Universal precautions

Identification of Chlamydia pneumoniae and NLRP3 inflammasome activation in Alzheimer's disease retina.

Chlamydia pneumoniae is an intracellular bacterium implicated in Alzheimer's disease (AD), but its role in retinal pathology and disease progression is unclear. Here we identify Chlamydia pneumoniae inclusions in the retina, showing higher burden in AD retina and brain, increasing with APOEε4, disease stage, and cognitive deficit. Retinal and cortical proteomics reveal bacterial-infection and rela...

Water hyacinth detection for autonomous navigation mapping using image segmentation cascaded classifier.

Water hyacinth (Eichhornia crassipes) is among the world's most aggressive invasive aquatic weeds. Its rapid proliferation forms thick floating mats that block sunlight, deplete dissolved oxygen, impede navigation, degrade water quality, and severely threaten aquatic biodiversity and livelihoods, particularly in tropical and subtropical regions. We present a low-cost, fully autonomous catamaran sy...

A Machine Learning Approach to Deciphering the Genomic Basis of Host Specificity and Geographic Origin in Salmonella Kentucky.

In the U.S., Salmonella Kentucky is frequently isolated from food animals, but human cases are often linked to international travel. The objectives of...

Active Adversarial Noise Suppression for Image Forgery Localization.

Recent advances in deep learning have significantly propelled the development of image forgery localization. However, existing models remain highly vu...

Enhanced language models for predicting and understanding HIV care disengagement: a case study in Tanzania.

Sustained engagement in HIV care and adherence to ART are crucial for meeting the UNAIDS "95-95-95" targets. Disengagement from care remains a signifi...

Flemboda artificial intelligence: hybrid fuzzy-convolutional neural network for efficient chromosome abnormality classification.

Chromosomal abnormality detection is a fundamental task in clinical genetics, as accurate identification of structural and numerical defects is essent...

Classification and detection of Salmonella, Escherichia coli O157:H7, and Listeria monocytogenes using Fourier-transform near infrared spectroscopy coupled with machine learning.

This study was conducted to investigate the potential use of Fourier-transform near-infrared (FT-NIR) spectroscopy combined with machine learning (ML)...

Large-scale serum N-glycomics tracks N-glycosylation dynamics in hepatocellular carcinoma progression and enables early diagnosis.

Hepatocellular carcinoma (HCC) is a leading cause of cancer mortality, yet the role of N-glycosylation in its pathogenesis remains unclear. We applied...

Combining mass spectrometry and machine learning models for predicting Klebsiella pneumoniae antimicrobial resistance: a multicenter experience from clinical isolates in Italy.

BACKGROUND: Multidrug-resistant Klebsiella pneumoniae represents a significant challenge in healthcare settings, prompting numerous studies on the rap...

Construction of musculoskeletal quantitative model based on deep learning and study of musculoskeletal relationship in patients with osteoporosis.

INTRODUCTION: Lumbar CT and MRI scans are helpful for osteoporosis (OP) screening. Deep learning enhances the efficiency and accuracy of musculoskelet...

Liver cancer risk stratification using deep learning on nationwide longitudinal health screening data: a retrospective cohort study.

BACKGROUND: Current liver cancer screening in Korea focuses on viral hepatitis or cirrhosis, despite rising risks from metabolic and alcohol-related l...

AI-Assisted Multiplex SYBR Green I-Based qPCR for the Identification and Quantitative Analysis of Oral Microbiota.

Conventional multiplex qPCR faces challenges in quantitative multipathogen detection due to limited fluorescent channels, primer interference, and the...

Foundation models enable wearable signal screening for cardiovascular disease among people living with HIV.

BACKGROUND: Cardiovascular disease screening faces significant challenges in resource-limited settings, where infrastructure and computational constra...

An interpretable machine learning model for prediction of significant liver fibrosis in comorbid chronic hepatitis B and nonalcoholic fatty liver disease: a retrospective development and validation study.

Predictive modeling and spatiotemporal analysis of TB in Argentina: Advancing control efforts through machine learning.

OBJECTIVES: To improve prediction and understanding of TB dynamics in Argentina, identifying key risk factors and high-incidence areas to inform surve...

Alzheimer's disease prediction via an explainable CNN using genetic algorithm and SHAP values.

Convolutional neural networks (CNNs) are widely recognized for their high precision in image classification. Nevertheless, the lack of transparency in...

Ethnic fermentation secrets of Northeast India and emerging functional food insights.

Northeast India is home to an incredible variety of ethnic fermented foods, shaped over centuries by cultural wisdom and adaptation to the local envir...

A Comparative Study of Machine Learning Models for Identification of Antiviral Peptides Using Various Encoded Features.

Viruses are a significant threat to human life, as demonstrated by the global COVID-19 pandemic and the Ebola outbreak. Diseases such as smallpox, AID...

Digital CRISPR-based diagnostics for quantification of Candida auris and resistance mutations.

Candida auris, an increasingly prevalent fungal pathogen, requires both rapid identification and antifungal susceptibility testing to enable proper tr...

Automated high-throughput microscopy screening unveiled new Listeria monocytogenes genes involved in cell infection.

To uncover novel genetic factors required for Listeria monocytogenes cell infection, we developed an automated high-throughput microscopy screening pi...