Unraveling the Mysteries of Sleep: Exploring Phylogenomic Sleep Signals in the Recently Characterized Archaeal Phylum Lokiarchaeota near Loki's Castle.

Sleep is a universally conserved behavior whose origin and evolutionary purpose are uncertain. Using phylogenomics, this article investigates the evolutionary foundations of sleep from a never before used perspective. More specifically, it identifies orthologs of human sleep-related genes in the Lokiarchaeota of the Asgard superphylum and examines their functional role.

Apoptotic and Molecular Mechanisms of Carthamidin in Breast Cancer Therapy: An Integrated In Vitro and In Silico Study.

The current study examines the anticancer properties of the chemical carthamidin in breast cancer through in-vitro and in silico analysis. This study's results demonstrated that carthamidin strongly inhibited the proliferation of MCF 7 cells in vitro, as evidenced by an IC50 value of 128.65 µg/mL at 24 h, determined using the MTT test.

Clock-Sleep Communication.

Rhythmicity is a characteristic feature of the inanimate universe. The organization of biological rhythms in time is an adaptation to the cyclical environmental changes brought on by the earth's rotation on its axis and around the sun. Circadian (L.

Harnessing Simple Animal Models to Decode Sleep Mysteries.

Whether it involves human subjects or non-human animals, basic, translational, or clinical sleep research poses significant ethical challenges for researchers and ethical committees alike. Sleep research greatly benefits from using diverse animal models, each offering unique insights into sleep control mechanisms. The fruit fly (Drosophila melanogaster) is a superior genetic model due to its quick generation period, large progenies, and rich genetic tools.

Bibliometric analysis of artificial intelligence in healthcare research: Trends and future directions.

Objective: The presence of artificial intelligence (AI) in healthcare is a powerful and game-changing force that is completely transforming the industry as a whole. Using sophisticated algorithms and data analytics, AI has unparalleled prospects for improving patient care, streamlining operational efficiency, and fostering innovation across the healthcare ecosystem. This study conducts a comprehensive bibliometric analysis of research on AI in healthcare, utilising the SCOPUS database as the primary data source.

Revolutionizing GPCR-ligand predictions: DeepGPCR with experimental validation for high-precision drug discovery.

G-protein coupled receptors (GPCRs), crucial in various diseases, are targeted of over 40% of approved drugs. However, the reliable acquisition of experimental GPCRs structures is hindered by their lipid-embedded conformations. Traditional protein-ligand interaction models falter in GPCR-drug interactions, caused by limited and low-quality structures.

A deep learning based multi-model approach for predicting drug-like chemical compound's toxicity.

Ensuring the safety and efficacy of chemical compounds is crucial in small-molecule drug development. In the later stages of drug development, toxic compounds pose a significant challenge, losing valuable resources and time. Early and accurate prediction of compound toxicity using deep learning models offers a promising solution to mitigate these risks during drug discovery.

Importance of Inter-residue Contacts for Understanding Protein Folding and Unfolding Rates, Remote Homology, and Drug Design.

Inter-residue interactions in protein structures provide valuable insights into protein folding and stability. Understanding these interactions can be helpful in many crucial applications, including rational design of therapeutic small molecules and biologics, locating functional protein sites, and predicting protein-protein and protein-ligand interactions. The process of developing machine learning models incorporating inter-residue interactions has been improved recently.

Harbingers of Hope: Scientists and the Pursuit of World Peace.

The ongoing wars in many regions-such as the conflict between Israel and Hamas-as well as the effects of war on communities, social services, and mental health are covered in this special editorial. This article emphasizes the need for international efforts to promote peace, offer humanitarian aid, and address the mental health challenges faced by individuals and communities affected by war and violence.

Waking Up the Sleep Field: An Overview on the Implications of Genetics and Bioinformatics of Sleep.

Sleep genetics is an intriguing, as yet less understood, understudied, emerging area of biological and medical discipline. A generalist may not be aware of the current status of the field given the variety of journals that have published studies on the genetics of sleep and the circadian clock over the years. For researchers venturing into this fascinating area, this review thus includes fundamental features of circadian rhythm and genetic variables impacting sleep-wake cycles.

Hexadecanoic acid analogs as potential CviR-mediated quorum sensing inhibitors in : an study.

is a Gram-negative, rod-shaped and opportunistic human pathogen. is resistant to various antibiotics due to the production of quorum sensing (QS)-controlled virulence factor and biofilm formation. Hence, we need to find alternative strategies to overcome the antimicrobial resistance and biofilm formation in Gram-negative bacteria.

Carbon nanoparticles fabricated microfilm: A potent filter for microplastics debased water.

Microplastics were found to be the major pollutant across the globe. Plastic microbeads, like 0.5 mm, are very small and mainly used for exfoliation.

DeepBindGCN: Integrating Molecular Vector Representation with Graph Convolutional Neural Networks for Protein-Ligand Interaction Prediction.

The core of large-scale drug virtual screening is to select the binders accurately and efficiently with high affinity from large libraries of small molecules in which non-binders are usually dominant. The binding affinity is significantly influenced by the protein pocket, ligand spatial information, and residue types/atom types. Here, we used the pocket residues or ligand atoms as the nodes and constructed edges with the neighboring information to comprehensively represent the protein pocket or ligand information.

Advancement of targeted protein degradation strategies as therapeutics for undruggable disease targets.

Targeted protein degradation (TPD) aids in developing novel bifunctional small-molecule degraders and eliminates proteins of interest. The TPD approach shows promising results in oncological, neurogenerative, cardiovascular and gynecological drug development. We provide an overview of technology advancements in TPD, including molecular glues, proteolysis-targeting chimeras (PROTACs), lysosome-targeting chimeras, antibody-based PROTAC, GlueBody PROTAC, autophagy-targeting chimera, autophagosome-tethering compound, autophagy-targeting chimera and chaperone-mediated autophagy-based degraders.

Methanodibenzo[,][1,5]dioxocins as Novel Glutaminase Inhibitor with Anti-Glioblastoma Potential.

Glutamine metabolism is an important hallmark of several cancers with demonstrated antitumor activity in glioblastoma cancer cells (GBM). GBM cells regulate glutamine and use it as a major energy source for their proliferation through the glutaminolysis process. Enzymes, such as glutaminase in glutaminolysis, can be targeted by small-molecule inhibitors, thus exhibiting promising anticancer properties.

An Efficient Modern Strategy to Screen Drug Candidates Targeting RdRp of SARS-CoV-2 With Potentially High Selectivity and Specificity.

Desired drug candidates should have both a high potential binding chance and high specificity. Recently, many drug screening strategies have been developed to screen compounds with high possible binding chances or high binding affinity. However, there is still no good solution to detect whether those selected compounds possess high specificity.

DeepBindBC: A practical deep learning method for identifying native-like protein-ligand complexes in virtual screening.

Identifying native-like protein-ligand complexes (PLCs) from an abundance of docking decoys is critical for large-scale virtual drug screening in early-stage drug discovery lead searching efforts. Providing reliable prediction is still a challenge for most current affinity predicting models because of a lack of non-binding data during model training, lost critical physical-chemical features, and difficulties in learning abstract information with limited neural layers. In this work, we proposed a deep learning model, DeepBindBC, for classifying putative ligands as binding or non-binding.

Generating and screening de novo compounds against given targets using ultrafast deep learning models as core components.

Deep learning is an artificial intelligence technique in which models express geometric transformations over multiple levels. This method has shown great promise in various fields, including drug development. The availability of public structure databases prompted the researchers to use generative artificial intelligence models to narrow down their search of the chemical space, a novel approach to chemogenomics and de novo drug development.

Identification of oxazolo[4,5-g]quinazolin-2(1H)-one Derivatives as EGFR Inhibitors for Cancer Prevention.

Objective: Abnormal expression of EGFR (epidermal growth factor receptor) results in different types of human tumors. Quinazoline-containing derivative signify an attractive platform for EGFR inhibitors. The present study aims to discover the potential binders of a group of compounds belonging to oxazolo[4,5-g]quinazoline-2(1H)-one derivative as EGFR inhibitors.

E3 ligases: a potential multi-drug target for different types of cancers and neurological disorders.

Ubiquitylation is a posttranslational modification of proteins that is necessary for a variety of cellular processes. E1 ubiquitin activating enzyme, E2 ubiquitin conjugating enzyme, and E3 ubiquitin ligase are all involved in transferring ubiquitin to the target substrate to regulate cellular function. The objective of this review is to provide an overview of different aspects of E3 ubiquitin ligases that can lead to major biological system failure in several deadly diseases.