TECHNICAL FIELD

[0001] The present invention relates broadly, but not exclusively, to methods and systems for evaluating change in liver fibrosis.

BACKGROUND

[0002] Nonalcoholic fatty liver disease (NAFLD) such as nonalcoholic steatohepatitis (NASH) is a common cause of chronic liver disease worldwide, characterized by fibrosis and/or ballooning and/or inflammation and/or steatosis of the liver. Various therapeutics are being developed for NASH and clinical trials are conducted to assess efficacy of such therapeutics. Histological assessment of liver biopsy remains the basis for diagnosing NASH, grading activity and assessing stage of fibrosis. Clinical trials normally use the NASH Clinical Research Network (CRN) system for semi-quantitative histological assessment of disease severity.

[0003] It is noted that current scoring systems only provide a nonlinear, semi- quantitative, or categorical assessment of disease. This may limit precision and granularity of data, particularly in the context of subtle changes with therapy. For example, at the end of a clinical trial, a patient may be categorized as one of “progression”, “no change” or “regression”. However, the categorization may have inaccuracy brought over by limitations of the scoring systems, such that a “progression” or “regression” may be wrongly classified as “no change”.

[0004] A need therefore exists to provide an evaluation method and system that can address at least some of the above problems.

SUMMARY

[0005] An aspect of the present disclosure provides a method for evaluating change in liver fibrosis, comprising: determining a baseline value of a fibrosis parameter, wherein the baseline value is associated with a first liver biopsy sample of a subject at a first time; determining an end-of-treatment value of the fibrosis parameter, wherein the end-of-treatment value is associated with a second liver biopsy sample of the subject at a second time after the first time; applying at least one correction coefficient to at least the end-of-treatment value to obtain at least a corrected end-of-treatment value of the fibrosis parameter; and determining a change in liver fibrosis severity based on at least the corrected end-of-treatment value.

[0006] Determining the change in liver fibrosis severity based on at least the corrected end-of-treatment value may comprise: calculating a first percentage change from baseline based on the baseline value and the end-of-treatment value; calculating a second percentage change from baseline based on the baseline value and the corrected end-of-treatment value; and comparing the first percentage change from baseline with the second percentage change from baseline.

[0007] The at least one correction coefficient may comprise a liver volume correction coefficient, the liver volume correction coefficient is applied to only the end-of- treatment value, and the liver volume correction coefficient

.EncL-o f- treatment volume. -

( - Baseline volume ) ³ -

[0008] The at least one correction coefficient may further comprise a steatosis correction coefficient, the steatosis correction coefficient is applied to both the baseline value and the end-of-treatment value, and the steatosis correction coefficient = - l-%St -e -atosi -s, where %Steatosis comp ^r rises the p rercentag ae of steatosis area in a tissue area of a sample.

[0009] Determining the baseline value may comprise applying a trained machine learning model to image data extracted from the first liver biopsy sample, and determining the end-of-treatment value may comprise applying the trained machine learning model to image data extracted from the second liver biopsy sample. [0010] The image data may be extracted by second harmonic generation (SHG) microscopy and/or two photon excitation fluorescence (TPEF) microscopy.

[0011] The first liver biopsy sample of the subject at the first time may comprise a liver biopsy sample before administration of a therapeutic intervention and the second liver biopsy sample of the subject at the second time may comprise a liver biopsy sample after administration of the therapeutic intervention. The method may further comprise evaluating efficacy of therapeutic intervention based on the determined change in liver fibrosis severity.

[0012] Another aspect of the present disclosure provides a system for evaluating change in liver fibrosis, the system comprising: a processor; and a computer-readable memory coupled to the processor and having instructions stored thereon that are executable by the processor to: determine a baseline value of a fibrosis parameter, wherein the baseline value is associated with a first liver biopsy sample of a subject at a first time; determine an end-of-treatment value of the fibrosis parameter, wherein the end-of-treatment value is associated with a second liver biopsy sample of the subject at a second time after the first time; apply at least one correction coefficient to at least the end-of-treatment value to obtain at least a corrected end-of-treatment value of the fibrosis parameter; and determine a change in liver fibrosis severity based on at least the corrected end-of-treatment value.

[0013] The at least one correction coefficient may comprise a liver volume correction coefficient, the liver volume correction coefficient is applied to only the end-of- treatment value, and the liver volume correction coefficient

.EncL-o - treatment volume. -

( - Baseline volume ) ³ .

[0014] The at least one correction coefficient may further comprise a steatosis correction coefficient, the steatosis correction coefficient is applied to both the baseline value and the end-of-treatment value, and the steatosis correction coefficient = - l-%St -e -atosi -s, where %Steatosis comp ^r rises the p rercentag ae of steatosis area in a tissue area of a sample.

BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Embodiments of the invention will be better understood and readily apparent to one of ordinary skill in the art from the following written description, by way of example only, and in conjunction with the drawings, in which:

[0016] Figure 1 shows images of liver samples at baseline and end-of-treatment with identified zones.

[0017] Figure 2 shows an enlarged image of a liver sample with different zones labelled.

[0018] Figure 3 shows a flow chart illustrating a method for evaluating change in liver fibrosis according to an example embodiment.

[0019] Figure 4 shows bar charts illustrating the effect of liver volume correction on the parameter of Zone 2/Tissue area for a group of patients receiving placebo and a group of patients receiving drug.

[0020] Figure 5 shows bar charts illustrating the effect of liver volume correction on other Zone 2 parameters for a group of patients receiving placebo and a group of patients receiving drug.

[0021] Figure 6 shows example images of fibrosis intersections in Zone 2.

[0022] Figure 7 shows bar charts illustrating the effect of liver volume correction on the number of vessels per unit area for a group of patients receiving placebo and a group of patients receiving drug.

[0023] Figure 8 shows the qFibrosis parameter level analysis with liver volume correction.

[0024] Figure 9 shows the qFibrosis parameter level analysis with both steatosis and liver volume correction.

[0025] Figure 10 shows the changes of the % area for different zones. [0026] Figure 11 shows the changes of the collagen proportionate area (CPA) in different zones.

[0027] Figure 12 shows an example for the impact of various corrections on CPA data.

[0028] Figures 13a-13c show the proportions of “progression”, “no change” and “regression” when the parameters of Table 1 are uncorrected and when they are steatosis-corrected.

[0029] Figure 14 shows a block diagram of a computing device suitable for implementing the method according example embodiments.

[0030] Skilled artisans will appreciate that elements in the figures are illustrated for simplicity and clarity and have not necessarily been depicted to scale. For example, the dimensions of some of the elements in the illustrations, block diagrams or flowcharts may be exaggerated in respect to other elements to help to improve understanding of the present embodiments.

DETAILED DESCRIPTION

[0031] The present disclosure recognises that certain relevant physiological changes in liver, such as liver volume reduction, fat reduction, are not easily visible in images of stained liver samples. As further shown in Figure 1 , even with a 25% volume reduction, pathologists are normally unable to visually detect the changes in Percentage Zone 2 area. This change of Percentage Zone 2 area is only significant in F1/F2 patients, but not F3 patients. Hence, in the case of severe fibrosis in F3 patients, it does not appear clearly in conventional methods of detection.

[0032] The present disclosure provides a method that uses an artificial intelligence (Al)-based tool that can automatically identify and annotate zonal regions, portal tracts, central vein and other morphological features with subsequent quantification of fibrosis parameters (also referred to as qFibrosis) within these zones. Figure 2 provides an example illustration of the various zones and morphological features. Further, at least one correction coefficient is applied to correct the end of treatment (EOT) parameters impacted by the physiological changes. For example, it is recognised that the liver volume may reduce by up to 20% over the duration of a treatment while fibrosis does not disappear in 12-36 weeks; hence, the density of fibrosis is higher due to liver volume reduction. In other words, the EOT fibrosis parameters are normally higher due to liver volume reduction. By applying a liver volume correction coefficient to the EOT fibrosis parameters, the method and system of the present disclosure can detect changes liver fibrosis severity more accurately.

[0033] Embodiments will be described, by way of example only, with reference to the drawings. Like reference numerals and characters in the drawings refer to like elements or equivalents.

[0034] Some portions of the description which follows are explicitly or implicitly presented in terms of algorithms and functional or symbolic representations of operations on data within a computer memory. These algorithmic descriptions and functional or symbolic representations are the means used by those skilled in the data processing arts to convey most effectively the substance of their work to others skilled in the art. An algorithm is here, and generally, conceived to be a self-consistent sequence of steps leading to a desired result. The steps are those requiring physical manipulations of physical quantities, such as electrical, magnetic or optical signals capable of being stored, transferred, combined, compared, and otherwise manipulated.

[0035] Unless specifically stated otherwise, and as apparent from the following, it will be appreciated that throughout the present specification, discussions utilizing terms such as “scanning”, “calculating”, “determining”, “applying”, “extracting”, “generating”, “initializing”, “outputting”, or the like, refer to the action and processes of a computer system, or similar electronic device, that manipulates and transforms data represented as physical quantities within the computer system into other data similarly represented as physical quantities within the computer system or other information storage, transmission or display devices.

[0036] The present specification also discloses apparatus for performing the operations of the methods. Such apparatus may be specially constructed for the required purposes, or may comprise a computer or other device selectively activated or reconfigured by a computer program stored in the computer. The algorithms and displays presented herein are not inherently related to any particular computer or other apparatus. Various machines may be used with programs in accordance with the teachings herein. Alternatively, the construction of more specialized apparatus to perform the required method steps may be appropriate. The structure of a conventional computer will appear from the description below. [0037] In addition, the present specification also implicitly discloses a computer program, in that it would be apparent to the person skilled in the art that the individual steps of the method described herein may be put into effect by computer code. The computer program is not intended to be limited to any particular programming language and implementation thereof. It will be appreciated that a variety of programming languages and coding thereof may be used to implement the teachings of the disclosure contained herein. Moreover, the computer program is not intended to be limited to any particular control flow. There are many other variants of the computer program, which can use different control flows without departing from the scope of the disclosure.

[0038] Furthermore, one or more of the steps of the computer program may be performed in parallel rather than sequentially. Such a computer program may be stored on any computer readable medium. The computer readable medium may include storage devices such as magnetic or optical disks, memory chips, or other storage devices suitable for interfacing with a computer. The computer readable medium may also include a hard-wired medium such as exemplified in the Internet system, or wireless medium such as exemplified in the GSM, GPRS, 3G, 4G or 5G mobile telephone systems, as well as other wireless systems such as Bluetooth, ZigBee, Wi-Fi. The computer program when loaded and executed on such a computer effectively results in an apparatus that implements the steps of the preferred method.

[0039] The present disclosure may also be implemented as hardware elements. More particularly, in the hardware sense, an element is a functional hardware unit designed for use with other components or elements. For example, an element may be implemented using discrete electronic components, or it can form a portion of an entire electronic circuit such as an Application Specific Integrated Circuit (ASIC) or Field Programmable Gate Array (FPGA). Numerous other possibilities exist. Those skilled in the art will appreciate that the system can also be implemented as a combination of hardware and software elements.

[0040] According to various embodiments, a “circuit” may be understood as any kind of a logic implementing entity, which may be special purpose circuitry or a processor executing software stored in a memory, firmware, or any combination thereof. Thus, in an embodiment, a “circuit” may be a hard-wired logic circuit or a programmable logic circuit such as a programmable processor, e.g. a microprocessor (e.g. a Complex Instruction Set Computer (CISC) processor or a Reduced Instruction Set Computer (RISC) processor). A “circuit” may also be a processor executing software, e.g. any kind of computer program, e.g. a computer program using a virtual machine code such as e.g. Java. Any other kind of implementation of the respective functions which may be described in more detail herein may also be understood as a “circuit” in accordance with an alternative embodiment.

[0041] Figure 3 shows a flow chart 300 illustrating a method for evaluating change in liver fibrosis according to an example embodiment. At step 302, a baseline value of a fibrosis parameter is determined. The baseline value is associated with a first liver biopsy sample of a subject at a first time (e.g. before receiving a therapeutic intervention). At step 304, an end-of-treatment value of the fibrosis parameter is determined. The end-of- treatment value is associated with a second liver biopsy sample of the subject at a second time after the first time (e.g. after receiving the therapeutic intervention for a predetermined period). At step 306, at least one correction coefficient is applied to at least the end-of-treatment value to obtain at least a corrected end-of-treatment value of the fibrosis parameter. At step 308, a change in liver fibrosis severity is determined based on at least the corrected end-of-treatment value.

[0042] Steps 302 and 304 of the method in Figure 3 may be carried out using the quantitative approach described in WO 2022/191781 , the contents of which are hereby incorporated by reference. Briefly, the approach involves creating a trained machine learning model for assessing a selected histopathological feature (e.g. fibrosis) using labelled samples and applying the trained machine learning model to assess actual samples. In other words, determining the baseline value involves applying the trained machine learning model to image data extracted from the first liver biopsy sample, and determining the end-of-treatment value involves applying the trained machine learning model to image data extracted from the second liver biopsy sample. In one implementation, the image data is extracted by second harmonic generation (SHG) microscopy and/or two photon excitation fluorescence (TPEF) microscopy, for example, a Genesis system (Histoindex Pte. Ltd, Singapore), in which SHG microscopy is used to visualize collagen, and the other cell structures are visualized using TPEF microscopy. As a result, fibrosis is estimated as a continuous variable (a qFibrosis value) using SHG imaging of unstained tissue sections. SHG imaging can provide visual mapping of collagen burden and distribution and permit the measurement of quantifiable collagen fibrillar properties. [0043] In example embodiments, the at least one correction coefficient includes a liver volume correction coefficient, which is applied only to the end-of-treatment value of the fibrosis parameter, and which can be calculated according to formula (1):

Liver volume correction coefficient = (1 )

[0044] For example, the baseline volume and end-of-treatment volume of the liver of a patient/subject can be determined using magnetic resonance imaging (MRI) scans or other means, as would be appreciated by a person skilled in the art. Once the volumes are obtained, the liver volume correction coefficient can be calculated according to formula (1 ) and then applied to the end-of-treatment value of the fibrosis parameter to obtain the corrected end-of-treatment value of the fibrosis parameter in step 306 as described above. Since the liver volume correction coefficient is less than 1 , the correction has the effect of reducing the end-of-treatment value of the fibrosis parameter which would otherwise be higher due to higher fibrosis density as discussed above.

[0045] Alternatively or in addition, the at least one correction coefficient includes a steatosis correction coefficient, which is applied to both the baseline value and the end-of-treatment value of the fibrosis parameter, and which can be calculated according to formula (2):

Steatosis correction coefficient = - l—%St -e -atosi -s ( '2) ' where %Steatosis is the percentage of steatosis area in a tissue area of a sample. In other words, the steatosis correction coefficient to be applied to the baseline value is calculated using the %Steatosis of a baseline sample, while the steatosis correction coefficient to be applied to the end-of-treatment value is calculated using the %Steatosis of an end-of-treatment sample.

[0046] The steatosis correction coefficient can be multiplied with the uncorrected baseline or end-of-treatment parameters, hence:

Uncorrected parameters Steatosis-corrected parameters = -

1 — %Steatosis

[0047] For example, the %Steatosis value can be determined by analysing biopsy samples. An example approach can be found in WO 2022/101853 under the section “Steatosis Measurement and Analysis”, the contents of which are hereby incorporated by cross-reference. The steatosis correction coefficient effectively removes the steatosis area with fat content to correct the effect of fat reduction, which can be up to 50%, as a result of the treatment by the therapeutic intervention.

[0048] In some embodiments, the corrected end-of-treatment value of the fibrosis parameter can be directly compared with the uncorrected end-of-treatment value to determine whether there has been a change in liver fibrosis severity. In other embodiments, determining the change in liver fibrosis severity based on at least the corrected end-of-treatment value includes calculating a first percentage change from baseline (%CFB) based on the baseline value and the end-of-treatment value, calculating a second %CFB based on the baseline value and the corrected end-of- treatment value, and comparing the first %CFB with the second %CFB.

[0049] Appropriate ranges or thresholds can be set for the difference between the two values (EOT - Baseline) that correspond to “progression”, “no change” and “regression” classifications, respectively. In an implementation, the standard error of means (SEM) of the parameter, which may be specific to each patient or training cohort, is used to set the ranges or thresholds according to Table 1 below:

Table 1

[0050] Accordingly, when the first liver biopsy sample of the subject at the first time is a liver biopsy sample before administration of a therapeutic intervention and the second liver biopsy sample of the subject at the second time is a liver biopsy sample after administration of the therapeutic intervention, the method according to the present disclosure may additionally include evaluating efficacy of therapeutic intervention based on the determined change in liver fibrosis severity, and appropriate recommendations can be made. For example, the subject may be ascertained to be one of “progression”, “no change” or “regression” with greater confidence. Further, if the subject is initially categorized as “no change” using other scoring systems, but is identified to be one of “progression” based on the present method, the subject may be moved to a new treatment regime.

EXAMPLES

[0051] Figure 4 shows bar charts illustrating the effect of liver volume correction on the parameter of Zone 2/Tissue area for a group of patients receiving placebo and a group of patients receiving drug. As shown in Figure 4, when liver volume is uncorrected for, the percentage of Zone 2 fibrosis changes are indistinguishable between the Placebo group versus Drug group. After correcting for liver volume reduction, fibrosis changes occur mainly in Zone 2 in the Drug group. It can also be concluded that the liver volume impact on % Zone 2 area for F1 , F2 vs F3 baseline biopsies are not the same. More specifically, the impact of liver volume is most significant for patients with F1 or F2 fibrosis stages at baseline.

[0052] Figure 5 shows bar charts illustrating the effect of liver volume correction on other Zone 2 parameters for a group of patients receiving placebo and a group of patients receiving drug. It can be observed that the changes of perisinusoidal (PS) fibrosis are significant after liver volume correction. Perisinusoidal (PS) fibrosis in zone 2 is reduced further in the Drug group. At the parameter level, a decrease can be observed in the number of collagen fiber intersections in Drug-treated patients vs Placebo-treated patients, which strongly suggests fibrosis regression due to treatment. In some implementations, the median %CFBs at baseline and end-of-treatment are calculated. The median %CFB of individual parameter can indicate the effect for patients with placebo or drug. For example, it is expected that the PS fibrosis area in Zone 2 for patients with drug would reduce more than placebo after treatment. This trend can be found for the parameter with liver volume correction.

[0053] Figure 6 shows example images of fibrosis intersections in Zone 2. At baseline, the number of intersections in Zone 2 is 482, while at the end of treatment, the number of intersections in Zone 2 significantly decreases to 121 .

[0054] Figure 7 shows bar charts illustrating the effect of liver volume correction on the number of vessels per unit area for a group of patients receiving placebo and a group of patients receiving drug. It can be seen that after correcting for liver volume reduction, the number of portal tract (PT) and central vein (CV) decrease for Drug-treated versus Placebo-treated patients. Appearance of “fused” vessels making the numbers of vessels appear to decrease is an artifact of the volume reduction and biopsy sectioning.

[0055] Figure 8 shows the qFibrosis parameter level analysis with liver volume correction. With liver volume correction, 31/184 regression parameters demonstrate significant changes with Drug treatment (p<0.05).

[0056] Figure 9 further shows the qFibrosis parameter level analysis with both steatosis and liver volume correction. When liver volume and steatosis correction coefficients are combined, 101/184 regression parameters are significantly reduced with Drug treatment (p<0.05).

[0057] Figure 10 shows the changes of the % area for different zones. It can be seen that, after correcting for liver volume reduction, the areas of all zones show significant decrease in the Drug-treated group.

[0058] Figure 11 shows the changes of the collagen proportionate area (CPA) in different zones. It can be seen that, after correcting for liver volume reduction, the collagen proportionate areas of all zones show decrease in the Drug-treated group, whereas the Placebo-treated group has a mix of increase, decrease and no change.

[0059] Figure 12 shows an example for the impact of various corrections on CPA data. It will be appreciated that the volume correction coefficient in Figure 12 is different from that in formula (1) above.

[0060] Table 2 below provides a list of 15 example fibrosis parameters that are used to establish the qFibrosis model, together with their respective definitions. Figures 13a-13c show the proportions of “progression”, “no change” and “regression” when these parameters are uncorrected and when they are steatosis-corrected in a sample size of 768. It can be seen from Figures 13a-13c that the steatosis correction has the effect of increasing the proportion of samples that are categorised as “regression”, while decreasing the proportion of samples that are categorised as “progression”.

Table 2 - Fibrosis Parameters and Definitions

[0061] As described, the Al-based algorithm in example embodiments can identify and annotate zonal regions, portal tracts, central veins and other morphological features with subsequent quantification of qFibrosis parameters within these zones, in order to assess and correct for the liver volume reduction and/or fat reduction on fibrosis changes. The liver volume corrected data and steatosis corrected data obtained from the method according to example embodiments clearly shows the impact of drug treatment on fibrosis regression which cannot be detected by NASH CRN system.

[0062] Figure 14 depicts an exemplary computing device 1400, hereinafter interchangeably referred to as a computer system 1400, where one or more such computing devices 1400 may be used for implementing the method as described above. The following description of the computing device 1400 is provided by way of example only and is not intended to be limiting.

[0063] As shown in Figure 14, the example computing device 1400 includes a processor 1404 for executing software routines. Although a single processor is shown for the sake of clarity, the computing device 1400 may also include a multi-processor system. The processor 1404 is connected to a communication infrastructure 1406 for communication with other components of the computing device 1400. The communication infrastructure 1406 may include, for example, a communications bus, cross-bar, or network.

[0064] The computing device 1400 further includes a main memory 1408, such as a random access memory (RAM), and a secondary memory 1410. The secondary memory 1410 may include, for example, a hard disk drive 1412 and/or a removable storage drive 1414, which may include a floppy disk drive, a magnetic tape drive, an optical disk drive, or the like. The removable storage drive 1414 reads from and/or writes to a removable storage unit 1418 in a well-known manner. The removable storage unit 1418 may include a floppy disk, magnetic tape, optical disk, or the like, which is read by and written to by removable storage drive 1414. As will be appreciated by persons skilled in the relevant art(s), the removable storage unit 1418 includes a computer readable storage medium having stored therein computer executable program code instructions and/or data.

[0065] In an alternative implementation, the secondary memory 1410 may additionally or alternatively include other similar means for allowing computer programs or other instructions to be loaded into the computing device 1400. Such means can include, for example, a removable storage unit 1422 and an interface 1420. Examples of a removable storage unit 1422 and interface 1420 include a program cartridge and cartridge interface (such as that found in video game console devices), a removable memory chip (such as an EPROM or PROM) and associated socket, and other removable storage units 1422 and interfaces 1420 which allow software and data to be transferred from the removable storage unit 1422 to the computer system 1400.

[0066] The computing device 1400 also includes at least one communication interface 1424. The communication interface 1424 allows software and data to be transferred between computing device 1400 and external devices via a communication path 1426. In various embodiments of the disclosure, the communication interface 1424 permits data to be transferred between the computing device 1400 and a data communication network, such as a public data or private data communication network. The communication interface 1424 may be used to exchange data between different computing devices 1400 which such computing devices 1400 form part an interconnected computer network. Examples of a communication interface 1424 can include a modem, a network interface (such as an Ethernet card), a communication port, an antenna with associated circuitry and the like. The communication interface 1424 may be wired or may be wireless. Software and data transferred via the communication interface 1424 are in the form of signals which can be electronic, electromagnetic, optical or other signals capable of being received by communication interface 1424. These signals are provided to the communication interface via the communication path 1426.

[0067] As shown in Figure 14, the computing device 1400 further includes a display interface 1402 which performs operations for rendering images to an associated display 1430 and an audio interface 1432 for performing operations for playing audio content via associated speaker(s) 1434.

[0068] As used herein, the term "computer program product" may refer, in part, to removable storage unit 1418, removable storage unit 1422, a hard disk installed in hard disk drive 1412, or a carrier wave carrying software over communication path 1426 (wireless link or cable) to communication interface 1424. Computer readable storage media refers to any non-transitory tangible storage medium that provides recorded instructions and/or data to the computing device 1400 for execution and/or processing. Examples of such storage media include floppy disks, magnetic tape, CD-ROM, DVD, Blu-ray™ Disc, a hard disk drive, a ROM or integrated circuit, USB memory, a magnetooptical disk, or a computer readable card such as a PCMCIA card and the like, whether or not such devices are internal or external of the computing device 1400. Examples of transitory or non-tangible computer readable transmission media that may also participate in the provision of software, application programs, instructions and/or data to the computing device 1400 include radio or infra-red transmission channels as well as a network connection to another computer or networked device, and the Internet or Intranets including e-mail transmissions and information recorded on Websites and the like.

[0069] The computer programs (also called computer program code) are stored in main memory 1408 and/or secondary memory 1410. Computer programs can also be received via the communication interface 1424. Such computer programs, when executed, enable the computing device 1400 to perform one or more features of embodiments discussed herein. In various embodiments, the computer programs, when executed, enable the processor 1404 to perform features of the above-described embodiments. Accordingly, such computer programs represent controllers of the computer system 1400.

[0070] Software may be stored in a computer program product and loaded into the computing device 1400 using the removable storage drive 1414, the hard disk drive 1412, or the interface 1420. Alternatively, the computer program product may be downloaded to the computer system 1400 over the communications path 1426. The software, when executed by the processor 1404, causes the computing device 1400 to perform functions of embodiments described herein.

[0071] It is to be understood that the embodiment of Figure 4 is presented merely by way of example. Therefore, in some embodiments one or more features of the computing device 1400 may be omitted. Also, in some embodiments, one or more features of the computing device 1400 may be combined together. Additionally, in some embodiments, one or more features of the computing device 1400 may be split into one or more component parts.

[0072] It will be appreciated that the elements illustrated in Figure 4 function to provide means for performing the various functions and operations of the servers as described in the above embodiments.

[0073] In an implementation, a server may be generally described as a physical device comprising at least one processor and at least one memory including computer program code. The at least one memory and the computer program code are configured to, with the at least one processor, cause the physical device to perform the requisite operations.

[0074] It will be appreciated by a person skilled in the art that numerous variations and/or modifications may be made to the present invention as shown in the specific embodiments without departing from the scope of the invention as broadly described. The present embodiments are, therefore, to be considered in all respects to be illustrative and not restrictive.