Antimicrobial resistance (AMR) is a global crisis for human public health which threatens the effective prevention and control of ever-increasing infectious diseases. The advent of pandrug-resistant bacteria makes most, if not all, available antibiotics invalid. Meanwhile, the pipeline of novel antibiotics development stagnates, which prompts scientists and pharmacists to develop unconventional antimicrobials. Bacteriophage-derived endolysins are cell wall hydrolases which could hydrolyze the peptidoglycan layer from within and outside of bacterial pathogens. With high specificity, rapid action, high efficiency, and low risk of resistance development, endolysins are believed to be among the best alternative therapeutic agents to treat multidrug resistant (MDR) bacteria. As of now, endolysins have been applied to diverse aspects. In this review, we comprehensively introduce the structures and activities of endolysins and summarize the latest application progress of recombinant endolysins in the fields of medical treatment, pathogen diagnosis, food safety, and agriculture.

Antimicrobial resistance (AMR) is a major threat to global public health. The current review synthesizes to address the possible role of Artificial Intelligence and Machine Learning (AI/ML) in mitigating AMR. Supervised learning, unsupervised learning, deep learning, reinforcement learning, and natural language processing are some of the main tools used in this domain. AI/ML models can use various data sources, such as clinical information, genomic sequences, microbiome insights, and epidemiological data for predicting AMR outbreaks. Although AI/ML are relatively new fields, numerous case studies offer substantial evidence of their successful application in predicting AMR outbreaks with greater accuracy. These models can provide insights into the discovery of novel antimicrobials, the repurposing of existing drugs, and combination therapy through the analysis of their molecular structures. In addition, AI-based clinical decision support systems in real-time guide healthcare professionals to improve prescribing of antibiotics. The review also outlines how can AI improve AMR surveillance, analyze resistance trends, and enable early outbreak identification. Challenges, such as ethical considerations, data privacy, and model biases exist, however, the continuous development of novel methodologies enables AI/ML to play a significant role in combating AMR.

Nowadays, the emergence of multidrug-resistant bacterial strains initiates the urgent need for the elucidation of the new drug targets for the discovery of antimicrobial drugs. Filamenting temperature-sensitive mutant Z (FtsZ), a eukaryotic tubulin homolog, is a GTP-dependent prokaryotic cytoskeletal protein and is conserved among most bacterial strains. In vitro studies revealed that FtsZ self-assembles into dynamic protofilaments or bundles and forms a dynamic Z-ring at the center of the cell in vivo, leading to septation and consequent cell division. Speculations on the ability of FtsZ in the blockage of cell division make FtsZ a highly attractive target for developing novel antibiotics. Researchers have been working on synthetic molecules and natural products as inhibitors of FtsZ. Accumulating data suggest that FtsZ may provide the platform for the development of novel antibiotics. In this review, we summarize recent advances in the properties of FtsZ protein and bacterial cell division, as well as in the development of FtsZ inhibitors.

Ramadan fasting is a religious practice observed by millions of Muslims worldwide. Firstly, the experimental studies were searched on different websites like PubMed, Science direct, and google scholar to find out the latest research regarding Ramadan fasting. Studies have shown that Ramadan fasting influences body weight, blood glucose, insulin, lipid levels, and blood pressure. However, the effects of fasting on mental health and specific health conditions such as cardiovascular diseases, cancer or metabolic disorders vary depending on individual factors. Because, fasting duration, the presence of chronic diseases, and obesity are all factors that can alter the outcomes. Until now, Multiple studies on Ramadan fasting's impact on health have been conducted but certain limitations make the results inconclusive. We hope to gain a better understanding of the potential benefits and risks associated with this religious practice. Ultimately, this knowledge can be used to inform healthcare professionals and individuals who are participating in Ramadan fasting. Further research is necessary to explore underlying mechanisms and optimize fasting protocols for chronic disease patients. Overall, this review emphasizes the need for further research on the effects of Ramadan fasting on human health and its potential as a tool for improving health outcomes in diverse populations.