Liver and cardiometabolic markers and conditions in a cross-sectional study of three Australian communities living with environmental per- and polyfluoroalkyl substances contamination.

BACKGROUND: Per- and polyfluoroalkyl substances (PFAS) have been associated with higher cholesterol and liver function markers in some studies, but the evidence for specific cardiometabolic conditions has been inconclusive. OBJECTIVES: We quantified the associations of single and combined PFAS with cardiometabolic markers and conditions in a cross-sectional study of three Australian communities with PFAS-contaminated water from the historical use of aqueous film-forming foam in firefighting activities, and three comparison communities. METHODS: Participants gave blood samples for measurement of nine PFAS, four lipids, six liver function markers, and completed a survey on sociodemographic characteristics and eight cardiometabolic conditions. We estimated differences in mean biomarker concentrations per doubling in single PFAS concentrations (linear regression) and per interquartile range increase in the PFAS mixture (Bayesian kernel machine regression). We estimated prevalence ratios of biomarker concentrations outside reference limits and self-reported cardiometabolic conditions (Poisson regression). RESULTS: We recruited 881 adults in exposed communities and 801 in comparison communities. We observed higher mean total cholesterol with higher single and mixture PFAS concentrations in blood serum (e.g., 0.18 mmol/L, 95% credible interval -0.06 to 0.42, higher total cholesterol concentrations with an interquartile range increase in all PFAS concentrations in Williamtown, New South Wales), with varying certainty across communities and PFAS. There was less consistency in direction of associations for liver function markers. Serum perfluorooctanoic acid (PFOA) concentrations were positively associated with the prevalence of self-reported hypercholesterolemia in one of three communities, but PFAS concentrations were not associated with self-reported type II diabetes, liver disease, or cardiovascular disease. DISCUSSION: Our study is one of few that has simultaneously quantified the associations of blood PFAS concentrations with multiple biomarkers and cardiometabolic conditions in multiple communities. Our findings for total cholesterol were consistent with previous studies; however, substantial uncertainty in our estimates and the cross-sectional design limit causal inference.

Could the kynurenine pathway be the key missing piece of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) complex puzzle?

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a complex and debilitating disease with a substantial social and economic impact on individuals and their community. Despite its importance and deteriorating impact, progresses in diagnosis and treatment of ME/CFS is limited. This is due to the unclear pathophysiology of the disease and consequently lack of prognostic biomarkers. To investigate pathophysiology of ME/CFS, several potential pathologic hallmarks have been investigated; however, these studies have failed to report a consistent result. These failures in introducing the underlying reason for ME/CFS have stimulated considering other possible contributing mechanisms such as tryptophan (TRP) metabolism and in particular kynurenine pathway (KP). KP plays a central role in cellular energy production through the production of nicotinamide adenine dinucleotide (NADH). In addition, this pathway has been shown to mediate immune response and neuroinflammation through its metabolites. This review, we will discuss the pathology and management of ME/CFS and provide evidence pertaining KP abnormalities and symptoms that are classic characteristics of ME/CFS. Targeting the KP regulation may provide innovative approaches to the management of ME/CFS.

Hepatitis B virus infection in Nigeria: a systematic review and meta-analysis of data published between 2010 and 2019.

BACKGROUND: Hepatitis B virus (HBV) is an infectious disease of global significance, causing a significant health burden in Africa due to complications associated with infection, such as cirrhosis and liver cancer. In Nigeria, which is considered a high prevalence country, estimates of HBV cases are inconsistent, and therefore additional clarity is required to manage HBV-associated public health challenges. METHODS: A systematic review of the literature (via PubMed, Advanced Google Scholar, African Index Medicus) was conducted to retrieve primary studies published between 1 January 2010 and 31 December 2019, with a random-effects model based on proportions used to estimate the population-based prevalence of HBV in the Nigerian population. RESULTS: The final analyses included 47 studies with 21,702 participants that revealed a pooled prevalence of 9.5%. A prevalence estimate above 8% in a population is classified as high. Sub-group analyses revealed the highest HBV prevalence in rural settings (10.7%). The North West region had the highest prevalence (12.1%) among Nigeria's six geopolitical zones/regions. The estimate of total variation between studies indicated substantial heterogeneity. These variations could be explained by setting and geographical region. The statistical test for Egger's regression showed no evidence of publication bias (p = 0.879). CONCLUSIONS: We present an up-to-date review on the prevalence of HBV in Nigeria, which will provide critical data to optimise and assess the impact of current prevention and control strategies, including disease surveillance and diagnoses, vaccination policies and management for those infected.

An Isolated Complex V Inefficiency and Dysregulated Mitochondrial Function in Immortalized Lymphocytes from ME/CFS Patients.

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is an enigmatic condition characterized by exacerbation of symptoms after exertion (post-exertional malaise or "PEM"), and by fatigue whose severity and associated requirement for rest are excessive and disproportionate to the fatigue-inducing activity. There is no definitive molecular marker or known underlying pathological mechanism for the condition. Increasing evidence for aberrant energy metabolism suggests a role for mitochondrial dysfunction in ME/CFS. Our objective was therefore to measure mitochondrial function and cellular stress sensing in actively metabolizing patient blood cells. We immortalized lymphoblasts isolated from 51 ME/CFS patients diagnosed according to the Canadian Consensus Criteria and an age- and gender-matched control group. Parameters of mitochondrial function and energy stress sensing were assessed by Seahorse extracellular flux analysis, proteomics, and an array of additional biochemical assays. As a proportion of the basal oxygen consumption rate (OCR), the rate of ATP synthesis by Complex V was significantly reduced in ME/CFS lymphoblasts, while significant elevations were observed in Complex I OCR, maximum OCR, spare respiratory capacity, nonmitochondrial OCR and "proton leak" as a proportion of the basal OCR. This was accompanied by a reduction of mitochondrial membrane potential, chronically hyperactivated TOR Complex I stress signaling and upregulated expression of mitochondrial respiratory complexes, fatty acid transporters, and enzymes of the ß-oxidation and TCA cycles. By contrast, mitochondrial mass and genome copy number, as well as glycolytic rates and steady state ATP levels were unchanged. Our results suggest a model in which ME/CFS lymphoblasts have a Complex V defect accompanied by compensatory upregulation of their respiratory capacity that includes the mitochondrial respiratory complexes, membrane transporters and enzymes involved in fatty acid ß-oxidation. This homeostatically returns ATP synthesis and steady state levels to "normal" in the resting cells, but may leave them unable to adequately respond to acute increases in energy demand as the relevant homeostatic pathways are already activated.

Weighting of orthostatic intolerance time measurements with standing difficulty score stratifies ME/CFS symptom severity and analyte detection.

BACKGROUND: Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is clinically defined and characterised by persistent disabling tiredness and exertional malaise, leading to functional impairment. METHODS: This study introduces the weighted standing time (WST) as a proxy for ME/CFS severity, and investigates its behaviour in an Australian cohort. WST was calculated from standing time and subjective standing difficulty data, collected via orthostatic intolerance assessments. The distribution of WST for healthy controls and ME/CFS patients was correlated with the clinical criteria, as well as pathology and cytokine markers. Included in the WST cytokine analyses were activins A and B, cytokines causally linked to inflammation, and previously demonstrated to separate ME/CFS from healthy controls. Forty-five ME/CFS patients were recruited from the CFS Discovery Clinic (Victoria) between 2011 and 2013. Seventeen healthy controls were recruited concurrently and identically assessed. RESULTS: WST distribution was significantly different between ME/CFS participants and controls, with six diagnostic criteria, five analytes and one cytokine also significantly different when comparing severity via WST. On direct comparison of ME/CFS to study controls, only serum activin B was significantly elevated, with no significant variation observed for a broad range of serum and urine markers, or other serum cytokines. CONCLUSIONS: The enhanced understanding of standing test behaviour to reflect orthostatic intolerance as a ME/CFS symptom, and the subsequent calculation of WST, will encourage the greater implementation of this simple test as a measure of ME/CFS diagnosis, and symptom severity, to the benefit of improved diagnosis and guidance for potential treatments.

Effects of an In-Frame Deletion of the 6k Gene Locus from the Genome of Ross River Virus.

UNLABELLED: The alphaviral6kgene region encodes the two structural proteins 6K protein and, due to a ribosomal frameshift event, the transframe protein (TF). Here, we characterized the role of the6kproteins in the arthritogenic alphavirus Ross River virus (RRV) in infected cells and in mice, using a novel6kin-frame deletion mutant. Comprehensive microscopic analysis revealed that the6kproteins were predominantly localized at the endoplasmic reticulum of RRV-infected cells. RRV virions that lack the6kproteins 6K and TF [RRV-(Δ6K)] were more vulnerable to changes in pH, and the corresponding virus had increased sensitivity to a higher temperature. While the6kdeletion did not reduce RRV particle production in BHK-21 cells, it affected virion release from the host cell. Subsequentin vivostudies demonstrated that RRV-(Δ6K) caused a milder disease than wild-type virus, with viral titers being reduced in infected mice. Immunization of mice with RRV-(Δ6K) resulted in a reduced viral load and accelerated viral elimination upon secondary infection with wild-type RRV or another alphavirus, chikungunya virus (CHIKV). Our results show that the6kproteins may contribute to alphaviral disease manifestations and suggest that manipulation of the6kgene may be a potential strategy to facilitate viral vaccine development. IMPORTANCE: Arthritogenic alphaviruses, such as chikungunya virus (CHIKV) and Ross River virus (RRV), cause epidemics of debilitating rheumatic disease in areas where they are endemic and can emerge in new regions worldwide. RRV is of considerable medical significance in Australia, where it is the leading cause of arboviral disease. The mechanisms by which alphaviruses persist and cause disease in the host are ill defined. This paper describes the phenotypic properties of an RRV6kdeletion mutant. The absence of the6kgene reduced virion release from infected cells and also reduced the severity of disease and viral titers in infected mice. Immunization with the mutant virus protected mice against viremia not only upon exposure to RRV but also upon challenge with CHIKV. These findings could lead to the development of safer and more immunogenic alphavirus vectors for vaccine delivery.

Activin B is a novel biomarker for chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME) diagnosis: a cross sectional study.

BACKGROUND: Investigations of activin family proteins as serum biomarkers for chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME). CFS/ME is a disease with complex, wide-ranging symptoms, featuring persistent fatigue of 6 months or longer, particularly post exertion. No definitive biomarkers are available. METHODS: A cross-sectional, observational study of CFS/ME patients fulfilling the 2003 Canadian Consensus Criteria, in parallel with healthy non-fatigued controls, was conducted. Comparisons with a previously defined activin reference population were also performed. For the total study cohort the age range was 18-65 years with a female: male participant ratio of greater than 3:1. All participants were assessed via a primary care community clinic. Blood samples were collected for pathology testing after physical examination and orthostatic intolerance assessment. Cytokines, activin A, activin B and follistatin were also measured in sera from these samples. All data were compared between the CFS/ME and control cohorts, with the activins and follistatin also compared with previously defined reference intervals. RESULTS: Serum activin B levels for CFS/ME participants were significantly elevated when compared to the study controls, as well as the established reference interval. Serum activin A and follistatin were within their normal ranges. All routine and special pathology markers were within the normal laboratory reference intervals for the total study cohort, with no significant differences detected between CFS/ME and control groups. Also, no significant differences were detected for IL-2, IL-4, IL-6, IL-10, IL-17A, TNF or IFN-gamma. CONCLUSION: Elevated activin B levels together with normal activin A levels identified patients with the diagnostic symptoms of CFS/ME, thus providing a novel serum based test. The activins have multiple physiological roles and capture the diverse array of symptoms experienced by CFS/ME patients.

Arthritogenic alphaviral infection perturbs osteoblast function and triggers pathologic bone loss.

Arthritogenic alphaviruses including Ross River virus (RRV), Sindbis virus, and chikungunya virus cause worldwide outbreaks of musculoskeletal disease. The ability of alphaviruses to induce bone pathologies remains poorly defined. Here we show that primary human osteoblasts (hOBs) can be productively infected by RRV. RRV-infected hOBs produced high levels of inflammatory cytokine including IL-6. The RANKL/OPG ratio was disrupted in the synovial fluid of RRV patients, and this was accompanied by an increase in serum Tartrate-resistant acid phosphatase 5b (TRAP5b) levels. Infection of bone cells with RRV was validated using an established RRV murine model. In wild-type mice, infectious virus was detected in the femur, tibia, patella, and foot, together with reduced bone volume in the tibial epiphysis and vertebrae detected by microcomputed tomographic (µCT) analysis. The RANKL/OPG ratio was also disrupted in mice infected with RRV; both this effect and the bone loss were blocked by treatment with an IL-6 neutralizing antibody. Collectively, these findings provide previously unidentified evidence that alphavirus infection induces bone loss and that OBs are capable of producing proinflammatory mediators during alphavirus-induced arthralgia. The perturbed RANKL/OPG ratio in RRV-infected OBs may therefore contribute to bone loss in alphavirus infection.

Enhancement of hepatitis virus immunoassay outcome predictions in imbalanced routine pathology data by data balancing and feature selection before the application of support vector machines.

BACKGROUND: Data mining techniques such as support vector machines (SVMs) have been successfully used to predict outcomes for complex problems, including for human health. Much health data is imbalanced, with many more controls than positive cases. METHODS: The impact of three balancing methods and one feature selection method is explored, to assess the ability of SVMs to classify imbalanced diagnostic pathology data associated with the laboratory diagnosis of hepatitis B (HBV) and hepatitis C (HCV) infections. Random forests (RFs) for predictor variable selection, and data reshaping to overcome a large imbalance of negative to positive test results in relation to HBV and HCV immunoassay results, are examined. The methodology is illustrated using data from ACT Pathology (Canberra, Australia), consisting of laboratory test records from 18,625 individuals who underwent hepatitis virus testing over the decade from 1997 to 2007. RESULTS: Overall, the prediction of HCV test results by immunoassay was more accurate than for HBV immunoassay results associated with identical routine pathology predictor variable data. HBV and HCV negative results were vastly in excess of positive results, so three approaches to handling the negative/positive data imbalance were compared. Generating datasets by the Synthetic Minority Oversampling Technique (SMOTE) resulted in significantly more accurate prediction than single downsizing or multiple downsizing (MDS) of the dataset. For downsized data sets, applying a RF for predictor variable selection had a small effect on the performance, which varied depending on the virus. For SMOTE, a RF had a negative effect on performance. An analysis of variance of the performance across settings supports these findings. Finally, age and assay results for alanine aminotransferase (ALT), sodium for HBV and urea for HCV were found to have a significant impact upon laboratory diagnosis of HBV or HCV infection using an optimised SVM model. CONCLUSIONS: Laboratories looking to include machine learning via SVM as part of their decision support need to be aware that the balancing method, predictor variable selection and the virus type interact to affect the laboratory diagnosis of hepatitis virus infection with routine pathology laboratory variables in different ways depending on which combination is being studied. This awareness should lead to careful use of existing machine learning methods, thus improving the quality of laboratory diagnosis.

A simulation model to estimate the risk of transfusion-transmitted arboviral infection.

BACKGROUND: The arboviruses West Nile virus (WNV), dengue virus (DENV) and Ross River virus (RRV) have been demonstrated to be blood transfusion-transmissible. A model to estimate the risk of WNV to the blood supply using a Monte Carlo approach has been developed and also applied to Chikungunya virus. Also, a probabilistic model was developed to assess the risk of DENV to blood safety, which was later adapted to RRV. To address efficacy and limitations within each model we present a hybrid model that promises improved accuracy, and is broadly applicable to assess the risk of arboviral transmission by blood transfusion. MATERIAL AND METHODS: Data were drawn from the Cairns Public Health Unit (Australia) and published literature. Based on the published models and using R code, a novel 'combined' model was developed and validated against the BP model using sensitivity testing. RESULTS: The mean risk per 10,000 of the combined model is 0.98 with a range from 0.79 to 1.25, while the maximum risk was 4.45 ranging from 2.62 to 7.67 respectively. These parameters for the BP model were 1.20 ranging from 0.84 to 1.55, and 2.86 ranging from 1.33 to 5.23 respectively. CONCLUSION: The combined simulation model is simple and robust. We propose it can be applied as a 'generic' arbovirus model to assess the risk from known or novel arboviral threats to the blood supply.

Characterization of Barmah Forest virus pathogenesis in a mouse model.

Alphaviruses including Barmah Forest virus (BFV) and Ross River virus (RRV) cause arthritis, arthralgia and myalgia in humans. The rheumatic symptoms in human BFV infection are very similar to those of RRV. Although RRV disease has been studied extensively, little is known about the pathogenesis of BFV infection. We sought to establish a mouse model for BFV to facilitate our understanding of BFV infectivity, tropism and pathogenesis, and to identify key pathological and immunological mechanisms of BFV infection that may distinguish between infections with BFV and RRV. Here, to the best of our knowledge, we report the first study assessing the virulence and replication of several BFV isolates in a mouse model. We infected newborn Swiss outbred mice with BFV and established that the BFV2193 prototype was the most virulent strain. BFV2193 infection resulted in the highest mortality among all BFV variant isolates, comparable to that of RRV. In comparison with RRV, C57BL/6 mice infected with BFV showed delayed onset, moderate disease scores and early recovery of the disease. BFV replicated poorly in muscle and did not cause the severe myositis seen in RRV-infected mice. The mRNAs for the inflammatory mediators TNF-α, IL-6, CCL2 and arginase-1 were highly upregulated in RRV- but not BFV-infected muscle. To our knowledge, this is the first report of a mouse model of BFV infection, which we have used to demonstrate differences between BFV and RRV infections and to further understand disease pathogenesis. With an increasing number of BFV cases occurring annually, a better understanding of the disease mechanisms is essential for future therapeutic development.

An 1H-MRS framework predicts the onset of Alzheimer's disease symptoms in PSEN1 mutation carriers.

BACKGROUND: Alzheimer's disease (AD) is the most common cause of dementia; the main risk factors are age and several recently identified genes. A major challenge for AD research is the early detection of subjects at risk. The aim of this study is to develop a predictive model using proton magnetic resonance spectroscopy (1H-MRS), a noninvasive technique that evaluates brain chemistry in vivo, for monitoring the clinical outcome of carriers of a fully penetrant mutation that causes AD. METHODS: We studied 75 subjects from the largest multigenerational pedigree in the world (∼5000 people) that segregates a unique form of early-onset Alzheimer's disease (EOAD) caused by a fully penetrant mutation in the Presenilin-1 gene (PSEN1 p.Glu280Ala [E280 A]). Forty-four subjects were carriers of the mutation, and 31 were noncarriers. Seventeen carriers had either mild cognitive impairment (MCI) or early-stage AD (collectively MCI-AD). In right and left parietal white mater and parasagittal parietal gray matter (RPPGM and LPPGM) of the posterior cingulate gyrus and precuneus, we measured levels of the brain metabolites N-acetylaspartate (NAA), inositol (Ins), choline (Cho), and glutamate-glutamine complex (Glx) relative to creatine (Cr) levels (NAA/Cr, Ins/Cr, Cho/Cr, and Glx/Cr, respectively) with two-dimensional 1H-MRS. Using advanced recursive partition analysis and random forest analysis, we built classificatory decision trees for both mutation carrier status and the presence of MCI-AD symptoms, fitting them to 1H-MRS data while controlling for age, educational level, and sex. RESULTS: We found that (1) the combination of LPPGM Cho/Cr<0.165 and RPPGM Glx/Cr>1.54 fully excluded carriers; (2) LPPGM Cho/Cr>0.165, RPPGM Glx/Cr<1.54, and left parietal white mater NAA/Cr>1.16 identified asymptomatic carriers with sensitivity of 97.7% and specificity of 77.4%; and (3) RPPGM NAA/Cr>1.05 defined asymptomatic subjects (independent of carrier status) with sensitivity of 100% and a specificity of 96.6%. CONCLUSIONS: Brain metabolites measured by 1H-MRS in the posterior cingulate gyrus and precuneus are optimally sensitive and specific potential noninvasive biomarkers of subclinical emergence of AD caused by the PSEN1 p.Glu280Ala (E280 A) mutation.

Infection status outcome, machine learning method and virus type interact to affect the optimised prediction of hepatitis virus immunoassay results from routine pathology laboratory assays in unbalanced data.

BACKGROUND: Advanced data mining techniques such as decision trees have been successfully used to predict a variety of outcomes in complex medical environments. Furthermore, previous research has shown that combining the results of a set of individually trained trees into an ensemble-based classifier can improve overall classification accuracy. This paper investigates the effect of data pre-processing, the use of ensembles constructed by bagging, and a simple majority vote to combine classification predictions from routine pathology laboratory data, particularly to overcome a large imbalance of negative Hepatitis B virus (HBV) and Hepatitis C virus (HCV) cases versus HBV or HCV immunoassay positive cases. These methods were illustrated using a never before analysed data set from ACT Pathology (Canberra, Australia) relating to HBV and HCV patients. RESULTS: It was easier to predict immunoassay positive cases than negative cases of HBV or HCV. While applying an ensemble-based approach rather than a single classifier had a small positive effect on the accuracy rate, this also varied depending on the virus under analysis. Finally, scaling data before prediction also has a small positive effect on the accuracy rate for this dataset. A graphical analysis of the distribution of accuracy rates across ensembles supports these findings. CONCLUSIONS: Laboratories looking to include machine learning as part of their decision support processes need to be aware that the infection outcome, the machine learning method used and the virus type interact to affect the enhanced laboratory diagnosis of hepatitis virus infection, as determined by primary immunoassay data in concert with multiple routine pathology laboratory variables. This awareness will lead to the informed use of existing machine learning methods, thus improving the quality of laboratory diagnosis via informatics analyses.

Predicting the presence of hepatitis B virus surface antigen in Chinese patients by pathology data mining.

Hepatitis B virus (HBV) is a pathogen of worldwide health significance, associated with liver disease. A vaccine is available, yet HBV prevalence remains a concern, particularly in developing countries. Pathology laboratories have a primary role in the diagnosis and monitoring of HBV infection, through hepatitis B surface antigen (HBsAg) immunoassay and associated tests. Analysis of HBsAg immunoassay and associated pathology data from 821 Chinese patients applied 10-fold cross-validation to establish classification decision trees (CDTs), with CDT results used subsequently to develop a logistic regression model. The robustness of logistic regression model was confirmed by the Hosmer-Lemeshow test, Pseudo-R(2) and an area under receiver operating characteristic curve (AUROC) result that showed the logistic regression model was capable of accurately discriminating the HBsAg positive from HBsAg negative patients at 95% accuracy. Overall CDT sensitivity and specificity was 94.7% (± 5.0%) and 89.5% (± 5.7%), respectively, close to the sensitivity and specificity of the immunoassay, providing an alternative to predict HBsAg status. Both the CDT and logistic regression modeling demonstrated the importance of the routine pathology variables alanine aminotransferase (ALT), serum albumin (ALB), and alkaline phosphatase (ALP) to accurately predict HBsAg status in a Chinese patient cohort. The study demonstrates that CDTs and a linked logistic regression model applied to routine pathology data were an effective supplement to HBsAg immunoassay, and a possible replacement method where immunoassays are not requested or not easily available for the laboratory diagnosis of HBV infection.

Routine blood test markers for predicting liver disease post HBV infection: precision pathology and pattern recognition.

BACKGROUND: Early stages of hepatitis B virus (HBV) infection usually involve inflammation of the liver. Patients with chronic infection have an increased risk of progressive liver fibrosis, cirrhosis, and life-threatening clinical complications of end-stage hepatocellular carcinoma (HCC). CONTENT: Early diagnosis of hepatic fibrosis and timely clinical management are critical to controlling disease progression and decreasing the burden of end-stage liver cancer. Fibrosis staging, through its current gold standard, liver biopsy, improves patient outcomes, but the clinical procedure is invasive with unpleasant post-procedural complications. Routine blood test markers offer promising diagnostic potential for early detection of liver disease without biopsy. There is a plethora of candidate routine blood test markers that have gone through phases of biomarker validation and have shown great promise, but their current limitations include a predictive ability that is limited to only a few stages of fibrosis. However, the advent of machine learning, notably pattern recognition, presents an opportunity to refine blood-based non-invasive models of hepatic fibrosis in the future. SUMMARY: In this review, we highlight the current landscape of routine blood-based non-invasive models of hepatic fibrosis, and appraise the potential application of machine learning (pattern recognition) algorithms to refining these models and optimising clinical predictions of HBV-associated liver disease. OUTLOOK: Machine learning via pattern recognition algorithms takes data analytics to a new realm, and offers the opportunity for enhanced multi-marker fibrosis stage prediction using pathology profile that leverages information across patient routine blood tests.

Clinical Validity of a Machine Learning Decision Support System for Early Detection of Hepatitis B Virus: A Binational External Validation Study.

HepB LiveTest is a machine learning decision support system developed for the early detection of hepatitis B virus (HBV). However, there is a lack of evidence on its generalisability. In this study, we aimed to externally assess the clinical validity and portability of HepB LiveTest in predicting HBV infection among independent patient cohorts from Nigeria and Australia. The performance of HepB LiveTest was evaluated by constructing receiver operating characteristic curves and estimating the area under the curve. Delong's method was used to estimate the 95% confidence interval (CI) of the area under the receiver-operating characteristic curve (AUROC). Compared to the Australian cohort, patients in the derivation cohort of HepB LiveTest and the hospital-based Nigerian cohort were younger (mean age, 45.5 years vs. 38.8 years vs. 40.8 years, respectively; p < 0.001) and had a higher incidence of HBV infection (1.9% vs. 69.4% vs. 57.3%). In the hospital-based Nigerian cohort, HepB LiveTest performed optimally with an AUROC of 0.94 (95% CI, 0.91-0.97). The model provided tailored predictions that ensured most cases of HBV infection did not go undetected. However, its discriminatory measure dropped to 0.60 (95% CI, 0.56-0.64) in the Australian cohort. These findings indicate that HepB LiveTest exhibits adequate cross-site transportability and clinical validity in the hospital-based Nigerian patient cohort but shows limited performance in the Australian cohort. Whilst HepB LiveTest holds promise for reducing HBV prevalence in underserved populations, caution is warranted when implementing the model in older populations, particularly in regions with low incidence of HBV infection.

The development of a machine learning algorithm for early detection of viral hepatitis B infection in Nigerian patients.

Access to Hepatitis B Virus (HBV) testing for people in low-resource settings has long been challenging due to the gold standard, enzyme immunoassay, being prohibitively expensive, and requiring specialised skills and facilities that are not readily available, particularly in remote and isolated laboratories. Routine pathology data in tandem with cutting-edge machine learning shows promising diagnostic potential. In this study, recursive partitioning ("trees") and Support Vector Machines (SVMs) were applied to interrogate patient dataset (n = 916) that comprised results for Hepatitis B Surface Antigen (HBsAg) and routine clinical chemistry and haematology blood tests. These algorithms were used to develop a predictive diagnostic model of HBV infection. Our SVM-based diagnostic model of infection (accuracy = 85.4%, sensitivity = 91%, specificity = 72.6%, precision = 88.2%, F1-score = 0.89, Area Under the Receiver Operating Curve, AUC = 0.90) proved to be highly accurate for discriminating HBsAg positive from negative patients, and thus rivals with immunoassay. Therefore, we propose a predictive model based on routine blood tests as a novel diagnostic for early detection of HBV infection. Early prediction of HBV infection via routine pathology markers and pattern recognition algorithms will offer decision-support to clinicians and enhance early diagnosis, which is critical for optimal clinical management and improved patient outcomes.

Machine learning prediction models for clinical management of blood-borne viral infections: a systematic review of current applications and future impact.

BACKGROUND: Machine learning (ML) prediction models to support clinical management of blood-borne viral infections are becoming increasingly abundant in medical literature, with a number of competing models being developed for the same outcome or target population. However, evidence on the quality of these ML prediction models are limited. OBJECTIVE: This study aimed to evaluate the development and quality of reporting of ML prediction models that could facilitate timely clinical management of blood-borne viral infections. METHODS: We conducted narrative evidence synthesis following the synthesis without meta-analysis guidelines. We searched PubMed and Cochrane Central Register of Controlled Trials for all studies applying ML models for predicting clinical outcomes associated with hepatitis B virus (HBV), human immunodeficiency virus (HIV), or hepatitis C virus (HCV). RESULTS: We found 33 unique ML prediction models aiming to support clinical decision making. Overall, 12 (36.4%) focused on HBV, 10 (30.3%) on HCV, 10 on HIV (30.3%) and two (6.1%) on co-infection. Among these, six (18.2%) addressed the diagnosis of infection, 16 (48.5%) the prognosis of infection, eight (24.2%) the prediction of treatment response, two (6.1%) progression through a cascade of care, and one (3.03%) focused on the choice of antiretroviral therapy (ART). Nineteen prediction models (57.6%) were developed using data from high-income countries. Evaluation of prediction models was limited to measures of performance. Detailed information on software code accessibility was often missing. Independent validation on new datasets and/or in other institutions was rarely done. CONCLUSION: Promising approaches for ML prediction models in blood-borne viral infections were identified, but the lack of robust validation, interpretability/explainability, and poor quality of reporting hampered their clinical relevance. Our findings highlight important considerations that can inform standard reporting guidelines for ML prediction models in the future (e.g., TRIPOD-AI), and provides critical data to inform robust evaluation of the models.

Identification and characterization of a ross river virus variant that grows persistently in macrophages, shows altered disease kinetics in a mouse model, and exhibits resistance to type I interferon.

Alphaviruses, such as chikungunya virus, o'nyong-nyong virus, and Ross River virus (RRV), cause outbreaks of human rheumatic disease worldwide. RRV is a positive-sense single-stranded RNA virus endemic to Australia and Papua New Guinea. In this study, we sought to establish an in vitro model of RRV evolution in response to cellular antiviral defense mechanisms. RRV was able to establish persistent infection in activated macrophages, and a small-plaque variant (RRV(PERS)) was isolated after several weeks of culture. Nucleotide sequence analysis of RRV(PERS) found several nucleotide differences in the nonstructural protein (nsP) region of the RRV(PERS) genome. A point mutation was also detected in the E2 gene. Compared to the parent virus (RRV-T48), RRV(PERS) showed significantly enhanced resistance to beta interferon (IFN-ß)-stimulated antiviral activity. RRV(PERS) infection of RAW 264.7 macrophages induced lower levels of IFN-ß expression and production than infection with RRV-T48. RRV(PERS) was also able to inhibit type I IFN signaling. Mice infected with RRV(PERS) exhibited significantly enhanced disease severity and mortality compared to mice infected with RRV-T48. These results provide strong evidence that the cellular antiviral response can direct selective pressure for viral sequence evolution that impacts on virus fitness and sensitivity to alpha/beta IFN (IFN-α/ß).

Ross River Virus Immune Evasion Strategies and the Relevance to Post-viral Fatigue, and Myalgic Encephalomyelitis Onset.

Ross River virus (RRV) is an endemic Australian arbovirus, and member of the Alphavirus family that also includes Chikungunya virus (CHIK). RRV is responsible for the highest prevalence of human disease cases associated with mosquito-borne transmission in Australia, and has long been a leading suspect in cases of post-viral fatigue syndromes, with extrapolation of this link to Myalgic Encephalomyelitis (ME). Research into RRV pathogenesis has revealed a number of immune evasion strategies, impressive for a virus with a genome size of 12 kb (plus strand RNA), which resonate with insights into viral pathogenesis broadly. Drawing from observations on RRV immune evasion, mechanisms of relevance to long term idiopathic fatigue are featured as a perspective on infection and eventual ME symptoms, which include considerations of; (1) selective pro-inflammatory gene suppression post antibody-dependent enhancement (ADE) of RRV infection, (2) Evidence from other virus families of immune disruption and evasion post-ADE, and (3) how virally-driven immune evasion may impact on mitochondrial function via target of rapamycin (TOR) complexes. In light of these RRV measures to counter the host immune - inflammatory responses, links to recent discoveries explaining cellular, immune and metabolomic markers of ME will be explored and discussed, with the implications for long-COVID post SARS-CoV-2 also considered. Compelling issues on the connections between virally-induced alterations in cytokine expression, for example, will be of particular interest in light of energy pathways, and how these perturbations manifest clinically.