Infectious Disease

BERT-AmPEP60: A BERT-Based Transfer Learning Approach to Predict the Minimum Inhibitory Concentrations of Antimicrobial Peptides for and .

Antimicrobial peptides (AMPs) are a promising alternative for combating bacterial drug resistance. While current computer prediction models excel at binary classification of AMPs based on sequences, there is a lack of regression methods to accurately quantify AMP activity against specific bacteria, making the identification of highly potent AMPs a challenge. Here, we present a deep learning method...

Circulating lncRNAs as biomarkers for severe dengue using a machine learning approach.

OBJECTIVES: Dengue virus (DENV) infection is a significant global health concern, causing severe morbidity and mortality. While many cases present as a mild febrile illness, some progress to life-threatening severe dengue (SD). Early intervention is essential to improve outcomes, but current predictive methods lack specificity, burdening healthcare systems in endemic regions. Circulating long non-...

An explainable web application based on machine learning for predicting fragility fracture in people living with HIV: data from Beijing Ditan Hospital, China.

PURPOSE: This study aimed to develop and validate a novel web-based calculator using machine learning algorithms to predict fragility fracture risk in...

Explainable machine learning models enhance prediction of PFAS bioactivity using quantitative molecular surface analysis-derived representation.

The extensive use of per- and polyfluoroalkyl substances (PFAS) in industrial and consumer products poses health risks due to their toxicity. Computat...

Integration of Photodiagnosis and Therapy Guided by Micro/Nanorobots.

Micro/Nanorobots(MNRs)integrated with phototherapy represent an emerging approach to cancer treatment and hold significant potential for addressing ba...

Exploration of Novel Antimicrobial Agents against Foodborne Pathogens via a Deep Learning Approach.

The emergence of antibiotic-resistant bacteria poses a severe threat to food safety and human health, necessitating an urgent search for novel antimic...

Exploring the repository of de novo-designed bifunctional antimicrobial peptides through deep learning.

Antimicrobial peptides (AMPs) are attractive candidates to combat antibiotic resistance for their capability to target biomembranes and restrict a wid...

Enhancing yeast cell tracking with a time-symmetric deep learning approach.

Accurate tracking of live cells using video microscopy recordings remains a challenging task for popular state-of-the-art image processing-based objec...

Liquid-bodied antibiofilm robot with switchable viscoelastic response for biofilm eradication on complex surface topographies.

Recalcitrant biofilm infections pose a great challenge to human health. Micro- and nanorobots have been used to eliminate biofilm infections in hard-t...

Hierarchical agent transformer network for COVID-19 infection segmentation.

Accurate and timely segmentation of COVID-19 infection regions is critical for effective diagnosis and treatment. While convolutional neural networks ...

Lung cancer detection with machine learning classifiers with multi-attribute decision-making system and deep learning model.

Diseases of the airways and the other parts of the lung cause chronic respiratory diseases. The major cause of lung disease is tobacco smoke, along wi...

Forecasting dengue across Brazil with LSTM neural networks and SHAP-driven lagged climate and spatial effects.

BACKGROUND: Dengue fever is a mosquito-borne viral disease that poses significant health risks and socioeconomic challenges in Brazil, necessitating a...

Modifiable risk factors of vaccine hesitancy: insights from a mixed methods multiple population study combining machine learning and thematic analysis during the COVID-19 pandemic.

BACKGROUND: Vaccine hesitancy, the delay in acceptance or reluctance to vaccinate, ranks among the top threats to global health. Identifying modifiabl...

Capillariid diversity in archaeological material from the New and the Old World: clustering and artificial intelligence approaches.

BACKGROUND: Capillariid nematode eggs have been reported in archaeological material in both the New and the Old World, mainly in Europe and South Amer...

Machine learning-based prediction of mortality risk in AIDS patients with comorbid common AIDS-related diseases or symptoms.

OBJECTIVE: Early assessment and intervention of Acquired Immune Deficiency Syndrome (AIDS) patients at high risk of mortality is critical. This study ...

Early differential diagnosis models of Talaromycosis and Tuberculosis in HIV-negative hosts using clinical data and machine learning.

BACKGROUND: Talaromyces marneffei is an emerging pathogen, and the number of infections in HIV-negative individuals is increasing. In HIV-negative ind...

The dawn of the revolution that will allow us to precisely describe how microbiomes function.

The community of microorganisms inhabiting a specific environment, such as the human gut - including bacteria, fungi, archaea, viruses, protozoa, and ...

Advancing sepsis diagnosis and immunotherapy machine learning-driven identification of stable molecular biomarkers and therapeutic targets.

Sepsis represents a significant global health challenge, necessitating early detection and effective treatment for improved outcomes. While traditiona...

Unraveling the power of NAP-CNB's machine learning-enhanced tumor neoantigen prediction.

In this study, we present a proof-of-concept classical vaccination experiment that validates the in silico identification of tumor neoantigens (TNAs) ...

An assistive robot that enables people with amyotrophia to perform sequences of everyday activities.

Mobile manipulation aids aim at enabling people with motor impairments to physically interact with their environment. To facilitate the operation of s...