FDA General

Potential of AI and ML in oncology research including diagnosis, treatment and future directions: A comprehensive prospective.

Artificial intelligence (AI) and machine learning (ML) have emerged as transformative tools in cancer research, offering the ability to process huge data rapidly and make precise therapeutic decisions. Over the last decade, AI, particularly deep learning (DL) and machine learning (ML), has significantly enhanced cancer prediction, diagnosis, and treatment by leveraging algorithms such as convoluti...

MultiCycPermea: accurate and interpretable prediction of cyclic peptide permeability using a multimodal image-sequence model.

BACKGROUND: Cyclic peptides, known for their high binding affinity and low toxicity, show potential as innovative drugs for targeting "undruggable" proteins. However, their therapeutic efficacy is often hindered by poor membrane permeability. Over the past decade, the FDA has approved an average of one macrocyclic peptide drug per year, with romidepsin being the only one targeting an intracellular...

Predicting oleogels properties using non-invasive spectroscopic techniques and machine learning.

Oleogelators are considered food additives that require approval from regulatory authorities. Therefore, classifying these ingredients that may have c...

From text to insight: A natural language processing-based analysis of burst and research trends in HER2-low breast cancer patients.

With the intensification of population aging, the proportion of elderly breast cancer patients is continuously increasing, among which those with low ...

Learning monocular depth estimation for defect measurement from civil RGB-D dataset.

A large quantity of civil infrastructure in North America is near the end of their design life. Consequently, the routine visual structural inspection...

Diagnostic Accuracy of IDX-DR for Detecting Diabetic Retinopathy: A Systematic Review and Meta-Analysis.

PURPOSE: Diabetic retinopathy (DR) is a leading cause of vision loss worldwide, making early detection critical to prevent blindness. IDX-DR, an FDA-a...

An extensive experimental analysis for heart disease prediction using artificial intelligence techniques.

The heart is an important organ that plays a crucial role in maintaining life. Unfortunately, heart disease is one of the major causes of mortality gl...

AI-Driven Analysis of Drug Marketing Efficiency: Unveiling FDA Approval to Market Release Dynamics.

This paper explores a novel approach using generative AI to enhance drug marketing strategies in the US pharmaceutical sector. By leveraging an offici...

Thermo-responsive and phase-separated hydrogels for cardiac arrhythmia diagnosis with deep learning algorithms.

Adhesive epidermal hydrogel electrodes are essential for achieving robust signal transduction and cardiac arrhythmia diagnosis, but detachment of conv...

Exploring Potential Medications for Alzheimer's Disease with Psychosis by Integrating Drug Target Information into Deep Learning Models: A Data-Driven Approach.

Approximately 50% of Alzheimer's disease (AD) patients develop psychotic symptoms, leading to a subtype known as psychosis in AD (AD + P), which is as...

ADR-DQPU: A Novel ADR Signal Detection Using Deep Reinforcement and Positive-Unlabeled Learning.

The medical community has grappled with the challenge of analysis and early detection of severe and unknown adverse drug reactions (ADRs) from Spontan...

Exploring the impact of lenticels on the detection of soluble solids content in apples and pears using hyperspectral imaging and one-dimensional convolutional neural networks.

In this work, the effect of lenticels on the predictive performance of apple and pear soluble solids content (SSC) models developed based on hyperspec...

Artificial Intelligence in Natural Product Drug Discovery: Current Applications and Future Perspectives.

Drug discovery, a multifaceted process from compound identification to regulatory approval, historically plagued by inefficiencies and time lags due t...

Future prospective of AI in drug discovery.

Drug discovery and development is very expensive and long with an inferior success rate. It is quite inefficient and costly due to huge R&D costs and ...

Deep Learning-Enhanced Chemiluminescence Vertical Flow Assay for High-Sensitivity Cardiac Troponin I Testing.

Democratizing biomarker testing at the point-of-care requires innovations that match laboratory-grade sensitivity and precision in an accessible forma...

Integrating Machine Learning-Based Approaches into the Design of ASO Therapies.

Rare diseases impose a significant burden on affected individuals, caregivers, and healthcare systems worldwide. Developing effective therapeutics for...

Regulatory approaches towards AI Medical Devices: A comparative study of the United States, the European Union and China.

The swift progression of AI within the realm of medical devices has precipitated an imperative for stringent regulatory oversight. The United States, ...

Sexual dimorphism of the humerus bones in a French sample: comparison of several statistical models including machine learning models.

Sex estimation is an important part of skeletal analysis and forensic identification. Traditionally pelvic traits are utilized for accurate sex estima...

The research progress of biologics in elderly-onset rheumatoid arthritis (EORA).

is a distinct subtype of characterized by heightened treatment challenges due to immune aging and the complexity of comorbidities. This review syste...

Identification of dequalinium as a potent inhibitor of human organic cation transporter 2 by machine learning based QSAR model.

Human organic cation transporter 2 (hOCT2/SLC22A2) is a key drug transporter that facilitates the transport of endogenous and exogenous organic cation...