Search
163 articles for “Inhibitors”
-
Host Invasion to Immune Evasion: Emerging Concepts in Pathogen Virulence Strategies
Abstract: The interaction between hosts and pathogens represents a continuous evolutionary struggle, shaping both microbial virulence and host immunity. Pathogens employ diverse strategies to evade immune surveillance, ranging from antigenic variation and molecular mimicry to hijacking of host immune checkpoints. Simultaneously, hosts refine innate and adaptive immune mechanisms to counteract infection and limit damage. This reciprocal adaptation, known as host–pathogen coevolution, influences pathogen persistence, transmission, and fitness, while also sculpting immune …
Published in International Journal of Pathogens · Vol. 3, Issue 1, 2026 Read article
-
Copper Sulfide Semiconducting Nanoparticles for Antibacterial Applications: Synthesis Strategies, Mechanisms and Performance – A Review
Abstract: Copper sulfide nanoparticles (CuS NPs) have drawn growing attention as a next-generation antibacterial platform, owing to their tunable structural, optical, and catalytic properties. This review consolidates current evidence on CuS NP synthesis, mechanisms, and antibacterial performance, comparing chemical and green synthesis routes alongside the effects of morphology, doping, and surface modification. Mechanistically, CuS NPs act through several overlapping pathways, including reactive oxygen species generation, bacterial membrane disruption, ion release, and …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
-
Neuro-Symbolic Agentic AI for Autonomous Scientific Discovery: Integrating Deep Reinforcement Learning, Quantum Simulation, and XAI-Audited LLM Hypothesis Generation in Drug Target Identification
Abstract: The exponential growth of multi-omics data and the increasing complexity of disease-associated protein interactomes have rendered conventional drug target identification pipelines computationally and epistemologically inadequate. This paper presents the Neuro-Symbolic Agentic AI for Scientific Discovery (NS-AASD) framework, a unified architecture that cohesively integrates deep reinforcement learning (DRL) exploration strategies, variational quantum simulation (VQS) of protein conformational dynamics, and XAI-audited large language model (LLM) hypothesis generation within an autonomous scientific discovery …
Published in Research and Reviews : Journal of Computational Biology · Vol. 15, Issue 2, 2026 Read article