from the participants themselves, if competent, or their caregivers. Patients diagnosed with either sporadic Alzheimer's disease (AD) or mild cognitive impairment (MCI) were recruited from the Memory Clinic of the Jewish General Hospital (JGH), a teaching hospital of McGill University. Normal elderly controls (NEC) were recruited from community-based volunteer cohorts through public lectures and newspaper advertisements.

[64] All subjects underwent thorough neurological and cognitive assessment, with a full neuropsychological battery. The normal subjects reported no subjective memory complaints, scored over 25 on the Mini-Mental State Examination (MMSE) [30], and tested within normal limits on the neuropsychological battery. All other subjects were assessed in the Memory Clinic by a neurologist or geriatrician skilled in evaluating memory-impaired elderly individuals. All subjects classified as MCI met the criteria defined by Winblad's working group [27]. All had a history of memory decline in the last 1-4 years reported by the patient, caregiver (usually the spouse), or both, of a sufficient degree to bring them to medical attention.

[65] All subjects were documented to have objective memory impairment on a standardized mental status exam, the JGH Memory Clinic Assessment (unpublished), which contains elements of the CERAD, CDR, and Toronto Behavioural Neurology Assessment batteries appropriate for mild dementia subjects [28]. The diagnosis of MCI was primarily clinical. The MCI subjects did not meet NINCDS-ADRDA criteria for the diagnosis of probable AD, or the DSM-III criteria for dementia, due to the lack of significant impairment in other cognitive domains or impairment in functional abilities [29]. Subjects diagnosed as AD met clinical criteria for dementia and probable AD [29]. The Mini-Mental State Examination (MMSE) [30] was administered to all subjects but not used in diagnosis, only in analysis. 2. Collection of blood and isolation of RNA

[66] Ficoll-Paque Plus™ (GE Healthcare, Piscataway, NJ) was used to isolate the plasma fraction from 30 mL blood samples collected in EDTA Vacutainers™. RNA was extracted from plasma samples using the miRNeasy Serum Plasma Kit (Qiagen, Venlo, Limberg, Netherlands) following the manufacturer's instructions, with the addition of a second extraction step following the initial chloroform extraction, adapted from a previous study [31]. Cel-miR-39 (33 fmol) was added to the samples after denaturation and before adding chloroform, for control of cDNA synthesis step in qPCR assays. The samples were quantified using the NanoVue Plus™ (GE Healthcare, Pscataway, NJ) and integrity was assessed on an Agilent 2100™ bioanalyzer (Agilent Technologies, Waldbronn, Germany).

3. Sample selection

[67] Samples were selected based on a number of factors including age, MMSE and MoCA scores, ApoE allele status, and the visible color of the collected plasma. Samples from participants under the age of 71 were excluded. MMSE scores for AD samples were required to be below 25; for NEC participants, MMSE scores were 25 or higher. Samples from MCI participants scoring in the 19-28 range on the MoCA [26] were included. Plasma samples which were orange in color were rejected, due to the interference of hemolysis in miR-16 levels [32].

4. Real Time qPCR using Taqman microRNA kit [68] MicroRNAs isolated from the aqueous plasma phase were used to generate cDNAs by means of the Taqman ^® MicroRNA Reverse Transcription Kit (Applied Biosystems™, Carlsbad, CA). Purified RNA was used to synthesize first strand cDNA, using specific miRNA stem-loop primers for each microRNA target according to the catalog number from Life Technologies as per the following Table 1 :

TABLE 1

[69] The qPCR reactions were carried out on a Veriti-96 thermal cycler (Applied Biosystems) with Bullseye EvaGreen qPCR 2x Mastermix (ABI ^® , Foster City, California); all qPCR reactions were performed in triplicate to reduce variation. Data normalization was carried out as described below.

5. Statistical Analyses

[70] All statistical analyses were conducted using MS Excel 2010 and SPSS 21.0 statistical software (IBM). For two-way comparisons, a Student's t-test was used to determine significance levels; for multiple group comparisons, a one-way ANOVA followed by Fisher's Least Significant Difference (LSD) test was implemented [33]. Statistical significance was defined as a p-value of p < 0.05 for all analyses.

[71] Normalization was performed by calculating the median of averages of all cel-39 Ct values - the average Ct of each cel-39 sample; this normalization factor is added to the raw Ct value of the target microRNA, and used for calculating normalized relative expression values [34]. For determining relative expression, the AACt method was utilized, where ACt = normalized Ct value of target microRNA - Ct value of endogenous reference microRNA. [72] The miR-16 circulating level was used as the endogenous reference for each assay, to obtain differential expression levels for target microRNAs in AD, MCI and NEC samples. The person of skill will readily realize that other circulating molecule(s) can be used for this purpose and the person of skill can therefore substitute the miR-16 for such other molecule(s) without undue effort.

[73] Receiver-Operating Characteristic (ROC) curves were analyzed by calculating sensitivity, specificity, and accuracy as follows:

Accuracy: (TN + TP)/(TN+TP+FN+FP) = (Number of correct

assessments )/Number of all assessments); Sensitivity: TP/(TP + FN) = (Number of true positive

assessments )/(Number of all positive assessments);

Specificity: TN/(TN + FP) = (Number of true negative

assessments )/(Number of all negative assessments), where TN indicates true negative, TP true positive, FN false negative and FP false positive [35, 36]. The dotted line for each plot represents the area under the curve; the solid line represents the reference line. The ROC curve plots sensitivity against 1 - specificity. The sensitivity can be defined as the ability of a test to identify all individuals who have a specific disease within a given population. The specificity can be defined as the ability of a test to identify all individuals who do not have a specific disease within a given population. The area under the curve yields a value that is representative of the overall accuracy of a test on a scale from 0.5-1.0, with 1.0 indicating a perfectly accurate test with 100% sensitivity and specificity.

6. Results

I - Selection of samples and identification of endogenous control

[74] High quality plasma samples used for this study were selected from three cohorts, which included individuals with sporadic Alzheimer's disease (AD), mild cognitive impairment (MCI), as well as normal elderly controls (NEC). All samples were age-matched, with an average age of 73 for NEC, 76.6 for MCI, and 74.4 for AD. In addition, AD individuals with MMSE scores > 24 were excluded from our study, as were NEC individuals scoring below this cut-off. The MMSE score of the three cohorts compared against education level, age, and the ApoE status for all the cohorts are shown in Figures 1A, IB and 1C.

[75] Overall, the average MMSE score for AD individuals was 14.5, and for NEC individuals was 28.7 (Table 2):

TABLE 2

[76] Because the MMSE score is subjective and not the most accurate exam for determining whether a patient has progressed to the MCI stage, the Montreal Cognitive Assessment exam was also administered to these individuals. Following the initial selection of samples for each group, additional acceptance criteria involving RNA sample quality, evaluated by the RNA integrity profile, were also considered [37]. Samples with no background and with a single peak at the 24 nucleotide base position were considered optimal, and selected for use in the study. We employed two further steps to control the efficiency and accuracy of quantitative PCR (qPCR) analysis for specific target microRNA expressions: 1) use of C. elegans synthetic microRNA to monitor the quality of cDNA synthesized; and 2) the use of a consistently expressed endogenous reference to standardize baseline level calculations. Following published reports [12, 33], we selected miR-16 as the endogenous control for qPCR assays. However, miR-16 expression is elevated in red blood cells, and thus the presence of modest hemolysis (red blood cells lysis) in the sample can lead to unreliable qPCR results, as shown by Muller [38] in qPCR assays of cerebrospinal fluid.

[77] To validate the use of miR-16 as a suitable endogenous control, we evaluated the levels of this miRNA in all (hemo lysis-free) samples used in our study. Figure 2 shows no significant change in the levels of miR-16 in AD, MCI and NEC samples, showing that the circulating miR-16 level is suitable for use as an endogenous control. The consistency of circulating miR- 16 levels in the qPCR assays of all three cohorts (AD, MCI, and NEC) suggests that these assays were stringent, that the cDNA synthesized was of optimal quality, and that there was minimal putative RNA inhibitors present. II - Circulating miR-let-7a levels in plasma samples

[78] Figures 3A and 3B show a graphical representation of qPCR results for circulating miR-let-7a levels in plasma normalized with the circulating level of miR-16 determined for AD samples (n = 12), MCI samples (n = 10) and NEC samples (n = 11). From this representation, one can see that there are significant changes in circulating level of miR-let-7a in MCI samples relatively to NEC samples with a p-value < 0.001 for LSD test and p < 0.001 for Scheffe test. From this representation, one can also see that there are significant changes in circulating level of miR-let-7a in MCI samples relatively to AD samples with a p-value < 0.001 for LSD test and p = 0.002 for Scheffe test. From this representation, one can also see that there are no significant changes in circulating level of miR-let-7a in AD samples relatively to NEC samples with a p-value = 0.514 for LSD test and p = 0.806 for Scheffe test.

[79] These results demonstrate that processing a sample to obtain a biological fluid sample from a subject to obtain a substantially cell free biological fluid sample without cell lysis, and processing the resulting sample to determine the circulating miR-let-7a level therein and processing this circulating level at least in part based on a reference level (e.g. of miR-16) can be used to derive information conveying a level of likelihood for assisting in determining the presence of MCI and, alternatively or additionally, can be used for assisting in distinguishing between AD and MCI in a non-normal elderly (non-NEC) subject, for example a subject already suspected of having AD and/or MCI for instance based on MMSE and/or MoCA scores, and/or based on other cognitive assessment.

III - Circulating levels of miR-let-7b in plasma samples

[80] Figures 4A and 4B show a graphical representation of qPCR results for circulating miR-let-7b levels in plasma normalized with the circulating level of miR-16 determined for AD samples (n = 12), MCI samples (n = 1 1) and NEC samples (n = 12). From this representation, one can see that there are no significant changes in circulating level of miR-let- 7b in MCI samples relatively to NEC samples with a p-value = 0.257 for LSD test and p = 0.520 for Scheffe test. From this representation, one can also see that there are no significant changes in circulating level of miR-let-7b in MCI samples relatively to AD samples with a p- value = 0.329 for LSD test and p = 0.616 for Scheffe test. From this representation, one can also see that there are no significant changes in circulating level of miR-let-7b in AD samples relatively to NEC samples with a p-value = 0.868 for LSD test and p-value = 0.986 for Scheffe test.

[81] These results demonstrate that processing a sample to obtain a biological fluid sample from a subject to obtain a substantially cell free biological fluid sample without cell lysis, and processing the resulting sample to determine the circulating miR-let-7b level therein and processing this circulating level at least in part based on a reference level (e.g. of miR-16) cannot be used to derive information conveying a level of likelihood for assisting in determining the presence of AD and/or MCI, and additionally or alternatively, can be used for assisting in distinguishing between AD and MCI in a non-NEC subject. IV - Circulating levels of miR-let-7c in plasma samples

[82] Figures 5A and 5B show a graphical representation of qPCR results for circulating miR-let-7c levels in plasma normalized with the circulating level of miR- 16 determined for AD samples (n = 12), MCI samples (n = 1 1) and NEC samples (n = 12). From this representation, one can see that there are no significant changes in circulating level of miR-let-7c in MCI samples relatively to NEC samples with a p-value = 0.057 for LSD test and p = 0.160 for Scheffe test. From this representation, one can also see that there are no significant changes in circulating level of miR-let-7c in MCI samples relatively to AD samples with a p-value = 0.783 for LSD test and p = 0.962 for Scheffe test. From this representation, one can also see that there are significant changes in circulating level of miR-let-7c in AD samples relatively to NEC samples with a p-value = 0.028 for LSD test and p-value = 0.087 for Scheffe test.

[83] These results demonstrate that processing a sample to obtain a biological fluid sample from a subject to obtain a substantially cell free biological fluid sample without cell lysis, and processing the resulting sample to determine the circulating miR-let-7c level therein and processing this circulating level at least in part based on a reference level (e.g. of miR-16) can be used to derive information conveying a level of likelihood for assisting in determining the presence of AD.

V - Circulating levels of miR-let-7d in plasma samples

[84] Figures 6A and 6B show a graphical representation of qPCR results for circulating miR-let-7d levels in plasma normalized with the circulating level of miR-16 determined for AD samples (n = 12), MCI samples (n = 1 1) and NEC samples (n = 12). From this representation, one can see that there are significant changes in circulating level of miR-let-7d in MCI samples relatively to NEC samples with a p-value = 0.022 for LSD test and p = 0.068 for Scheffe test. From this representation, one can also see that there are significant changes in circulating level of miR-let-7d in AD samples relatively to NEC samples with a p-value < 0.001 for LSD test and p < 0.001 for Scheffe test. From this representation, one can also see that there are no significant changes in circulating level of miR-let-7d in AD samples relatively to MCI samples.

[85] Figures 6C and 6D show a graphical representation of the the Receiver Operating Characteristic (ROC) curves for circulating miR-let-7d levels in plasma for (C) AD relatively to NEC and (D) MCI relatively to NEC, with area under the curve values shown. The dotted line for each plot represents the area under the curve, with the solid line representing the reference line.

[86] When comparing AD and NEC, the ROC curves have an overall area under the curve value of 1.00, along with a value of 1.00 for each parameter tested. When comparing MCI and NEC, the ROC curves have an overall area under the curve value of 0.98, along with a sensitivity value of 0.90, a specificity value of 0.83, and accuracy value of 0.87 (Table 3).

TABLE 3

[87] These results demonstrate that processing a sample to obtain a biological fluid sample from a subject to obtain a substantially cell free biological fluid sample without cell lysis, and processing the resulting sample to determine the circulating miR-let-7d level therein and processing this circulating level at least in part based on a reference level (e.g. of miR-16) can be used to derive information conveying a level of likelihood for assisting in determining the presence of AD and/or MCI. VI - Circulating levels of miR-let-7e in plasma samples

[88] Figures 7A and 7B show a graphical representation of circulating miR-let-7e levels in plasma normalized with the circulating level of miR-16 determined for AD samples (n = 1 1), MCI samples (n = 1 1) and NEC samples (n = 12). From this representation, one can see that there are no significant changes in circulating level of miR-let-7e in MCI samples relatively to NEC samples with a p-value = 0.079 for LSD test and p = 0.208 for Scheffe test. From this representation, one can also see that there are significant changes in circulating level of miR- let-7e in AD samples relatively to MCI samples with a p-value = 0.037 for LSD test and p = 0.109 for Scheffe test. From this representation, one can also see that there are significant changes in circulating level of miR-let-7e in AD samples relatively to NEC samples with a p- value < 0.001 for LSD test and p-value = 0.001 for Scheffe test.

[89] Figures 7C and 7D show a graphical representation of the Receiver Operating Characteristic (ROC) curves for circulating miR-let-7e levels in plasma for (C) AD relatively to MCI and (D) AD relatively to NEC, with area under the curve values shown. The dotted line for each plot represents the area under the curve, with the solid line representing the reference line.

[90] When comparing AD and MCI, the ROC curves have an overall area under the curve value of 0.74, a sensitivity value of 0.55, a specificity value of 0.82, and an accuracy value of 0.68 (Table 4). When comparing AD and NEC, the ROC curves have a value of 1.00 for each parameter tested (Table 4).

TABLE 4

[91] These results demonstrate that processing a sample to obtain a biological fluid sample from a subject to obtain a substantially cell free biological fluid sample without cell lysis, and processing the resulting sample to determine the circulating miR-let-7e level therein and processing this circulating level at least in part based on a reference level (e.g. of miR-16) can be used to derive information conveying a level of likelihood for assisting in determining the presence of AD and, alternatively or additionally, can be used for assisting in distinguishing between AD and MCI in a non-NEC subject, for example a subject already suspected of having AD and/or MCI for instance based on MMSE and/or MoCA scores, and/or based on other cognitive assessment.

VII - Circulating levels of miR-let-7f in plasma samples

[92] Figures 8A and 8B show a graphical representation of circulating miR-let-7f levels in plasma normalized with the circulating level of miR-16 determined for AD samples (n = 12), MCI samples (n = 8) and NEC samples (n = 12). From this representation, one can see that there are no significant changes in circulating level of miR-let-7f in AD samples relatively to NEC samples with a p-value < 0.855 for LSD test and p = 0.983 for Scheffe test. From this representation, one can also see that there are significant changes in circulating level of miR- let-7f in MCI samples relatively to AD samples with a p-value < 0.001 for LSD test and p < 0.001 for Scheffe test. From this representation, one can also see that there are significant changes in circulating level of miR-let-7f in MCI samples relatively to NEC samples with a p- value < 0.001 for LSD test and p-value = 0.001 for Scheffe test.

[93] These results demonstrate that processing a sample to obtain a biological fluid sample from a subject to obtain a substantially cell free biological fluid sample without cell lysis, and processing the resulting sample to determine the circulating miR-let-7f level therein and processing this circulating level at least in part based on a reference level (e.g. of miR-16) can be used to derive information conveying a level of likelihood for assisting in determining the presence of MCI and, alternatively or additionally, can be used for assisting in distinguishing between AD and MCI in a non-normal elderly (non-NEC) subject, for example a subject already suspected of having AD and/or MCI for instance based on MMSE and/or MoCA scores, and/or based on other cognitive assessment.

VIII - Circulating levels of miR-let-7g in plasma samples

[94] Figures 9A and 9B show a graphical representation of circulating miR-let-7g levels in plasma normalized with the circulating level of miR-16 determined for AD samples (n = 12), MCI samples (n = 10) and NEC samples (n = 12). From this representation, one can see that there are no significant changes in circulating level of miR-let-7g in MCI samples relatively to NEC samples with a p-value = 0.156 for LSD test and p = 0.359 for Scheffe test. From this representation, one can also see that there are no significant changes in circulating level of miR-let-7g in AD samples relatively to NEC samples with a p-value = 0.525 for LSD test and p = 0.814 for Scheffe test. From this representation, one can also see that there are significant changes in circulating level of miR-let-7g in AD samples relatively to MCI samples with a p- value = 0.047 for LSD test and p-value = 0.135 for Scheffe test.

[95] These results demonstrate that processing a sample to obtain a biological fluid sample from a subject to obtain a substantially cell free biological fluid sample without cell lysis, and processing the resulting sample to determine the circulating miR-let-7g level therein and processing this circulating level at least in part based on a reference level (e.g. of miR-16) can be used to derive information conveying a level of likelihood for assisting in distinguishing between AD and MCI in a non-NEC subject, for example a subject already suspected of having AD and/or MCI for instance based on MMSE and/or MoCA scores, and/or based on other cognitive assessment.

IX - Circulating levels of miR-let-7i in plasma samples [96] Figures 10A and 10B show a graphical representation of circulating miR-let-7i levels in plasma normalized with the circulating level of miR-16 determined for AD samples (n = 6), MCI samples (n = 6) and NEC samples (n = 9). From this representation, one can see that there are no significant changes in circulating level of miR-let-7i in AD samples relatively to MCI samples with a p-value = 0.053 for LSD test and p = 0.147 for Scheffe test. From this representation, one can also see that there are significant changes in circulating level of miR- let-7i in MCI samples relatively to NEC samples with a p-value < 0.001 for LSD test and p = 0.001 for Scheffe test. From this representation, one can also see that there are significant changes in circulating level of miR-let-7i in AD samples relatively to NEC samples with a p- value = 0.034 for LSD test and p-value = 0.100 for Scheffe test. [97] These results demonstrate that processing a sample to obtain a biological fluid sample from a subject to obtain a substantially cell free biological fluid sample without cell lysis, and processing the resulting sample to determine the circulating miR-let-7i level therein and processing this circulating level at least in part based on a reference level (e.g. of miR-16) can be used to derive information conveying a level of likelihood for assisting in determining the presence of AD and/or MCI. X - Circulating levels of miR-200a in plasma samples

[98] Figures 1 1A and 1 IB show a graphical representation of circulating miR-200a levels in plasma normalized with the circulating level of miR-16 determined for AD samples (n = 12), MCI samples (n = 1 1) and NEC samples (n = 11). From this representation, one can see that there are no significant changes in circulating level of miR-200a in AD samples relatively to NEC samples with a p-value = 0.397 for LSD test and p = 0.695 for Scheffe test. From this representation, one can also see that there are significant changes in circulating level of miR- 200a in MCI samples relatively to AD samples with a p-value < 0.001 for LSD test and p = 0.002 for Scheffe test. From this representation, one can also see that there are significant changes in circulating level of miR-200a in MCI samples relatively to NEC samples with a p- value = 0.005 for LSD test and p-value = 0.017 for Scheffe test.

[99] These results demonstrate that processing a sample to obtain a biological fluid sample from a subject to obtain a substantially cell free biological fluid sample without cell lysis, and processing the resulting sample to determine the circulating miR-200a level therein and processing this circulating level at least in part based on a reference level (e.g. of miR-16) can be used to derive information conveying a level of likelihood for assisting in determining the presence of MCI and, alternatively or additionally, can be used for assisting in distinguishing between AD and MCI in a non-NEC subject, for example a subject already suspected of having AD and/or MCI for instance based on MMSE and/or MoCA scores, and/or based on other cognitive assessment.

XI - Circulating levels of miR-200c in plasma samples

[100] Figures 12A and 12B show a graphical representation of circulating miR-200c levels in plasma normalized with the circulating level of miR-16 determined for AD samples (n = 12), MCI samples (n = 1 1) and NEC samples (n = 12). From this representation, one can see that there are no significant changes in circulating level of miR-200c in MCI samples relatively to NEC samples with a p-value = 0.441 for LSD test and p = 0.740 for Scheffe test. From this representation, one can also see that there are no significant changes in circulating level of miR-200c in MCI samples relatively to AD samples with a p-value = 0.203 for LSD test and p = 0.439 for Scheffe test. From this representation, one can also see that there are significant changes in circulating level of miR-200c in AD samples relatively to NEC samples with a p- value = 0.041 for LSD test and p-value = 0.121 for Scheffe test. [101] These results demonstrate that processing a sample to obtain a biological fluid sample from a subject to obtain a substantially cell free biological fluid sample without cell lysis, and processing the resulting sample to determine the circulating miR-200c level therein and processing this circulating level at least in part based on a reference level (e.g. of miR-16) can be used to derive information conveying a level of likelihood for assisting in determining the presence of AD.

XII - Circulating levels of miR-141 in plasma samples

[102] Figures 13A and 13B show a graphical representation of circulating miR-141 levels in plasma normalized with the circulating level of miR-16 determined for AD samples (n = 12), MCI samples (n = 1 1) and NEC samples (n = 11). From this representation, one can see that there are no significant changes in circulating level of miR-141 in MCI samples relatively to NEC samples with a p-value = 0.368 for LSD test and p = 0.662 for Scheffe test. From this representation, one can also see that there are significant changes in circulating level of miR- 141 in AD samples relatively to MCI samples with a p-value < 0.001 for LSD test and p = 0.004 for Scheffe test. From this representation, one can also see that there are significant changes in circulating level of miR-141 in AD samples relatively to NEC samples with a p- value = 0.010 for LSD test and p-value = 0.033 for Scheffe test.

[103] Figures 13C and 13D show a graphical representation of the Receiver Operating Characteristic (ROC) curves for circulating miR-141 levels in plasma for (C) AD relatively to MCI and (D) AD relatively to NEC, with area under the curve values shown.

[104] When comparing AD and MCI, the ROC curves have an overall area under the curve (0.86), along with sensitivity (0.83), specificity (0.91) and accuracy (0.87) values (Table 5). When comparing AD and NEC, the ROC curves have an overall AUC value of 0.67 along with sensitivity (0.58), specificity (0.64) and accuracy (0.61) values (Table 5).

TABLE 5

[105] These results demonstrate that processing a sample to obtain a biological fluid sample from a subject to obtain a substantially cell free biological fluid sample without cell lysis, and processing the resulting sample to determine the circulating miR-141 level therein and processing this circulating level at least in part based on a reference level (e.g. of miR-16) can be used to derive information conveying a level of likelihood for assisting in determining the presence of AD and, alternatively or additionally, can be used for assisting in distinguishing between AD and MCI in a non-NEC subject, for example a subject already suspected of having AD and/or MCI for instance based on MMSE and/or MoCA scores, and/or based on other cognitive assessment. XIII - Circulating levels of miR-146a in plasma samples

[106] Figures 14A and 14B show a graphical representation of circulating miR-146a levels in plasma normalized with the circulating level of miR-16 determined for AD samples (n = 12), MCI samples (n = 10) and NEC samples (n = 10). From this representation, one can see that there are no significant changes in circulating level of miR-146a in MCI samples relatively to NEC samples with a p-value = 0.096 for LSD test and p = 0.244 for Scheffe test. From this representation, one can also see that there are no significant changes in circulating level of miR-146a in MCI samples relatively to AD samples with a p-value = 0.115 for LSD test and p = 0.282 for Scheffe test. From this representation, one can also see that there are significant changes in circulating level of miR-146a in AD samples relatively to NEC samples with a p- value = 0.002 for LSD test and p-value = 0.007 for Scheffe test.

[107] These results demonstrate that processing a sample to obtain a biological fluid sample from a subject to obtain a substantially cell free biological fluid sample without cell lysis, and processing the resulting sample to determine the circulating miR-146a level therein and processing this circulating level at least in part based on a reference level (e.g. of miR-16) can be used to derive information conveying a level of likelihood for assisting in determining the presence of AD.

XIV - Circulating levels of miR- 181 a in plasma samples

[108] Figures 15A and 15B show a graphical representation of circulating miR-181a levels in plasma normalized with the circulating level of miR-16 determined for AD samples (n = 9), MCI samples (n = 10) and NEC samples (n = 12). From this representation, one can see that there are no significant changes in circulating level of miR- 181 a in AD samples relatively to NEC samples with a p-value = 0.234 for LSD test and p = 0.486 for Scheffe test. From this representation, one can also see that there are significant changes in circulating level of miR- 181a in MCI samples relatively to AD samples with a p-value = 0.020 for LSD test and p = 0.064 for Scheffe test. From this representation, one can also see that there are significant changes in circulating level of miR- 181 a in MCI samples relatively to NEC samples with a p- value < 0.001 for LSD test and p-value = 0.002 for Scheffe test.

[109] These results demonstrate that processing a sample to obtain a biological fluid sample from a subject to obtain a substantially cell free biological fluid sample without cell lysis, and processing the resulting sample to determine the circulating miR- 18 la level therein and processing this circulating level at least in part based on a reference level (e.g. of miR- 16) can be used to derive information conveying a level of likelihood for assisting in determining the presence of MCI (thus confirming the teachings of U.S. Patent Application Publication No. 2013/0040303) and, alternatively or additionally, can be used for assisting in distinguishing between AD and MCI in a non-NEC subject, for example a subject already suspected of having AD and/or MCI for instance based on MMSE and/or MoCA scores, and/or based on other cognitive assessment.

XV - Circulating levels of miR- 18 lb in plasma samples

[110] Figure 16A shows a graphical representation of circulating miR- 18 lb levels in plasma normalized with the circulating level of miR- 16 determined for AD samples (n = 29), MCI samples (n = 26) and NEC samples (n = 22). From this representation, one can see that there are significant changes in circulating level of miR- 18 lb in AD samples relatively to NEC samples with a p-value < 0.001 for LSD test. From this representation, one can also see that there are significant changes in circulating level of miR- 18 lb in MCI samples relatively to AD samples with a p-value = 0.035 for LSD test. From this representation, one can also see that there are significant changes in circulating level of miR- 18 lb in MCI samples relatively to NEC samples with a p-value = 0.034 for LSD test.

[Ill] Figures 16B, 16C and 16D show a graphical representation of the Receiver Operating Characteristic (ROC) curves for circulating miR- 18 lb levels in plasma for (B) AD relatively to MCI, (C) AD relatively to NEC, and (D) MCI relatively to NEC, with area under the curve values shown. The dotted line for each plot represents the area under the curve, with the solid line representing the reference line. [112] When comparing AD and MCI, the ROC curves have an overall area under the curve value of 0.64, along with a sensitivity value of 0.62, a specificity value of 0.54 and accuracy value of 0.58 (Table 6). When comparing AD and NEC, the ROC curves have an overall AUC value of 0.83 along with a sensitivity value of 0.79, a specificity value of 0.73 and accuracy value of 0.76 (Table 6). When comparing MCI and NEC, the ROC curves have an overall AUC value of 0.64 along with a sensitivity value of 0.62, a specificity value of 0.59 and accuracy value of 0.60 (Table 6).

TABLE 6

[113] These results demonstrate that processing a sample to obtain a biological fluid sample from a subject to obtain a substantially cell free biological fluid sample without cell lysis, and processing the resulting sample to determine the circulating miR-181b level therein and processing this circulating level at least in part based on a reference level (e.g. of miR-16) can be used to derive information conveying a level of likelihood for assisting in determining the presence of AD and/or MCI (thus confirming the teachings of U.S. Patent Application Publication No. 2013/0040303) and, alternatively or additionally, can be used for distinguishing between AD and MCI in a non-NEC subject, for example a subject already suspected of having AD and/or MCI for instance based on MMSE and/or MoCA scores, and/or based on other cognitive assessment. XVI - Circulating levels of miR-18 lc in plasma samples

[114] Figures 17A and 17B show a graphical representation of circulating miR-181c levels in plasma normalized with the circulating level of miR-16 determined for AD samples (n = 9),

MCI samples (n = 10) and NEC samples (n = 12). From this representation, one can see that there are no significant changes in circulating level of miR-181c in AD samples relatively to MCI samples with a p-value = 0.174 for LSD test and p = 0.390 for Scheffe test. From this representation, one can also see that there are significant changes in circulating level of miR- 181c in MCI samples relatively to NEC samples with a p-value = 0.007 for LSD test and p = 0.025 for Scheffe test. From this representation, one can also see that there are significant changes in circulating level of miR-181c in AD samples relatively to NEC samples with a p- value < 0.001 for LSD test and p-value < 0.001 for Scheffe test.

[115] Figures 17C and 17D show a graphical representation of the Receiver Operating Characteristic (ROC) curves for circulating miR-181c levels for (C) AD relatively to NEC and (D) MCI relatively to NEC, with area under the curve values shown. The dotted line for each plot represents the area under the curve, with the solid line representing the reference line. [116] When comparing AD and NEC, the ROC curves have an overall AUC value of 0.72 along with sensitivity value of 0.68, a specificity value of 0.78 and accuracy value of 0.73 (Table 7). When comparing MCI and NEC, the ROC curves have an overall AUC value of 0.90 along with a sensitivity value of 0.85, specificity value of 0.83 and accuracy value of 0.84 (Table 7).

TABLE 7

[117] These results demonstrate that processing a sample to obtain a biological fluid sample from a subject to obtain a substantially cell free biological fluid sample without cell lysis, and processing the resulting sample to determine the circulating miR-181c level therein and processing this circulating level at least in part based on a reference level (e.g. of miR-16) can be used to derive information conveying a level of likelihood for assisting in determining the presence of AD and/or MCI (thus confirming the teachings of U.S. Patent Application Publication No. 2013/0040303). XVII - Circulating levels of miR-429 in plasma samples

[118] Figures 18A and 18B show a graphical representation of circulating miR-429 levels in plasma normalized with the circulating level of miR-16 determined for AD samples (n = 1 1), MCI samples (n = 1 1) and NEC samples (n = 12). From this representation, one can see that there are no significant changes in circulating level of miR-429 in MCI samples relatively to NEC samples with a p-value = 0.874 for LSD test and p = 0.987 for Scheffe test. From this representation, one can also see that there are significant changes in circulating level of miR- 429 in AD samples relatively to MCI samples with a p-value < 0.001 for LSD test and p = 0.001 for Scheffe test. From this representation, one can also see that there are significant changes in circulating level of miR-429 in AD samples relatively to NEC samples with a p- value < 0.001 for LSD test and p-value < 0.001 for Scheffe test.

[119] Figures 18C and 18D show a graphical representation of the Receiver Operating Characteristic (ROC) curves for circulating level of miR-429 for (C) AD relatively to MCI and (D) AD relatively to NEC, with area under the curve values shown. The dotted line for each plot represents the area under the curve, with the solid line representing the reference line.

[120] When comparing AD and MCI, the ROC curves have an overall AUC value of 0.98 along with a sensitivity value of 0.82, a specificity value of 0.91 and accuracy value of 0.86 (Table 8). When comparing AD and NEC, the ROC curves have an overall AUC value of 1.00 along with a sensitivity value of 1.00, a specificity value of 1.00 and accuracy value of 1.00 (Table 8).

TABLE 8

[121] These results demonstrate that processing a sample to obtain a biological fluid sample from a subject to obtain a substantially cell free biological fluid sample without cell lysis, and processing the resulting sample to determine the circulating miR-429 level therein and processing this circulating level at least in part based on a reference level (e.g. of miR-16) can be used to derive information conveying a level of likelihood for assisting in determining the presence of AD.

7. Non-limiting example of a process and apparatus implementing an embodiment of the present disclosure [122] With reference to Figure 19, there is shown a block diagram of a system 150 comprising a microRNA detecting agent sensor 1 10, a user input device 118, an apparatus 100 implementing a user interface for displaying information at least derived from a circulating microRNA level, as described in the present specification, and a display unit 1 14. The person skilled in the art will understand that in the context of the present disclosure, the system 150 may include a number of additional sensors without detracting from the present disclosure.

[123] In accordance with a specific non-limiting implementation, the sensor 1 10 detects at least one detecting reagent in a substantially cell free sample (obtained without cell lysis) to generate a signal indicative of the circulating microRNA level in the sample. The resulting signal conveys information related to the circulating microRNA level in the sample. Such signal, hereinafter referred to as secondary signal, may be a signal generated by qPCR, radiolabeled-oligo northern hybridization, etc. Such sensor may thus be based on spectrophotometer devices (e.g., to detect fluorescence, etc.), scintillation counters (e.g., ionizing radiation, etc.), and the like. Sensors for detecting and measuring secondary signal are well known in the art and any suitable sensor may be used and as such, will not be described further here.

[124] Alternatively, certain embodiments of the system 150 may omit the sensor 110 and instead make use of a user-controlled input 118 for generating the information conveyed by the secondary signal. The user-controlled input allows a user to provide information relating to the secondary signal previously obtained, for example, via a qPCR reaction. The user input device 1 18 will be described later in the specification.

[125] The apparatus 100 is for implementing a graphical user interface module for displaying information at least derived from a circulating microRNA level, as described in the present specification. The information may be displayed in various forms as will become apparent later on in the specification. Optionally, the graphical user interface module implemented by the apparatus 100 selectively causes an alarm event based at least in part on the information at least derived from a circulating microRNA level, as described in the present specification. The apparatus 100 also releases a signal for causing the display unit 1 14 to display the graphical user interface module. Optionally, the apparatus is further adapted for releasing signals to a data output module 130 for causing the latter to convey information at least derived from a circulating microRNA level, as described in the present specification, to a user of the system 150. Specific examples of implementation of the apparatus 100 and of the graphical user interface module will be described later on in the specification.

[126] The user input device 118 is for receiving data from a user of the system 150. The user input device 118 may be used, for example, to enter information associated with the patient and/or to manipulate the information displayed by the user interface implemented by the apparatus 100. Optionally, the user may also create via the input device 1 18 an alarm event based at least in part on the information at least derived from a circulating microRNA level, as described in the present specification. Optionally still, the user input device 118 may be used to enter medical information conveying information associated to administration of a compound to the subject. The medical information may indicate whether (i) an anti-AD and/or MCI compound, and/or (ii) an anti-AD and/or MCI candidate compound, was administered and, optionally, the dosage of the compound that was administered. The user input device 1 18 may include any one or a combination of the following: keyboard, pointing device, touch sensitive surface, keypad or speech recognition unit. Certain embodiments of the system 150 may omit the user input device 1 18 without detracting from the spirit of the invention. [127] The display unit 114 is in communication with the apparatus 100 and receives a signal causing the display unit 1 14 to display a graphical user interface module implemented by apparatus 100. The display unit 1 14 may be in the form of a display screen, a printer or any other suitable device for conveying to the physician, or other health care professional, or other recipient, information associated to the patient. In embodiments where the display unit 114 is in the form of a display screen, it may be part of any suitable type of apparatus including, without being limited to, a desktop/laptop computing apparatus, a personal digital assistant (PDA), a telephone equipped with video display capability, a TV monitor or any other suitable device equipped with a display screen for visually conveying information to a user.

[128] Optionally, the system 150 may further include a data output module 130. The data output module 130 is in communication with the apparatus 100 and is suitable for receiving signals generated by the apparatus 100. In a first specific example of implementation, the data output module 130 includes an audio module for releasing audio signals on the basis of signals received from the apparatus 100. In a second specific example of implementation, the data output module 130 includes a data communication entity suitable for transmitting messages to remote devices causing the latter to convey to a user of the system 150 information at least derived from a circulating microRNA level, as described in the present specification. Examples of remote devices include, without being limited to, PDAs, telephones, pagers and computing terminals.

Apparatus 100

[129] A specific example of implementation of apparatus 100 will now be described with reference to Figure 20. The apparatus 100 includes an input 202 (labelled as first input in Figure 20), a processing unit 206 and an output 208. The first input 202 is for receiving a signal originating from a sensor (e.g., either sensor 1 10) and conveying information related to microRNA detecting agent level in a sample. The processing unit 206 is in communication with the first input 202 and implements a graphical user interface for displaying information at least derived from a circulating microRNA level, as described in the present specification. The output 208 is for releasing a signal for causing the display unit 114 to display the graphical user interface implemented by processing unit 206. Optionally, as shown in Figure 20, the apparatus further includes a second input 204 for receiving data from a user through input device 118. Optionally still, the apparatus 100 further includes a data interface 210 for exchanging signals with a data output module 130 for causing the latter to convey information at least derived from a circulating microRNA level, as described in the present specification, to a user of the system 150.

[130] Optionally, the user may input mini-mental state examination (MMSE) or Folstein test information or information relating to the medical background of the patient (for example, but without being limited thereto, familial antecedence, age, genetic markers, e.g., APP and/or Tau mutations, etc.) through input device 118. The apparatus 100 is adapted for processing this information together with the calculated or user entered circulating microRNA level to determine a likelihood as described in the present specification. For example, where the information that the user inputs through input device 118 includes MMSE information, the processor is adapted for processing this information together with the circulating microRNA level to determine a likelihood, e.g., for assisting in determining presence of AD and/or MCI, or for assisting in distinguishing between AD and MCI in non-NEC subjects, as described in the present specification. [131] The graphical user interface (GUI) module implemented by apparatus 100 will now be described in greater detail.

[132] The graphical user interface module receives the signal conveying information related to the level of detection agent (e.g. microRNA detecting agent) and displays first and second information. The first information conveys a circulating microRNA level which is derived at least in part on the basis of at least a portion of the signal received at input 202. The second information conveys a threshold level for the circulating microRNA.

[133] In one non-limiting embodiment, the threshold level defines boundaries of safe care and may be set in accordance with best practices or in accordance with hospital/care-giver facility policy.

[134] In another non-limiting embodiment, the threshold level defines boundaries of MMSE defined subgroups.

[135] The specific manner in which the information can be displayed to a user of the system 150 by the graphical user interface module may vary from one implementation to the other without detracting from the present disclosure. Specific non-limiting examples of implementation of a graphical user interface module are shown in Figures 24A and 24B.

[136] A first specific example of implementation of the graphical user interface module is shown in Figures 24A and 24B of the drawings. In the specific implementation shown in Figure 24A, the first information includes a first alphanumeric element 602A conveying a circulating microRNA level associated to an individual and the second information includes a second alphanumeric element 604 conveying a threshold level for this microRNA. The first alphanumeric element 602A and the second alphanumeric element 604 are displayed concurrently in viewing window 600A. In this specific implementation, the first alphanumeric element 602A reflects the current circulating microRNA level derived on the basis of a signal received at input 202. In this non-limiting example, the first alphanumeric element 602A conveys an absolute number which may represent an actual value, e.g. a concentration of microRNA, or may represent a factory-defined relative value where a specific concentration value of microRNA corresponds to a value on a scale, for example in a scale from 1 to 10 where 10 is the highest concentration and 1 is the lowest concentration. The person skilled in the art will understand that any other way of representing the circulating microRNA level can be used without departing from the present disclosure. [137] Advantageously, the implementation depicted in Figures 24A and 24B allows the user to also readily appreciate whether the current level is within the boundaries of safe care as conveyed by the second alphanumeric element 604.

[138] Optionally, as depicted in the specific examples shown in Figures 24A and 24B, the graphical user interface module also displays an alphanumeric indicator 610A in the form of a rating for conveying to the user of the graphical user interface module an indication of whether the current level is within the boundaries of safe care. In the example depicted in Figure 24A, the first alphanumeric element 602A conveys a level of 4, which is below the 5 threshold level conveyed by the second alphanumeric element 604. In this case the alphanumeric indicator 610A indicates the message "OK or SAFE" conveying that the current level is within an acceptable range. In Figure 24B, the same example of implementation of the graphical user interface module as that shown in Figure 24A is shown but with a different value of the current level. In this example, in the viewing window 600B, the first alphanumeric element 602B conveys a level of 7, which is above the 5 threshold level conveyed by the second alphanumeric element 604. In this case the alphanumeric indicator 610B indicates the message "ELEVATED RISK" conveying that the current level is not within an acceptable range. In alternative examples of implementation, the alphanumeric indicator 610B may be adapted for displaying graded risk levels such as for example "mildly elevated", "moderately elevated" and "critically elevated" for example or alternatively for displaying cognitive graded classification such as for example "Robust NEC", "Normal NEC", "ARAD" (At-Risk Alzheimer Disease), "Early MCI", "Late MCI", "Mild AD", "Moderate AD", "Severe AD", and the like.

[139] The person of skill will readily realize that although the specific examples depicted in Figures 24A and 24B showed a single alphanumeric element 602A conveying a circulating microRNA level, variants may also include more than one alphanumeric elements 602A conveying a circulating microRNA level for a respective number of different microRNAs, or for a respective number of samples in which the same or different circulating microRNA levels are determined.

Alarm Events

[140] The graphical user interface module is adapted for selectively causing an alarm event based at least in part on a circulating microRNA level and the threshold level of this microRNA. In a specific example of implementation, the alarm event is for alerting the user of the system (e.g. medical staff, patient, insurance company, lab technician, etc.) of a likelihood, as described in the present specification. The alarm event may be triggered in a number of situations and may be optionally at least partly based on user defined parameters and/or factory-based parameters. Non-limiting examples of the manners in which an alarm event may be selectively caused will be described later on in the specification.

[141] An alarm event, in accordance with a specific example of implementation of the present disclosure, may include one or more components for communicating information to a user of the graphical user interface module.

[142] In a first specific illustrative implementation, the alarm event includes displaying a visual indicator to convey to a user of the graphical user interface module the herein described likelihood. The visual indicator may be displayed as part of the graphical user interface module or in a separate display at a remote location. Any suitable type of visual indicator may be used. Examples of visual indicators that may be used include, without being limited to:

1. Variations in color. For example, a color scheme may be established whereby certain colors are associated with varying levels of risk. Portions of the graphical user interface may turn a certain color associated with a high level of risk when, for example, the circulating microRNA level falls outside a limit set by the threshold level. Alternatively, the entire display window or a portion of the window may be displayed may turn a certain color associated with a high level of risk based at least in part on a circulating microRNA level conveyed by the first information and the threshold level of this microRNA. A non-limiting example of a color scheme is green = normal; yellow: intermediate risk level; red: high level of risk, however any suitable color scheme may be used without departing from the present disclosure;

2. Variation in display intensity of the viewing window. For example, flashing or blinking of the viewing window may be used as a visual indicator to draw the attention of the user;

3. Variation in the size or position of the viewing window. For example, the viewing window may be made to appear more prominently on the display unit or at a location that is more likely to draw the attention of the user; 4. Displaying a message prompting/alerting the user. For example, in Figure 24B, an alphanumeric message 61 OB is displayed as "ELEVATED RISK" to convey that the current circulating microRNA level conveyed by the first alphanumeric element 602B has exceeded the threshold level conveyed by the second alphanumeric element 604. In this example, when the current circulating microRNA level falls below the threshold rate of 5, either no message may be displayed or a message conveying that the current circulating microRNA level is within the limit set by the threshold as shown in Figure 24A.

[143] In a second specific illustrative implementation, the alarm event includes causing an audio signal to be issued, alone or in combination with a visual indicator, to draw attention of a user of the graphical user interface module. In this second specific implementation, the processing unit 206 releases a signal at the data interface 210 for causing an audio unit (not shown in the figures) to issue an audio signal. The audio unit may be connected directly to the data interface 210 through either a wire-line link or a wireless link. Alternatively, the audio unit may be in communication with the data interface 210 over a network. Alternatively still, the audio unit may be an integral part of apparatus 100.

[144] In a third specific illustrative implementation, the alarm event includes causing a message signal to be transmitted to a remote device. The remote device may be, for example, a PDA, telephone, pager or a remote computing terminal. Other suitable types of remote devices may also be envisaged in other specific implementations of the present disclosure. In this third specific implementation, the processing unit 206 releases a signal at the data interface 210 for causing a message signal to be transmitted to the remote device. The remote device may be connected directly to the data interface 210 though either a wire-line link or a wireless link. Alternatively, the remote device may be in communication with the data interface 210 over a network.

[145] In a first practical example of interaction, the remote device is a PDA assigned to a lab technician responsible for overseeing a screening process for identifying and selecting candidate compounds for treatment and/or prevention of AD and/or MCI, where a compound is administered to AD and/or MCI transgenic mice models and a resulting modulation of the circulating microRNA level is indicative that the compound may be a candidate compound for treatment and/or prevention of AD and/or MCI. At least based in part on a circulating microRNA level conveyed by the first information and the threshold level of this microRNA, the graphical user interface module selectively sends a message through the data interface 210 and over a network to the PDA of the lab technician to alert that lab technician. The message may include any suitable useful information including, but not limited to, the identification of the candidate compound, the transgenic mouse, the level of microRNA, and the like. Optionally, the message may also enable the PDA of the lab technician to display all or part of the user interface module described in the present specification. For example, the message may enable the PDA of the lab technician to display a user interface of the type depicted in Figures 24A and/or 24B. Alternatively, the message may only indicate that a certain mouse requires closer monitoring of the microRNA. The specific format of the message is not critical to the invention and as such will not be discussed further here.

[146] In second practical example of interaction, the remote device is a remote computing terminal located at a centralised nursing station in a hospital centre. At least based in part on a circulating microRNA level conveyed by the first information and the threshold level thereof, the graphical user interface module selectively causes a message to be sent to the remote computing terminal. Advantageously, by allowing a message to be transmitted to a remote device, the clinical staff need not be located near the patient or in proximity to the patient to be alerted to potentially problematic situations.

A Process

[147] An exemplary embodiment of the process implemented by the graphical user interface will now be described with reference to Figure 25.

[148] With reference to Figure 25, at step 300, the microRNA detecting agent signal is received by the graphical user interface module. At step 302, the graphical user interface module computes a circulating microRNA level on the basis of the microRNA detecting agent signal received at step 300. [149] The specific manner in which the circulating microRNA level is computed will depend on the format of the microRNA detecting agent signal. In a first specific example, the microRNA detecting agent signal is obtained through a qPCR resulting signal conveying the amount of circulating microRNA in the sample.

[150] In a specific implementation, the graphical user interface module computes a circulating microRNA level in the sample. The circulating microRNA level in the sample may be computed in a number of suitable manners as described elsewhere in the present specification. It will be readily apparent to the person skilled in the art, in light of the present description, that other well-known techniques for computing a circulating microRNA level on the basis of a microRNA detecting agent signal may be used without detracting from the present disclosure.

[151] At step 304, the graphical user interface module, implemented by the processing unit 206, displays the first information conveying the level derived at step 302. At step 306, the graphical user interface module, implemented by the processing unit 206, displays concurrently with the first information, the second information conveying a threshold level. Specific non- limiting examples of formats for the first information and second information were described with reference to Figures 24A and 24B of the drawings.

[152] At step 308, the graphical user interface module determines, at least in part on the basis of the computed level and the threshold level, whether an alarm event should be caused.

[153] As will become apparent to the person skilled in the art in light of the present specification, different conditions may bring the graphical user interface module to cause an alarm event.

[154] In a first specific example of implementation, an alarm event is triggered depending on the specific circumstances conveyed by the computed level and the threshold level alone.

[155] In a second specific example of implementation, an alarm event is triggered depending on the specific circumstances conveyed by the computed level and the threshold level in combination with other factors. Such other factors may include, without being limited to, MMSE information and the like.

[156] In either one of the above described specific examples of implementation, the conditions for causing an alarm event may be determined on the basis of a hospital policy or in accordance with best recognised practices in health care.

[157] In a specific example of implementation, step 308 shown in Figure 25 includes multiple sub-steps for determining whether an alarm event should be caused. A non-limiting example of implementation of process step 308 may proceed as follows: the graphical user interface module determines whether the computed level exceeds the limit set by threshold level, (i) if the answer is in the negative and the computed level does not exceed the limit set by threshold level, step 308 determines that no alarm should be caused and the graphical user interface proceeds to step 300;

(ii) if step 308 is answered in the affirmative and the computed level exceeds the limit set by threshold level, the graphical user interface may proceed to test an additional condition, or may proceed to step 310.

[158] The additional condition may include, for example, in the context of a screening process for identifying candidate compounds for treatment and/or prevention of AD, evaluating the level (or dosage) of administered compound if any was changed and use that information in effecting the decision step.

[159] In the specific example of implementation shown in Figure 25, the multiple sub-steps are optional steps which may be included or omitted from specific implementations of the present invention. In addition, it will be appreciated in light of the present specification that other suitable manners of determining whether an alarm event should be caused on the basis of the computed level and the threshold level may be used without detracting from the present disclosure. As such, it should be understood that the example depicted in Figure 25 was presented for the purpose of illustration only.

[160] Returning now to Figure 25, if step 308 determines that an alarm event should be caused, the graphical user interface module proceeds to step 310 where an alarm event is triggered. Examples of alarm events were described previously in the specification. The graphical user interface module can then return to step 300 where another signal may be received and subsequently processed.

[161] If step 308 determines that no alarm event should be caused, the graphical user interface module can return to step 300 where another signal may be received and subsequently processed.

[162] As can be observed, the process illustrated in Figure 25 can be an iterative process whereby steps 300 to 308 (and selectively step 310 when an alarm event is caused to occur) are repeated as time progresses and as new segments of the signal are received by the apparatus. Over time, the graphical user interface module processes the signal to derive a set of biomarker level data elements, where each level data element in the set of level data elements is associated to a segment of the signal. In a non-limiting example, the graphical user interface module may compute a running average of level in the signal to derive the set of level data elements. Such may be useful for example to derive a signal over time progression, which may be indicative, for example in the context of a specific implementation relating to the screening for a candidate compound for treatment of prevention of AD and/or MCI, of the relative mid- to long-term therapeutic efficacy of a candidate compound.

[163] Although the exemplary embodiment of the process implemented by the graphical user interface described with reference to Figure 25 made reference to a single alarm event presented in box 310, it will be appreciated that different types of alarm events may be caused by the graphical user interface. More specifically, different circumstances conveyed by the computed level, medication information, and optionally other conditions may be associated to respective types of alarm events. Therefore, although the specification described causing a given alarm event, it should be understood that different types of alarm events may be caused and that the type of alarm event caused may be conditioned at least in part on the basis of the circumstances conveyed by the computed level, (optionally) medication information, and optionally other conditions.

Specific Physical Implementation

[164] Those skilled in the art should appreciate that in some non-limiting embodiments, all or part of the functionality previously described herein with respect to the apparatus for implementing a user interface for displaying information at least derived from a circulating microRNA level, as described in the present specification, may be implemented as preprogrammed hardware or firmware elements (e.g., application specific integrated circuits (ASICs), electrically erasable programmable read-only memories (EEPROMs), etc.), or other related components. [165] In other non-limiting embodiments, all or part of the functionality previously described herein with respect to the apparatus for implementing a user interface for information at least derived from a circulating microRNA level, as described in the present specification, may be implemented as software consisting of a series of instructions for execution by a computing unit. The series of instructions could be stored on a medium which is fixed, tangible and readable directly by the computing unit, (e.g., removable diskette, CD-ROM, ROM, PROM, EPROM or fixed disk), or the instructions could be stored remotely but transmittable to the computing unit via a modem or other interface device (e.g., a communications adapter) connected to a network over a transmission medium. The transmission medium may be either a tangible medium (e.g., optical or analog communications lines) or a medium implemented using wireless techniques (e.g., microwave, infrared or other transmission schemes). [166] The apparatus implementing a user interface for displaying information at least derived from a circulating microRNA level, as described in the present specification, may be configured as a computing unit of the type depicted in Figure 21, including a processing unit 702 and a memory 704 connected by a communication bus 708. The memory 704 includes data 710 and program instructions 706. In a specific example of implementation, the data 710 stored in memory 704 includes: one or more threshold level, alternatively or additionally one or more MMSE score value, alternatively or additionally one or more correlation between biological / psychological parameters and MCI / AD status, and the like. The processing unit 702 is adapted to process the data 710 and the program instructions 706 in order to implement the functional blocks described in the specification and depicted in the drawings. In a non-limiting implementation, the program instructions 706 implement the functionality of processing unit 206 described above. The processing unit 702 may also comprise a number of interfaces 712, 714, 716 for receiving or sending data elements to external devices. For example, interface 712 is used for receiving data streams indicative of biomarker detecting agent signal and interface 714 is used for receiving control signals and/or information from the user. Interface 716 is for releasing a signal causing a display unit to display the user interface generated by the program instructions 706.

[167] It will be appreciated that the system for implementing a user interface for displaying information at least derived from a circulating microRNA level, as described in the present specification, may also be of a distributed nature where the detecting agent signal is collected at one location by a sensor and transmitted over a network to a server unit implementing the graphical user interface. The server unit may then transmit a signal for causing a display unit to display the graphical user interface. The display unit may be located in the same location as the sensor, in the same location as the server unit or in yet another location. Figure 22 illustrates a network-based client-server system 900 for displaying information at least derived from a circulating microRNA level, as described in the present specification. The client-server system

900 includes a plurality of client systems 912, 914, 916, 918 connected to a server system 910 through network 920. The communication links 950 between the client systems 912, 914, 916,

918 and the server system 910 can be metallic conductors, optical fibers or wireless, without departing from the present invention. The network 920 may be any suitable network including but not limited to a global public network such as the Intranet, a private network and a wireless network. The server 910 may be adapted to process and issue signals to display multiple biomarker levels derived from multiple sensors 926, 928 concurrently using suitable methods known in the computer related arts.

[168] The server system 910 includes a program element 960 for execution by a CPU. Program element 960 implements similar functionality as program instructions 706 and includes the necessary networking functionality to allow the server system 910 to communicate with the client systems 912, 914, 916, 918 over network 920. In a non-limiting implementation, program element 960 includes a number of program element components, each program element components implementing a respective portion of the functionality of the user interface for displaying information at least derived from a circulating microRNA level, as described in the present specification.

[169] Figure 23 shows a non-limiting example of the architecture of program element 960 at the server system. As shown, the program element 960 includes three program element components:

[170] the first program element component 800 is executed on server system 910 and is for receiving a biomarker detecting agent signal (e.g., a signal from a qPCR result) conveying information related to the circulating microRNA level in a sample; [171] the second program element component 802 is executed on server system 910 and is for sending messages to a client system, say client system 914, for causing client system 914 to:

(i) display first information conveying circulating microRNA level, the first information being derived at least in part on the basis of at least a portion of the detecting agent signal; and (ii) display, concurrently with the first information, second information conveying a threshold level of this microRNA;

[172] the third program element component 804 is executed on server system 910 and is for selectively sending messages to client system 914 for causing an alarm event based at least in part on the level of biomarker conveyed by said first information and the threshold level of this biomarker. Alternatively, the third program element component 804 is executed on server system 910 and is for selectively sending messages to a client system distinct from the client system 914 for causing an alarm event at the distinct client system. The messages for causing an alarm event may include alarm program elements for execution at the client system, the alarm program elements implementing the alarm events when executed at the client system. Alternatively, alarm program elements for implementing the alarm events are stored at the client system and the messages for causing an alarm event transmitted from the server system 910 include instructions for causing the alarm program elements at the client system to be executed.

[173] Those skilled in the art should further appreciate that the program instructions 706 and 960 may be written in a number of programming languages for use with many computer architectures or operating systems. For example, some embodiments may be implemented in a procedural programming language (e.g., "C") or an object oriented programming language (e.g., "C++" or "JAVA").

Variants [174] The person of skill will readily understand that the above may be implemented in other variations within the spirit of the present disclosure. For example, this may be implemented as per one of the following:

[175] One implementation relates to a graphical user interface for use in distinguishing between AD and MCI status in a subject, the graphical user interface comprising a plurality of graphical user interface pages arranged in a hierarchical format and displayed on a communication apparatus, a first one of the graphical user interface pages including a first graphical area for entry of circulating microRNA level data relating to a circulating microRNA level in a substantially cell free biological fluid sample from the subject, said substantially cell free biological fluid sample being obtained without cell lysis, and said circulating microRNA being selected from the group consisting of miR-let7a, miR-let-7e, miR-let-7f, miR-let-7g, miR-200a, miR-141, miR-181a, miR-181b, miR-429, and any combinations thereof, and a second activatable area which, when activated, causes transmission to a server of the data entered in the first graphical area, a second one of the graphical user interface pages including a third graphical area indicative of a likelihood of the AD or MCI status, said likelihood having been encoded in data received from the server in response to transmission of the circulating microRNA level data thereto. [176] Another implementation relates to a graphical user interface for use in distinguishing between AD and MCI status in a subject, the graphical user interface comprising a plurality of graphical user interface pages arranged in a hierarchical format and displayed on a communication apparatus, a first subset of the graphical user interface pages including a first graphical area for entry of an identifier of one or more circulating microRNA selected from the group consisting of miR-let7a, miR-let-7e, miR-let-7f, miR-let-7g, miR-200a, miR-141, miR- 181a, miR-181b, miR-429, and any combinations thereof, a second graphical area for entry of circulating microRNA level data relating to a level of said circulating microRNA in a substantially cell free biological fluid sample from the subject, said substantially cell free biological fluid sample being obtained without cell lysis, and a third activatable area which, when activated, causes transmission to a server of the data entered in the first and second graphical areas, an additional one of the graphical user interface pages including a fourth graphical area indicative of a likelihood for use in distinguishing between AD and MCI status, said likelihood having been encoded in data received from the server in response to transmission of the identified circulating microRNA and circulating microRNA level data thereto.

[177] In one embodiment of any of the above implementations, the third graphical area has a color that conveys the indication of the likelihood.

[178] In one embodiment of any of the above implementations, the third graphical area is animated to convey the indication of the likelihood.

[179] Another implementation relates to a database system comprising a plurality of data structures readable by a processor of a computing device, said data structures each comprising a respective first field, a respective second field and a respective third field, the respective first field identifying a circulating microRNA selected from the group consisting of miR-let7a, miR-let-7e, miR-let-7f, miR-let-7g, miR-200a, miR-141, miR-181a, miR-181b, miR-429, and any combinations thereof, the respective second field conveying a level of circulating microRNA, the respective third field conveying a likelihood for distinguishing between AD and MCI status when a substantially cell free biological fluid sample from the subject and obtained without cell lysis contains the circulating microRNA level conveyed in the second field of the circulating microRNA identified in the first field. [180] Another implementation relates to a non-transitory computer-readable medium comprising instructions executable by a processor of a computer server, wherein the instructions, when executed by the processor, cause the processor to recognize data received from a requesting party as data relating to a circulating microRNA level in a substantially cell free biological fluid sample from a subject, said substantially cell free biological fluid sample being obtained without cell lysis, and said circulating microRNA being selected from the group consisting of miR-let7a, miR-let-7e, miR-let-7f, miR-let-7g, miR-200a, miR-141, miR-181a, miR-181b, miR-429, and any combinations thereof, and wherein the instructions further cause the processor to consult a database on a basis of the received data and to receive, from the database and in response to said data, a corresponding likelihood for distinguishing between AD and MCI status, and wherein the instructions further cause the computer server to transmit a tangible signal conveying the likelihood to the requesting party.

[181] In one embodiment of this implementation, the database is accessible over the Internet and wherein to consult the database, the computer server is configured to access the database over the Internet.

[182] In one embodiment of this implementation, the database is accessible to the computer server in a local memory thereof.

[183] In any of the above implementations, the microRNA can be alternatively selected from the group consisting of miR-let7a, miR-let7c, miR-let7d, miR-let7e, miR-let-7f, miR-200c, miR-141, miR-429, and any combinations thereof, the implementation being for assisting in determining the presence of AD and/or MCI in a subject in accordance with the teachings of the present disclosure.

[184] The person of skill will readily realize that other features may be implemented such as (i) authentication of the requestor, which could include verifying the identity of the requestor and/or the IP address or location of where the request is coming from (e.g., hospital); (ii) billing/invoicing of the requestor; (iii) error handling (if the level that it is being asked to match does not exist in the database); and the like.

[185] Note that titles or subtitles may be used throughout the present disclosure for convenience of a reader, but in no way these should limit the scope of the invention. Moreover, certain theories may be proposed and disclosed herein; however, in no way they, whether they are right or wrong, should limit the scope of the invention so long as the invention is practiced according to the present disclosure without regard for any particular theory or scheme of action.

[186] All references cited throughout the specification are hereby incorporated by reference in their entirety for all purposes. [187] As used herein, the terms "individual," "subject," and "patient," generally refer to a human subject, unless indicated otherwise, e.g., in the context of a non-human mammal useful in an in vivo model (e.g., for testing drug toxicity), which generally refers to murines, simians, canines, felines, ungulates and the like (e.g., mice, rats, other rodents, rabbits, dogs, cats, swine, cattle, sheep, horses, primates, etc.). [188] It will be understood by those of skill in the art that throughout the present specification, the term "a" used before a term encompasses embodiments containing one or more to what the term refers. It will also be understood by those of skill in the art that throughout the present specification, the term "comprising", which is synonymous with "including," "containing," or "characterized by," is inclusive or open-ended and does not exclude additional, un-recited elements or method steps.

[189] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. In the case of conflict, the present document, including definitions will control.

[190] As used in the present disclosure, the terms "around", "about" or "approximately" shall generally mean within the error margin generally accepted in the art. Hence, numerical quantities given herein generally include such error margin such that the terms "around", "about" or "approximately" can be inferred if not expressly stated.

[191] Although the present disclosure has described certain non-limiting embodiments, variations and refinements are possible and will become apparent to persons skilled in the art in light of the teachings of the present description as a whole. The invention is better defined by the attached claims. References

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