Infectious Disease

Prevalence of fungal colonization among patients with psoriasis in difficult-to-treat areas: impact of apremilast on mycotic burden and clinical outcomes.

INTRODUCTION: Fungi, including , may be a trigger or exacerbate psoriasis, especially in difficult to treat (DTT) areas, through the activation of IL-17/23 axis.

An explainable deep learning platform for molecular discovery.

Deep learning approaches have been increasingly applied to the discovery of novel chemical compounds. These predictive approaches can accurately model compounds and increase true discovery rates, but they are typically black box in nature and do not generate specific chemical insights. Explainable deep learning aims to 'open up' the black box by providing generalizable and human-understandable rea...

Schistosomiasis transmission: A machine learning analysis reveals the importance of agrochemicals on snail abundance in Rwanda.

BACKGROUND: Schistosomiasis is an important snail-borne parasitic disease whose transmission is exacerbated by water resource management activities. I...

Comparative study of DCNN and image processing based classification of chest X-rays for identification of COVID-19 patients using fine-tuning.

The conventional detection of COVID-19 by evaluating the CT scan images is tiresome, often experiences high inter-observer variability and uncertainty...

Genomic analysis, culturing optimization, and characterization of bacteriophage OSYSP, previously studied as effective pathogen control on fresh produce.

Advances in bacteriophage genome sequencing and regulatory approvals of some bacteriophages in various applications have renewed interest in these ant...

Machine learning-enhanced assessment of potential probiotics from healthy calves for the treatment of neonatal calf diarrhea.

Neonatal calf diarrhea (NCD) remains a significant contributor to calf mortality within the first 3 weeks of life, prompting widespread antibiotic use...

The isolation, bioactivity, and synthesis of natural products from with anti-HIV activities.

Natural products isolated from have attracted considerable attention from the chemical community due to their unique structures and promising anti-HI...

Machine learning based algorithms for virtual early detection and screening of neurodegenerative and neurocognitive disorders: a systematic-review.

BACKGROUND AND AIM: Neurodegenerative disorders (e.g., Alzheimer's, Parkinson's) lead to neuronal loss; neurocognitive disorders (e.g., delirium, deme...

Combined effect of Tetracycline compounds and essential oils on antimicrobial resistant isolated from the swine food chain.

Antimicrobial resistance (AMR) poses risks for food stakeholders because of the spread of resistant microbes and potential foodborne diseases. In exam...

Antimycobacterial and Antifungal Activities of Leaf Extracts From .

The global problem of infectious and deadly diseases caused by microbes such as candida and mycobacteria presents major scientific and medical challen...

A novel approach to antimicrobial resistance: Machine learning predictions for carbapenem-resistant Klebsiella in intensive care units.

This study was conducted at Kocaeli University Hospital in Turkey and aimed to predict carbapenem-resistant Klebsiella pneumoniae infection in intensi...

VaxOptiML: leveraging machine learning for accurate prediction of MHC-I and II epitopes for optimized cancer immunotherapy.

Cancer immunotherapy hinges on accurate epitope prediction for advancing vaccine development. VaxOptiML (available at https://vaxoptiml.streamlit.app/...

Deciphering the climate-malaria nexus: A machine learning approach in rural southeastern Tanzania.

OBJECTIVES: Malaria remains a critical public health challenge, especially in regions like southeastern Tanzania. Understanding the intricate relation...

[PSI]-CIC: A Deep-Learning Pipeline for the Annotation of Sectored Saccharomyces cerevisiae Colonies.

The prion phenotype in yeast manifests as a white, pink, or red color pigment. Experimental manipulations destabilize prion phenotypes, and allow col...

Prediction of mortality in sepsis patients using stacked ensemble machine learning algorithm.

INTRODUCTION: Machine learning (ML) has been tried in predicting outcomes following sepsis. This study aims to identify the utility of stacked ensembl...

A machine learning-based risk score for prediction of mechanical ventilation in children with dengue shock syndrome: A retrospective cohort study.

BACKGROUND: Patients with severe dengue who develop severe respiratory failure requiring mechanical ventilation (MV) support have significantly increa...

Heparin in sepsis: current clinical findings and possible mechanisms.

Sepsis is a clinical syndrome resulting from the interaction between coagulation, inflammation, immunity and other systems. Coagulation activation is ...

Novel active Trp- and Arg-rich antimicrobial peptides with high solubility and low red blood cell toxicity designed using machine learning tools.

BACKGROUND: Given the rising number of multidrug-resistant (MDR) bacteria, there is a need to design synthetic antimicrobial peptides (AMPs) that are ...

Machine learning-based prediction of antibiotic resistance in Mycobacterium tuberculosis clinical isolates from Uganda.

BACKGROUND: Efforts toward tuberculosis management and control are challenged by the emergence of Mycobacterium tuberculosis (MTB) resistance to exist...

MLVICX: Multi-Level Variance-Covariance Exploration for Chest X-Ray Self-Supervised Representation Learning.

Self-supervised learning (SSL) reduces the need for manual annotation in deep learning models for medical image analysis. By learning the representati...