To investigate the antimicrobial potential of methanol fruit extract of Solanum xanthocarpum against Methicillin-Resistant Staphylococcus aureus (MRSA) and elucidate its mode of action. The rise of antimicrobial resistance (AMR) demands the exploration of alternative therapeutic strategies to combat resistant pathogens. To evaluate the efficacy of Solanum xanthocarpum methanol extract against MRSA, and identify its active constituents and mechanism of action. The fruits of Solanum xanthocarpum were extracted using various solvents, with hexane and methanol yielding the highest results. Microbroth dilution assays assessed antimicrobial activity, while in vitro assays such as Alamar blue, Scanning Electron Microscopy (SEM), protein, and nucleic acid leakage examined metabolic disruption and cell membrane integrity. Gas Chromatography- Mass Spectrometry (GC-MS) was used to identify active compounds, and molecular docking studies assessed interactions with key MRSA proteins. The methanol extract demonstrated significant antimicrobial activity against MRSA, causing metabolic disruption and leakage of cellular contents as evidenced by various in vitro assays including alarm blue, SEM, and protein and nucleic acid leakage assay. GC-MS analysis identified alpha-linoleic acid and palmitic acid as key active components. Molecular docking studies confirmed their inhibition of beta-lactamase activity, cell wall biosynthesis, efflux pumps, and virulence factors. The findings suggest that Solanum xanthocarpum methanol fruit extract has promising potential as a natural remedy against AMR associated with MRSA.

Healthcare organizations are complicated and demanding for all stakeholders, but artificial intelligence (AI) has revolutionized several sectors, especially healthcare, with the potential to enhance patient outcomes and standard of life. Quick advancements in AI can transform healthcare by implementing it into clinical procedures. Reporting AI's involvement in clinical settings is vital for its successful adoption by providing medical professionals with the necessary information and tools. This paper offers a thorough and up-to-date summary of the present condition of AI in medical settings, including its possible uses in patient interaction, treatment suggestions, and disease diagnosis. It also addresses the challenges and limitations, including the necessity for human expertise along with future directions. In doing so, it improves the understanding of AI's relevance in healthcare and supports medical institutions in successfully implementing AI technologies. The structured literature review, with its dependable and reproducible research process, allowed the authors to acquire 337 peer-reviewed publications from indexing databases, such as Scopus and EMBASE, without any time restrictions. The researchers utilized both qualitative and quantitative factors to assess authors, publications, keywords, and collaboration networks. AI implementation in healthcare holds enormous potential for enhancing patient outcomes, treatment recommendations, and disease diagnosis. AI technologies can use massive datasets and recognize patterns to beat human performance in various healthcare domains. AI provides improved accuracy, reduced expenses, and time savings. It can transform customized medicine, optimize drug dosages, improve management of population health, set guidelines, offer digital medical assistants, promote mental health services, boost patient knowledge, and maintain patientclinician trust. AI can be utilized to detect diseases, develop customized therapy plans, and support medical professionals with their clinical decision-making. Instead of just automating jobs, AI focuses on creating technologies that can improve patient care in several healthcare settings. However, challenges such as biasness, data confidentiality, and data quality must be resolved for the appropriate and successful integration of AI in healthcare.

Herein, we present an in-depth review focused on the application of different artificial intelligence (AI) approaches for developing protein kinase inhibitors (PKIs) targeting anticancer activity, focusing on how the AI-based tools are making promising advances in drug design and development, by predicting active compounds for essential targets involved in cancer. In this context, the machine learning (ML) approach performs a critical role by promoting a fast analysis of a thousand potential inhibitors within a small gap of time by processing large datasets of chemical data, putting it at a higher level than other traditionally used methods for screening molecules. In general, AI-based screening of compounds reduces the time of the work and increases the chances of success in the end. Additionally, we have covered recent studies focused on the application of deep neural networks (DNNs) and quantitative structure-activity relationships (QSAR) to identify PKIs. Furthermore, the paper covers new AI-based methodologies for filtering or improving datasets of potential compounds or even targets, such as generative models for the creation of novel compounds and ML-based strategies to collect information from different databases, increasing the efficiency in drug design and development. Finally, this review highlights how AI-based tools are increasing and improving the field of PKIs targeting cancer, making them an alternative for the future in the medicinal chemistry field.

This review is delving into determining the content and significance of hypericin, a pharmacologically important constituent commonly known as St. John's Wort (SJW). The paper explores the rich history of Hypericin's traditional use in alternative medicine and the recent surge in scientific interest surrounding its bioactive properties. This review aims to comprehensively analyse the therapeutic potentials of hypericin, focusing on its chemistry, extraction, sources, stability, pharmacokinetics, safety profile, and multifunctional applications in drug and medicinal fields as well as advancements in Bioengineering Approaches for Enhanced Hypericin Delivery. We performed a non-systematic search of journals. Literature using computerized methods was conducted, utilizing databases such as Medline (PubMed), ChemSciFinder, China National Knowledge Infrastructure (CNKI) and Scirus Library. To effectively identify the most important and relevant research articles, scientific studies, clinical studies and review articles on hypericin were searched using different keywords: "Hypericum", "traditional use", "phytochemistry", "pharmacology", "drug delivery" and "bioactivity". Thus, articles available from 1984 to 2024 were analyzed and collected. The selection process for the review primarily considered the originality of the paper and its clinical applications. Although hypericin is not a novel compound within the research community, it is gaining renewed recognition and showing great effectiveness as a promising agent in the field of medical diagnostics and has promising applications as a therapeutic. Here, our current comprehensive review of hypericin, its potential and its activities is intended to contribute to this ongoing process actively. Overall, this review provided theoretical direction for future hypericin research. Future studies should, therefore, focus further on the pharmacological processes, pharmacokinetics, and chemistry of hypericin and hypericin-based drug delivery systems. Our most recent overview of hypericin's functions and potentials is meant to aid in this process.

Genistein (GEN), a phytoestrogen primarily sourced from soy plants, is recognized for its anticancer properties attributed to its roles as a tyrosine kinase inhibitor, an estrogen receptor agonist, and its influence on various cancer hallmarks by modulating diverse signaling pathways. Recent research has highlighted the considerable potential of GEN in combating drug resistance in cancer cells. This attribute of GEN has been demonstrated by its capacity to modulate tyrosine kinases such as HER2, HER3, and EGFR which are implicated in tumorigenesis, as well as prosurvival signaling pathways including NF-κB and Akt/mTOR. Moreover, GEN impacts drug accumulation, AR-driven transcriptional regulation, ER signaling, and various genes that are involved in autophagy, pro/anti-apoptosis, DNA methylation, and histone acetylation. Further, GEN demonstrated efficacy in combinatorial therapy with various standard anticancer agents like 5-FU, cetuximab, cisplatin, clofarabine, doxorubicin, tamoxifen, TRAIL, trastuzumab, and other agents with anticancer activities such as capsaicin, curcumin, daidzein, lycopene, resveratrol, sulforaphane, etc., across a spectrum of cancers including the cancers of bone, brain, breast, cervix, colorectal, endometrium, esophagus, head and neck, leukemia, liver, lung, ovary, pancreas and stomach. Thus, further clinical validation of these potential combinations involving GEN is warranted to confirm the preclinical findings.

Artemisinin and its semisynthetic derivatives are a group of bioactive chemicals obtained mostly from the extracts of Artemisia species that exert a significant amount of antimalarial activity while remaining relatively safe and tolerable. However, their effectiveness is not limited to malaria; it extends to a variety of human infectious diseases. Mostly the mode of action includes the generation of free radicals by breaking the endoperoxide link in its molecular structure, which facilitates the eradication of microbial species. Artemisinins are found to inhibit bacterial, viral, protozoal, helminth, and fungal infections. Their derivatives, like artemisone, a reversible inhibitor, target the viral replication cycle, and artesunate suppresses EBV infection by inhibiting the production of early EBV proteins. They were also found to be highly effective against Helicobacter pylori and Mycobacterium tuberculosis by generating peroxides in a time and concentrationdependent way. Other derivatives, such as artesunate, artemether, and arteether, have also shown antimicrobial activity that can be administered orally, rectally, intramuscularly, or intravenously. This review aims to provide a complete update on the antimicrobial applications of Artemisinin and its semi-synthetic derivatives, as well as derived dimers/trimers. The paper reflects a significant potential for the discovery of novel Artemisinin medications that can be used as supplementary treatments for various diseases. However, further translational and experimental research is required for optimization and the establishment of pharmacokinetic profiles. In addition, health authorities are also required to regulate the present Artemisinins and newly discovered derivatives to ensure their long-term effectiveness in the worldwide fight against antibiotic resistance.

The TLR4 (Toll-like receptor 4)/MD2 (Myeloid differentiation protein-2) is a crucial target for developing novel anti-inflammatory drugs. Nevertheless, current inhibitors often have significant adverse effects, necessitating the discovery of safer alternatives. The investigation aims to identify novel TLR4/MD2 inhibitors with potential antiinflammatory activity using machine learning and virtual screening technology. A machine-learning model was created using the MACCS (Molecular ACCess Systems) key fingerprint. Subsequently, virtual screening and molecular docking were used to evaluate candidate compounds' binding free energy to the TLR4/MD2 complex. Furthermore, ADMET (absorption, distribution, metabolism, excretion, and toxicity) prediction was used to assess the druggable properties of compounds. The most promising compound, T19093, was considered for molecular dynamic simulation. Finally, the anti-inflammatory efficacy of T19093 was further validated using LPS-treated THP-1 cells. T19093, a polyphenolic compound isolated from the Gnaphalium plant genus, showed strong binding to key residues of the TLR4/MD2 complex, with a docking score of -11.29 kcal/mol. Furthermore, ADMET predicted that T19093 has good pharmacokinetic properties and balanced physicochemical properties. Moreover, molecular dynamics simulation confirmed stable binding between T19093 and TLR4/MD2 complex. Finally, it was found that T19093 alleviated LPSinduced inflammatory response by inhibiting the activation of TLR4/MD2 downstream signaling pathways and disrupting the TLR4/MD2 interaction. T19093 was discovered as a potential novel TLR4/MD2 inhibitor using machine learning and virtual screening techniques and showed potent anti-inflammatory activity, which could provide a new therapeutic alternative for the treatment of inflammation-related diseases.

Diosmetin, also known as 3´, 5, 7-trihydroxy-4´-methoxyflavone, is a bioflavonoid that can be found in several food sources. These include mainly citrus fruits and plants, such as Rosmarinus officinalis, Citrus sinensis, Galium verum, and Rosa agrestis. It has been shown that diosmetin has pharmacological actions that include neuroprotective, antioxidant, and anticancer properties. The objective of this manuscript is to provide a comprehensive review of the pharmacological, pharmacokinetic, and toxicological profile of diosmetin. The information has been collected from several research papers found in various databases, including Web of Science, Pubmed, Scopus, and ScienceDirect. Diosmetin has the potential to reduce inflammation, and it can also improve vascular functions and protect against oxidative stress, both of which are essential for the prevention and management of several diseases.

Currently, there are pharmacological treatments for type 2 diabetes (T2D), but these are ineffective. Quercetin is a flavonoid with antidiabetic properties. This research aimed to identify the molecular mechanism of Quercetin in T2D from network pharmacology. We obtained T2D-related genes from MalaCards and DisGeNET, while potential targets for Quercetin were sourced from SwissTargetPrediction and Comparative Toxicogenomics databases. The overlapping genes were identified and analyzed using ShinyGO 0.77. Subsequently, we constructed a protein-protein interaction network using Cytoscape, conducted molecular docking analyses with SwissDock, and validated the results through molecular dynamics simulation in GROMACS. Quercetin is involved in apoptotic processes and in the regulation of insulin activity, estrogen, prolactin and EGFR receptor. The key driver genes AKT1, GSK3B, SRC, IGF1R, MMP9, ESR2, PIK3R1, and MMP2 showed high concordance in the molecular docking study, and molecular dynamics showed stability between Quercetin and ESR2 and PIK3R1. Our work helps to identify the molecular mechanism and antidiabetic effect of quercetin, which needs to be studied experimentally.

CCN6/WISP3 is a member of the CCN adipokines family that can exert multiple effects on metabolic pathways. So far, the function of CCN6 in the pathogenesis of NAFLD has not been known well. Hence, we aimed to examine CCN6 serum levels in patients with NAFLD compared to healthy individuals and its association with some risk factors for the first time. This case-control study measured serum levels of CCN6, TNF-α, IL-6, adiponectin, and fasting insulin using ELISA kits in 88 NAFLD patients and 88 controls. In addition, other biochemical variables, including AST, ALT, lipid profiles, and FBG, were determined using an Auto analyzer instrument. A remarkable decrease in CCN6 levels was found in the NAFLD patients (1501.9543 ± 483.414 pg/ml) compared to the healthy group (1899.4856 ± 559.704 pg/ml, P < 0.001). In NAFLD patients, a negatively notable correlation was observed between CCN6 and the levels of insulin (r = -0.278, P = 0.011), HOMA-IR (r = -0.268, P = 0.014), as well as TNF-α (r = -0.343, P = 0.001). A remarkable association was found between CCN6 and the risk factor of NAFLD in the adjusted model for gender, age, and BMI with OR = 0.867 (95% CI, [0.806-0.931], P < 0.001). Our findings showed a significant reduction in CCN6 levels in the NAFLD patients compared to the healthy group, as well as the developing risk of NAFLD enhanced with the decrease of CCN6 levels.