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Introduction to Memristive Mechanisms and Models.
Recent Pat Nanotechnol
January 2025
Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, Nijenborgh 9, 9747 AG Groningen, The Netherlands.
The increase in computational power demand led by the development of Artificial Intelligence is rapidly becoming unsustainable. New paradigms of computation, which potentially differ from digital computation, together with novel hardware architecture and devices, are anticipated to reduce the exorbitant energy demand for data-processing tasks. Memristive systems with resistive switching behavior are under intense research, given their prominent role in the fabrication of memory devices that promise the desired hardware revolution in our intensive data-driven era.
View Article and Find Full Text PDFAnalysis of seroprevalence and risk factors for syphilis and HIV among female sex workers and transgender individuals in different cities of Sindh, Pakistan.
PLoS One
January 2025
Department of Biomedical Sciences, Nazarbayev University School of Medicine, Astana, Kazakhstan.
Introduction: Co-infections of syphilis and HIV have been found to exacerbate the impact on sexual and reproductive health, especially among key population groups such as Female Sex Workers (FSWs) and Transgender Individuals (TGs). The data on the prevalence and determinants of syphilis and HIV in Pakistan, particularly in Sindh province, is limited. This prospective cross-sectional study aimed to determine the seroprevalence and risk factors for HIV and syphilis infections among FSWs and TGs in different cities of Sindh, Pakistan.
View Article and Find Full Text PDFMachine learning for antimicrobial peptide identification and design.
Nat Rev Bioeng
May 2024
Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Artificial intelligence (AI) and machine learning (ML) models are being deployed in many domains of society and have recently reached the field of drug discovery. Given the increasing prevalence of antimicrobial resistance, as well as the challenges intrinsic to antibiotic development, there is an urgent need to accelerate the design of new antimicrobial therapies. Antimicrobial peptides (AMPs) are therapeutic agents for treating bacterial infections, but their translation into the clinic has been slow owing to toxicity, poor stability, limited cellular penetration and high cost, among other issues.
View Article and Find Full Text PDFIdentifying candidate RNA-seq biomarkers for severity discrimination in chemical injuries: A machine learning and molecular dynamics approach.
Int Immunopharmacol
January 2025
Chemical Injuries Research Center, Systems Biology and Poisonings Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran.
Introduction: Biomarkers play a crucial role across various fields by providing insights into biological responses to interventions. High-throughput gene expression profiling technologies facilitate the discovery of data-driven biomarkers through extensive datasets. This study focuses on identifying biomarkers in gene expression data related to chemical injuries by mustard gas, covering a spectrum from healthy individuals to severe injuries.
View Article and Find Full Text PDFIntroduction: Tuberculosis (TB) poses a significant threat to global health, with millions of new infections and approximately one million deaths annually. Various modeling efforts have emerged, offering tailored data-driven and physiologically-based solutions for novel and historical compounds. However, this diverse modeling panorama may lack consistency, limiting result comparability.
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