A graph neural network-based model with out-of-distribution robustness for enhancing antiretroviral therapy outcome prediction for HIV-1.

Predicting the outcome of antiretroviral therapies (ART) for HIV-1 is a pressing clinical challenge, especially when the ART includes drugs with limited effectiveness data. This scarcity of data can arise either due to the introduction of a new drug to the market or due to limited use in clinical settings, resulting in clinical dataset with highly unbalanced therapy representation. To tackle this issue, we introduce a novel joint fusion model, which combines features from a Fully Connected (FC) Neural Network and a Graph Neural Network (GNN) in a multi-modality fashion.

Spectrum of Non-Nucleoside Reverse Transcriptase Inhibitor-Associated Drug Resistance Mutations in Persons Living with HIV-1 Receiving Rilpivirine.

Introduction: Few data are currently available on the nonnucleoside reverse transcriptase (RT) inhibitors (NNRTI) resistance mutations selected in persons living with HIV-1 (PLWH) who develop virological failure while receiving rilpivirine (RPV).

Methods: We analyzed pooled HIV-1 RT genotypic data from 280 PLWH in the multicenter EuResist database and 115 PLWH in the Stanford HIV Drug Resistance Database (HIVDB) who received RPV as their only NNRTI.

Results: Among the 395 PLWH receiving RPV, 180 (45.

Incorporating temporal dynamics of mutations to enhance the prediction capability of antiretroviral therapy's outcome for HIV-1.

Motivation: In predicting HIV therapy outcomes, a critical clinical question is whether using historical information can enhance predictive capabilities compared with current or latest available data analysis. This study analyses whether historical knowledge, which includes viral mutations detected in all genotypic tests before therapy, their temporal occurrence, and concomitant viral load measurements, can bring improvements. We introduce a method to weigh mutations, considering the previously enumerated factors and the reference mutation-drug Stanford resistance tables.

Cohort Profile: A European Multidisciplinary Network for the Fight against HIV Drug Resistance (EuResist Network).

The EuResist cohort was established in 2006 with the purpose of developing a clinical decision-support tool predicting the most effective antiretroviral therapy (ART) for persons living with HIV (PLWH), based on their clinical and virological data. Further to continuous extensive data collection from several European countries, the EuResist cohort later widened its activity to the more general area of antiretroviral treatment resistance with a focus on virus evolution. The EuResist cohort has retrospectively enrolled PLWH, both treatment-naïve and treatment-experienced, under clinical follow-up from 1998, in nine national cohorts across Europe and beyond, and this article is an overview of its achievement.

A machine-learning based bio-psycho-social model for the prediction of non-obstructive and obstructive coronary artery disease.

Background: Mechanisms of myocardial ischemia in obstructive and non-obstructive coronary artery disease (CAD), and the interplay between clinical, functional, biological and psycho-social features, are still far to be fully elucidated.

Objectives: To develop a machine-learning (ML) model for the supervised prediction of obstructive versus non-obstructive CAD.

Methods: From the EVA study, we analysed adults hospitalized for IHD undergoing conventional coronary angiography (CCA).

Molecular Epidemiology of HIV-1 in Eastern Europe and Russia.

The HIV epidemic in Eastern Europe and Russia is large and not well-controlled. To describe the more recent molecular epidemiology of HIV-1, transmitted drug resistance, and the relationship between the epidemics in this region, we sequenced the and genes of HIV-1 from 812 people living with HIV from Ukraine ( = 191), Georgia ( = 201), and Russia ( = 420) before the initiation of antiretroviral therapy. In 190 Ukrainian patients, the gene sequence was also determined.

Spectrum of Atazanavir-Selected Protease Inhibitor-Resistance Mutations.

Ritonavir-boosted atazanavir is an option for second-line therapy in low- and middle-income countries (LMICs). We analyzed publicly available HIV-1 protease sequences from previously PI-naïve patients with virological failure (VF) following treatment with atazanavir. Overall, 1497 patient sequences were identified, including 740 reported in 27 published studies and 757 from datasets assembled for this analysis.