Infectious Disease

Extending visual range of bacteria with upconversion nanoparticles and constructing NIR-responsive bio-microrobots.

The motility of bacteria is crucial for navigating competitive environments and is closely linked to physiological activities essential for their survival, such as biofilm development. Precise regulation of bacterial motility enhances our understanding of these complex processes. While optogenetic tools have been used to control and investigate bacterial motility, the excitation light in most exis...

Application of machine learning for delirium prediction and analysis of associated factors in hospitalized COVID-19 patients: A comparative study using the Korean Multidisciplinary cohort for delirium prevention (KoMCoDe).

BACKGROUND: The incidence of delirium in hospitalized coronavirus disease 2019 (COVID-19) patients is linked to adverse health outcomes. Predicting the occurrence and risk factors of delirium is key to preventing its sudden onset.

Graphene FET biochip on PCB reinforced by machine learning for ultrasensitive parallel detection of multiple antibiotics in water.

Antibiotics like Ciprofloxacin (Cfx), tetracycline (Tet) and Tobramycin (Tob) are commonly used against a broad-spectrum of bacterial infection. Recen...

Neural network procedures for the cholera disease system with public health mediations.

Severe gastrointestinal infections and watery diseases like cholera are still a major worldwide medical concern in the developing nations. A mathemati...

Lightweight convolutional neural network for chest X-ray images classification.

In this study, we developed a lightweight and rapid convolutional neural network (CNN) architecture for chest X-ray images; it primarily consists of a...

Machine learning analysis of the effects of COVID-19 on migration patterns.

This study investigates the impact of the COVID-19 pandemic on European tourist mobility patterns from 2019 to 2021 by conceptualizing countries as mo...

Evaluation of rapid detection methods for H5N1 virus using biosensors: An AI-based study.

High mortality and zoonotic potential predispose the H5N1 avian influenza virus as a critical threat. knowing that an epidemic could be occurring, qui...

Identification of Staphylococcus aureus, Enterococcus faecium, Klebsiella pneumoniae, Pseudomonas aeruginosa and Acinetobacter baumannii from Raman spectra by Artificial Intelligent Raman Detection and Identification System (AIRDIS) with machine learning.

BACKGROUND: Rapid and accurate identification of bacteria is required in order to develop effective treatment strategies. Traditional culture-based me...

Integrating Interpretability in Machine Learning and Deep Neural Networks: A Novel Approach to Feature Importance and Outlier Detection in COVID-19 Symptomatology and Vaccine Efficacy.

In this study, we introduce a novel approach that integrates interpretability techniques from both traditional machine learning (ML) and deep neural n...

Inferring strain-level mutational drivers of phage-bacteria interaction phenotypes arising during coevolutionary dynamics.

The enormous diversity of bacteriophages and their bacterial hosts presents a significant challenge to predict which phages infect a focal set of bact...

Dynamics of infectious disease mathematical model through unsupervised stochastic neural network paradigm.

The viruses has spread globally and have been impacted lives of people socially and economically, which causes immense suffering throughout the world....

How peptide migration and fraction bioactivity are modulated by applied electrical current conditions during electromembrane process separation: A comprehensive machine learning-based peptidomic approach.

Industrial wastewaters are significant global concerns due to their environmental impact. Yet, protein-rich wastewaters can be valorized by enzymatic ...

Severity prediction markers in dengue: a prospective cohort study using machine learning approach.

BACKGROUND: Dengue virus causes illnesses with or without warning indicators for severe complications. There are no clear prognostic signs linked to t...

A hybrid model for monthly runoff forecasting based on mixed signal processing and machine learning.

Monthly runoff forecasting plays a critically supportive role in water resources planning and management. Various signal decomposition techniques have...

Rapid diagnosis of latent and active pulmonary tuberculosis by autofluorescence spectroscopy of blood plasma combined with artificial neural network algorithm.

The existing clinical diagnostic methods of pulmonary tuberculosis (TB) usually have some of the following limitations, such as time-consuming, invasi...

Machine learning-enhanced immunopeptidomics applied to T-cell epitope discovery for COVID-19 vaccines.

Next-generation T-cell-directed vaccines for COVID-19 focus on establishing lasting T-cell immunity against current and emerging SARS-CoV-2 variants. ...

Analysis of four long non-coding RNAs for hepatocellular carcinoma screening and prognosis by the aid of machine learning techniques.

Hepatocellular carcinoma (HCC) represents a significant health burden in Egypt, largely attributable to the endemic prevalence of hepatitis B and C vi...

Predicting patients with septic shock and sepsis through analyzing whole-blood expression of NK cell-related hub genes using an advanced machine learning framework.

BACKGROUND: Sepsis is a life-threatening condition that causes millions of deaths globally each year. The need for biomarkers to predict the progressi...

-targeted AI-driven vaccines: a paradigm shift in gastric cancer prevention.

, a globally prevalent pathogen Group I carcinogen, presents a formidable challenge in gastric cancer prevention due to its increasing antimicrobial r...

Machine Learning Models as Early Warning Systems for Neonatal Infection.

Neonatal infections pose a significant threat to the health of newborns. Associated morbidity and mortality risks underscore the urgency of prompt dia...