Antibiotic resistance is a problem of deep scientific concern both in hospital and community settings. Rapid detection in clinical laboratories is essential for the judicious recognition of antimicrobial resistant organisms. Production of extended-spectrum β-lactamases (ESBLs) is a significant resistance-mechanism that impedes the antimicrobial treatment of infections caused by Enterobacteriaceae and is a serious threat to the currently available antibiotic armory. ESBLs are classified into several groups according to their amino acid sequence homology. Proper infection control practices and barriers are essential to prevent spread and outbreaks of ESBL producing bacteria. As bacteria have developed different strategies to counter the effects of antibiotics, the identification of the resistance mechanism may help in the discovery and design of new antimicrobial agents. The carbapenems are widely regarded as the drugs of choice for the treatment of severe infections caused by ESBL-producing Enterobacteriaceae, although comparative clinical trials are scarce. Hence, more expeditious diagnostic testing of ESBL-producing bacteria and the feasible modification of guidelines for community-onset bacteremia associated with different infections are prescribed.

Multiple drug-resistant bacteria are a severe and growing public health concern. Because relatively few antibiotics have been approved over recent years and because of the inability of existing antibiotics to combat bacterial infections fully, demand for unconventional biocides is intense. Metallic nanoparticles (NPs) offer a novel potential means of fighting bacteria. Although metallic NPs exert their effects through membrane protein damage, superoxide radicals and the generation of ions that interfere with the cell granules leading to the formation of condensed particles, their antimicrobial potential, and mechanisms of action are still debated. This article discusses the action of metallic NPs as antibacterial agents, their mechanism of action, and their effect on bacterial drug resistance. Based on encouraging data about the antibacterial effects of NP/antibiotic combinations, we propose that this concept be thoroughly researched to identify means of combating drug-resistant bacteria.

Journal of Animal Science and Technology (JAST) is a peer-reviewed, open access journal publishing original research, review articles and notes in all fields of animal science. Topics covered by the journal include: genetics and breeding, physiology, nutrition of monogastric animals, nutrition of ruminants, animal products (milk, meat, eggs and their by-products) and their processing, grasslands and roughages, livestock environment, animal biotechnology, animal behavior and welfare.

The convergence of artificial intelligence (AI) and drug discovery is accelerating the pace of therapeutic target identification, refining of drug candidates, and streamlining processes from laboratory research to clinical applications. Despite these promising advances, the inherent opacity of AI-driven models, especially deep-learning (DL) models, poses a significant “black-box" problem, limiting interpretability and acceptance within the pharmaceutical researchers. Explainable artificial intelligence (XAI) has emerged as a crucial solution for enhancing transparency, trust, and reliability by clarifying the decision-making mechanisms that underpin AI predictions. This review systematically investigates the principles and methodologies underpinning XAI, highlighting various XAI tools, models, and frameworks explicitly designed for drug-discovery tasks. XAI applications in healthcare are explored with an in-depth discussion on the potential role in accelerating the drug-discovery processes, such as molecular modeling, therapeutic target identification, Absorption, Distribution, Metabolism, and Excretion (ADME) prediction, clinical trial design, personalized medicine, and molecular property prediction. Furthermore, this article critically examines how XAI approaches effectively address the black-box nature of AI models, bridging the gap between computational predictions and practical pharmaceutical applications. Finally, we discuss the challenges in deploying XAI methodologies, focusing on critical research directions to improve transparency and interpretability in AI-driven drug discovery. This review emphasizes the importance of researchers staying current on evolving XAI technologies to realize their transformative potential in fully improving the efficiency, reliability, and clinical impact of drug-discovery pipelines.

The world’s population continues to increase, meaning we require more consistent protein supply to meet demand. Despite the availability of plant-based protein alternatives, animal meat remains a popular, high-quality protein source. Research studies have focused on cultured meat (meat grown in vitro) as a safe and more efficient alternative to traditional meat. Cultured meat is produced by in vitro myogenesis, which involves the processing of muscle satellite and mature muscle cells. Meat culture efficiency is largely determined by the culture conditions, such as the cell type and cell culture medium used and the biomolecular composition. Protein production can be enhanced by providing the optimum biochemical and physical conditions for skeletal muscle cell growth, while myoblasts play important roles in skeletal muscle formation and growth. This review describes the cell types used to produce cultured meat and the biological effects of various myokines and cytokines, such as interleukin-6, leukemia inhibitory factor, interleukin-4, interleukin-15, and interleukin-1β, on skeletal muscle and myogenesis and their potential roles in cultured meat production.