Infectious Disease

Omics-Based Computational Approaches for Biomarker Identification, Prediction, and Treatment of Long COVID

Long COVID, also referred to as post-acute sequelae of COVID-19 (PASC), is a substantial global health concern estimated to have affected over 145 million individuals worldwide. Characterized by persistent and new symptoms extending beyond four weeks from the initial infection—such as fatigue, breathlessness, and cognitive impairments—Long COVID poses significant challenges to healthcare systems d...

Genus-level transfer learning of Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry data predicts antibiotic resistance with greater accuracy

Bacterial resistance, driven by excessive antibiotic use, has rendered many traditional antibiotics ineffective. Despite the advantages of applying matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) to predict bacterial antimicrobial resistance, limited databases and a lack of high-quality data hinder this effort. This study aimed to address this lack of da...

Fine-tuned large language models enhance influenza forecasting

Influenza-like illness (ILI) continues to present significant challenges to global health, highlighting the need for accurate forecasting to guide tim...

Combining Mass Spectrometry with Machine Learning to Identify Novel Protein Signatures: The Example of Multisystem Inflammatory Syndrome in Children

We demonstrate an approach that integrates biomarker analysis with machine learning to identify protein signatures, using the example of SARS-CoV-2-in...

Machine learning identifies clinical sepsis phenotypes that translate to the plasma proteome: a prospective cohort study

Sepsis therapy is still limited to treatment of the underlying infection and supportive measures. To date, various sepsis subtypes were proposed, but ...

Machine learning-based short-term forecasting of COVID-19 hospital admissions using routine hospital patient data

During the COVID-19 pandemic, the field of infectious disease modeling advanced rapidly, with forecasting tools developed to track trends in transmiss...

Creation of an Open-Access Lung Ultrasound Image Database For Deep Learning and Neural Network Applications

Lung ultrasound (LUS) offers advantages over traditional imaging for diagnosing pulmonary conditions, with superior accuracy compared to chest X-ray a...

ARTIFICIAL INTELLIGENCE AND COMPUTATIONAL METHODS FOR MODELLING AND FORECASTING INFLUENZA AND INFLUENZA-LIKE ILLNESS: A SCOPING REVIEW

The persistnt resurgence of influence and influenza-like illness despite concerted vaccination interventions is a global health burden, thus necessita...

Predicting Bacterial Vaginosis Development using Artificial Neural Networks

Bacterial vaginosis (BV) is a dysbiosis of the vaginal microbiome, characterized by the depletion of protective Lactobacillus spp. and overgrowth of a...

Estimating the worst-case scenario for malaria parasite rate in sub-Saharan Africa

Malaria remains a leading cause of morbidity and mortality worldwide, with sub-Saharan Africa bearing the highest burden. Stalled progress under an in...

Fast and Trustworthy Nowcasting of Dengue Fever: A Case Study Using Attention-Based Probabilistic Neural Networks in São Paulo, Brazil

Nowcasting methods are crucial in infectious disease surveillance, as reporting delays often lead to underestimation of recent incidence and can impai...

Contextualized Biomedical Language Processing Enhances ICU Survival Prediction

Cerebrospinal fluid (CSF) culture is the diagnostic gold standard for neuroinfectious diseases such as bacterial meningitis, but its sensitivity is li...

Reinforcement learning-based control of epidemics on networks of communities and correctional facilities

Correctional facilities can act as amplifiers of infectious disease outbreaks. Small community outbreaks can cause larger prison outbreaks, which can ...

Leveraging probabilistic forecasts for dengue preparedness and control: the 2024 Dengue Forecasting Sprint in Brazil

Forecast models are a key decision-support tool for public health authorities in managing epi- demics, feeding into early warning systems, scenario ev...

The allostatic overload in pregnancy during the COVID-19 pandemic and potential effects on the health of the mother-child dyad: Study Protocol

Allostatic load refers to the cumulative burden of stress and life events that involve the interaction of various physiological systems at differing l...

Machine learning models for the prediction of COVID-19 prognosis in the primary health care setting

This study aimed to identify prognostic factors associated with poor outcomes of COVID-19 at diagnosis in Primary Health Care (PHC). We conducted a re...

Activation status of immune cells in the airway is a defining feature of severe fungal asthma

Airborne fungi are potent inducers of respiratory disease and cause the debilitating conditions severe asthma with fungal sensitisation (SAFS) and all...

Deep Learning for Pneumonia Diagnosis: A Custom CNN Approach with Superior Performance on Chest Radiographs

A major global health and wellness issue causing major health problems and death, pneumonia underlines the need of quickly and precisely identifying a...

Plasma proteomics identifies molecular subtypes in sepsis

The heterogeneity of sepsis represents a significant challenge to the development of personalized sepsis therapies. Sepsis subtyping has therefore eme...

PlasmoCount 2.0: Rapid Multi-Species Malaria Parasite Detection Using Deep Learning

Visual examination of Giemsa-stained red blood cell smears is the gold-standard for identification of malaria parasite infection. Despite its wide usa...