Training Outcomes and Satisfaction in Canadian MD/PhD and MD/MSc Programs: Findings from a National Survey.

PURPOSE: Despite the impact of physician-scientists on scientific discovery and translational medicine, several reports have signalled their declining workforce, reduced funding, and insufficient protected research time. Given the paucity of outcome data on Canadian MD/PhD programs, this study presents a national portrait of the sociodemographic characteristics, training trajectories, productivity, and satisfaction in trainees and alumni from Canadian MD/PhD and MD/MSc programs. METHODS: Quantitative data were collected in a national survey launched in 2021. Respondents included 74 MD/PhD alumni and 121 trainees across 12 Canadian MD/PhD and MD/MSc programs. RESULTS: Among MD/PhD alumni, 51% were independent practitioners/researchers while others underwent residency training. Most trainees (88%) were in MD/PhD programs. Significantly more alumni identified as men than did trainees. Significantly more alumni conducted clinical and health services research, while more trainees conducted basic science research. Average time to MD/PhD completion was 8 years, with no correlation to subsequent research outcomes. Self-reported research productivity was highest during MD/PhD training. Concerning training trajectories, most alumni completed residency, pursued additional training, and practised in Canada. Finally, regression models showed that trainees and alumni were satisfied with programs, with significant moderators in trainee models. CONCLUSION: Survey findings showed Canadian MD/PhD and MD/MSc programs recruit more diverse cohorts of trainees than before, provide productive research years, and graduate alumni who pursue training and academic employment in Canada. Both alumni and trainees are largely satisfied with these training programs. The need to collect in-depth longitudinal data on Canadian MD/PhD graduates to monitor diversity and success metrics is discussed.

Molecular Mechanism of Cynodon dactylon Phytosterols Targeting MAPK3 and PARP1 to Combat Epithelial Ovarian Cancer: A Multifaceted Computational Approach.

Epithelial Ovarian Cancer (EOC) presents a global health concern, necessitating the development of innovative therapeutic strategies to combat its impact. This study was employed to investigate the unexplored therapeutic efficacy of Cynodon dactylon phytochemicals against EOC using a multifaceted computational approach. A total of 19 out of 89 rigorously curated phytochemicals were assessed as potential drug targets via ADMET profiling, while protein-protein interaction analysis scrutinized the top 20 hub genes among 264 disease targets, revealing their involvement in cancer-related pathways and underscoring their significance in EOC pathogenesis. In molecular docking, Stigmasterol acetate showed the highest binding affinity (-10.9 kcal/mol) with Poly [ADP-ribose] polymerase-1 (PDB: 1UK1), while Arundoin and Beta-Sitosterol exhibited strong affinities (-10.4 kcal/mol and -10.1 kcal/mol, respectively); additionally, Beta-Sitosterol interacting with Mitogen-activated protein kinase 3 (PDB: 4QTB) showed a binding affinity of -10.1 kcal/mol, forming 2 hydrogen bonds and a total of 10 bonds with 10 residues. Molecular dynamics simulations exhibited the significant structural stability of the Beta-Sitosterol-4QTB complex with superior binding free energy (-36.61 kcal/mol) among the three complexes. This study identified C. dactylon phytosterols, particularly Beta-Sitosterol, as effective in targeting MAPK3 and PARP1 to combat EOC, laying the groundwork for further experimental validation and drug development efforts.

The Effects and Mechanisms of AM1241 in Alleviating Cerebral Ischemia-Reperfusion Injury.

OBJECTIVE: Research has shown that cerebral ischemia-reperfusion injury (CIRI) involves a series of physiological and pathological mechanisms, including inflammation, oxidative stress, and cell apoptosis. The cannabinoid receptor 2 agonist AM1241 has been found to have anti-inflammatory and anti-oxidative stress effects. However, it is unclear whether AM1241 has a protective effect against brain ischemia-reperfusion injury, and its underlying mechanisms are not yet known. METHODS: In this study, we investigated the anti-inflammatory, anti-oxidative stress, and anti-apoptotic effects of AM1241 and its mechanisms in BV2 cells stimulated with H2O2 and in a C57BL/6 mouse model of CIRI in vitro and in vivo, respectively. RESULTS: In vitro, AM1241 significantly inhibited the release of pro-inflammatory cytokines TNF-α and IL-6, reactive oxygen species (ROS), and the increase in Toll-like receptor 4/myeloid differentiation protein 2 (MD2/TLR4) complex induced by H2O2. Under H2O2 stimulation, MD2 overexpression resulted in increased levels of MD2/TLR4 complex, TNF-α, IL-6, NOX2, BAX, and Cleaved-Caspase3 (C-Caspase3), as well as the activation of the MAPK pathway and NF-κB, which were reversed by AM1241. In addition, molecular docking experiments showed that AM1241 directly interacted with MD2. Surface Plasmon Resonance (SPR) experiments further confirmed the binding of AM1241 to MD2. In vivo, AM1241 significantly attenuated neurofunctional impairment, brain edema, increased infarct volume, oxidative stress levels, and neuronal apoptosis in CIRI mice overexpressing MD2. CONCLUSION: Our study demonstrates for the first time that AM1241 alleviates mouse CIRI by inhibiting the MD2/TLR4 complex, exerting anti-inflammatory, anti-oxidative stress and anti-apoptotic effects.

6-Bromo quinazoline derivatives as cytotoxic agents: design, synthesis, molecular docking and MD simulation.

Based on unselectively, several side effects and drug resistance of available anticancer agents, the development and research for novel anticancer agents is necessary. In this study, a new series of quinazoline-4(3H)-one derivatives having a thiol group at position 2 of the quinazoline ring (8a-8 h) were designed and synthesized as potential anticancer agents. The Chemical structures of all compounds were characterized by 1H-NMR, 13C-NMR, and Mass spectroscopy. The antiproliferative activity of all derivatives were determined against two cancer cell lines (MCF-7 and SW480) and one normal cell lines (MRC-5) by the MTT method. Cisplatin, Erlotinib and Doxorubicin were used as positive controls. The results of in vitro screening showed that 8a with an aliphatic linker to SH group was the most potent compound with IC50 values of 15.85 ± 3.32 and 17.85 ± 0.92 µM against MCF-7 and SW480 cell lines, respectively. 8a indicated significantly better potency compared to Erlotinib in the MCF-7 cell line. The cytotoxic results obtained from testing compound 8a on the normal cell line, revealing an IC50 value of 84.20 ± 1.72 µM, provide compelling evidence of its selectivity in distinguishing between tumorigenic and non-tumorigenic cell lines. Structure-activity relationship indicated that the variation in the anticancer activities of quinazoline-4(3H)-one derivatives was affected by different substitutions on the SH position. Molecular docking and MD simulation were carried out for consideration of the binding affinity of compounds against EGFR and EGFR-mutated. The binding energy of compounds 8a and 8c were calculated at -6.7 and - 5.3 kcal.mol- 1, respectively. Compounds 8a and 8c were found to establish hydrogen bonds and some other important interactions with key residue. The DFT analysis was also performed at the B3LYP/6-31 + G(d, p) level for compounds 8a, 8c and Erlotinib. Compound 8a was thermodynamically more stable than 8c. Also, the calculated theoretical and experimental data for the IR spectrum were in agreement. The obtained results delineated that the 8a can be considered an appropriate pharmacophore to develop as an anti-proliferative agent.

Comparative study for metabolomics, antioxidant activity, and molecular docking simulation of the newly bred Korean red rice accessions.

This study analyzed the metabolite profiles and antioxidant capacities of two waxy and non-waxy Korean red rice accessions newly bred. Fifteen phenolic compounds were detected in the rice samples. Accession1 had high fatty acids, phytosterols, and vitamin E; accession3 had high vitamin E and phytosterol; and accession4 had a high total flavonoid. The correlation analysis findings from this study validated the positive association between all the metabolites and antioxidant activity. in silico results revealed that protocatechuic acid had a docking score of -9.541, followed by luteolin, quercetin, and caffeic acid, all of which had significant docking scores and a significant number of contacts. Similarly, molecular dynamics simulations showed that phytochemicals had root mean square deviation values of <2.8 Å with Keap 1, indicating better stability. This study provides valuable insights into potential directions for future investigations and improvements in the functional qualities of other colored rice varieties.

Identification of aryl hydrocarbon receptor allosteric antagonists from clinically approved drugs.

The human aryl hydrocarbon receptor (AhR), a ligand-dependent transcription factor, plays a pivotal role in a diverse array of pathways in biological and pathophysiological events. This position AhR as a promising target for both carcinogenesis and antitumor strategies. In this study we utilized computational modeling to screen and identify FDA-approved drugs binding to the allosteric site between α2 of bHLH and PAS-A domains of AhR, with the aim of inhibiting its canonical pathway activity. Our findings indicated that nilotinib effectively fits into the allosteric pocket and forms interactions with crucial residues F82, Y76, and Y137. Binding free energy value of nilotinib is the lowest among top hits and maintains stable within its pocket throughout entire (MD) simulations time. Nilotinib has also substantial interactions with F295 and Q383 when it binds to orthosteric site and activate AhR. Surprisingly, it does not influence AhR nuclear translocation in the presence of AhR agonists; instead, it hinders the formation of the functional AhR-ARNT-DNA heterodimer assembly, preventing the upregulation of regulated enzymes like CYP1A1. Importantly, nilotinib exhibits a dual impact on AhR, modulating AhR activity via the PAS-B domain and working as a noncompetitive allosteric antagonist capable of blocking the canonical AhR signaling pathway in the presence of potent AhR agonists. These findings open a new avenue for the repositioning of nilotinib beyond its current application in diverse diseases mediated via AhR.

Elevating Residency Match Success: The Potential Impact of a Home Program on the Surgical Match Rate.

INTRODUCTION: Home residency programs can provide medical students with opportunities for networking, mentorship, research, and exposure to surgeries. The goal of this project was to understand the potential impact of home surgical residencies on student match rates into specific surgical specialties. METHODS: This 5-year retrospective study (2019-2023) analyzed 12,916 matched applicants from 155 United States MD programs through publicly available match lists. Odds ratios (ORs) were used to determine the likelihood of students from institutions with home surgical residency programs (home programs) matching into desired surgical specialties compared to students from institutions without home programs. Additional variables included the Alpha Omega Alpha and the Gold Humanism Honor Society statuses of the medical school, the number of faculty, and the type of residency program. RESULTS: Of the matched applicants, 11,442 had home programs resulting in a 39.1% match rate into surgical specialties compared to a 22.3% match rate for students without a home program (OR: 1.76) (P < 0.001). Of the applicants with a home program compared to those without a home program, 69.2% matched into an academic residency (OR: 1.06), 7.7% matched into a community residency (OR: 0.90), 13.6% matched into a combined residency (OR: 0.95), and 2.5% matched into a military residency (OR: 1.31). CONCLUSIONS: Medical students graduating from institutions with home programs were 1.76 times more likely to match into a surgical residency program compared to those graduating from institutions without a home program. Future studies should look at how access to certain resources may influence a student's match rate.

Molecular Dynamics Simulations in Protein-Protein Docking.

Concerted interactions between all the cell components form the basis of biological processes. Protein-protein interactions (PPIs) constitute a tremendous part of this interaction network. Deeper insight into PPIs can help us better understand numerous diseases and lead to the development of new diagnostic and therapeutic strategies. PPI interfaces, until recently, were considered undruggable. However, it is now believed that the interfaces contain "hot spots," which could be targeted by small molecules. Such a strategy would require high-quality structural data of PPIs, which are difficult to obtain experimentally. Therefore, in silico modeling can complement or be an alternative to in vitro approaches. There are several computational methods for analyzing the structural data of the binding partners and modeling of the protein-protein dimer/oligomer structure. The major problem with in silico structure prediction of protein assemblies is obtaining sufficient sampling of protein dynamics. One of the methods that can take protein flexibility and the effects of the environment into account is Molecular Dynamics (MD). While sampling of the whole protein-protein association process with plain MD would be computationally expensive, there are several strategies to harness the method to PPI studies while maintaining reasonable use of resources. This chapter reviews known applications of MD in the PPI investigation workflows.

Identification of most representative hub-genes for diagnosis, prognosis, and therapies of hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related deaths globally. To reduce HCC-related mortality, early diagnosis and therapeutic improvement are essential. Hub differentially expressed genes (HubGs) may serve as potential diagnostic and prognostic biomarkers, also offering therapeutic targets for precise therapies. Therefore, we aimed to identify top-ranked hub genes for the diagnosis, prognosis, and therapy of HCC. METHODS: Through a systematic literature review, 202 HCC-related HubGs were derived from 59 studies, yet consistent detection across these was lacking. Then, we identified top-ranked HubGs (tHubGs) by integrated bioinformatics analysis, highlighting their functions, pathways, and regulators that might be more representative of the diagnosis, prognosis, and therapies of HCC. RESULTS: In this study, eight HubGs (CDK1, AURKA, CDC20, CCNB2, TOP2A, PLK1, BUB1B, and BIRC5) were identified as the tHubGs through the protein-protein interaction (PPI) network and survival analysis. Their differential expression in different stages of HCC, validated using The Cancer Genome Atlas (TCGA) Program database, suggests their potential as early HCC markers. The enrichment analyses revealed some important roles in HCC-related biological processes (BPs), molecular functions (MFs), cellular components (CCs), and signaling pathways. Moreover, the gene regulatory network analysis highlighted key transcription factors (TFs) and microRNAs (miRNAs) that regulate these tHubGs at transcriptional and post-transcriptional. Finally, we selected three drugs (CD437, avrainvillamide, and LRRK2-IN-1) as candidate drugs for HCC treatment as they showed strong binding with all of our proposed and published protein receptors. CONCLUSIONS: The findings of this study may provide valuable resources for early diagnosis, prognosis, and therapies for HCC.

Modulation of aryl hydrocarbon receptor activity by tyrosine kinase inhibitors (ponatinib and tofacitinib).

Ponatinib and tofacitinib, established kinase inhibitors and FDA-approved for chronic myeloid leukemia and rheumatoid arthritis, are recently undergoing investigation in diverse clinical trials for potential repurposing. The aryl hydrocarbon receptor (AhR), a transcription factor influencing a spectrum of physiological and pathophysiological activities, stands as a therapeutic target for numerous diseases. This study employs molecular modelling tools and in vitro assays to identify ponatinib and tofacitinib as AhR ligands, elucidating their binding and molecular interactions in the AhR PAS-B domain. Molecular docking analyses revealed that ponatinib and tofacitinib occupy the central pocket within the primary cavity, similar to AhR agonists 2,3,7,8-tetrachlorodibenzodioxin (TCDD) and (benzo[a]pyrene) B[a]P. Our simulations also showed that these compounds exhibit good stability, stabilizing many hot spots within the PAS-B domain, including the Dα-Eα loop, which serves as a regulatory element for the binding pocket. Binding energy calculations highlighted ponatinib's superior predicted affinity, revealing F295 as a crucial residue in maintaining strong interaction with the two compounds. Our in vitro data suggest that ponatinib functions as an AhR antagonist, blocking the downstream signaling of AhR pathway induced by TCDD and B[a]P. Additionally, both tofacitinib and ponatinib cause impairment in AhR-regulated CYP1A1 enzyme activity induced by potent AhR agonists. This study unveils ponatinib and tofacitinib as potential modulators of AhR, providing valuable insights into their therapeutic roles in AhR-associated diseases and enhancing our understanding of the intricate relationship between kinase inhibitors and AhR.

Exploring fingerprints for antidiabetic therapeutics related to peroxisome proliferator-activated receptor gamma (PPARγ) modulators: A chemometric modeling approach.

This study demonstrated the correlation of molecular structures of Peroxisome proliferator-activated receptor gamma (PPARγ) modulators and their biological activities. Bayesian classification, and recursive partitioning (RP) studies have been applied to a dataset of 323 PPARγ modulators with diverse scaffolds. The results provide a deep insight into the important sub-structural features modulating PPARγ. The molecular docking analysis again confirmed the significance of the identified sub-structural features in the modulation of PPARγ activity. Molecular dynamics simulations further underscored the stability of the complexes formed by investigated modulators with PPARγ. Overall, the integration of many computational approaches unveiled key structural motifs essential for PPARγ modulatory activity that will shed light on the development of effective modulators in the future.

Betulin: a novel triterpenoid anti-cancerous agent targeting cervical cancer through epigenetic proteins.

Worldwide, cervical cancer (CCa) is a major killer of women. As the conventional drugs used to treat cervical cancer are expensive and expose severe side effects, there is a growing demand to search for novel modifications. Therefore, in the current investigation employing a bioinformatic approach, we explored triterpenoids for their anti-cancer efficacy by targeting cervical cancer epigenetic proteins, namely DNMT3A, HDAC4, and KMT2C. The study utilized molecular docking, ADMET assay, Molecular Dynamic simulation, and DFT calculation to unveil Betulin (BE) as the potential lead compound. Comparative analysis with that standard drug indicated that BE has a better glide score with the target protein KM2TC (- 9.893 kcal/mol), HDAC4 (- 9.720 kcal/mol), and DNMT3A (- 7.811 kcal/mol), which depicts that BE could be a potent inhibitor of these three epigenetic proteins and exhibits favorable pharmacokinetic, pharmacodynamics and toxicity properties. Molecular Dynamics simulation revealed noteworthy structural stability and compactness. DFT analysis revealed higher molecular activity of BE and showed the most increased kinetic stability (δE = 0.254647 eV). Further, we employed In vitro analysis through MTT assay and found that BE has IC50 of 15 µg/ml. In conclusion, BE can potentially treat CCa upon further investigations using in vivo models for better understanding.

MDFit: automated molecular simulations workflow enables high throughput assessment of ligands-protein dynamics.

Molecular dynamics (MD) simulation is a powerful tool for characterizing ligand-protein conformational dynamics and offers significant advantages over docking and other rigid structure-based computational methods. However, setting up, running, and analyzing MD simulations continues to be a multi-step process making it cumbersome to assess a library of ligands in a protein binding pocket using MD. We present an automated workflow that streamlines setting up, running, and analyzing Desmond MD simulations for protein-ligand complexes using machine learning (ML) models. The workflow takes a library of pre-docked ligands and a prepared protein structure as input, sets up and runs MD with each protein-ligand complex, and generates simulation fingerprints for each ligand. Simulation fingerprints (SimFP) capture protein-ligand compatibility, including stability of different ligand-pocket interactions and other useful metrics that enable easy rank-ordering of the ligand library for pocket optimization. SimFPs from a ligand library are used to build & deploy ML models that predict binding assay outcomes and automatically infer important interactions. Unlike relative free-energy methods that are constrained to assess ligands with high chemical similarity, ML models based on SimFPs can accommodate diverse ligand sets. We present two case studies on how SimFP helps delineate structure-activity relationship (SAR) trends and explain potency differences across matched-molecular pairs of (1) cyclic peptides targeting PD-L1 and (2) small molecule inhibitors targeting CDK9.

How health seeking behavior develops in patients with type 2 diabetes: a qualitative study based on health belief model in China.

Background: Type 2 diabetes(T2DM) is a global health problem which is accompanied with multi-systemic complications, and associated with long-term health burden and economic burden. Effective health seeking behavior (HSB) refers to reasonably utilize health resources, effectively prevent and treat diseases, and maintain health. Effective health seeking behavior (HSB) is vital to mitigate the risk of T2DM complications. However, health seeking behavior for T2DM patients remains sub-optimal worldwide. Objective: The study aimed to explore the internal logic of how health seeking behavior of T2DM patients develops and the influencing factors of health seeking behavior. With a view to provide a reference basis for improving the health seeking behavior situation of T2DM patients. Methods: This study was conducted at an integrated tertiary hospital in China. People who were diagnosed with T2DM, capable of expressing clearly and had no mental illness, were approached based on a purposive sampling. The experience of T2DM and health seeking behavior were collected via in-depth interviews. A theory-driven thematic analysis based on Health Belief Model (HBM) was applied for data analysis. Inductive reasoning was used to identify emerging themes which were not included in HBM. Results: 26 patients with T2DM were included in the current study. Seven themes were identified, including: (1) T2DM diagnosis and severity; (2) T2DM treatment and management; (3) Perceived susceptibility of diabetes progression; (4) Perceived severity of diabetes progression; (5) Perceived benefits of health seeking behavior; (6) Perceived barriers of health seeking behavior; (7) Perception of behavioral cues. Generally, patients with T2DM lacked reliable sources of information, considered T2DM to be slow-progressing and without posing an immediate threat to life. Consequently, they did not fully grasp the long-term risks associated with T2DM or the protective effects of health seeking behavior. Conclusion: This study highlighted the challenges in health seeking behavior for patients with T2DM. It suggested that future interventions and strategies should involve multi-faceted approaches, targeting healthcare providers (HCPs), patients with T2DM, and their support networks. This comprehensive strategy can help patients better understand their condition and the importance of effective health seeking behavior. Ultimately, enhancing their capacity for adopting appropriate health-seeking practices.

Identification of imidazole-based small molecules to combat cognitive disability caused by Alzheimer's disease: A molecular docking and MD simulations based approach.

Alzheimer's disease (AD) is a chronic neurodegenerative disorder that is the primary cause of dementia. It is characterised by the gradual loss of brain cells, which results in memory loss and cognitive dysfunction. One of the hallmarks of AD is an abnormally upregulated glutaminyl-peptide cyclotransferase (QPCT or QC) enzyme. Not only AD, but QC has also been implicated with pathological conditions like Huntington's disease (HD), melanomas, carcinomas, atherosclerosis, and septic arthritis. Therefore, the inhibition of QC emerged as a potential strategy for preventing multiple pathological conditions. Considering this, we screened a library of 153,536 imidazole-based compounds against a doubly mutant (Y115E-Y117E) QC target. Molecular docking based virtual screening and absorption, distribution, metabolism, excretion/toxicity (ADME/T) predictions identified five compounds, namely 118981836, 136459842, 139388116, 139388226, and 139958725. Furthermore, molecular dynamics (MD) simulations of 500 ns were conducted to investigate the behaviour of the identified compounds with the target receptor. The results were compared to the co-ligand by analysing RMSD, RMSF, and SASA parameters. To our knowledge, this is the first computational study that employed a protein with double mutation to identify new imidazole-based QC-inhibitors.

Investigating the Potential of 2-Ethylbutyl-(Phenoxy)phosphoryl-D-Alaninate Against RNA-Dependent RNA Polymerase (RdRp) of SARS-CoV-2.

There was an emergency call globally when COVID-19 was detected in December 2019. The SARS-CoV-2 virus, a modified virus, is what causes this contagious disease. Although research is being conducted throughout the world, the main target is still to find the promising candidate to target RNA-dependent RNA polymerase (RdRp) to provide possible drug against COVID-19. Aim of this work is to find a molecule to inhibit the translational process of viral protein synthesis. Density Functional Theory calculations revealed information about the formation of the desired ligand (RD). Molecular docking of RD with RdRp was performed and compared with some reported molecules and the data revealed that RD had the best docking score with RdRp (-6.7 kcal/mol). Further, molecular dynamics (MD) simulations of RD with RdRp of SARS-CoV-2 revealed the formation of stable complex with a maximum number of seven hydrogen bonds. Root mean square deviations values are in acceptable range and root mean square fluctuations has less fluctuation indicate stable complex formation. Further, based on MM-GBSA calculation, RD formed a stable complex with RdRp of nCoV with ΔG° of -12.28 kcal·mol-1.

Erratum: Exploring HMMR as a therapeutic frontier in breast cancer treatment, its interaction with various cell cycle genes, and targeting its overexpression through specific inhibitors.

[This corrects the article DOI: 10.3389/fphar.2024.1361424.].

Multireceptor Analysis for Evaluating the Antidiabetic Efficacy of Karanjin: A Computational Approach.

BACKGROUND: Diabetes mellitus, notably type 2, is a rising global health challenge, prompting the need for effective management strategies. Common medications such as metformin, insulin, repaglinide and sitagliptin can induce side effects like gastrointestinal disturbances, hypoglycemia, weight gain and specific organ risks. Plant-derived therapies like Karanjin from Pongamia pinnata present promising alternatives due to their historical use, holistic health benefits and potentially fewer adverse effects. This study employs in silico analysis to explore Karanjin's interactions with diabetes-associated receptors, aiming to unveil its therapeutic potential while addressing the limitations and side effects associated with conventional medications. METHODOLOGY: The research encompassed the selection of proteins from the Protein Data Bank (PDB), followed by structural refinement processes and optimization. Ligands such as Karanjin and standard drugs were retrieved from PubChem, followed by a comprehensive analysis of their ADMET profiling and pharmacokinetic properties. Protein-ligand interactions were evaluated through molecular docking using AutoDockTools 1.5.7, followed by the analysis of structural stability using coarse-grained simulations with CABS Flex 2.0. Molecular dynamics simulations were performed using Desmond 7.2 and the OPLS4 force field to explore how Karanjin interacts with proteins over 100 nanoseconds, focusing on the dynamics and structural stability. RESULTS: Karanjin, a phytochemical from Pongamia pinnata, shows superior drug candidate potential compared to common medications, offering advantages in efficacy and reduced side effects. It adheres to drug-likeness criteria and exhibits optimal ADMET properties, including moderate solubility, high gastrointestinal absorption and blood-brain barrier penetration. Molecular docking revealed Karanjin's highest binding energy against receptor 3L2M (Pig pancreatic alpha-amylase) at -9.1 kcal/mol, indicating strong efficacy potential. Molecular dynamics simulations confirmed stable ligand-protein complexes with minor fluctuations in RMSD and RMSF, suggesting robust interactions with receptors 3L2M. CONCLUSION: Karanjin demonstrates potential in pharmaceutical expansion for treating metabolic disorders such as diabetes, as supported by computational analysis. Prospects for Karanjin in pharmaceutical development include structural modifications for enhanced efficacy and safety. Nanoencapsulation may improve bioavailability and targeted delivery to pancreatic cells, while combination therapies could optimize treatment outcomes in diabetes management. Clinical trials and experimental studies are crucial to validate its potential as a novel therapeutic agent.

Artificial Intelligence-Powered Molecular Docking and Steered Molecular Dynamics for Accurate scFv Selection of Anti-CD30 Chimeric Antigen Receptors.

Chimeric antigen receptor (CAR) T cells represent a revolutionary immunotherapy that allows specific tumor recognition by a unique single-chain fragment variable (scFv) derived from monoclonal antibodies (mAbs). scFv selection is consequently a fundamental step for CAR construction, to ensure accurate and effective CAR signaling toward tumor antigen binding. However, conventional in vitro and in vivo biological approaches to compare different scFv-derived CARs are expensive and labor-intensive. With the aim to predict the finest scFv binding before CAR-T cell engineering, we performed artificial intelligence (AI)-guided molecular docking and steered molecular dynamics analysis of different anti-CD30 mAb clones. Virtual computational scFv screening showed comparable results to surface plasmon resonance (SPR) and functional CAR-T cell in vitro and in vivo assays, respectively, in terms of binding capacity and anti-tumor efficacy. The proposed fast and low-cost in silico analysis has the potential to advance the development of novel CAR constructs, with a substantial impact on reducing time, costs, and the need for laboratory animal use.

Regulation and mechanism of enzyme metabolism in germinated hemp seeds by ultrasound combined with exogenous calcium chloride treatment.

γ-aminobutyric acid (GABA) plays an important role in anti-anxiety by inhibiting neurotransmitter in the central nervous system (CNS) of mammals, which is generated in the germinating seeds. The key enzymes activity of GABA metabolism pathway and nutrients content in hemp seeds during germination were studied after treated with ultrasound and CaCl2. The mechanism of exogenous stress on key enzymes in GABA metabolism pathway was investigated by molecular dynamics simulation. The results showed that ultrasonic combined with 1.5 mmol·L-1CaCl2 significantly increased the activities of glutamate decarboxylase (GAD) and GABA transaminase (GABA-T) in seeds, and promoted the conversion of glutamate to GABA, resulting in the decrease of glutamate content and the accumulation of GABA. Molecular dynamics simulations revealed that Ca2+ environment enhanced the activity of GAD and GABA-T enzymes by altering their secondary structure, exposing their hydrophobic residues. Ultrasound, germination and CaCl2 stress improved the nutritional value of hemp seeds.