Infectious Disease

Accelerating Low- Dielectric Material Discovery: From Graph Machine Learning to Synthesis.

The exploration of dielectric materials with a specific permittivity remains a significant challenge. Dielectric materials with low permittivity are widely used in semiconductor interlayers and communication substrates for reducing parasitic capacitance and latency transmission. However, discovering novel dielectric materials often relies on a trial-and-error strategy, which is inefficient and tim...

Harnessing AI for Improved Diagnosis and Management of Pediatric Sepsis: Current Advances, Challenges, and Future Directions.

Artificial intelligence (AI) has been applied to early recognition and management of rapidly progressive, community-acquired pediatric sepsis, a leading cause of childhood mortality. The broad adoption of electronic health records combined with rapid advances in digital technologies have enabled the federated training of both knowledge-driven AI, known as expert systems, trained by teams of collab...

Improving Tuberculosis Detection in Chest X-Ray Images Through Transfer Learning and Deep Learning: Comparative Study of Convolutional Neural Network Architectures.

BACKGROUND: Tuberculosis (TB) remains a significant global health challenge, as current diagnostic methods are often resource-intensive, time-consumin...

Machine learning reveals genes impacting oxidative stress resistance across yeasts.

Reactive oxygen species (ROS) are highly reactive molecules encountered by yeasts during routine metabolism and during interactions with other organis...

Experimentally profiling dielectric properties of Escherichia coli and Staphylococcus aureus by movement velocity and force.

The gradual research in integrating artificial intelligence in the Dielectrophoresis system is rapid since the evolution of AI in every aspect of tech...

Language models learn to represent antigenic properties of human influenza A(H3) virus.

Given that influenza vaccine effectiveness depends on a good antigenic match between the vaccine and circulating viruses, it is important to assess th...

Human protein interaction networks of ancestral and variant SARS-CoV-2 in organ-specific cells and bodily fluids.

Understanding SARS-CoV-2 human protein-protein interactions (PPIs) and the host response to infection is essential for developing effective COVID-19 a...

A textual dataset of de-identified health records in Spanish and Catalan for medical entity recognition and anonymization.

The advancement of clinical natural language processing systems is crucial to exploit the wealth of textual data contained in medical records. Diverse...

Gradual poisoning of a chest x-ray convolutional neural network with an adversarial attack and AI explainability methods.

Given artificial intelligence's transformative effects, studying safety is important to ensure it is implemented in a beneficial way. Convolutional ne...

Identifying real time surveillance indicators to estimate COVID-19 hospital admissions in Colorado during and after the public health emergency.

Questions remain about how best to focus surveillance efforts for COVID-19 and other emerging respiratory diseases. We used an archive of COVID-19 dat...

Personalized prediction model generated with machine learning for kidney function one year after living kidney donation.

Living kidney donors typically experience approximately a 30% reduction in kidney function after donation, although the degree of reduction varies amo...

Survival analysis for sepsis patients: A machine learning approach to feature selection and predictive modeling.

Sepsis is a life-threatening condition that presents substantial challenges to healthcare and pharmacological management due to its high mortality rat...

Rapid detection of antibiotic resistance in Burkholderia pseudomallei using MALDI-TOF mass spectrometry.

Antibiotic resistance in Burkholderia pseudomallei (Bp) is a growing public health concern requiring urgent attention. Matrix-assisted laser desorptio...

A comprehensive study based on machine learning models for early identification Mycoplasma pneumoniae infection in segmental/lobar pneumonia.

Segmental/lobar pneumonia in children following Mycoplasma pneumoniae (MP) infection has a significant threat to the children's health, so early recog...

Mitochondrial haplogroup A2 is associated with increased COVID-19 mortality in an admixed Brazilian population.

Mitochondria play a crucial role in cellular respiration and immune responses. Mitochondrial DNA (mtDNA) haplogroups and variants have been associated...

Machine learning and transformer models for prediction of postoperative pneumonia risk in patients with lower limb fractures.

Postoperative pneumonia, a prevalent complication arising from lower limb fracture surgery, can significantly prolong hospitalization periods and elev...

Deep learning-based automated detection and multiclass classification of soil-transmitted helminths and Schistosoma mansoni eggs in fecal smear images.

In this work, we developed an automated system for the detection and classification of soil-transmitted helminths (STH) and Schistosoma (S.) mansoni e...

Mapping global risk of bat and rodent borne disease outbreaks to anticipate emerging threats.

Future epidemics and/or pandemics may likely arise from zoonotic viruses with bat- and rodent-borne pathogens being among the prime candidates. To imp...

AI-driven analysis by identifying risk factors of VL relapse in HIV co-infected patients.

Visceral Leishmaniasis (VL), also known as Kala-Azar, poses a significant global public health challenge and is a neglected disease, with relapses and...

Profiling short-term longitudinal severity progression and associated genes in COVID-19 patients using EHR and single-cell analysis.

Here we propose CovSF, a deep learning model designed to track and forecast short-term severity progression of COVID-19 patients using longitudinal cl...