Genotype switching in hepatitis B virus as a potential risk for vertical transmission from mother-to-child was first reported

This study reports #HBV #MTCT despite reduced maternal viral loads & infant #immunoprophylaxis, showing #ViralLoad is key but not the sole factor; genotype switching and #quasispecies diversity reveal added mechanisms in transmission. #PublicHealth

#OpenAccess: doi.org/10.1016/j.dc...

Genotype switching in hepatitis B virus as a potential risk for vertical transmission from mother-to-child was first reported

First report of #HBV mother‑to‑child transmission despite low #maternal #viralload and #immunoprophylaxis, revealing #GenotypeSwitching and quasispecies diversity as key factors in #MTCT mechanisms.
#IDSky #EpiSky #ImmunoSky #MedSky #PublicHealth
#OpenAccess: doi.org/10.1016/j.dc...

We know treatment as prevention (#TasP) works, #HIV incidence comes down & the world advances toward epidemic control. The effort now is getting prevention to everyone who needs it. Thanks Mike Cohen for sharing HPTN's contributions #MTCT #U=U

#24hourstosaveAIDSresearch

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A Data-Driven Approach to Assessing Hepatitis B Mother-to-Child Transmission Risk Prediction Model: Machine Learning Perspective Background: Hepatitis B virus (HBV) can be transmitted from mother to child either through transplacental infection or via blood-to-blood contact during or immediately after delivery. Early and accurate risk assessments are essential for guiding clinical decisions and implementing effective preventive measures. Data mining techniques are powerful tools for identifying key predictors in medical diagnostics. Objective: This study aims to develop a robust predictive model for mother-to-child transmission (MTCT) of HBV using decision tree algorithms, specifically Iterative Dichotomiser 3 (ID3) and classification and regression trees (CART). The study identifies clinically and paraclinically relevant predictors, particularly hepatitis B e antigen (HBeAg) status and peripheral blood mononuclear cell (PBMC) concentration, for effective risk stratification and prevention. Additionally, we will assess the model’s reliability and generalizability through cross-validation with various training-test split ratios, aiming to enhance its applicability in clinical settings and inform improved preventive strategies against HBV MTCT. Methods: This study used decision tree algorithms—ID3 and CART—on a data set of 60 hepatitis B surface antigen (HBsAg)–positive pregnant women. Samples were collected either before or at the time of delivery, enabling the inclusion of patients who were undiagnosed or had limited access to treatment. We analyzed both clinical and paraclinical parameters, with a particular focus on HBeAg status and PBMC concentration. Additional biochemical markers were evaluated for their potential contributory or inhibitory effects on MTCT risk. The predictive models were validated using multiple training-test split ratios to ensure robustness and generalizability. Results: Our analysis showed that 20 out of 48 (based on a split ratio of 0.8 from a total of 60 cases, 42%) to 27 out of 57 (based on a split ratio of 0.95 from a total of 60 cases, 47%) training cases with HBeAg-positive status were associated with a significant risk of MTCT of HBV (χ28=21.16, P=.007, df=8). Among HBeAg-negative women, those with PBMC concentrations ≥8 × 106 cells/mL exhibited a low risk of MTCT, whereas individuals with PBMC concentrations

JMIR Formative Res: A Data-Driven Approach to Assessing Hepatitis B Mother-to-Child Transmission Risk Prediction Model: Machine Learning Perspective #HepatitisB #MTCT #MachineLearning #DataMining #PredictiveModel