Exploring the aroma profile and biomedical applications of Scutellaria nuristanica Rech. F.: A new insight as a natural remedy.

Background: The Scutellaria genus has promising therapeutic capabilities as an aromatherapy. Based on that and local practices of S. nuristanica Rech.

Vitamin B6 p-JNK/Nrf-2/NF-κB Signaling Ameliorates Cadmium Chloride-Induced Oxidative Stress Mediated Memory Deficits in Mice Hippocampus.

Background: Cadmium chloride (Cd) is a pervasive environmental heavy metal pollutant linked to mitochondrial dysfunction, memory loss, and genetic disorders, particularly in the context of neurodegenerative diseases like Alzheimer's disease (AD).

Methods: This study investigated the neurotherapeutic potential of vitamin B6 (Vit. B6) in mitigating Cd-induced oxidative stress and neuroinflammation-mediated synaptic and memory dysfunction.

Treatment of a resistant case of schizoaffective disorder with lumateperone: A case report.

To this day, there exists skepticism about the reliability and clinical utility of the diagnostic criteria and classification of schizoaffective disorder. In addition, the treatment of schizoaffective disorder, especially of treatment-resistant cases, has been minimally investigated. As a result, formulating official treatment guidelines for schizoaffective disorder has been challenging.

Evaluation of machine learning models for predicting TiO photocatalytic degradation of air contaminants.

The escalation of global urbanization and industrial expansion has resulted in an increase in the emission of harmful substances into the atmosphere. Evaluating the effectiveness of titanium dioxide (TiO) in photocatalytic degradation through traditional methods is resource-intensive and complex due to the detailed photocatalyst structures and the wide range of contaminants. Therefore in this study, recent advancements in machine learning (ML) are used to offer data-driven approach using thirteen machine learning techniques namely XG Boost (XGB), decision tree (DT), lasso Regression (LR2), support vector regression (SVR), adaBoost (AB), voting Regressor (VR), CatBoost (CB), K-Nearest Neighbors (KNN), gradient boost (GB), random Forest (RF), artificial neural network (ANN), ridge regression (RR), linear regression (LR1) to address the problem of estimation of TiO photocatalytic degradation rate of air contaminants.

In silico design of peptide inhibitors for Dengue virus to treat Dengue virus-associated infections.

Dengue virus is a single positive-strand RNA virus that is composed of three structural proteins including capsid, envelope, and precursor membrane while seven non-structural proteins (NS1, NS2A, NS2B, NS3A, NS3B, NS4, and NS5). Dengue is a viral infection caused by the dengue virus (DENV). DENV infections are asymptomatic or produce only mild illness.

Chemoenzymatic synthesis of (1R,3R)-3-hydroxycyclopentanemethanol: An intermediate of carbocyclic-ddA.

The synthesis of carbocyclic-ddA, a potent antiviral agent against hepatitis B, relies significantly on (1R,3R)-3-hydroxycyclopentanemethanol as a key intermediate. To effectively produce this intermediate, our study employed a chemoenzymatic approach. The selection of appropriate biocatalysts was based on substrate similarity, leading us to adopt the CrS enoate reductase derived from Thermus scotoductus SA-01.

Molecular modeling and simulation approaches to characterize potential molecular targets for burdock inulin to instigate protection against autoimmune diseases.

In the current study, we utilized molecular modeling and simulation approaches to define putative potential molecular targets for Burdock Inulin, including inflammatory proteins such as iNOS, COX-2, TNF-alpha, IL-6, and IL-1β. Molecular docking results revealed potential interactions and good binding affinity for these targets; however, IL-1β, COX-2, and iNOS were identified as the best targets for Inulin. Molecular simulation-based stability assessment demonstrated that inulin could primarily target iNOS and may also supplementarily target COX-2 and IL-1β during DSS-induced colitis to reduce the role of these inflammatory mechanisms.

Investigating the role of functional mutations in leucine binding to Sestrin2 in aging and age-associated degenerative pathologies using structural and molecular simulation approaches.

Leucine is the native known ligand of Sestrin2 (Sesn2) and its interaction with Sesn2 is particularly important, as it influences the activity of mTOR in aging and its associated pathologies. It is important to find out how leucine interacts with Sesn2 and how mutations in the binding pocket of leucine affect the binding of leucine. Therefore, this study was committed to investigating the impact of non-synonymous mutations by incorporating a broad spectrum of simulation techniques, from molecular dynamics to free energy calculations.

A robust computational quest: Discovering potential hits to improve the treatment of pyrazinamide-resistant Mycobacterium tuberculosis.

The rise of pyrazinamide (PZA)-resistant strains of Mycobacterium tuberculosis (MTB) poses a major challenge to conventional tuberculosis (TB) treatments. PZA, a cornerstone of TB therapy, must be activated by the mycobacterial enzyme pyrazinamidase (PZase) to convert its active form, pyrazinoic acid, which targets the ribosomal protein S1. Resistance, often associated with mutations in the RpsA protein, complicates treatment and highlights a critical gap in the understanding of structural dynamics and mechanisms of resistance, particularly in the context of the G97D mutation.

Unraveling the mechanisms of Sofosbuvir resistance in HCV NS3/4A protease: Structural and molecular simulation-based insights.

Current management of HCV infection is based on Direct-Acting Antiviral Drugs (DAAs). However, resistance-associated mutations, especially in the NS3 and NS5B regions are gradually decreasing the efficacy of DAAs. Among the most effective HCV NS3/4A protease drugs, Sofosbuvir also develops resistance due to mutations in the NS3 and NS5B regions.

Targeting human progesterone receptor (PR), through pharmacophore-based screening and molecular simulation revealed potent inhibitors against breast cancer.

Breast cancer, the prevailing malignant tumor among women, is linked to progesterone and its receptor (PR) in both tumorigenesis and treatment responsiveness. Despite thorough investigation, the precise molecular mechanisms of progesterone in breast cancer remain unclear. The human progesterone receptor (PR) serves as an essential therapeutic target for breast cancer treatment, warranting the rapid design of small molecule therapeutics that can effectively inhibit HPR.

Machine learning and molecular simulation-based protocols to identify novel potential inhibitors for reverse transcriptase against HIV infections.

Acquired immunodeficiency syndrome (AIDS) is a potentially fatal condition affecting the human immune system, which is attributed to the human immunodeficiency virus (HIV). The suppression of reverse transcriptase activity is a promising and feasible strategy for the therapeutic management of AIDS. In this study, we employed machine learning algorithms, such as support vector machines (SVM), k-nearest neighbor (k-NN), random forest (RF), and Gaussian naive base (GNB), which are fast and effective tools commonly used in drug design.

Immunoinformatics-driven In silico vaccine design for Nipah virus (NPV): Integrating machine learning and computational epitope prediction.

The Nipah virus (NPV) is a highly lethal virus, known for its significant fatality rate. The virus initially originated in Malaysia in 1998 and later led to outbreaks in nearby countries such as Bangladesh, Singapore, and India. Currently, there are no specific vaccines available for this virus.

exploration of the potential inhibitory activities of in-house and ZINC database lead compounds against alpha-glucosidase using structure-based virtual screening and molecular dynamics simulation approach.

Inhibitors of α-glucosidase have been used to treat type-2 diabetes (T2DM) by preventing the breakdown of carbohydrates into glucose and prevent enhancing glucose conversion. Structure-based virtual screening (SBVS) was used to generate novel chemical scaffold-ligand α-glucosidase inhibitors. The databases were screened against the receptor α-glucosidase using SBVS and molecular dynamics simulation (MDS) techniques in this study.

Exploring Phytochemicals as Potential Inhibitors for Breast Cancer Genes BRCA1 and BRCA2 Using Pharmacophore Modeling, Molecular Docking, MD Simulations, and DFT Analysis.

Background: Structure-activity relationship (SAR) is considered to be an effective in silico approach when discovering potential antagonists for breast cancer due to gene mutation. Major challenges are faced by conventional SAR in predicting novel antagonists due to the discovery of diverse antagonistic compounds. Methodologyand Results: In predicting breast cancer antagonists, a multistep screening of phytochemicals isolated from the seeds of the plant was applied using feasible complementary methodologies.

Innovative Approaches to Safe Surgery: A Narrative Synthesis of Best Practices.

By encompassing a wide range of best practices within the ever-changing realm of modern surgical care, this exhaustive narrative compendium attempts to unravel the complex tapestry of novel approaches to safe surgery. Within the context of a dynamic surgical environment, this research endeavors to illuminate and integrate state-of-the-art methods that collectively methodically improve patient safety. The narrative elucidates a diverse array of practices that seek to revolutionize the paradigm of safe surgery, emphasizing technological progress, patient-centric approaches, and global viewpoints.

Comparison of Mechanical Properties of Non-ridged Versus Ridged Backslabs in Lower Limb Fractures.

Introduction Lower limb fractures frequently require immobilization with backslabs to promote healing. This study investigates a novel approach involving the incorporation of a single ridge to enhance backslab strength while maintaining cost-effectiveness. Objective The aim of this study was to assess the mechanical performance of ridged backslabs in comparison to traditional non-ridged backslabs, specifically focusing on their load-bearing capacity and cost-effectiveness when used in lower limb fractures.

Identification of potential inhibitors targeting Ebola virus VP35 protein: a computational strategy.

Ebola virus (EBOV) poses a severe threat as a highly infectious pathogen, causing devastating hemorrhagic fever in both humans and animals. The EBOV virus VP35 protein plays a crucial role in viral replication and exhibits the ability to suppress the host interferon cascade, leading to immune system depletion. As a potential drug target, VP35 protein inhibition holds promise for combating EBOV.

In silico design of an epitope-based vaccine against PspC in Streptococcus pneumoniae using reverse vaccinology.

Background: Streptococcus pneumoniae is a major pathogen that poses a significant hazard to global health, causing a variety of infections including pneumonia, meningitis, and sepsis. The emergence of antibiotic-resistant strains has increased the difficulty of conventional antibiotic treatment, highlighting the need for alternative therapies such as multi-epitope vaccines. In this study, immunoinformatics algorithms were used to identify potential vaccine candidates based on the extracellular immunogenic protein Pneumococcal surface protein C (PspC).

Exploring potent aldose reductase inhibitors for anti-diabetic (anti-hyperglycemic) therapy: integrating structure-based drug design, and MMGBSA approaches.

Aldose reductase (AR) is an important target in the development of therapeutics against hyper-glycemia-induced health complications such as retinopathy, etc. In this study, we employed a combination of structure-based drug design, molecular simulation, and free energy calculation approaches to identify potential hit molecules against anti-diabetic (anti-hyperglycemic)-induced health complications. The 3D structure of aldoreductase was screened for multiple compound libraries (1,00,000 compounds) and identified as ZINC35671852, ZINC78774792 from the ZINC database, Diamino-di nitro-methyl dioctyl phthalate, and Penta-o-galloyl-glucose from the South African natural compounds database, and Bisindolylmethane thiosemi-carbazides and Bisindolylme-thane-hydrazone from the Inhouse database for this study.

Napthyridine-derived compounds as promising inhibitors for CrtM: a primer for the discovery of potential anti- agents.

The disease-free existence of humans is constantly under attack by a variety of infections caused by a variety of organisms including bacteria. Notable among the bacteria is which is an etiological organism for infections including impetigo, folliculitis, and furuncles. The response of the human immune system against this disease is often neutralized by the production of a pigment called Staphyloxanthin (STX) via a series of reactions mediated by several enzymes.

Structure-based virtual screening, molecular simulation and free energy calculations of traditional Chinese medicine, ZINC database revealed potent inhibitors of estrogen-receptor α (ERα).

Breast Cancer, a heterogeneous disease at the molecular level, is the most common cause of woman mortality worldwide. We used molecular screening and simulation approaches to target nuclear receptor protein-estrogen receptor alpha (Erα) protein to design and develop of specific and compelling drugs from traditional Chinese medicine (TCM), and ZINC database against pathophysiology of breast cancer. Using virtual screening, only six hits TCM22717, TCM23524, TCM31953, while ZINC05632920, ZINC05773243, and ZINC12780336 demonstrated better pharmacological potential than the 4-hydroxytamoxifen (OHT) taken as control.