Infectious Disease

Latest AI and machine learning research in infectious disease for healthcare professionals.

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UniAMP: enhancing AMP prediction using deep neural networks with inferred information of peptides.

Antimicrobial peptides (AMPs) have been widely recognized as a promising solution to combat antimicr...

Leveraging explainable AI and large-scale datasets for comprehensive classification of renal histologic types.

Recently, as the number of cancer patients has increased, much research is being conducted for effic...

Utilizing artificial intelligence and cellular population data for timely identification of bacteremia in hospitalized patients.

BACKGROUND: Bacteremia is a critical condition with high mortality that requires prompt detection to...

A graph neural network-based model with out-of-distribution robustness for enhancing antiretroviral therapy outcome prediction for HIV-1.

Predicting the outcome of antiretroviral therapies (ART) for HIV-1 is a pressing clinical challenge,...

FocusUNet: Pioneering dual attention with gated U-Net for colonoscopic polyp segmentation.

The detection and excision of colorectal polyps, precursors to colorectal cancer (CRC), can improve ...

deep-AMPpred: A Deep Learning Method for Identifying Antimicrobial Peptides and Their Functional Activities.

Antimicrobial peptides (AMPs) are small peptides that play an important role in disease defense. As ...

Prediction of mortality risk in patients with severe community-acquired pneumonia in the intensive care unit using machine learning.

The aim of this study was to develop and validate a machine learning-based mortality risk prediction...

AI based medical imagery diagnosis for COVID-19 disease examination and remedy.

COVID-19, caused by the SARS-CoV-2 coronavirus, has spread to more than 200 countries, affecting mil...

Combining machine learning and single-cell sequencing to identify key immune genes in sepsis.

This research aimed to identify novel indicators for sepsis by analyzing RNA sequencing data from pe...

Developing and validating a machine learning model to predict multidrug-resistant -related septic shock.

BACKGROUND: Multidrug-resistant Klebsiella pneumoniae (MDR-KP) infections pose a significant global ...

Amphotericin B tissue penetration and pharmacokinetics in healthy and -infected rats: insights from microdialysis and population modeling.

INTRODUCTION: This study evaluated the relationship between total plasma and free kidney concentrati...

The influence of factors related to public health campaigns on vaccination behavior among population of Wuxi region, China.

BACKGROUND: Public health campaigns are essential for promoting vaccination behavior, but factors su...

Machine Learning Approach for Sepsis Risk Assessment in Ischemic Stroke Patients.

BackgroundIschemic stroke is a critical neurological condition, with infection representing a signif...

Prediction of urinary tract infection using machine learning methods: a study for finding the most-informative variables.

BACKGROUND: Urinary tract infection (UTI) is a frequent health-threatening condition. Early reliable...

Multi-region infectious disease prediction modeling based on spatio-temporal graph neural network and the dynamic model.

Human mobility between different regions is a major factor in large-scale outbreaks of infectious di...

Prognostic value of HSP27 in 28-day mortality in septic ICU patients: a retrospective cohort study.

BACKGROUND: This study aimed to investigate the association between serum heat shock protein 27 (HSP...

Phenotypic antibiotic resistance prediction using antibiotic resistance genes and machine learning models in Mannheimia haemolytica.

Mannheimia haemolytica is one of the most common causative agents of bovine respiratory disease (BRD...

Virtual Gram staining of label-free bacteria using dark-field microscopy and deep learning.

Gram staining has been a frequently used staining protocol in microbiology. It is vulnerable to stai...

Open-set deep learning-enabled single-cell Raman spectroscopy for rapid identification of airborne pathogens in real-world environments.

Pathogenic bioaerosols are critical for outbreaks of airborne disease; however, rapidly and accurate...

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