Infectious Disease

Latest AI and machine learning research in infectious disease for healthcare professionals.

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Identification of potential riboswitch elements in Homo sapiens mRNA 5'UTR sequences using positive-unlabeled machine learning.

Riboswitches are a class of noncoding RNA structures that interact with target ligands to cause a co...

IDENTIFYING A SEPSIS SUBPHENOTYPE CHARACTERIZED BY DYSREGULATED LIPOPROTEIN METABOLISM USING A SIMPLIFIED CLINICAL DATA ALGORITHM.

Background: Cholesterol metabolism is dysregulated in sepsis contributing to patient heterogeneity. ...

Precision Strike Strategy for Liver Diseases Trilogy with Xiao-Chai-Hu Decoction: A Meta-Analysis with Machine Learning.

BACKGROUND AND PURPOSE: The progression from hepatitis to liver fibrosis (LF) and ultimately to hepa...

Integrating AI for infectious disease prediction: A hybrid ANN-XGBoost model for leishmaniasis in Pakistan.

Addressing leishmaniasis infection remains a substantial challenge in KP-Pakistan due to the increas...

Pato: prediction of probiotic bacteria using metabolic features.

Probiotics have gained recognition for their health-promoting benefits, particularly in the gastroin...

Interpretable machine learning model for predicting delirium in patients with sepsis: a study based on the MIMIC data.

OBJECTIVE: The aim of this study was to construct interpretable machine learning models to predict t...

Machine learning assessment of zoonotic potential in avian influenza viruses using PB2 segment.

BACKGROUND: Influenza A virus (IAV) is a major global health threat, causing seasonal epidemics and ...

Optimizing microfluidic chip for rapid SARS-CoV-2 detection using Taguchi method and artificial neural network PSO.

Microfluidic biosensors offer a promising solution for real-time analysis of coronaviruses with mini...

Host Biomarkers and Antibiotic Tissue Penetration in Sepsis: Insights from Moxifloxacin.

BACKGROUND AND OBJECTIVE: Sepsis-induced pathophysiological changes may lead to pharmacokinetic vari...

Advancing antimalarial drug discovery: ensemble machine learning models for predicting PfPK6 inhibitor activity.

Malaria is a significant global health challenge, causing high morbidity and mortality. The rise of ...

Deep learning based ensemble model for accurate tomato leaf disease classification by leveraging ResNet50 and MobileNetV2 architectures.

Global food security depends on tomato growing, but several fungal, bacterial, and viral illnesses s...

Deep learning-enhanced hyperspectral imaging for rapid screening of Co-metabolic microplastic-degrading bacteria in environmental samples.

Microbial biodegradation of microplastic (MP) emerges as an environmentally benign and highly promis...

Detection and prevalence of avian influenza epidemic in the southwest of Poyang Lake and analysis of the influence of meteorological factors.

Recently, the epidemiological profile of avian influenza has changed dramatically worldwide. Avian i...

A machine learning model for predicting severe mycoplasma pneumoniae pneumonia in school-aged children.

OBJECTIVE: To develop an interpretable machine learning (ML) model for predicting severe Mycoplasma ...

A comparative study on TB incidence and HIVTB coinfection using machine learning models on WHO global TB dataset.

Tuberculosis, a deadly and contagious disease caused by Mycobacterium tuberculosis, remains a signif...

Design of nanobody targeting SARS-CoV-2 spike glycoprotein using CDR-grafting assisted by molecular simulation and machine learning.

The design of proteins capable effectively binding to specific protein targets is crucial for develo...

CAD-Unet: A capsule network-enhanced Unet architecture for accurate segmentation of COVID-19 lung infections from CT images.

Since the outbreak of the COVID-19 pandemic in 2019, medical imaging has emerged as a primary modali...

Utilizing artificial intelligence to predict and analyze socioeconomic, environmental, and healthcare factors driving tuberculosis globally.

Tuberculosis (TB) is a major global health issue, contributing significantly to mortality and morbid...

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