Infectious Disease

Large-scale deep learning identifies the antiviral potential of PKI-179 and MTI-31 against coronaviruses.

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has led to the global pandemic of Coronavirus Disease (2019) (COVID-19), underscoring the urgency for effective antiviral drugs. Despite the development of different vaccination strategies, the search for specific antiviral compounds remains crucial. Here, we combine machine learning (ML) techniques with in vitro validation to efficientl...

A machine learning-based electronic nose for detecting neonatal sepsis: Analysis of volatile organic compound biomarkers in fecal samples.

BACKGROUND: Neonatal sepsis is a global health threat, contributing to high morbidity and mortality rates among newborns. Recognizing the profound impact of neonatal sepsis on long-term health outcomes emphasizes the critical need for timely detection to mitigate its consequences and ensure optimal health for the affected newborns. Currently, various diagnostic approaches have been implemented, bu...

Multiplexed bacterial recognition based on "All-in-One" semiconducting polymer dots sensor and machine learning.

The accurate discrimination of bacterial infection is imperative for precise clinical diagnosis and treatment. Here, this work presents a simplified s...

Does COVID-19 lockdown matter for air pollution in the short and long run in China? A machine learning approach to policy evaluation.

This paper leverages a data-driven two-step approach to effectively evaluate the effects of COVID-19 lockdown on air pollution in both the short and l...

Crowd-sourced machine learning prediction of long COVID using data from the National COVID Cohort Collaborative.

BACKGROUND: While many patients seem to recover from SARS-CoV-2 infections, many patients report experiencing SARS-CoV-2 symptoms for weeks or months ...

"Artificial intelligence-driven infection risk prediction in ventriculoperitoneal shunting: a novel approach for normal pressure hydrocephalus treatment".

Idiopathic normal pressure hydrocephalus (iNPH) affects approximately 1.5% of the population, with a higher prevalence in men than women. Ventriculope...

Development and validation of a machine-learning model for predicting postoperative pneumonia in aneurysmal subarachnoid hemorrhage.

Pneumonia is a common postoperative complication in patients with aneurysmal subarachnoid hemorrhage (aSAH), which is associated with poor prognosis a...

Prevention of adverse HIV treatment outcomes: machine learning to enable proactive support of people at risk of HIV care disengagement in Tanzania.

OBJECTIVES: This study aimed to develop a machine learning (ML) model to predict disengagement from HIV care, high viral load or death among people li...

Deep learning and feature reconstruction assisted vis-NIR calibration method for on-line monitoring of key growth indicators during kombucha production.

Artificial intelligence (AI) technology is advancing the digitization and intelligence development of the food industry. A promising application is us...

Metabolomic profiling of dengue infection: unraveling molecular signatures by LC-MS/MS and machine learning models.

BACKGROUND & OBJECTIVE: The progression of dengue fever to severe dengue (SD) is a major public health concern that impairs the capacity of the medica...

AI-Safe-C score: Assessing liver-related event risks in patients without cirrhosis after successful direct-acting antiviral treatment.

BACKGROUND & AIMS: Direct-acting antivirals (DAAs) have considerably improved chronic hepatitis C (HCV) treatment; however, follow-up after sustained ...

Optimization of fungal secondary metabolites production via response surface methodology coupled with multi-parameter optimized artificial neural network model.

Filamentous fungi's secondary metabolites (SMs) possess significant application owing to their distinct structure and diverse bioactivities, yet their...

Integrating deep learning techniques for effective river water quality monitoring and management.

Effective river water quality monitoring is essential for sustainable water resource management. In this study, we established a comprehensive monitor...

Diagnostic performance of an automated robot for MALDI target preparation in microbial identification.

The MBT Pathfinder is an automated colony-picking robot designed for efficient sample preparation in matrix-assisted laser desorption/ionization time-...

Investigating Older Adults' Use of a Socially Assistive Robot via Time Series Clustering and User Profiling: Descriptive Analysis Study.

BACKGROUND: The aging population and the shortage of geriatric care workers are major global concerns. Socially assistive robots (SARs) have the poten...

Predictive modeling of hepatitis B viral dynamics: a caputo derivative-based approach using artificial neural networks.

A fractional model for the kinetics of hepatitis B transmission was developed. The hepatitis B virus significantly affects the world's economic and he...

Identifying severe community-acquired pneumonia using radiomics and clinical data: a machine learning approach.

Evaluating Community-Acquired Pneumonia (CAP) is crucial for determining appropriate treatment methods. In this study, we established a machine learni...

What explains adolescents' physical activity and sports participation during the COVID-19 pandemic? - an interpretable machine learning approach.

Adolescents' physical activity (PA) and sports participation declined due to the COVID-19 pandemic. This study aimed to determine the critical socio-e...

Text mining of verbal autopsy narratives to extract mortality causes and most prevalent diseases using natural language processing.

Verbal autopsy (VA) narratives play a crucial role in understanding and documenting the causes of mortality, especially in regions lacking robust medi...

Human lung cancer classification and comprehensive analysis using different machine learning techniques.

Lung cancer is the most common causes of death among all cancer-related diseases. A lung scan examination of the patient is the primary diagnostic tec...