Infectious Disease

Practical adaptability of a pre-hospital prognostic prediction model for patients following out-of-hospital cardiac arrest during the COVID-19 pandemic.

OBJECTIVES: The overwhelmed situation under the COVID-19 pandemic has worsened the quality of emergency medical care and the mortality rate due to out-of-hospital cardiac arrest (OHCA). However, there has been no research conducted for the validation of prognostic prediction models for OHCA patients using data collected during the pandemic. We sought to develop a pre-hospital prediction model for ...

Telehealth Approaches for Pediatric Otitis Media and Clinical Outcomes: Scoping Review.

BACKGROUND: Otitis media (OM) is a common pediatric infection worldwide. Conventionally, accurate diagnosis depends on in-person pneumatic otoscopy, which is not always accessible, contributing to delayed care and inappropriate prescribing, especially in underserved settings. Rapid advances in telemedicine and digital tools have accelerated the development of remote approaches for assessing pediat...

Development and Validation of Machine Learning Model to Predict Refractory Septic Shock.

This study developed and validated a machine learning model to predict refractory septic shock in patients with sepsis admitted to a tertiary care cen...

Machine learning-assisted Zr-MOFFe@SECHA fluorescence and colorimetric dual-mode intelligent platform for sensitive LPS detection.

BACKGROUND: Lipopolysaccharide (LPS) is a crucial diagnosis biomarker for sepsis. Accurate and sensitive determination of LPS in real samples (human s...

Context-aware synthetic promoter design using neural networks enables rewiring of eukaryotic transcriptional networks.

Gene regulation through promoter engineering is a cornerstone of synthetic biology, enabling precise control over transcriptional networks. However, e...

A generative explainable model for antimicrobial peptide prediction using bidirectional temporal convolutional neural network.

Advances in artificial intelligence (AI) and multi-omics integration are reshaping precision oncology by enabling deeper mechanistic understanding, im...

Corticomorphic Hybrid CNN-SNN Architecture for EEG-Based Low-Footprint Low-Latency Auditory Attention Detection.

PURPOSE: In the context of a multi-speaker "cocktail party" scenario where listeners selectively focus on specific speakers, human auditory attention ...

Machine learning-based prediction of BV-relevant probiotic functional potential in vaginal-derived Lactobacillus crispatus strains.

The vaginal microbiota plays a pivotal role in maintaining female reproductive health and homeostasis. However, most currently available Lactobacillus...

Machine learning prediction of tuberculosis mortality: a comparative analysis of random survival forest and cox regression models.

Characterising responses in group-housed pigs to Salmonella typhimurium infection through integrated computer vision-based behavioural monitoring and statistical analyses.

BACKGROUND: Health monitoring is crucial for early disease detection and prompt intervention to mitigate the disease. Computer vision is one of the no...

Metabolite-mediated plant immunity: From traditional defensors to artificial elicitors.

The accelerating global population growth and increasing environmental pressures driven by climate change have made food security an urgent scientific...

Predicting risk of Plasmodium vivax microscopy-detected episodes using serological markers in patients with Plasmodium falciparum malaria: a multi-country diagnostic performance evaluation.

BACKGROUND: Plasmodium vivax presents a significant obstacle to malaria elimination due to its capacity to form dormant liver-stage hypnozoites that c...

Noninvasive Detection of Occult yet Significant Liver Pathology in Alanine Aminotransferase-Normal Chronic Hepatitis B: A Multicenter Study.

BACKGROUND: Individuals with chronic hepatitis B (CHB) may harbor occult yet significant liver pathology despite normal alanine aminotransferase (ALT)...

Development and Validation of a Machine Learning-Based Nomogram for Predicting Severe Pneumocystis jirovecii Pneumonia in Kidney Transplant Recipients.

BACKGROUND Pneumocystis jirovecii pneumonia (PJP) is a life-threatening opportunistic infection in kidney transplant recipients (KTRs). Early identifi...

Novel Deep-Learning Unsupervised Domain Adaptation Method for Mitigating Batch, Strain, and Instrument Variations to Enhance Raman Spectroscopy-Based Bacterial Pathogen Identification.

The escalating threat of antibiotic resistance demands a rapid and accurate pathogen identification. While Raman spectroscopy combined with deep learn...

Machine learning identifies novel signatures of antifungal drug resistance in Saccharomycotina yeasts.

Antifungal drug resistance is a major challenge in fungal infection management. Numerous genomic changes are known to contribute to acquired drug resi...

Value of Endobronchial Ultrasound-Derived Radiomics in Differentiating Pulmonary Sarcoidosis from Mediastinal Lymph Node Tuberculosis.

INTRODUCTION: To explore the feasibility of an ultrasound radiomics machine learning model based on endobronchial ultrasound (EBUS) for differentiatin...

Single-Cell Nanomotion and Machine Learning for Parallel Bacterial Identification and Antibiotic Screening.

Rapid and accurate identification of bacterial infections and their resistance to antibiotics is critical to effective clinical decision-making and co...

The Integration of a Diplomacy Lab in a Registered Nurse to Bachelor of Science in Nursing Research and Informatics Course: A Qualitative Study.

BACKGROUND: Nursing education requires innovative models to prepare practice-ready nurses with global health competencies and real-world research expe...

A lightweight deep learning framework for reliable microscopy-based diagnosis of cutaneous leishmaniasis.

Cutaneous leishmaniasis (CL) is a neglected tropical and zoonotic disease affecting both human and animal health, for which microscopic examination of...