The present study aimed to purify and identify the metabolites from T. atroviride using high-performance liquid chromatography (HPLC) and 1H and 13C nuclear magnetic resonance spectrometer (NMR) followed by analyzing their toxicological, antibacterial and anticancer properties. This work identified two metabolites - TM1 and TM2. TM1 was in two forms: (i) 1, 3-dione-5, 5-dimethylcyclohexane; and, (ii) 2-enone-3hydroxy -5,5-dimethylcylohex, while TM2 was 4H-1,3-dioxin-4-one-2,3,6-trimethyl. These metabolites did not exhibit any irritant or allergic reaction as revealed by HET- CAM test. TM2 significantly inhibited the growth of H. pylori and Shigella toxin producing Escherichia coli (STEC) as evident by in vitro and microscopic observations of bacterial cell death. TM2 also induced the cell death and cytotoxicity, as revealed by cell viability test and western blot analysis. According to microscopic, flow cytometer and western blot analysis, TM2 treated cells displayed higher ROS, cell death, and apoptosis-related protein expression than TM1 and control. This study concluded that TM2 derived from T. atroviride was a potential therapeutic agent for anti-prostate cancer and antibiotic agent against MDR- H. pylori and STEC and it is also recommended to carry out further in vivo animal model experiments with improved stability of the metabolites for future pharmaceutical trails.
Medical conditions and systemic diseases often manifest as distinct facial characteristics, making identification of these unique features crucial for disease screening. However, detecting diseases using facial photography remains challenging because of the wide variability in human facial features and disease conditions. The integration of artificial intelligence (AI) into facial analysis represents a promising frontier offering a user-friendly, non-invasive, and cost-effective screening approach. This review explores the potential of AI-assisted facial analysis for identifying subtle facial phenotypes indicative of health disorders. First, we outline the technological framework essential for effective implementation in healthcare settings. Subsequently, we focus on the role of AI-assisted facial analysis in disease screening. We further expand our examination to include applications in health monitoring, support of treatment decision-making, and disease follow-up, thereby contributing to comprehensive disease management. Despite its promise, the adoption of this technology faces several challenges, including privacy concerns, model accuracy, issues with model interpretability, biases in AI algorithms, and adherence to regulatory standards. Addressing these challenges is crucial to ensure fair and ethical use. By overcoming these hurdles, AI-assisted facial analysis can empower healthcare providers, improve patient care outcomes, and enhance global health.
In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field.