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Preventive Care

Latest AI and machine learning research in preventive care for healthcare professionals.

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SLOGEN: A Structure-based Lead Optimization Model Unifying Fragment Generation and Screening

Lead optimization plays an important role in preclinical drug discovery. While deep learning has accelerated this process, structure-based approaches that leverage 3D protein-ligand information remain underexplored. Existing models could improve predicted affinity but often yield synthetically inaccessible compounds, whereas screening-based methods limit chemical novelty by relying on fixed fragme...

AI-Driven and 3D-Bioprinted New Approach Methodology (NAM) Identifies NEO100 as Potent Ultrasound-Activated Therapeutic for Primary and Metastatic Brain Tumors

Primary and metastatic brain tumors are among the deadliest and treatment-resistant cancers, mainly because of their inherent resistance to chemoradiation and limited drug delivery across the blood–brain barrier (BBB). Identifying molecules that can cross the BBB and serve as sonosensitizers is crucial for developing noninvasive, targeted therapies such as sonodynamic therapy (SDT). To overcome th...

Iterative immunogen optimization to focus immune responses on a conserved, subdominant viral epitope

Designing effective vaccination strategies against genetically diverse viruses, such as HIV or influenza, is hindered by the ability of these pathogen...

Ultrahigh throughput screening to train generative protein models for engineering specificity into unspecific peroxygenases

Enzyme engineering is central to developing biocatalysts with improved activity and specificity, yet traditional approaches are often limited by the s...

Viral protease-Initiated Pyroptosis Activator mRNA therapy as a Universal Antiviral Strategy

Although therapeutic drugs targeting gasdermin (GSDM)-mediated pyroptosis have made remarkable progress in treating various diseases, their potential ...

Predicting Future SARS-CoV-2 Mutations using Deep Learning

SARS-CoV-2 continues to spread over the world steadily as opposed to many earlier estimations that it would disappear in less than two years. Even tho...

Predicting Toxicity and Bioactivity of the Chemical Exposome: A Case Study for the Blood Exposome Database

Humans are exposed to thousands of chemicals throughout their life. Many of these chemicals are detected in blood and have been catalogued in the Bloo...

TEIP: A Compact, Open-Source Framework for Predicting Tumor Epitope Immunogenicity in Glioblastoma Using Deep Learning and Multi-Modal Biological Features

This work introduces a modular, open-source computational pipeline for glioblastoma (GBM) vaccine design that integrates omics-based OIP5 target disco...

HyperBind2: Multi-Shot Learning Enables Progressive Improvement in Computational Antibody Discovery

Antibody discovery remains constrained by resource-intensive experimental screening approaches that offer limited control over critical properties. He...

A Computational Pipeline for Glioblastoma Vaccine Development: Integrating Novel Omics-Driven OIP5 Target Discovery to Create a Deep Learning-Based Immunogenicity Framework for Personalized Immunotherapy

This work introduces a modular, open-source computational pipeline for glioblastoma (GBM) vaccine design that integrates omics-based OIP5 target disco...

Ensemble-DeepSets: an interpretable deep learning framework for single-cell resolution profiling of immunological aging

Immunological aging (immunosenescence) drives increased susceptibility to infections and reduced vaccine efficacy in elderly populations. Current bulk...

Phenotypic Screening Coupled with AI-Driven Target Deconvolution Identifies α-Terthienyl as a Dual DPP-IV/HSD17β13 Modulator with Efficacy in a Mouse Model of MASLD

Metabolic dysfunction-associated steatotic liver disease (MASLD) is a highly prevalent condition characterized by fat build-up in the liver and ranges...

Integration of artificial intelligence and high-content screening enabled identification of drugs for long-term treatment of cerebral cavernous malformation disease

Adults and children with cerebral cavernous malformations (CCMs) are at risk of experiencing lifelong complications such as hemorrhagic strokes, neuro...

VaxjoGNN: A Graph Neural Network for Ontology-Grounded Vaccine Adjuvant Recommendation

The selection of an effective adjuvant is a critical bottleneck in vaccine development, particularly for emerging diseases where experimental data is ...

SYSTEMS AND NETWORK BIOLOGY ANALYSIS COMBINED WITH MACHINE LEARNING IDENTIFIES KEY IMMUNE RESPONSE PROFILES AND POTENTIAL CORRELATES OF PROTECTION FOR THE M72/AS01E TUBERCULOSIS VACCINE

Tuberculosis claims around 1.5 million lives annually. The M72/AS01E vaccine candidate is an innovative effort demonstrating a 50% reduction in the in...

Fusing Data from CT Deep Learning, CT Radiomics and Peripheral Blood Immune profiles to Diagnose Lung Cancer in Symptomatic Patients

Lung cancer is the leading cause of cancer-related deaths. Diagnosis at late stages is common due to the largely non-specific nature of presenting sym...

Impact of Mydriasis on Image Gradability and Automated Diabetic Retinopathy Screening with a Handheld Camera in Real-World Settings

Diabetic retinopathy (DR) screening in low- and middle-income countries (LMICs) faces challenges due to limited access to specialized care. Portable r...

Using large language models to understand the public discourse towards vaccination in Brazil between January 2013 and December 2019

Vaccination against infectious diseases prevents diseases, saves lives, and reduces healthcare costs. However, trust, accessibility, and public percep...

Hazard-aware adaptations bridge the generalization gap in large language models: a nationwide study

Despite growing excitement in deploying large language models (LLMs) for healthcare, most machine learning studies show success on the same few limite...

The role of artificial intelligence in the application of the integrated electronic health records and patient-generated health data

This scoping review aims to identify and understand the role of artificial intelligence in the application of integrated electronic health records (EH...

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