Infectious Disease

Perspective: Limiting Antimicrobial Resistance with Artificial Intelligence/Machine Learning.

The author traces his experience with the application of computers in clinical microbiology over the past 60 years, specifically in directing clinicians to treat bacterial infections diagnosed by the laboratory and the antibacterial agent(s) that could be used to treat those infections. Appropriate use of antibiotics will result in reduced antimicrobial resistance, which is increasing worldwide. A...

Characterizing adjuvants' effects at murine immunoglobulin repertoire level.

Generating large-scale, high-fidelity sequencing data is challenging and, furthermore, not much has been done to characterize adjuvants' effects at the repertoire level. Thus, we introduced an IgSeq pipeline that standardized library prep protocols and data analysis functions for accurate repertoire profiling. We then studied systemically effects of CpG and Alum on the Ig heavy chain repertoire us...

Effects of User-Reported Risk Factors and Follow-Up Care Activities on Satisfaction With a COVID-19 Chatbot: Cross-Sectional Study.

BACKGROUND: The COVID-19 pandemic influenced many to consider methods to reduce human contact and ease the burden placed on health care workers. Conve...

Deployment and assessment of a deep learning model for real-time detection of anal precancer with high frame rate high-resolution microendoscopy.

Anal cancer incidence is significantly higher in people living with HIV as HIV increases the oncogenic potential of human papillomavirus. The incidenc...

From the diagnosis of infectious keratitis to discriminating fungal subtypes; a deep learning-based study.

Infectious keratitis (IK) is a major cause of corneal opacity. IK can be caused by a variety of microorganisms. Typically, fungal ulcers carry the wor...

EFFICIENT ESTIMATION OF THE MAXIMAL ASSOCIATION BETWEEN MULTIPLE PREDICTORS AND A SURVIVAL OUTCOME.

This paper develops a new approach to post-selection inference for screening high-dimensional predictors of survival outcomes. Post-selection inferenc...

Diagnosis of COVID-19 with simultaneous accurate prediction of cardiac abnormalities from chest computed tomographic images.

COVID-19 has potential consequences on the pulmonary and cardiovascular health of millions of infected people worldwide. Chest computed tomographic (C...

Machine learning identification of Pseudomonas aeruginosa strains from colony image data.

When grown on agar surfaces, microbes can produce distinct multicellular spatial structures called colonies, which contain characteristic sizes, shape...

Effect of a 2-week interruption in methotrexate treatment on COVID-19 vaccine response in people with immune-mediated inflammatory diseases (VROOM study): a randomised, open label, superiority trial.

BACKGROUND: Methotrexate is the first-line treatment for immune-mediated inflammatory diseases and reduces vaccine-induced immunity. We evaluated if a...

Diagnosis and detection of pneumonia using weak-label based on X-ray images: a multi-center study.

PURPOSE: Development and assessment the deep learning weakly supervised algorithm for the classification and detection pneumonia via X-ray.

MRI advances in the imaging diagnosis of tuberculous meningitis: opportunities and innovations.

Tuberculous meningitis (TBM) is not only one of the most fatal forms of tuberculosis, but also a major public health concern worldwide, presenting gra...

A new method for clustered survival data: Estimation of treatment effect heterogeneity and variable selection.

We recently developed a new method random-intercept accelerated failure time model with Bayesian additive regression trees (riAFT-BART) to draw causal...

Evaluating the efficacy of artificial intelligence tools for the automation of systematic reviews in cancer research: A systematic review.

To evaluate the performance accuracy and workload savings of artificial intelligence (AI)-based automation tools in comparison with human reviewers in...

Accuracy of artificial intelligence CT quantification in predicting COVID-19 subjects' prognosis.

BACKGROUND: Artificial intelligence (AI)-aided analysis of chest CT expedites the quantification of abnormalities and may facilitate the diagnosis and...

Artificial intelligence applied to analyzes during the pandemic: COVID-19 beds occupancy in the state of Rio Grande do Norte, Brazil.

The COVID-19 pandemic is already considered one of the biggest global health crises. In Rio Grande do Norte, a Brazilian state, the RegulaRN platform ...

The Pediatric Data Science and Analytics Subgroup of the Pediatric Acute Lung Injury and Sepsis Investigators Network: Use of Supervised Machine Learning Applications in Pediatric Critical Care Medicine Research.

OBJECTIVE: Perform a scoping review of supervised machine learning in pediatric critical care to identify published applications, methodologies, and i...

Optimizing Signal Management in a Vaccine Adverse Event Reporting System: A Proof-of-Concept with COVID-19 Vaccines Using Signs, Symptoms, and Natural Language Processing.

INTRODUCTION: The Vaccine Adverse Event Reporting System (VAERS) has already been challenged by an extreme increase in the number of individual case s...

An adaptive ensemble deep learning framework for reliable detection of pandemic patients.

Nurses, often considered the backbone of global health services, are disproportionately vulnerable to COVID-19 due to their front-line roles. They con...

Comprehensive stiffness regulation on multi-section snake robot with considering the parasite motion and friction effects.

Snake robots have been widely used in challenging environments, such as confined spaces. However, most existing snake robots with large length/diamete...

Machine learning-driven development of a disease risk score for COVID-19 hospitalization and mortality: a Swedish and Norwegian register-based study.

AIMS: To develop a disease risk score for COVID-19-related hospitalization and mortality in Sweden and externally validate it in Norway.