Infectious Disease

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 the risk of developing delirium in patients with sepsis and to explore the impact of delirium on the 28-day survival rate of patients.

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

BACKGROUND AND OBJECTIVE: Sepsis-induced pathophysiological changes may lead to pharmacokinetic variability which alters antibiotic concentrations at the host infection site. This poses a challenge in clinical practice, as sufficient antibiotic concentrations in tissue are necessary to effectively eradicate bacterial pathogens. In this exploratory study, we aimed to evaluate the potential of routi...

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 seriously reduce productivity and quality, therefor...

Association between the (neutrophil + monocyte)/albumin ratio and all-cause mortality in sepsis patients: a retrospective cohort study and predictive model establishment according to machine learning.

INTRODUCTION: Sepsis is a life-threatening condition characterized by widespread inflammatory response syndrome in the body resulting from infection. ...

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 influenza sampling and surveillance of wholesale an...

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 significant global public health threat. HIV co-infectio...

A machine learning model for predicting acute respiratory distress syndrome risk in patients with sepsis using circulating immune cell parameters: a retrospective study.

BACKGROUND: Acute respiratory distress syndrome (ARDS) is a severe complication associated with a high mortality rate in patients with sepsis. Early 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 pneumoniae pneumonia (SMPP) in order to provide re...

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 developing therapies, diagnostics, and vaccine candidate...

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 modality for diagnosing COVID-19 pneumonia. In clinical ...

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 morbidity rates worldwide. Socioeconomic, environmental,...

Artificial Intelligence Models for Pediatric Lung Sound Analysis: Systematic Review and Meta-Analysis.

BACKGROUND: Pediatric respiratory diseases, including asthma and pneumonia, are major causes of morbidity and mortality in children. Auscultation of l...

Predicting mortality and risk factors of sepsis related ARDS using machine learning models.

Sepsis related acute respiratory distress syndrome (ARDS) is a common and serious disease in clinic. Accurate prediction of in-hospital mortality of p...

Machine learning-led semi-automated medium optimization reveals salt as key for flaviolin production in Pseudomonas putida.

Although synthetic biology can produce valuable chemicals in a renewable manner, its progress is still hindered by a lack of predictive capabilities. ...

MALDI-TOF mass spectrometry combined with machine learning algorithms to identify protein profiles related to malaria infection in human sera from Côte d'Ivoire.

BACKGROUND: In sub-Saharan Africa, Plasmodium falciparum is the most prevalent species of malaria parasites. In endemic areas, malaria is mainly diagn...

Performance and hypothetical clinical impact of an mNGS-based machine learning model for antimicrobial susceptibility prediction of five ESKAPEE bacteria.

UNLABELLED: Antimicrobial resistance is an escalating global health crisis, underscoring the urgent need for timely and targeted therapies to ensure e...

Multiscale metal-based nanocomposites for bone and joint disease therapies.

Bone and joint diseases are debilitating conditions that can result in significant functional impairment or even permanent disability. Multiscale meta...

Deep recurrent neural network with fractional addax optimization algorithm for influenza virus host prediction.

The accurate prediction of the host of influenza viruses is a significant challenge in bioinformatics, as it is crucial for understanding viral transm...

Large-Scale Deep Learning-Enabled Infodemiological Analysis of Substance Use Patterns on Social Media: Insights From the COVID-19 Pandemic.

BACKGROUND: The COVID-19 pandemic intensified the challenges associated with mental health and substance use (SU), with societal and economic upheaval...

Early prediction of sepsis associated encephalopathy in elderly ICU patients using machine learning models: a retrospective study based on the MIMIC-IV database.

BACKGROUND: Sepsis associated encephalopathy (SAE) is prevalent among elderly patients in the ICU and significantly affects patient prognosis. Due to ...