COVID-19

Machine Learning Identification of Neutrophil Extracellular Trap-Related Genes as Potential Biomarkers and Therapeutic Targets for Bronchopulmonary Dysplasia.

Neutrophil extracellular traps (NETs) play a key role in the development of bronchopulmonary dysplasia (BPD), yet their molecular mechanisms in contributing to BPD remain unexplored. Using the GSE32472 dataset, which includes 100 blood samples from postnatal day 28, we conducted comprehensive bioinformatics analyses to identify differentially expressed genes (DEGs) and construct gene modules. We i...

Machine learning algorithms applied to the diagnosis of COVID-19 based on epidemiological, clinical, and laboratory data.

OBJECTIVE: To predict COVID-19 in hospitalized patients with SARS in a city in southern Brazil by using machine learning algorithms.

Innovative IoT-enabled mask detection system: A hybrid deep learning approach for public health applications.

The integration of IoT and deep learning has revolutionized real-time monitoring systems, particularly in public health applications such as face mask...

Developing a Multisensor-Based Machine Learning Technology (Aidar Decompensation Index) for Real-Time Automated Detection of Post-COVID-19 Condition: Protocol for an Observational Study.

BACKGROUND: Post-COVID-19 condition is emerging as a new epidemic, characterized by the persistence of COVID-19 symptoms beyond 3 months, and is antic...

Machine learning-based risk prediction model for pertussis in children: a multicenter retrospective study.

BACKGROUND: Pertussis is a highly contagious respiratory disease. Even though vaccination has reduced the incidence, cases have resurfaced in certain ...

Deep neural networks excel in COVID-19 disease severity prediction-a meta-regression analysis.

COVID-19 is a disease in which early prognosis of severity is critical for desired patient outcomes and for the management of limited resources like i...

Fluorescence 'turn-on' sensing of glial fibrillary acidic protein using graphene oxide-quenched copper nanoclusters.

This study introduces a fluorescence based sensing platform made to detect glial fibrillary acidic protein (GFAP), a critical biomarker associated wit...

A new era in nephrology: the role of super-resolution microscopy in research, medical diagnostic, and drug discovery.

For decades, electron microscopy has been the primary method to visualize ultrastructural details of the kidney, including podocyte foot processes and...

aurora: a machine learning gwas tool for analyzing microbial habitat adaptation.

A primary goal of microbial genome-wide association studies is identifying genomic variants associated with a particular habitat. Existing tools fail ...

Evaluating Chat Generative Pretrained Transformer (GPT-4o) Problem-Solving Performance in the Japan Certificate Examination for Biomedical Engineering Class 1.

Introduction Chat generative pretrained transformer (ChatGPT; OpenAI, San Francisco, CA) has developed rapidly and is used in various fields, includin...

Critical scenarios adversarial generation method for intelligent vehicles testing based on hierarchical reinforcement architecture.

The widespread deployment of intelligent vehicles necessitates comprehensive testing across diverse driving scenarios. A significant challenge is gene...

Multi-modal MRI synthesis with conditional latent diffusion models for data augmentation in tumor segmentation.

Multimodality is often necessary for improving object segmentation tasks, especially in the case of multilabel tasks, such as tumor segmentation, whic...

FaciaVox: A diverse multimodal biometric dataset of facial images and voice recordings.

FaciaVox is a multimodal biometric dataset that consists of face images and voice recordings under both masked and unmasked conditions. The term ``Fac...

Flip Learning: Weakly supervised erase to segment nodules in breast ultrasound.

Accurate segmentation of nodules in both 2D breast ultrasound (BUS) and 3D automated breast ultrasound (ABUS) is crucial for clinical diagnosis and tr...

Development and validation of a machine learning-based nomogram for predicting prognosis in lung cancer patients with malignant pleural effusion.

Malignant pleural effusion (MPE) is a common complication in patients with advanced lung cancer, significantly impacting their survival rates and qual...

Automatic detection and extraction of key resources from tables in biomedical papers.

BACKGROUND: Tables are useful information artifacts that allow easy detection of missing data and have been deployed by several publishers to improve ...

Reducing hepatitis C diagnostic disparities with a fully automated deep learning-enabled microfluidic system for HCV antigen detection.

Viral hepatitis remains a major global health issue, with chronic hepatitis B (HBV) and hepatitis C (HCV) causing approximately 1 million deaths annua...

Predicting coronavirus disease 2019 severity using explainable artificial intelligence techniques.

Predictive models for determining coronavirus disease 2019 (COVID-19) severity have been established; however, the complexity of the interactions amon...

Forecasting stock prices using long short-term memory involving attention approach: An application of stock exchange industry.

The Stability of the economy is always a great challenge across the world, especially in under developed countries. Many researchers have contributed ...

Current experimental, statistical, and mechanistic approaches to optimizing biomolecule separations in aqueous two-phase systems.

Aqueous two-phase systems (ATPS) have been used to purify a range of biomolecules, including small molecules, monoclonal antibodies, viruses, and whol...