Search Results (235)

Combustion of aviation jet fuel emits a complex mixture of pollutants linked to adverse health outcomes among airport personnel and nearby communities. While epidemiological studies showed the detrimental effects of aviation-derived air pollutants on human health, the molecular mechanisms of the interactions of these pollutants with cellular biomolecules like proteins that drive the adverse health effects remain poorly understood. In this study, we performed molecular docking simulations of 272 pollutant–protein complexes using AutoDock Vina 1.2.7 to characterize the binding strength of the pollutants with the selected proteins. We selected 34 aviation-derived pollutants that constitute three chemical categories of pollutants: volatile organic compounds (VOCs), polyaromatic hydrocarbons (PAHs), and organophosphate esters (OPEs). Each pollutant was docked to eight proteins that play critical roles in endocrine, metabolic, transport, and neurophysiological functions, where functional disruption is implicated in disease. The effect of binding of multiple pollutants was analyzed. Our results indicate that aliphatic and monoaromatic VOCs display low (<6 kcal/mol) binding affinities while PAHs and organophosphate esters exhibit strong (>7 kcal/mol) binding affinities. Furthermore, the binding strength of PAHs exhibits a positive correlation with the increasing number of aromatic rings in the pollutants, ranging from nearly 7 kcal/mol for two aromatic rings to more than 15 kcal/mol for five aromatic rings. Analysis of intermolecular interactions showed that these interactions are predominantly stabilized by hydrophobic, pi-stacking, and hydrogen bonding interactions. Simultaneous docking of multiple pollutants revealed the increased binding strength of the resulting complexes, highlighting the detrimental effect of exposure to pollutant mixtures found in ambient air near airports. We provide a priority list of pollutants that regulatory authorities can use to further develop targeted mitigation strategies to protect the vulnerable personnel and communities near airports. Full article

Background and aim: The COVID-19 pandemic, caused by SARS-CoV-2, remains a global health crisis despite vaccination efforts, necessitating novel therapeutic strategies. Natural compounds from Syzygium aromaticum (clove), such as eugenol and β-caryophyllene, exhibit antiviral and anti-inflammatory properties, while host genetic factors, including miR-21 rs1292037 polymorphism, may influence disease susceptibility and severity. This study investigates the dual approach of targeting SARS-CoV-2 via Syzygium aromaticum phytoconstituents while assessing the role of miR-21 rs1292037 in COVID-19 pathogenesis. Methods: Firstly, molecular docking and molecular dynamics simulations were employed to assess the binding affinities of eugenol and caryophyllene against seven key SARS-CoV-2 proteins—including Spike-RBD, 3CLpro, and RdRp—using SwissDock (AutoDock Vina) and the Desmond software package, respectively. Secondly, GC-MS was used to characterize the composition of clove extract. Thirdly, pharmacokinetic profiles were predicted using in silico models. Finally, miR-21 rs1292037 genotyping was performed in 100 COVID-19 patients and 100 controls, with cytokine and coagulation markers analyzed. Results: Docking revealed strong binding of eugenol to viral Envelope Protein (−5.267 kcal/mol) and caryophyllene to RdRp (−6.200 kcal/mol). ADMET profiling indicated favorable absorption and low toxicity. Molecular dynamics simulations confirmed stable binding of methyl eugenol and caryophyllene to SARS-CoV-2 proteins, with caryophyllene–7Z4S showing the highest structural stability, highlighting its strong antiviral potential. Genotyping identified the TC genotype as prevalent in patients (52%), correlating with elevated IL-6 and D-dimer levels (p ≤ 0.01), suggesting a hyperinflammatory phenotype. Males exhibited higher ferritin and D-dimer (p < 0.0001), underscoring sex-based disparities. Conclusion: The bioactive constituents of Syzygium aromaticum exhibit strong potential as multi-target antivirals, with molecular simulations highlighting caryophyllene’s particularly stable interaction with the 7Z4S protein. Methyl eugenol also maintained consistent binding across several SARS-CoV-2 targets. Additionally, the miR-21 rs1292037 polymorphism may influence COVID-19 severity through its role in inflammatory regulation. Together, these results support the combined application of phytochemicals and genetic insights in antiviral research, pending further clinical verification. Full article

Background: Chronic wounds pose a significant public health challenge due to high treatment costs and the limited efficacy of current therapies. This study aims to evaluate the in vitro wound-healing activity and in silico interactions of two antimicrobial cationic peptides, derived from Galleria mellonella cecropin D, whose receptors are involved in tissue healing. Methods: Two peptides were tested: a long peptide (∆M2, 39 amino acids) and a short peptide (CAMP-CecD, 18 amino acids). Their cytotoxicity, as well as their effects on fibroblast proliferation and migration, were assessed using Detroit 551 cells. In parallel, molecular docking studies were conducted with AutoDock Vina to predict the binding affinities of these peptides to the key receptors involved in wound healing: the epidermal growth factor receptor (EGFR), the transforming growth factor beta receptor (TGFRβ2), and the vascular endothelial growth factor receptor (VEGFR). Results: In vitro assays showed that the short peptide exhibited lower cytotoxicity and significantly enhanced cell proliferation and migration, leading to a greater percentage of gap closure compared to the long peptide. A docking analysis revealed binding affinities of −6.7, −7.2, and −5.6 kcal/mol for VEGFR, EGFR, and TGFRβ2, respectively, with the RMSD values below 2 Å, indicating stable binding interactions. Conclusions: These findings suggest that the structure and cationic charge of the short peptide facilitate robust interactions with growth factor receptors, enhancing re-epithelialization and tissue regeneration. Consequently, this peptide is a promising candidate ligand for the treatment of chronic wounds and associated infections. Full article

Objective: This study aims to elucidate the potential mechanisms by which Fespixon cream promotes diabetic foot ulcer (DFU) healing using network pharmacology, molecular docking, and RT-qPCR validation in clinical tissue samples. Methods: Active components of Fespixon cream were screened from the Traditional Chinese Medicine Systems Pharmacology Database (TCMSP) and relevant literature, and their corresponding targets were standardized using the Universal Protein Resource (UniProt) database. Diabetic foot ulcer (DFU)-related targets were retrieved and filtered from the GeneCards database and the Online Mendelian Inheritance in Man (OMIM) database. The intersection of drug and disease targets was identified, and a protein–protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database. The interaction network was visualized using Cytoscape version 3.7.2 software. The potential mechanisms of the shared targets were analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis using R software packages, and results were visualized through Bioinformatics online tools. Molecular docking was performed to validate the binding between key active compounds of Fespixon cream and core DFU targets using AutoDock Vina version 1.1.2 and PyMOL software. Furthermore, RT-qPCR analysis was performed on wound edge tissue samples from DFU patients treated with Fespixon cream to experimentally verify the mRNA expression levels of predicted hub genes. Results: Network pharmacology analysis identified eight active compounds in Fespixon cream, along with 153 potential therapeutic targets related to diabetic foot ulcer (DFU). Among these, 21 were determined as core targets, with the top five ranked by degree value being RAC-αserine/threonine-protein kinase (AKT1), Cellular tumor antigen p53 (TP53), Tumor necrosis factor (TNF), Interleukin-6 (IL6), and Mitogen-activated protein kinase 1 (MAPK1). GO enrichment analysis indicated that the targets of Fespixon cream were primarily involved in various biological processes related to cellular stress responses. KEGG pathway enrichment revealed that these targets were significantly enriched in pathways associated with diabetic complications, atherosclerosis, inflammation, and cancer. Molecular docking confirmed stable binding interactions between the five major active compounds—quercetin, apigenin, rosmarinic acid, salvigenin, and cirsimaritin—and the five core targets (AKT1, TP53, TNF, IL6, MAPK1). Among them, quercetin exhibited the strongest binding affinity with AKT1. RT-qPCR validation in clinical DFU tissue samples demonstrated consistent expression trends with computational predictions: AKT1 was significantly upregulated, while TP53, TNF, IL6, and MAPK1 were markedly downregulated in the Fespixon-treated group compared to controls (p < 0.001), supporting the proposed multi-target therapeutic mechanism. Conclusions: Our study reveals the potential mechanisms by which Fespixon cream exerts therapeutic effects on DFUs. The efficacy of Fespixon cream in treating DFUs is attributed to the synergistic actions of its bioactive components through multiple targets and multiple signaling pathways. Full article

Background: Malaria remains a significant global health burden, particularly in sub-Saharan Africa, accounting for high rates of illness and death. The growing resistance to frontline antimalarial therapies underscores the urgent need for novel drug targets and therapeutics. Bromodomain-containing proteins, which regulate gene expression through chromatin remodeling, have gained attention as potential targets. Plasmodium falciparum bromodomain protein 1 (PfBDP1), a 55 kDa nuclear protein, plays a key role in recognizing acetylated lysine residues and facilitating transcription during parasite development. Methods: This study investigated ex vivo PfBDP1 gene mutations and identified potential small molecule inhibitors using computational approaches. Malaria-positive blood samples were collected. Genomic DNA was extracted, assessed for quality, and amplified using PfBDP1-specific primers. DNA sequencing and alignment were performed to determine single-nucleotide polymorphism (SNP). Structural modeling used the PfBDP1 crystal structure (PDB ID: 7M97), and active site identification was conducted using CASTp 3.0. Virtual screening and pharmacophore modeling were performed using Pharmit and AutoDock Vina, followed by ADME/toxicity evaluations with SwissADME, OSIRIS, and Discovery Studio. GROMACS was used for 100 ns molecular dynamics simulations. Results: The malaria prevalence rate stood at 12.24%, and the sample size was 165. Sequencing results revealed conserved PfBDP1 gene sequences compared to the 3D7 reference strain. Virtual screening identified nine lead compounds with binding affinities ranging from −9.8 to −10.7 kcal/mol. Of these, CHEMBL2216838 had a binding affinity of −9.9 kcal/mol, with post-screening predictions of favorable drug-likeness (8.60), a high drug score (0.78), superior pharmacokinetics, and a low toxicity profile compared to chloroquine. Molecular dynamics simulations confirmed its stable interaction within the PfBDP1 active site. Conclusions: Overall, this study makes a significant contribution to the ongoing search for novel antimalarial drug targets by providing both molecular and computational evidence for PfBDP1 as a promising therapeutic target. The prediction of CHEMBL2216838 as a lead compound with favorable binding affinity, drug-likeness, and safety profile, surpassing those of existing drugs like chloroquine, sets the stage for preclinical validation and further structure-based drug design efforts. These findings are supported by prior experimental evidence showing significant parasite inhibition and gene suppression capability of predicted hits. Full article

Background/Objectives: The current need for new antibacterial compounds that target non-classical pathways is highlighted by the emergence of multidrug-resistant Klebsiella pneumoniae. In the development of antibiotics, DNA adenine methyltransferase (Dam), a key regulator of bacterial gene expression and pathogenicity, is still underutilized. Epoxy-functionalized analogues of isatin derivatives have not been adequately investigated for their antibacterial activity, particularly as Dam inhibitors. In the pursuit of antimicrobial agents, this study synthesized an epoxy-functionalized isatin derivative (L3) using a one-pot reaction. The compound was characterized using FT-IR, ¹H-NMR, ¹³C-NMR, HR-MS, and UV–Vis spectroscopy. Methods: In silico evaluation performed by using ADMETlab3 and SwissADME. While molecular docking studies were achieved by AutoDock and Vina to find L3’s interaction with potential antibacterial target (Dam protein in K. pneumoniae). In addition, the antibacterial potential of L3 was evaluated using minimum inhibitory concentration (MIC) assays against Bacillus cereus, Bacillus pumilus, Escherichia coli, and K. pneumoniae. Results: Among these, L3 exhibited potential inhibitory activity against K. pneumoniae, with a MIC value of 93.75 μg/mL. In silico evaluations confirmed L3’s favorable drug-like properties, including potential oral bioavailability, blood–brain barrier (BBB) permeability, and low plasma protein binding (PPB). The compound satisfied Lipinski’s and other drug-likeness rules as well as getting a quantitative estimate of drug-likeness (QED) score of 0.52. Here, a homology model of Dam protein in K. pneumoniae was generated using the SWISS-MODEL server and validated using computational tools. Targeted docking analysis revealed that L3 exhibited significant potential binding affinity against Dam protein, with binding energies of −6.4 kcal/mol and −4.85 kcal/mol, as determined by Vina and AutoDock, respectively. The associated inhibition constant was calculated as 280.35 µM. Further interaction analysis identified the formation of hydrogen bonds with TRP7 and PHE32, along with Van der Waals’ interactions involving GLY9, ASP51, and ASP179. Conclusions: These findings highlight L3 as a promising scaffold for antimicrobial drug development, particularly in targeting Dam protein in K. pneumoniae. Furthermore, the ADMET profiling and physicochemical properties of L3 support its potential as a drug-like candidate. Full article

Norovirus, a major cause of acute gastroenteritis, possesses a single-stranded positive-sense RNA genome. The viral 3C-like cysteine protease (3CL^pro) plays a critical role in processing the viral polyprotein into mature non-structural proteins, a step essential for viral replication. Targeting 3CL^pro has emerged as a promising strategy for developing small-molecule inhibitors against Norovirus. In this study, we employed a combination of virtual screening and the FlipGFP assay to identify potential inhibitors targeting the 3CL^pro of Norovirus genotype GII.4. A library of approximately 58,800 compounds was screened using AutoDock Vina tool, yielding 20 candidate compounds based on their Max Affinity scores. These compounds were subsequently evaluated using a cell-based FlipGFP assay. Among them, eight compounds demonstrated significant inhibitory activity against 3CL^pro, with Gedatolisib showing the most potent effect (IC₅₀ = 0.06 ± 0.01 μM). Molecular docking and molecular dynamics simulations were conducted to explore the binding mechanisms and structural stability of the inhibitor–3CL^pro complexes. Our findings provide valuable insights into the development of antiviral drugs targeting Norovirus 3CL^pro, offering potential therapeutic strategies to combat Norovirus infections. Full article

Background/Objectives: Glycogen synthase kinase-3 beta (GSK3β) is a key enzyme involved in neurodegenerative diseases such as Alzheimer’s and Parkinson’s, contributing to tau hyperphosphorylation, amyloid-beta (Aβ) aggregation, and neuronal dysfunction. Methods: This study applied a machine learning-driven virtual screening approach to identify potent natural inhibitors of GSK3β. A dataset of 3092 natural compounds was analyzed using Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbors (KNN), with feature selection focusing on key molecular descriptors, including lipophilicity (ALogP: −0.5 to 5.0), hydrogen bond acceptors (0–10), and McGowan volume (0.5–2.5). RF outperformed SVM and KNN, achieving the highest test accuracy (83.6%), specificity (87%), and lowest RMSE (0.3214). Results: Virtual screening using AutoDock Vina and molecular dynamics simulations (100 ns, GROMACS 2022) identified artemisinin as the top GSK3β inhibitor, with a binding affinity of −8.6 kcal/mol, interacting with key residues ASP200, CYS199, and LEU188. Dihydroartemisinin exhibited a binding affinity of −8.3 kcal/mol, reinforcing its neuroprotective potential. Pharmacokinetic predictions confirmed favorable drug-likeness (TPSA: 26.3–70.67 Ų) and non-toxicity. Conclusions: While these findings highlight artemisinin-based inhibitors as promising candidates, experimental validation and structural optimization are needed for clinical application. This study demonstrates the effectiveness of machine learning and computational screening in accelerating neurodegenerative drug discovery. Full article

Bifenthrin (BF) is a widely used pyrethroid pesticide recognized as an endocrine-disrupting chemical (EDC). Previous studies have confirmed that chronic exposure to BF is associated with various health risks. However, its potential association with recurrent implantation failure (RIF) and recurrent pregnancy loss (RPL) remains unclear. In this study, the potential targets of BF were identified using several databases, including the Comparative Toxicogenomics Database (CTD), TargetNet, GeneCards, SwissTargetPrediction, and STITCH. Differentially expressed genes (DEGs) associated with RIF were obtained from bulk RNA-seq datasets in the GEO database. Candidate targets were identified by intersecting the predicted BF-related targets with the RIF-associated DEGs, followed by functional enrichment analysis using the DAVID and g:Profiler platforms. Subsequently, hub genes were identified based on the STRING database and Cytoscape. A diagnostic model was then constructed based on these hub genes in the RIF cohort and validated in an independent recurrent pregnancy loss (RPL) cohort. Additionally, we performed single-cell type distribution analysis and immune infiltration profiling based on single-cell RNA-seq and bulk RNA-seq data, respectively. Molecular docking analysis using AutoDock Vina was conducted to evaluate the binding affinity between BF and the four hub proteins, as well as several hormone-related receptors. Functional enrichment results indicated that the candidate genes were mainly involved in apoptotic and oxidative stress-related pathways. Ultimately, four hub genes—BCL2, HMOX1, CYCS, and PTGS2—were identified. The diagnostic model based on these genes exhibited good predictive performance in the RIF cohort and was successfully validated in the RPL cohort. Single-cell transcriptomic analysis revealed a significant increase in the proportion of myeloid cells in RPL patients, while immune infiltration analysis showed a consistent downregulation of M2 macrophages in both RIF and RPL. Moreover, molecular docking analysis revealed that BF exhibited high binding affinity to all four hub proteins and demonstrated strong binding potential with multiple hormone receptors, particularly pregnane X receptor (PXR), estrogen receptor α (ESRα), and thyroid hormone receptors (TR). In conclusion, the association of BF with four hub genes and multiple hormone receptors suggests a potential link to immune and endocrine dysregulation observed in RIF and RPL. However, in vivo and in vitro experimental evidence is currently lacking, and further studies are needed to elucidate the mechanisms by which BF may contribute to RIF and RPL. Full article

Diabetes mellitus is characterized by insulin resistance, impaired glucose homeostasis, and dysregulated glucose metabolism, leading to complications. Inonotus obliquus (Chaga) has shown potential anti-diabetic effects, but the bioactive compounds and molecular targets remain unclear. This study aimed to identify the bioactive components of Chaga and elucidate their anti-diabetic mechanisms using LC-MS compound screening, network pharmacology, molecular docking, and bioinformatics analyses. Chaga extract was prepared using 95% ethanol, and bioactive compounds were identified through UHPLC-QE-MS analysis. Target prediction was conducted using Swiss Target Prediction and SEA databases, while diabetes-related targets were retrieved from GeneCards. A PPI network was constructed using STRING and analyzed for GO and KEGG enrichment. Molecular docking was performed using AutoDock Vina, and gene expression was validated using the GSE7014 dataset and GEPIA database, with immune cell infiltration analyzed through CIBERSORT. UHPLC-QE-MS identified 30 bioactive compounds from Chaga, including 21 triterpenoids, four flavonoids, and two diterpenoids. Network pharmacology predicted 432 anti-diabetic targets, with 167 core targets enriched in key pathways, primarily the PI3K/Akt signaling pathway. Molecular docking revealed strong binding affinities of five key compounds with seven core targets. Bioinformatics analysis validated significant expression changes in ESR1, IL6, and SRC, while immune cell infiltration analysis showed correlations between core targets and immune cell subtypes. This study highlights the anti-diabetic potential of Chaga by identifying key bioactive compounds and their interactions with central diabetic targets. Further in vitro and in vivo studies are needed to validate these findings. Full article

(1) Backgroud Astragalus, a traditional Chinese medicine, demonstrates therapeutic effectiveness in acute pancreatitis (AP). Nevertheless, its precise pharmacological mechanism remains unclear, and clinical guidelines have not been established. This study aims to systematically elucidate the active compounds and molecular mechanisms underlying Astragalus’ therapeutic effects in AP, and provide clinical evidence supporting its efficacy. (2) Methods: TCMSP and Swiss Target Prediction identified drug targets; GeneCards, DrugBank, and OMIM provided disease targets. Venny determined the therapeutic targets, while STRING constructed a protein–protein interaction network. Cytoscape 3.10.3 validated core targets. DAVID was used to conduct GO and KEGG pathway analyses, visualized via Bioinformatic platform. Cytoscape 3.10.3 was used to build a “drug–ingredients–targets–pathways–disease” network. AutoDock Vina 1.1.2 and AutoDockTools 1.5.7 was used to performed molecular docking, with PyMOL 3.0 visualizing the results. PubMed, Embase, Cochrane, Web of Science, CNKI, Wanfang, VIP, and CBMdisc were searched. The literature was screened, extracted, and evaluated, followed by a meta-analysis, using RevMan 5.4.1 and Stata 18. (3) Results: We identified 539 targets for the active ingredients of astragalus. Among 1974 disease-related targets, 232 were found to be therapeutic targets. The GO analysis yielded 589 entries, while the KEGG pathway enrichment analysis identified 147 relevant pathways. The top five active ingredients were quercetin, kaempferol, isorhamnetin, formononetin, and calycosin. Molecular docking analysis revealed potential synergistic effects between these components and core targets. The meta-analysis, comprising six randomized controlled trials, demonstrated a significantly higher total effective rate of clinical efficacy in the astragalus group compared to the control group. (4) Conclusions: Astragalus treats AP through the synergistic action of its components, targets, and pathways. Key active compounds, such as quercetin, kaempferol, isorhamnetin, formononetin, and calycosin, engage with pivotal targets, including TP53, AKT1, TNF, IL6, EGFR, CASP3, MYC, and HIF1A, within primary pathways, such as pathways in cancer, PI3K-Akt signaling pathway, and lipid metabolism, and atherosclerosis. Astragalus effectively treats AP and alleviates clinical symptoms by reducing the time for gas or defecation passage, the disappearance time of abdominal pain or distension, and the recovery time of bowel sounds. Full article

Background: The increasing global incidence of breast cancer calls for the identification of new therapeutic targets and the assessment of possible neem-derived inhibitors by means of computational modeling and integrated genomic research. Methods: Originally looking at 59,424 genes throughout 42 samples, we investigated gene expression data from The Cancer Genome Atlas—Breast Cancer (TCGA-BRCA) dataset. We chose 286 genes for thorough investigation following strict screening for consistent expression. R’s limma package was used in differential expression analysis. The leading candidate’s protein modeling was done with Swiss-ADME and Discovery Studio. Molecular docking studies, including 132 neem compounds, were conducted utilizing AutoDock Vina. Results: Among the 286 examined, mitochondrially encoded cytochrome C oxidase III (MT—CO3) turned out to be the most greatly overexpressed gene, showing consistent elevation across all breast cancer samples. Protein modeling revealed a substantial hydrophobic pocket (volume: 627.3 ų) inside the structure of MT—CO3. Docking investigations showed five interesting neem-derived inhibitors: 7-benzoylnimbocinol, nimolicinol, melianodiol, isonimocinolide, and stigmasterol. Strong binding affinities ranging from −9.2 to −11.5 kcal/mol and diverse interactions with MT—CO3, mostly involving the residues Phe214, Arg221, and Trp58, these molecules displayed. With hydrophobic interactions dominant across all chemicals, fragment contribution analysis revealed that scaffold percentage greatly influences binding effectiveness. Stigmasterol revealed greater drug-likeness (QED = 0.79) despite minimal interaction variety, while 7-benzoylnimbocinol presented the best-balanced physicochemical profile. Conclusion: Connecting traditional medicine with current genomics and computational biology, this work proposes a methodology for structure-guided drug design and development using neem-derived chemicals and finds MT—CO3 as a potential therapeutic target for breast cancer. Full article

The objective of this research was to investigate the efficacy of lotus leaf in the prevention and treatment of inflammatory diarrhea in pigs, utilizing network pharmacology and in vitro methodologies. Initially, LC-MS was employed to analyze the constituents of lotus leaf extract (LLE); then, the TCMSP database was utilized to identify the active components and their corresponding targets. The GeneCards database was consulted to identify disease-related targets pertinent to inflammatory diarrhea in pigs. A drug ingredient–disease target network was constructed using Cytoscape software. Subsequently, the STRING database facilitated protein interaction analysis, which was also visualized through Cytoscape. Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were conducted based on the genes shared between disease and LLE targets. Molecular docking of the active ingredients with key targets was performed using Autodock Vina. Subsequently, an in vitro LPS-induced inflammation model was established using IPEC-J2 cells to validate the predictions made through network pharmacology. Verification was conducted via flow cytometry and Western blot analysis. The LC-MS assay and TCMSP retrieval results revealed that Quercetin, Nuciferine, Kaempferol, Leucodelphinidin, and Catechin were identified as the main compounds of LLE, associated with 181 potential targets. A total of 5995 targets were linked to inflammatory diarrhea in pigs, with 159 overlapping targets identified between the bioactive compounds and the disease. Notable key targets included TNF-α, IL-6, caspase-3, TP53, and AKT, which are integral to inflammation and apoptosis processes. GO functional annotation indicated significant enrichment in biological processes such as gene expression regulation and transcription from RNA polymerase II promoters. KEGG pathway analysis highlighted critical pathways, including TNF signaling and apoptosis. Furthermore, molecular docking analyses demonstrated that the bioactive components of lotus leaf exhibited a strong binding affinity for essential targets, including AKT1, BAX, caspase-3, CCL2, IL-6, IL-10, MPK14, NOS3, PTGS1, and TNF-α. In vitro experiments confirmed that LLE significantly inhibited LPS-induced apoptosis in IPEC-J2 cells and suppressed the activation of the TNF-α-mediated apoptosis pathway. This study offers novel insights into the therapeutic potential of Chinese medicine and its constituents in addressing inflammatory diarrhea in pigs. Full article

Cyclin-dependent kinase 1 (CDK1) has emerged as a critical regulator of cell cycle progression, yet its role in liver fibrosis-associated hepatocellular carcinoma (LF-HCC) remains underexplored. This study aimed to systematically evaluate CDK1’s prognostic significance, immune regulatory functions, and therapeutic potential in LF-HCC pathogenesis. Integrated bioinformatics approaches were applied to multi-omics datasets from GEO, TCGA, and TIMER databases. Differentially expressed genes were identified through enrichment analysis and protein–protein interaction networks. Survival outcomes were assessed via Kaplan–Meier analysis, while immune cell infiltration patterns were quantified using CIBERSORT. Molecular docking simulations evaluated CDK1’s binding affinity with pharmacologically active compounds (alvocidib, seliciclib, alsterpaullone) using AutoDock Vina. CDK1 demonstrated significant overexpression in LF-HCC tissues compared to normal controls (p < 0.001). Elevated CDK1 expression correlated with reduced overall survival (HR = 2.41, 95% CI:1.78–3.26, p = 0.003) and advanced tumor staging (p = 0.007). Immune profiling revealed strong associations between CDK1 levels and immunosuppressive cell infiltration, particularly regulatory T cells (r = 0.63, p = 0.001) and myeloid-derived suppressor cells (r = 0.58, p = 0.004). Molecular docking confirmed high-affinity binding of CDK1 to kinase inhibitors through conserved hydrogen-bond interactions (binding energy ≤ −8.5 kcal/mol), with alvocidib showing optimal binding stability. This multimodal analysis establishes CDK1 as both a prognostic biomarker and immunomodulatory regulator in LF-HCC pathogenesis. The enzyme’s dual role in driving tumor progression and reshaping the immune microenvironment positions it as a promising therapeutic target. Computational validation of CDK1 inhibitors provides a rational basis for developing precision therapies against LF-HCC, bridging translational gaps between biomarker discovery and clinical application. Full article

Acetylcholinesterase (AChE) is a critical enzyme involved in neurotransmission by hydrolyzing acetylcholine at the synaptic cleft, making it a key target for drug discovery, particularly in the treatment of neurodegenerative disorders such as Alzheimer’s disease. Computational approaches, particularly molecular docking and molecular dynamics (MD) simulations, have become indispensable tools for identifying and optimizing AChE inhibitors by predicting ligand-binding affinities, interaction mechanisms, and conformational dynamics. This review serves as a comprehensive guide for future research on AChE using molecular docking and MD simulations. It compiles and analyzes studies conducted over the past five years, providing a critical evaluation of the most widely used computational tools, including AutoDock, AutoDock Vina, and GROMACS, which have significantly contributed to the advancement of AChE inhibitor screening. Furthermore, we identify PDB ID: 4EY7, the most frequently used AChE crystal structure in docking studies, and highlight Donepezil, a well-established reference molecule widely employed as a control in computational screening for novel inhibitors. By examining these key aspects, this review aims to enhance the accuracy and reliability of virtual screening approaches and guide researchers in selecting the most appropriate computational methodologies. The integration of docking and MD simulations not only improves hit identification and lead optimization but also provides deeper mechanistic insights into AChE–ligand interactions, contributing to the rational design of more effective AChE inhibitors. Full article