Infectious Disease

Evolutionary Dynamics and Functional Differences in Clinically Relevant Pen β-Lactamases from spp.

Antimicrobial resistance (AMR) is a global threat, with species contributing significantly to difficult-to-treat infections. The Pen family of β-lactamases are produced by all spp., and their mutation or overproduction leads to the resistance of β-lactam antibiotics. Here we investigate the dynamic differences among four Pen β-lactamases (PenA, PenI, PenL and PenP) using machine learning driven ...

Machine Learning Predicts Regioselectivity in Pd-Catalyzed Directing Group-Assisted C-H Activation.

Regioselectivity in C-H activation is crucial for applications in pharmaceuticals and advanced materials. Machine learning (ML) techniques enhance the speed and accuracy of regioselectivity prediction in palladium-catalyzed directing group-assisted C-H activation in aryl substrates. Among the models tested, the standard support vector machine (SVM) model demonstrated better generalizability, achie...

Neural networks to model COVID-19 dynamics and allocate healthcare resources.

This study presents a neural network-based framework for COVID-19 transmission prediction and healthcare resource optimization. The model achieves hig...

Data-driven machine learning algorithm model for pneumonia prediction and determinant factor stratification among children aged 6-23 months in Ethiopia.

INTRODUCTION: Pneumonia is the leading cause of child morbidity and mortality and accounts for 5.6 million under-five child deaths. Pneumonia has a si...

Efficacy of eprinomectin 5 mg/mL topical solution administered twice pour on at 1 mg per kg body weight against sarcoptic mange (Sarcoptes scabiei) in sheep.

Sarcoptic mange of sheep is a skin disease which compromises the health and welfare of the animals, impacts their productivity and is zoonotic. In Eur...

ADT²R: Adaptive Decision Transformer for Dynamic Treatment Regimes in Sepsis.

Dynamic treatment regimes (DTRs), which comprise a series of decisions taken to select adequate treatments, have attracted considerable attention in t...

Predictive survival modelings for HIV-related cryptococcosis: comparing machine learning approaches.

INTRODUCTION: HIV-associated cryptococcosis is marked by unpredictable disease trajectories and persistently high mortality rates worldwide. Although ...

AMPCliff: Quantitative definition and benchmarking of activity cliffs in antimicrobial peptides.

INTRODUCTION: Activity cliff (AC) is a phenomenon that a pair of similar molecules differ by a small structural alternation but exhibit a large differ...

Natural Language Processing and Machine Learning Techniques for Analyzing Conversations About Nutritional Yeasts in the United States and France: Retrospective Social Media Listening Study.

BACKGROUND: Nutritional yeast, an inactive form of Saccharomyces cerevisiae, has recently become increasingly popular as a food supplement and healthy...

The application of machine learning in clinical microbiology and infectious diseases.

With the development of artificial intelligence(AI) in computer science and statistics, it has been further applied to the medical field. These applic...

The future of HIV diagnostics: an exemplar in infectious diseases.

Over the past 40 years, diagnostics have become the backbone of HIV prevention, treatment, and retention in care, and are central to the achievement o...

A digital photography dataset for Vaccinia Virus plaque quantification using Deep Learning.

Virological plaque assay is the major method of detecting and quantifying infectious viruses in research and diagnostic samples. Furthermore, viral pl...

Synergistic detection of E. coli using ultrathin film of functionalized graphene with impedance spectroscopy and machine learning.

Bacterial detection and classification are critical challenges in healthcare, environmental monitoring, and food safety, demanding selective and effic...

Use of Retrieval-Augmented Large Language Model for COVID-19 Fact-Checking: Development and Usability Study.

BACKGROUND: The COVID-19 pandemic has been accompanied by an "infodemic," where the rapid spread of misinformation has exacerbated public health chall...

A systematic evaluation of the language-of-viral-escape model using multiple machine learning frameworks.

Predicting the evolutionary patterns of emerging and endemic viruses is key for mitigating their spread. In particular, it is critical to rapidly iden...

Performance of AI Approaches for COVID-19 Diagnosis Using Chest CT Scans: The Impact of Architecture and Dataset.

AI is emerging as a promising tool for diagnosing COVID-19 based on chest CT scans. The aim of this study was the comparison of AI models for COVID-19...

A simple yet effective approach for predicting disease spread using mathematically-inspired diffusion-informed neural networks.

The COVID-19 outbreak has highlighted the importance of mathematical epidemic models like the Susceptible-Infected-Recovered (SIR) model, for understa...

Multifractal analysis and support vector machine for the classification of coronaviruses and SARS-CoV-2 variants.

This study presents a novel approach for the classification of coronavirus species and variants of SARS-CoV-2 using Chaos Game Representation (CGR) an...

Transformer-based deep learning enables improved B-cell epitope prediction in parasitic pathogens: A proof-of-concept study on Fasciola hepatica.

BACKGROUND: The identification of B-cell epitopes (BCEs) is fundamental to advancing epitope-based vaccine design, therapeutic antibody development, a...

Introduction to WBE case estimation: A practical toolset for public health practitioners.

Public health practitioners can use wastewater data to grasp disease dynamics, including incidence, prevalence, and potential disease trajectory. The ...