Search Results (3,426)

Pregnane X receptor (PXR) is an important nuclear receptor that regulates diverse physiological functions, including drug metabolism. Although PXR activation is potentially involved in adverse events, the full scope of its impact has yet to be elucidated. In this study, we developed a machine learning model to predict the activity of PXR agonists and applied the model to drugs listed in the US Food and Drug Administration Adverse Event Reporting System database. Analysis of the predicted agonist–active drug interactions and adverse event reports revealed statistically significant risks (lnROR > 1 and −logp > 1.3) for multiple cardiac disorders. These findings suggest that PXR activity is involved in cardiovascular adverse effects and may contribute to drug safety through the early identification of risks. Full article

Multiple sclerosis (MS) is a chronic, immune-mediated disorder of the central nervous system (CNS) characterized by inflammation, demyelination, axonal degeneration, and gliosis. Its pathophysiology involves a complex interplay of genetic susceptibility, environmental triggers, and immune dysregulation, ultimately leading to progressive neurodegeneration and functional decline. Although significant advances have been made in disease-modifying therapies (DMTs), many patients continue to experience disease progression and unmet therapeutic needs. Drug repurposing—the identification of new indications for existing drugs—has emerged as a promising strategy in MS research, offering a cost-effective and time-efficient alternative to traditional drug development. Several compounds originally developed for other diseases, including immunomodulatory, anti-inflammatory, and neuroprotective agents, are currently under investigation for their efficacy in MS. Repurposed agents, such as selective sphingosine-1-phosphate (S1P) receptor modulators, kinase inhibitors, and metabolic regulators, have demonstrated potential in promoting neuroprotection, modulating immune responses, and supporting remyelination in both preclinical and clinical settings. Simultaneously, artificial intelligence (AI) is transforming drug discovery and precision medicine in MS. Machine learning and deep learning models are being employed to analyze high-dimensional biomedical data, predict drug–target interactions, streamline drug repurposing workflows, and enhance therapeutic candidate selection. By integrating multiomics and neuroimaging data, AI tools facilitate the identification of novel targets and support patient stratification for individualized treatment. This review highlights recent advances in drug repurposing and discovery for MS, with a particular emphasis on the emerging role of AI in accelerating therapeutic innovation and optimizing treatment strategies. Full article

Biotechnological research increasingly focuses on developing new drugs to counter the rise of antibiotic-resistant strains in hospitals. This study aimed to create bacterial lysates from antibiotic-resistant pathogens isolated from patients and medical instruments across hospital departments. Identification was performed based on morphological, cultural, and biochemical characteristics, as well as 16S rRNA gene sequencing using the BLAST algorithm. Strain viability was assessed using the Miles and Misra method, while sensitivity to eight antibacterial drug groups and biosafety between cultures were evaluated using agar diffusion. From 15 clinical sources, 25 pure isolates were obtained, and their phenotypic and genotypic properties were studied. Carbohydrate fermentation testing confirmed that the isolates belonged to the genera Escherichia, Citrobacter, Klebsiella, Acinetobacter, Pseudomonas, Staphylococcus, Haemophilus, and Streptococcus. The cultures exhibited good viability (10⁹–10¹⁰ CFU/mL) and compatibility with each other. Based on prevalence and clinical significance, three predominant hospital pathogens (Klebsiella pneumoniae 12 BL, Pseudomonas aeruginosa 3 BL, and Acinetobacter baumannii 24 BL) were selected to develop a bacterial lysate consortium. Lysates were prepared with physical disruption using a French press homogenizer. The resulting product holds industrial value and may stimulate the immune system to combat respiratory pathogens prevalent in Kazakhstan’s healthcare settings. Full article

Background: The COVID-19 pandemic has profoundly altered the clinical and microbiological landscape of respiratory tract infections (RTIs), potentially reshaping pathogen distribution and antimicrobial resistance (AMR) profiles across care settings. Objectives: The objective of this study was to assess temporal trends in respiratory bacterial pathogens, antimicrobial resistance, and polymicrobial infections across three pandemic phases—pre-COVID (2018–2019), COVID (2020–2022), and post-COVID (2022–2024)—in hospitalized and ambulatory patients. Methods: We retrospectively analyzed 1827 respiratory bacterial isolates (hospitalized patients, n = 1032; ambulatory patients, n = 795) collected at a tertiary care center in Northern Italy. Data were stratified by care setting, anatomical site, and pandemic phase. Species identification and susceptibility testing followed EUCAST guidelines. Statistical analysis included chi-square and Fisher’s exact tests. Results: In hospitalized patients, a significant increase in Pseudomonas aeruginosa (from 45.5% pre-COVID to 58.6% post-COVID, p < 0.0001) and Acinetobacter baumannii (from 1.2% to 11.1% during COVID, p < 0.0001) was observed, with 100% extensively drug-resistant (XDR) rates for A. baumannii during the pandemic. Conversely, Staphylococcus aureus significantly declined from 23.6% pre-COVID to 13.7% post-COVID (p = 0.0012). In ambulatory patients, polymicrobial infections peaked at 41.2% during COVID, frequently involving co-isolation of Candida spp. Notably, resistance to benzylpenicillin in Streptococcus pneumoniae reached 80% (4/5 isolates) in hospitalized patients during COVID, and carbapenem-resistant P. aeruginosa (CRPA) significantly increased post-pandemic in ambulatory patients (0% pre-COVID vs. 23.5% post-COVID, p = 0.0014). Conclusions: The pandemic markedly shifted respiratory pathogen dynamics and resistance profiles, with distinct trends observed in hospital and community settings. Persistent resistance phenotypes and frequent polymicrobial infections, particularly involving Candida spp. in outpatients, underscore the need for targeted surveillance and antimicrobial stewardship strategies. Full article

This study was undertaken to identify foodborne bacteria and fungi from different parts of eggs depending on their storage duration, organoleptic properties, total viable count, and antibiotic resistance profile. Thirty-two samples were randomly collected from commercial layer farms in Mymensingh. Following the protocol of sample preparation, outer-surface and inner-content samples were streaked onto various selective media. Isolation and identification were carried out by observing Gram staining and biochemical properties. Molecular detection was confirmed through a PCR assay using specific primers for Salmonella spp., E. coli, Staphylococcus spp., and fungus (Simplicillium spp. and Saccharomyces spp.). To determine the antibiotic resistance profile, the disk diffusion method was followed against nine antibiotic disks. The isolation rate of E. coli, Salmonella spp., and Staphylococcus spp. was 53.13%, 40.63%, and 40.63%, respectively, in the outer eggshell and 15.63%, 25%, and 15.63%, respectively, in the inner content of the eggs. Regarding the fungus content (yeast and mold), 100% was obtained in the outer eggshell, whereas there was an absence of fungus in the inner content. It was observed that all the isolates of E. coli, Salmonella spp., and Staphylococcus spp. were highly sensitive to either Ciprofloxacin or Levofloxacin and extremely resistant to Amoxicillin or Azithromycin drug disks or both. The data also shows that storage duration had a proportional relationship with TVC and an inversely proportional relationship with organoleptic properties. This study indicates that eggs harbor multidrug-resistant foodborne bacteria, which might constitute a public health hazard if these antibiotic-resistant bacteria are transferred to humans. Full article

In the rapidly evolving fields of materials science, catalysis, electronics, drug delivery, and environmental remediation, the development of effective substrates for molecular deposition has become increasingly crucial. Ordered mesoporous silica materials have garnered significant attention due to their unique structural properties and exceptional potential as substrates for molecular immobilization across these diverse applications. This study compares three mesoporous silica powders: MCM-41, SBA-15, and SBA-16. A multi-technique characterization approach was employed, utilizing low- and wide-angle X-ray diffraction (XRD), nitrogen physisorption, and transmission electron microscopy (TEM) to elucidate the structure–property relationships of these materials. XRD analysis confirmed the amorphous nature of silica frameworks and revealed distinct pore symmetries: a two-dimensional hexagonal (P6mm) structure for MCM-41 and SBA-15, and three-dimensional cubic (Im$\overline{3}$m) structure for SBA-16. Nitrogen sorption measurements demonstrated significant variations in textural properties, with MCM-41 exhibiting uniform cylindrical mesopores and the highest surface area, SBA-15 displaying hierarchical meso- and microporosity confirmed by NLDFT analysis, and SBA-16 showing a complex 3D interconnected cage-like structure with broad pore size distribution. TEM imaging provided direct visualization of particle morphology and internal pore architecture, enabling estimation of lattice parameters and identification of structural gradients within individual particles. The integration of these complementary techniques proved essential for comprehensive material characterization, particularly for MCM-41, where its small particle size (45–75 nm) contributed to apparent structural inconsistencies between XRD and sorption data. This integrated analytical approach provides valuable insights into the fundamental structure–property relationships governing ordered mesoporous silica materials and demonstrates the necessity of combined characterization strategies for accurate structural determination. Full article

This narrative review explores the integration of theranostics and artificial intelligence (AI) in neuro-oncology, addressing the urgent need for improved diagnostic and treatment strategies for brain tumors, including gliomas, meningiomas, and pediatric central nervous system neoplasms. A comprehensive literature search was conducted through PubMed, Scopus, and Embase for articles published between January 2020 and May 2025, focusing on recent clinical and preclinical advancements in personalized neuro-oncology. The review synthesizes evidence on novel theranostic agents—such as Lu-177-based radiopharmaceuticals, CXCR4-targeted PET tracers, and multifunctional nanoparticles—and highlights the role of AI in enhancing tumor detection, segmentation, and treatment planning through advanced imaging analysis, radiogenomics, and predictive modeling. Key findings include the emergence of nanotheranostics for targeted drug delivery and real-time monitoring, the application of AI-driven algorithms for improved image interpretation and therapy guidance, and the identification of current limitations such as data standardization, regulatory challenges, and limited multicenter validation. The review concludes that the convergence of AI and theranostic technologies holds significant promise for advancing precision medicine in neuro-oncology, but emphasizes the need for collaborative, multidisciplinary research to overcome existing barriers and enable widespread clinical adoption. Full article

Pancreatic cancer is frequently accompanied by cancer-associated cachexia, a debilitating metabolic syndrome marked by progressive skeletal muscle wasting and systemic metabolic dysfunction. This study presents a systems biology framework to simultaneously identify therapeutic targets for both pancreatic ductal adenocarcinoma (PDAC) and its associated cachexia (PDAC-CX), using cell-specific genome-scale metabolic models (GSMMs). The human metabolic network Recon3D was extended to include protein synthesis, degradation, and recycling pathways for key inflammatory and structural proteins. These enhancements enabled the reconstruction of cell-specific GSMMs for PDAC and PDAC-CX, and their respective healthy counterparts, based on transcriptomic datasets. Medium-independent metabolic biomarkers were identified through Parsimonious Metabolite Flow Variability Analysis and differential expression analysis across five nutritional conditions. A fuzzy multi-objective optimization framework was employed within the anticancer target discovery platform to evaluate cell viability and metabolic deviation as dual criteria for assessing therapeutic efficacy and potential side effects. While single-enzyme targets were found to be context-specific and medium-dependent, eight combinatorial targets demonstrated robust, medium-independent effects in both PDAC and PDAC-CX cells. These include the knockout of SLC29A2, SGMS1, CRLS1, and the RNF20–RNF40 complex, alongside upregulation of CERK and PIKFYVE. The proposed integrative strategy offers novel therapeutic avenues that address both tumor progression and cancer-associated cachexia, with improved specificity and reduced off-target effects, thereby contributing to translational oncology. Full article

As the most unstable crystalline form of calcium carbonate, vaterite is rarely found in nature due to being highly prone to phase transitions. However, its high specific surface area, excellent biocompatibility, and high solubility properties have led to a research boom and the following breakthroughs in the last two decades: (1) From primitive calculations and spectroscopic analyses to modern multidimensional research methods combining calculations and experiments, the crystal structure of vaterite has turned from early identifications in orthorhombic and hexagonal crystal systems to a complex polymorphic structure within the monoclinic crystal system. (2) The formation process of vaterite not only conforms to the classical crystal growth theory but also encompasses the nanoparticle aggregation theory, which incorporates the concepts of oriented nanoparticle assembly and mesoscale transformation. (3) Regardless of the conditions, the formation of vaterite depends on an excess of CO₃²⁻ relative to Ca²⁺, and its stability duration relates to preservation conditions. (4) Vaterite demonstrates significant value in biomedical applications—including bone repair scaffolds, targeted drug carriers, and antibacterial coating materials—leveraging its porous structure, high specific surface area, and exceptional biocompatibility. While it also shows utility in environmental pollutant adsorption and general coating technologies, the current research remains predominantly concentrated on its medical applications. Currently, the rapid transformation of vaterite presents the primary limitation for its industrial application. Future research should prioritize investigating its formation kinetics and stability. Full article

Background/Objectives: The beneficial and multifaceted effects of Sideritis scardica Griseb. extracts are attributed to the presence of polyphenolic compounds, particularly phenolic acids. Methods: The research was carried out for S. scardica herb of different origins (Albania, Bulgaria, North Macedonia, and Türkiye). Identification of compounds was performed using the HPLC/ESI-QTOF-MS method; phenolic acids and flavonoids were determined spectrophotometrically. The antioxidant activity of methanol extracts from studied herbs was determined using the Folin–Ciocalteu, DPPH, and FRAP methods, and the antimicrobial activity was evaluated using the broth microdilution method in accordance with the guidelines of the European Committee on Antimicrobial Susceptibility Testing (EUCAST). Results: We demonstrated the presence 18–20 active compounds, depending on the origin of the raw material, with verbascoside being the predominant compound in all samples. The raw material was characterized by significant polyphenol content and high antioxidant activity. DPPH tests revealed the highest antioxidant activity, ranging from 86.5% to 87.9%, in samples from Bulgaria, North Macedonia, and Türkiye, and the latter showed the strongest antimicrobial activity, particularly against Gram-positive pathogens and Candida spp. Conclusions: This research is the first report comparing the chemical composition and biological activity of S. scardica raw material of different origins. Our findings indicate that S. scardica herb extracts have significant phytotherapeutic potential, although this varies depending on the origin of the raw material, and point to the need for further research on this plant material, particularly in terms of the level of active compounds and their possible synergistic effects with conventional drugs, as well as the need for standardization. Full article

Narcotics trafficking is a fundamental part of organized crime, posing significant and evolving challenges for forensic investigations. Addressing these challenges requires rapid, precise, and scientifically validated analytical methods for reliable identification of illicit substances. Over the past five years, forensic drug testing has advanced considerably, improving detection of traditional drugs—such as tetrahydrocannabinol, cocaine, heroin, amphetamine-type stimulants, and lysergic acid diethylamide—as well as emerging new psychoactive substances (NPS), including synthetic cannabinoids (e.g., 5F-MDMB-PICA), cathinones (e.g., α-PVP), potent opioids (e.g., carfentanil), designer psychedelics (e.g., 25I-NBOMe), benzodiazepines (e.g., flualprazolam), and dissociatives (e.g., 3-HO-PCP). Current technologies include colorimetric assays, ambient ionization mass spectrometry, and chromatographic methods coupled with various detectors, all enhancing accuracy and precision. Vibrational spectroscopy techniques, like Raman and Fourier transform infrared spectroscopy, have become essential for non-destructive identification. Additionally, new sensors with disposable electrodes and miniaturized transducers allow ultrasensitive on-site detection of drugs and metabolites. Advanced chemometric algorithms extract maximum information from complex data, enabling faster and more reliable identifications. An important emerging trend is the adoption of green analytical methods—including direct analysis, solvent-free extraction, miniaturized instruments, and eco-friendly chromatographic processes—that reduce environmental impact without sacrificing performance. This review provides a comprehensive overview of innovations over the last five years in forensic drug analysis based on the ScienceDirect database and highlights technological trends shaping the future of forensic toxicology. Full article

Background: Metabolic dysfunction-associated steatohepatitis (MASH) is a progressive disease that easily develops into cirrhosis and hepatocellular carcinoma, but its pathogenesis is not clear, and most therapeutic drugs have obvious limitations. However, Babao Dan (BBD) has a good therapeutic effect on liver disease, but its treatment mechanism is still to be studied. Therefore, we further investigated the mechanism of BBD in treating MASH. Methods: We predicted BBD-related targets through network pharmacology and further verified the binding ability of BBD-related targets through molecular docking. We also detected relevant indicators before and after model treatment, as well as metabolomics analysis and identification of the mechanism of action of BBD on MASH. Results: Through network pharmacology methods, 158 key cross targets and the top 10 core targets were identified, and it was determined that the PI3K-AKT signaling pathway plays an important regulatory role in the treatment of MASH with BBD. The molecular docking results indicate that the representative compounds quercetin and 17 Beta Estradiol have good binding activity with five core targets. Metabolomics has identified four metabolic biomarkers, such as Piceid, and it is determined that the key pathway for BBD treatment of MASH is the bile secretion pathway. Conclusions: BBD effectively treats MASH by modulating Piceid and other biomarkers, targeting ESR1 and other core proteins via quercetin and 17-beta-estradiol, and regulating the PI3K-AKT and bile secretion pathways to alleviate liver injury. Full article

The biggest challenge in cancer therapy is tumor resistance to the classical approach. Thus, research interest has shifted toward the cancer stem cell population (CSC). CSCs are a small subpopulation of cancer cells within tumors with self-renewal, differentiation, and metastasis/malignant potential. They are involved in tumor initiation and development, metastasis, and recurrence. Method. A narrative review of significant scientific publications related to the topic and its applicability in endometrial cancer (EC) was performed with the aim of identifying current knowledge about the identification of CSC populations in endometrial cancer, their biological significance, prognostic impact, and therapeutic targeting. Results: Therapy against the tumor population alone has no or negligible effect on CSCs. CSCs, due to their stemness and therapeutic resistance, cause tumor relapse. They target CSCs that may lead to noticeable persistent tumoral regression. Also, they can be used as a predictive marker for poor prognosis. Reverse transcription–polymerase chain reaction (RT-PCR) demonstrated that the cultured cells strongly expressed stemness-related genes, such as SOX-2 (sex-determining region Y-box 2), NANOG (Nanog homeobox), and Oct 4 (octamer-binding protein 4). The expression of surface markers CD133+ and CD44+ was found on CSC as stemness markers. Along with surface markers, transcription factors such as NF-kB, HIF-1a, and b-catenin were also considered therapeutic targets. Hypoxia is another vital feature of the tumor environment and aids in the maintenance of the stemness of CSCs. This involves the hypoxic activation of the WNT/b-catenin pathway, which promotes tumor survival and metastasis. Specific antibodies have been investigated against CSC markers; for example, anti-CD44 antibodies have been demonstrated to have potential against different CSCs in preclinical investigations. Anti-CD-133 antibodies have also been developed. Targeting the CSC microenvironment is a possible drug target for CSCs. Focusing on stemness-related genes, such as the transcription pluripotency factors SOX2, NANOG, and OCT4, is another therapeutic option. Conclusions: Stemness surface and gene markers can be potential prognostic biomarkers and management approaches for cases with drug-resistant endometrial cancers. Full article

Alpha-Synuclein (α-Syn) is a presynaptic neuronal protein implicated in the pathogenesis of Parkinson’s disease (PD) and other synucleinopathies, primarily through its aggregation into insoluble fibrils. The extended α-Syn half-life necessitates treatment durations that are incompatible with efficient high-throughput drug screening, can risk compound stability or cause cellular toxicity. To address this, we inserted a PEST sequence, a motif known to promote rapid protein degradation, at the C-terminus of the SNCA gene using CRISPR/Cas9 to create a novel cell line with reduced α-Syn half-life. This modification accelerates α-Syn turnover, providing a robust model for studying α-Syn dynamics and offering a platform that is applicable to other long-lived proteins. Our results demonstrate a six-fold reduction in α-Syn half-life, enabling the rapid detection of changes in protein levels and facilitating the identification of molecules that modulate α-Syn production and degradation pathways. Using inhibitors of the proteasome, transcription, and translation further validated the model’s utility in examining various mechanisms that impact protein levels. This novel cell line represents a significant advancement for studying α-Syn dynamics and offers promising avenues to develop therapeutics for α-synucleinopathies. Future research should focus on validating this model in diverse experimental settings and exploring its potential in high-throughput screening applications. Full article

The root of Plumbago indica L. is commercially available in herbal markets in both crude and powdered forms. P. indica root is a key ingredient in numerous polyherbal formulations. However, P. indica has two closely related species, P. zeylanica L. and P. auriculata Lam. Since only P. indica is traditionally used in Thai polyherbal products, adulteration with other species could potentially compromise the therapeutic efficacy and overall effectiveness of these formulations. To address this issue, a MassARRAY iPLEX assay was developed to accurately identify and differentiate P. indica from its closely related species. Five single nucleotide polymorphism (SNP) sites—positions 18, 112, 577, 623, and 652—within the internal transcribed spacer (ITS) region were selected as genetic markers for species identification. The assay demonstrated high accuracy in identifying P. indica and was capable of detecting the species at DNA concentrations as low as 0.01 ng/µL. Additionally, the assay successfully identified P. zeylanica in commercial crude drug samples, highlighting potential instances of adulteration. Furthermore, it was able to distinguish P. indica in mixed samples containing P. indica, along with either P. zeylanica or P. auriculata. The developed MassARRAY iPLEX assay proves to be a reliable and effective molecular tool for authenticating P. indica raw materials. Its application holds significant potential for ensuring the integrity of herbal products by preventing misidentification and adulteration. Full article