Infectious Disease

An extensive review on infectious disease diagnosis using machine learning techniques and next generation sequencing: State-of-the-art and perspectives.

UNLABELLED: Infectious diseases, including tuberculosis (TB), HIV/AIDS, and emerging pathogens like COVID-19 pose severe global health challenges due to their rapid spread and significant morbidity and mortality rates. Next-generation sequencing (NGS) and machine learning (ML) have emerged as transformative technologies for enhancing disease diagnosis and management.

Construction and validation of a predictive model for meningoencephalitis in pediatric scrub typhus based on machine learning algorithms.

To retrospectively analyze the clinical characteristics of pediatric scrub typhus (ST) with meningoencephalitis (STME) and to construct and validate predictive models using machine learning.Clinical data were collected from 100 cases of pediatric STME and matched with data from 100 ST cases without meningitis using propensity-score matching. Risk factors for STME in pediatrics were identified thro...

Deep mutational learning for the selection of therapeutic antibodies resistant to the evolution of Omicron variants of SARS-CoV-2.

Most antibodies for treating COVID-19 rely on binding the receptor-binding domain (RBD) of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2...

Artificial intelligence-based deep learning algorithms for ground-glass opacity nodule detection: A review.

Ground-glass opacities (GGOs) are hazy opacities on chest computed tomography (CT) scans that can indicate various lung diseases, including early COVI...

Epidemiology and risk factors of Clonorchis sinensis infection in the mountainous areas of Longsheng County, Guangxi: insights from automated machine learning.

Clonorchis sinensis (C. sinensis) is mainly prevalent in Northeast and South China, with Guangxi being the most severely affected region. This study a...

Characterizing Public Sentiments and Drug Interactions in the COVID-19 Pandemic Using Social Media: Natural Language Processing and Network Analysis.

BACKGROUND: While the COVID-19 pandemic has induced massive discussion of available medications on social media, traditional studies focused only on l...

Sustainable extraction of phytochemicals from Mentha arvensis using supramolecular eutectic solvent via microwave Irradiation: Unveiling insights with CatBoost-Driven feature analysis.

The present study revealed the higher extraction potential of sustainable choline chloride (ChCl) and ethylene glycol (EG) based deep eutectic solvent...

An AI-Based Clinical Decision Support System for Antibiotic Therapy in Sepsis (KINBIOTICS): Use Case Analysis.

BACKGROUND: Antimicrobial resistances pose significant challenges in health care systems. Clinical decision support systems (CDSSs) represent a potent...

Automated classification of chest X-rays: a deep learning approach with attention mechanisms.

BACKGROUND: Pulmonary diseases such as COVID-19 and pneumonia, are life-threatening conditions, that require prompt and accurate diagnosis for effecti...

Fluorescent sensor array for rapid bacterial identification using antimicrobial peptide-functionalized gold nanoclusters and machine learning.

Bacterial infectious diseases pose significant challenges to public health, emphasizing the need for rapid and accurate diagnostic tools. Here, we int...

CT Differentiation and Prognostic Modeling in COVID-19 and Influenza A Pneumonia.

RATIONALE AND OBJECTIVES: This study aimed to compare CT features of COVID-19 and Influenza A pneumonia, develop a diagnostic differential model, and ...

Deep learning models for early and accurate diagnosis of ventilator-associated pneumonia in mechanically ventilated neonates.

BACKGROUND: Early and accurate confirmation of critically ill neonates with a suspected diagnosis of ventilator-associated pneumonia (VAP) can optimiz...

A dataset for machine learning-based QSAR models establishment to screen beta-lactamase inhibitors using the FARM -BIOMOL chemical library.

OBJECTIVES: Beta-lactamase is a bacterial enzyme that deactivates beta-lactam antibiotics, and it is one of the leading causes of antibiotic resistanc...

Migrative armadillo optimization enabled a one-dimensional quantum convolutional neural network for supply chain demand forecasting.

Demand forecasting is a quite challenging task, which is sensitive to several factors such as endogenous and exogenous parameters. In the context of s...

Interpretable machine learning model for early morbidity risk prediction in patients with sepsis-induced coagulopathy: a multi-center study.

BACKGROUND: Sepsis-induced coagulopathy (SIC) is a complex condition characterized by systemic inflammation and coagulopathy. This study aimed to deve...

Antibacterial and antibiofilm activities of star anise-cinnamon essential oil against multidrug-resistant Thompson.

INTRODUCTION: The emergence of foodborne multidrug-resistant (MDR) has attracted considerable global attention. Given that food is the primary transm...

Efficacy and Safety of a Medical Robot for Non-Face-to-Face Nasopharyngeal Swab Specimen Collection: Nonclinical and Clinical Trial Findings for COVID-19 Testing.

ObjectivesTo meet the high demand for polymerase chain reaction (PCR) tests to diagnose COVID-19 and rapidly control the outbreak, an efficient and sa...

A privacy-preserving dependable deep federated learning model for identifying new infections from genome sequences.

The traditional molecular-based identification (TMID) technique of new infections from genome sequences (GSs) has made significant contributions so fa...

CEL: A Continual Learning Model for Disease Outbreak Prediction by Leveraging Domain Adaptation via Elastic Weight Consolidation.

Continual learning is the ability of a model to learn over time without forgetting previous knowledge. Therefore, adapting new data in dynamic fields ...

Integrating convolutional layers and biformer network with forward-forward and backpropagation training.

Accurate molecular property prediction is crucial for drug discovery and computational chemistry, facilitating the identification of promising compoun...