Explore the vast range of titles available.

The increasing global incidence of cancer emphasizes the vital role of machine learning algorithms and artificial intelligence (AI) in identifying novel anticancer targets and developing new drugs. Computational approaches can significantly quicken research on complex disorders, enabling the discovery of effective treatments. This study explores anticancer targets by assessing the potential of naturally occurring compounds derived from various plants to cure colorectal cancer. Twenty compounds were sourced from PubChem, and the RPS20 protein structure was obtained from AlphaFold, and mutation “V50S” was added. Validation of mutated RPS20 protein was performed using the Ramachandran plot and ERRAT. Binding sites on the mutated RPS20 protein were identified with DeepSite, followed by virtual screening to pinpoint the most promising natural lead drug candidate. Indirubin emerged as the lead drug candidate, fulfilling all ADMET criteria and exhibiting a good binding affinity. Further development included designing an AI-based drug using the WADDAICA server, which was validated through molecular docking, molecular dynamics (MD) simulation, and MMGBSA. The electronic properties of indirubin were studied using DFT calculations. The results show a moderate HOMO-LUMO gap, indicating its potential reactivity and the possible capability for biological target interactions. These findings indicate that indirubin could serve as a potent and effective cancer inhibitor, offering high efficacy with minimal side effects.

Diabetes mellitus is a major health concern worldwide; lifestyle and rising urbanization are the key contributing factors. The genetic factors implicated in type 2 diabetes include the transcription factor 7-like 2 ( TCF7L2 ) gene on chromosome 10q25.3, which has been greatly linked with diabetes, but the mechanisms and therapeutic effect on this gene are yet to be clearly defined. The objective of the research was to discover and screen natural phytochemicals of Moringa oleifera , especially carvacrol, as promising TCF7L2 inhibitors through combined in-silico methods. We have used a computational pipeline that includes ADMET profiling, molecular docking, molecular, dynamics (MD) simulations, and density functional theory (DFT) analysis. The ADMET analysis demonstrated that carvacrol has desirable pharmacokinetics, such as high gastrointestinal absorption, drug-likeness, and low-predicted oral toxicity. Molecular docking studies showed that carvacrol has a high binding affinity with the TCF7L2 protein with a binding energy of -5.5 kcal/mol. The conformational stability of the carvacrol-TCF7L2 complex was further validated through an extended 200 ns MD simulation, where the protein backbone RMSD stabilized after ~ 40 ns within ~ 0.25–0.35 nm (2.5–3.5 A), while the ligand RMSD remained consistently low at ~ 0.01–0.02 nm (0.1–0.2 A), supported by a persistent hydrogen-bond network throughout the trajectory. The chemical stability and reactivity of the compound were confirmed by DFT calculations. These findings indicate that carvacrol has potential as a lead compound against TCF7L2 in the treatment of type 2 diabetes. These results are in support of the therapeutic relevance of carvacrol, though experiments are necessary to prove its efficacy and safety in biological systems.

Human astrovirus (HAsVs) is a significant viral agent responsible for acute gastroenteritis, primarily affecting children. Among HAsVs serotypes, HAsVs − 1 and HAsVs-2 are the most virulent serotypes, contributing to severe gastrointestinal infections, and having limited therapeutics. This study aims to design multi-epitope vaccine candidate with predicted glycosylation sites against HAsVs-1 and HAsVs-2 utilizing an immunoinformatic approach. B-cell and T-cell epitopes in which natural glycan sites were present were selected and linked via GPGPG, AAY, and KK linkers, with an adjuvant to stimulate a balanced immune response. The 3D structure of the vaccine was validated via Ramachandran plot, following molecular docking with human immune receptors, and then subjected to dual molecular dynamics (MD) simulations via AMBER and DESMOND to confirm interaction stability and to predict its immunogenic profile. The HAsVs vaccine demonstrated strong immunogenic properties, including more than 70% of global populations, with favorable physiochemical characteristics, including an antigenicity score of 0.534, instability index of 29.26, molecular weight of 24,230.71 Da, and GRAVY score of − 0.126, ensuring stability, solubility, and hydrophilicity. Molecular docking studies confirmed stable binding with human immune receptors, particularly with HLA-DR, showing a binding energy of − 272.83 kcal/mol, and 35 hydrogen bonds. In MD simulations, the RMSD reached a stable point at ~ 15–20 Å (Desmond) and ~ 1.5 Å (AMBER), indicating little movement. RMSF values were mainly less than 8 Å, with flexible parts around residues 50 and 150. The radius of Gyration (Rg) stabilized around 33.0–26.0 Å (Desmond) and ~ 5 Å (AMBER), confirming the compactness. Immune simulation predicted a strong, Th1-dominated response, with antigen concentrations peaking at nearly 700,000 antigens per mL, and IFN-γ levels reaching approximately 450,000 ng/mL, supporting effective adaptive immunity with minimal Th2 activation. Although this research is an in-silico study, the results demonstrate the strong potential of a multi-epitope vaccine candidate against HAsVs.

Batai orthobunyavirus (BATV) is a mosquito-borne orthobunyavirus that affects both humans and animals, and currently no licensed vaccine is available. The aim of this study was to design a multi-epitope vaccine candidate against BATV using a computational vaccinology pipeline. Predicted B-cell and T-cell epitopes from the nucleoprotein and envelopment polyprotein were selected and assembled into a single construct using an adjuvant and suitable linkers. The final construct comprised 247 amino acids and was predicted to be antigenic with an antigenicity score of 0.7676, non-allergenic, and non-toxic. Population coverage analysis showed a global coverage of 97.2%. Physicochemical evaluation indicated a theoretical pI of 10, instability index of 23.18, aliphatic index of 78.66, and GRAVY value of − 0.252, suggesting a stable and hydrophilic profile. Molecular docking with TLR3 produced a docking score of − 1418.9, suggesting a favorable interaction. Molecular dynamics simulations for 100 ns further indicated the stability of the vaccine–TLR3 complex. Immune simulation predicted the ability of the construct to stimulate immune responses in silico, and in silico cloning analysis supported its expression feasibility. Overall, these findings suggest that the proposed multi-epitope construct has promising predicted immunological and structural properties; however, experimental validation is required to confirm its immunogenicity and protective efficacy.

Menangle Virus (MenV) is a zoonotic pathogenic virus that can penetrate humans, causing infection in them. It is an essential threat to public health due to the possibility of cross-species transmissions. Traditional vaccine development methods are time-consuming and resource-intensive, highlighting the need for innovative approaches. This study uses computational techniques to develop a multiepitope vaccine for MenV. We employed in silico tools to find potential B-cell, T-cell, and MHC-binding epitopes in the viral proteome. To ensure their safety and efficacy, these epitopes were evaluated for antigenicity, allergenicity, and toxicity. The selected epitopes were assembled into a multiepitope construct optimized for molecular stability and immunogenicity. Studies of molecular docking and MD simulations were considered to assess the interaction of vaccine candidates with human receptors, indicating a strong immune response. The goal of developing this vaccine is to prepare for potential future outbreaks. This in-silico vaccine design proposes a promising, efficient path to developing effective preventative measures against MenV, potentially expediting vaccine production and contributing to world health security.

This review will examine how artificial intelligence, profound learning technologies, has affected drug discovery. Deep learning technology (DLT), a sub-field of AI that uses intricate algorithms and enormous datasets, is transforming every point along the road to drug development. Integrating clinical trial data, target identification or lead optimization, and personalized medicine have all become possible thanks to DLT. Given the explosion in IUPAC-compliant compounds registered with PubChem or derived from existing ones, DLT has given the pharmaceutical industry a massive booster shot. We will explore the key role generative models play in creating new drug compounds and why interdisciplinary collaboration is essential to entirely using AI's potential for drug discovery. In addition, the purpose of this article is to consider further perspectives concerning what problems exist at present in deep learning and AI-driven drug discovery. We focus on its potential as an accelerated, more effectiveeven tailored healthcare technology. As AI technology advances, a new field emerges in drug development, tipping the global balance between 'well' and 'ill.'Article HighlightsArtificial intelligence and deep learning are changing drug discovery, making target identification quicker and more effective. For instance, these technologies can analyze vast amounts of biological data to identify potential drug targets in a fraction of the time it would take a human researcher.Generative models, a key element in the generation of new drug compounds, underscore the significance of interdisciplinary partnerships in advancing drug discovery.While significant progress has been achieved in AI-driven drug discovery, the existence of challenges underscores the ongoing need for innovation and problem-solving in this dynamic field.

Background: Concerns about the accuracy and reliability of soft tissue landmarks using two-dimensional (2D) and three-dimensional (3D) imaging. Objective: The aim of the systematic review is to estimate accuracy and reliability of soft tissue landmarks with 2D imaging and 3D imaging for orthodontic diagnosis planning and treatment planning purposes. Data Sources: Electronic database search was performed in MEDLINE via PubMed, Embase via embase.com, and the Cochrane library website. Selection Criteria: The data were extracted according to two protocols based on Centre for Evidence-Based Medicine (CEBM) critical appraisal tools. Next, levels of evidence were categorized into three groups: low, medium, and high. Data Synthesis: Fifty-five publications were found through database search strategies. A total of nine publications were included in this review. Conclusion According to the available literature, 3D imaging modalities were more accurate and reliable as compared to 2D modalities. Cone beam computed tomography (CBCT) was considered the most reliable imaging tool for soft tissues.

MXenes have attracted increasing attention because of their rich surface functional groups, high electrical conductivity, and outstanding dispersibility in many solvents, and have demonstrated competitive efficiency in energy storage and conversion applications. However, the restacking nature of MXene nanosheets like other two-dimensional (2D) materials through van der Waals forces results in sluggish ionic kinetics, restricted number of active sites, and ultimate deterioration of MXene material/device performance. The strategy of raising 2D MXenes into three-dimensional (3D) structures has been considered an efficient way for reducing restacking, providing greater porosity, higher surface area, and shorter distances for mass transport of ions, surpassing standard one-dimensional (1D) and 2D structures. In multivalent ion batteries, the positive multivalent ions combine with two or more electrons at the same time, so their capacities are two or three times that of lithium-ion batteries (LIBs) under the same conditions, e.g., a magnesium ion battery has a high theoretical specific capacity of 2 205 mAh g −1 and a high volumetric capacity of 3 833 mAh cm −3 . In this review, we summarize the most recent strategies for fabricating 3D MXene architectures, such as assembly, template, 3D printing, electrospinning, aerogel, and gas foaming methods. Special consideration has been given to the applications of highly porous 3D MXenes in energy storage devices beyond LIBs, such as sodium ion batteries (SIBs), potassium ion batteries (KIBs), magnesium ion batteries (MIBs), zinc ion batteries (ZIBs), and aluminum ion batteries (AIBs). Finally, the authors provide a summary of the future opportunities and challenges for the construction of 3D MXenes and MXene-based electrodes for applications beyond LIBs. Graphic Abstract

Lindane is a broad-spectrum insecticide widely used on fruits, vegetables, crops, livestock and on animal premises to control the insects and pests. The extensive use of pesticides and their residues in the soil and water typically join the food chain and thus accumulate in the body tissues of human and animals causing severe health effects. The study was designed to determine the toxicity effects of sub-lethal concentrations of lindane on hemato-biochemical profile and histo-pathological changes in Rohu ( Labeo rohita ). A significant increase in the absolute (p<0.05) and relative (p<0.05) weights was observed along with severe histo-pathological alterations in liver, kidneys, gills, heart and brain at 30μg/L and 45μg/L concentration of lindane. A significant (p<0.05) decrease in RBCs count, PCV and Hb concentration while a significant (p<0.05) increased leukocytes were observed by 30μg/L and 45μg/L concentrations of lindane at 45 and 60 days of the experiment. Serum total protein and albumin were significantly (p<0.05) decreased while hepatic and renal enzymes were significantly (p<0.05) increased due to 30μg/L and 45μg/L concentrations of lindane at days-45 and 60 of experiment compared to control group. The observations of thin blood smear indicated significantly increased number of erythrocytes having nuclear abnormalities in the fish exposed at 30μg/L and 45μg/L concentrations of lindane. ROS and TBARS were found to be significantly increased while CAT, SOD, POD and GSH were significantly decreased with an increase in the concentration and exposure time of lindane. The results showed that lindane causes oxidative stress and severe hematological, serum biochemical and histo-pathological alterations in the fish even at sub-lethal concentrations.

LE300 is a novel dopamine receptor antagonist used to treat cocaine addiction. In the current study, a sensitive and fast liquid chromatography–tandem mass spectrometry (LC-MS/MS) has been established and validated for the simultaneous analysis of LE300 and its N-methyl metabolite, MLE300, in rat plasma with an application in a pharmacokinetic study. The chromatographic elution of LE300, MLE300, and Ponatinib (IS, internal standard), was carried out on a 50 mm C18 analytical column (ID: 2.1 mm and particle size: 1.8 μm) maintained at 22 ± 2 °C. The run time was 5 min at a flow rate of 0.3 mL/min. The mobile phase consisted of 42% aqueous solvent (10 mM ammonium formate, pH: 4.2 with formic acid) and 58% organic solvent (acetonitrile). Plasma samples were pretreated using protein precipitation with acetonitrile. The electrospray ionization (ESI) source was used to generate an ion-utilizing positive mode. A multiple reaction monitoring mass analyzer mode was utilized for the quantification of analytes. The linearity of the calibration curves in rat plasma ranged from 1 to 200 ng/mL (r2 = 0.9997) and from 2 to 200 ng/mL (r2 = 0.9984) for LE300 and MLE300, respectively. The lower limits of detection (LLOD) were 0.3 ng/mL and 0.7 ng/mL in rat plasma for LE300 and MLE300, respectively. Accuracy (RE%) ranged from −1.71% to −0.07% and −4.18% to −1.48% (inter-day), and from −3.3% to −1.47% and −4.89% to −2.15% (intra-day) for LE300 and MLE300, respectively. The precision (RSD%) was less than 2.43% and 1.77% for the inter-day, and 2.77% and 1.73% for intra-day of LE300 and MLE300, respectively. These results are in agreement with FDA guidelines. The developed LC-MS/MS method was applied in a pharmacokinetic study in Wistar rats. Tmax and Cmax were 2 h and 151.12 ± 12.5 ng/mL for LE300, and 3 h and 170.4 ± 23.3 ng/mL for MLE300.