Infectious Disease

New solutions for antibiotic discovery: Prioritizing microbial biosynthetic space using ecology and machine learning.

With the explosive increase in genome sequence data, perhaps the major challenge in natural-product-based drug discovery is the identification of gene clusters most likely to specify new chemistry and bioactivities. We discuss the challenges and state-of-the-art of antibiotic discovery based on ecological principles, genome mining and artificial intelligence.

Deep Ensemble for Central Serous Microscopic Retinopathy Detection in Retinal Optical Coherence Tomographic Images.

The retina is an important part of the eye that aids in focusing light and visual recognition to the brain. Hence, its damage causes vision loss in the human eye. Central serous retinopathy is a common retinal disorder in which serous detachment occurs at the posterior pole of the retina. Therefore, detection of CSR at an early stage with good accuracy can decrease the rate of vision loss and reco...

Targeting Bacterial RNA Polymerase: Harnessing Simulations and Machine Learning to Design Inhibitors for Drug-Resistant Pathogens.

The increase in antimicrobial resistance presents a major challenge in treating bacterial infections, underscoring the need for innovative drug discov...

Enhancing e-waste management: a novel light gradient AdaBoost support vector classification approach.

The global consequences of electronic waste significantly affect the environment and human health. Accurate classification is essential for effective ...

Development and evaluation of USCnet: an AI-based model for preoperative prediction of infectious and non-infectious urolithiasis.

BACKGROUND: Urolithiasis, a prevalent condition characterized by a high rate of incidence and recurrence, necessitates accurate preoperative diagnosti...

Artificial intelligence models predicting abnormal uterine bleeding after COVID-19 vaccination.

The rapid deployment of COVID-19 vaccines has necessitated the ongoing surveillance of adverse events, with abnormal uterine bleeding (AUB) emerging a...

Application of Machine Learning for FOS/TAC Soft Sensing in Bio-Electrochemical Anaerobic Digestion.

This study explores the application of various machine learning (ML) models for the real-time prediction of the FOS/TAC ratio in microbial electrolysi...

Machine Learning-Based Prediction of Postoperative Pneumonia Among Super-Aged Patients With Hip Fracture.

BACKGROUND: Hip fractures have become a significant health concern, particularly among super-aged patients, who were at a high risk of postoperative p...

Complete Blood Count and Monocyte Distribution Width-Based Machine Learning Algorithms for Sepsis Detection: Multicentric Development and External Validation Study.

BACKGROUND: Sepsis is an organ dysfunction caused by a dysregulated host response to infection. Early detection is fundamental to improving the patien...

Predicting the risk of acute kidney injury in patients with acute pancreatitis complicated by sepsis using a stacked ensemble machine learning model: a retrospective study based on the MIMIC database.

OBJECTIVE: This study developed and validated a stacked ensemble machine learning model to predict the risk of acute kidney injury in patients with ac...

Leveraging diverse cell-death patterns in diagnosis of sepsis by integrating bioinformatics and machine learning.

BACKGROUND: Sepsis is a life-threatening disease causing millions of deaths every year. It has been reported that programmed cell death (PCD) plays a ...

Label-free rapid antimicrobial susceptibility testing with machine-learning based dynamic holographic laser speckle imaging.

Antimicrobial resistance (AMR) presents a significant global challenge, creating an urgent need for rapid and sensitive antimicrobial susceptibility t...

Fast screening of COVID-19 inpatient samples by integrating machine learning and label-free SERS methods.

BACKGROUND: Advances in bio-analyte detection demonstrate the need for innovation to overcome the limitations of traditional methods. Emerging viruses...

Discrimination of inherent characteristics of susceptible and resistant strains of Anopheles gambiae by explainable artificial intelligence analysis of flight trajectories.

Understanding mosquito behaviours is vital for the development of insecticide-treated nets (ITNs), which have been successfully deployed in sub-Sahara...

Study on the prediction performance of AIDS monthly incidence in Xinjiang based on time series and deep learning models.

OBJECTIVE: AIDS is a highly fatal infectious disease of Class B, and Xinjiang is a high-incidence region for AIDS in China. The core of prevention and...

Machine learning for predicting antimicrobial resistance in critical and high-priority pathogens: A systematic review considering antimicrobial susceptibility tests in real-world healthcare settings.

BACKGROUND: Antimicrobial resistance (AMR) poses a worldwide health threat; quick and accurate identification of AMR enhances patient outcomes and red...

Using machine learning to predict poor adherence to antiretroviral therapy among adolescents with HIV in low resource settings.

OBJECTIVES: Achieving optimal adherence to antiretroviral therapy (ART) and viral suppression is still insufficient for attaining the UNAIDS 95-95-95 ...

Identification of Biomarkers for Response to Interferon in Chronic Hepatitis B Based on Bioinformatics Analysis and Machine Learning.

Interferon (IFN) is a pivotal agent against hepatitis B virus (HBV) in clinic, but there is a lack of accurate biomarkers to predict the response to I...

A machine learning method for predicting molecular antimicrobial activity.

In response to the increasing concern over antibiotic resistance and the limitations of traditional methods in antibiotic discovery, we introduce a ma...

Machine learning-based prediction model for patients with recurrent Staphylococcus aureus bacteremia.

BACKGROUND: Staphylococcus aureus bacteremia (SAB) remains a significant contributor to both community-acquired and healthcare-associated bloodstream ...