Infectious Disease

An optimized EfficientNetB0 framework with CLAHE-based preprocessing for accurate multi-class chest X-ray classification.

Chest radiography remains an essential diagnostic tool for thoracic diseases, yet interpreting overlapping anatomical structures is particularly challenging when multiple pathologies co-occur a common clinical scenario often oversimplified in deep learning approaches. This study presents an optimized EfficientNetB0 framework designed explicitly for multi-label classification of chest X-rays using ...

Harnessing machine learning with auditory tests and demographic factors to forecast children's reading abilities in children living with and without HIV.

Artificial intelligence and multi-omic empowered-tracing macrophage diagnostic and druggable implications in coronavirus pneumonia.

BACKGROUND: As the innate immune barrier, macrophages play an important role in coronavirus infection pneumonia and post-infection regeneration. OBJEC...

DoTT-ML: Condition-aware detection of transcriptional readthrough from RNA-seq with optional ML-based prioritization.

Disruption of transcription termination (DoTT) occurs when RNA polymerase II reads past a gene's normal 3' end, generating downstream "readthrough" RN...

Optimal machine learning models and factors for global infectious diseases: An ecological analysis.

BACKGROUND: Infectious diseases are a major global public health challenge. Socioeconomic and environmental changes have complicated the situation, un...

Adaptive acoustic streaming microbial system for oxytetracycline removal.

Microalgae-bacteria symbiosis has been demonstrated to possess the ability to remove antibiotics from water. Appropriate granulation strategies enhanc...

Proteomic profiling and pathway analyses reveal molecular signatures and immune networks in pediatric sepsis.

BACKGROUND: Sepsis remains a leading cause of childhood mortality worldwide. Most deaths occur within the first few days of presentation, underscoring...

Integrating metabolomics and machine learning with in silico analysis to identify early biomarkers and molecular interactions in sepsis-associated acute kidney injury.

A machine learning-based prediction model for treatment efficacy in smear and/or chest X-ray positive tuberculosis patients.

PTBD: a machine learning-based non-invasive diagnostic model for pulmonary tuberculosis using large-scale blood transcriptomes.

BACKGROUND: Accurate and rapid diagnosis is essential for controlling pulmonary tuberculosis (PTB) by enabling timely intervention and reducing diseas...

Combined use of serum KL-6 and respiratory functional parameters for identifying fibrotic lung damage in SARS-CoV-2-induced interstitial pneumonia.

BACKGROUND: Post-COVID19 pulmonary fibrosis (PCPF) has been reported in a significant proportion of patients who survive the acute SARS-CoV-2 infectio...

AI-MedCraft: A Strategy-Driven AI Platform for Multi-Objective Molecular Design.

Small-molecule drug discovery requires balancing potency, selectivity, solubility, safety, and synthetic feasibility, among other interdependent prope...

An improved dataset for predicting mammal infecting viruses from genetic sequence information.

There have been several attempts to develop machine learning (ML) models to identify human infecting viruses from their genomic sequences, with varyin...

Diagnostic accuracy of AI-assisted chest radiographs in tuberculosis screening: A Ghanaian clinical study.

BACKGROUND: Tuberculosis remains a major global health challenge, particularly in resource-limited settings where access to expert radiological interp...

Probabilistic forecasting of monthly dengue cases using epidemiological and climate signals: A BiLSTM-Negative Binomial Model versus Mechanistic and Count-Model Baselines.

Reliable short-term forecasts enable urban health systems to anticipate dengue surges and allocate resources effectively. We assembled monthly dengue ...

Global prevalence and distribution of bla-harboring Salmonella: A genome-wide association study of cephalosporin resistance mechanisms.

Given the increasing global concern over Salmonella resistance to cephalosporins, such resistance poses serious challenges to food safety and clinical...

At the Edge of Chaos: Complexity Science and Critical Care in the Era of Artificial Intelligence.

Critical care unfolds at the crossroads of highly complex, nonlinear systems. The intersection of pathophysiology, human behavior, and clinical operat...

Clinically Confirmed Cohort Reveals Antioxidant Genetic Polymorphisms as Potential Susceptibility Factors for Long COVID After Mild or Asymptomatic COVID-19.

Although the COVID-19 pandemic has now been down-graded, long COVID (LC) presents an ongoing risk of long-term disease for a significant percentage of...

Advanced neuroimaging for central nervous system infections: when, where, how, and why?

PURPOSE OF REVIEW: Central nervous system (CNS) infections remain a major cause of morbidity and mortality worldwide, particularly in children, older ...

Machine learning predicts sepsis deterioration trajectories.

Sepsis has heterogeneous clinical trajectories, but conventional severity scores offer only static risk estimates. Timely, dynamic prediction could en...