Infectious Disease

A scalable tool for analyzing genomic variants of humans using knowledge graphs and graph machine learning.

Advances in high-throughput genome sequencing have enabled large-scale genome sequencing in clinical practice and research studies. By analyzing genomic variants of humans, scientists can gain better understanding of the risk factors of complex diseases such as cancer and COVID-19. To model and analyze the rich genomic data, knowledge graphs (KGs) and graph machine learning (GML) can be regarded a...

Serum decorin and biglycan levels as predictive biomarkers for lung fibrosis severity and mortality risk in COVID-19 patients.

INTRODUCTION: Individuals experiencing severe symptoms of COVID-19 are at the greatest risk of developing post-COVID lung fibrosis, which significantly impacts long-term health outcomes. This study aims to investigate the predictive potential of serum biomarkers, specifically decorin and biglycan, in assessing the severity and mortality risk among COVID-19 patients.

Humoral and cell-mediated immune responses to COVID-19 vaccines up to 6 months post three-dose primary series in adults with inborn errors of immunity and their breakthrough infections.

PURPOSE: Many individuals with inborn errors of immunity (IEIs) have poor humoral immune (HI) vaccine responses. Only a few studies have examined spec...

Under-representation for Female Pelvis Cancers in Commercial Auto-segmentation Solutions and Open-source Imaging Datasets.

AIM: Artificial intelligence (AI) based auto-segmentation aids radiation therapy (RT) workflows and is being adopted in clinical environments facilita...

Classification of chicken Eimeria species through deep transfer learning models: A comparative study on model efficacy.

Eimeria is a protozoan parasite that causes coccidiosis in various animal species, especially in chickens, resulting in infections characterized by in...

Computational biology and artificial intelligence in mRNA vaccine design for cancer immunotherapy.

Messenger RNA (mRNA) vaccines offer an adaptable and scalable platform for cancer immunotherapy, requiring optimal design to elicit a robust and targe...

An effective COVID-19 classification in X-ray images using a new deep learning framework.

BackgroundThe global concern regarding the diagnosis of lung-related diseases has intensified due to the rapid transmission of coronavirus disease 201...

The efficiency of artificial intelligence for management and clinical decision-making in the identification of patients with hidden HCV infection (Intelligen-C strategy).

INTRODUCTION: Artificial intelligence (AI) allows the optimization of diagnostic processes for hepatitis C virus (HCV) patients. Our objective was to ...

Artificial Neural Network-Based Validation, DFT, Thermal and Biological Evaluation of 4-Aminoantipyrine-Derived Ru(III) Complexes.

New methodologies have been evaluated for validating analytical characterization with artificial neural networks (ANNs). Compared to previous machine ...

Supervised learning approaches for predicting Ebola-Human Protein-Protein interactions.

The goal of this research work is to predict protein-protein interactions (PPIs) between the Ebola virus and the host who is at risk of infection. Sin...

Task-oriented robotic rehabilitation for back mobility and functioning in a post-intensive care unit obese patient: A case report.

BackgroundIntensive care unit (ICU) acquired weakness is a detrimental condition characterized by muscle weakness, difficulty in weaning from mechanic...

Explainable deep learning and virtual evolution identifies antimicrobial peptides with activity against multidrug-resistant human pathogens.

Artificial intelligence (AI) is a promising approach to identify new antimicrobial compounds in diverse microbial species. Here we developed an AI-bas...

Bacterial Wastewater-Based Epidemiology Using Surface-Enhanced Raman Spectroscopy and Machine Learning.

Although wastewater-based epidemiology has been used extensively for the surveillance of viral diseases, it has not been used to a similar extent for ...

Mitigating bias in AI mortality predictions for minority populations: a transfer learning approach.

BACKGROUND: The COVID-19 pandemic has highlighted the crucial role of artificial intelligence (AI) in predicting mortality and guiding healthcare deci...

Field-scale detection of Bacterial Leaf Blight in rice based on UAV multispectral imaging and deep learning frameworks.

Bacterial Leaf Blight (BLB) usually attacks rice in the flowering stage and can cause yield losses of up to 50% in severely infected fields. The resul...

W70: a probiotic with capacity to enhance ammonia assimilation in the rumen.

Ruminants excrete both undigested and excess consumed nitrogen (N) through feces and urine, which results in the waste of feed protein and environment...

Predicting work ability impairment in post COVID-19 patients: a machine learning model based on clinical parameters.

The Post COVID-19 condition (PCC) is a complex disease affecting health and everyday functioning. This is well reflected by a patient's inability to w...

Artificial intelligence and machine learning in veterinary medicine: a regulatory perspective on current initiatives and future prospects.

The US FDA's Center for Veterinary Medicine (CVM) is advancing its leadership in veterinary science by integrating AI and machine learning (ML) into i...

Machine learning models for predicting indoor airborne fungal concentrations in public facilities utilizing environmental variables.

Airborne fungi are major contributors to substandard indoor air quality, with potential implications for public health, especially in public facilitie...

Applications of Machine Learning Approaches for the Discovery of SARS-CoV-2 PLpro Inhibitors.

The global impact of SARS-CoV-2 highlights the need for treatments beyond vaccination, given the limited availability of effective medications. While ...