University of Swat

The clinical microbiology laboratory is shifting from traditional culture-based, manual systems to automated, AI-driven smart laboratories. These platforms use real-time sensors, computer vision, and advanced algorithms to reduce human error, speed up workflows, and cut turnaround times significantly. Machine and deep learning models achieve over 95% accuracy in pathogen detection and can predict antimicrobial resistance using genomic, proteomic, or phenotypic data, supporting targeted therapy and stewardship. Data integrity is maintained through regulatory compliance, audit trails, and AI-based quality control, while privacy is protected using federated learning and encryption. Ethical oversight, regulation, and interdisciplinary collaboration remain essential.

Atherosclerosis is a chronic inflammatory disease influenced by host–microbiota interactions beyond traditional risk factors. Microbial communities in the oral cavity, gut, and blood contribute to vascular dysfunction through metabolic and immune mechanisms, yet an integrated perspective across these compartments remains lacking. This narrative review synthesizes current evidence on the distinct and interconnected roles of oral, gut, and blood microbiotas in atherosclerosis pathogenesis. We critically evaluate key microbial metabolites, trimethylamine N-oxide (TMAO), short-chain fatty acids (SCFAs), and secondary bile acids, and their mechanisms of host metabolic and immune modulation. We also examine cross-compartment interactions, emerging multi-omics approaches, and the translational potential of microbiota-targeted interventions. Oral pathogens promote systemic inflammation and endothelial activation. Gut-derived metabolites such as TMAO exacerbate foam cell formation and impair reverse cholesterol transport, whereas SCFAs exert protective effects via immune modulation and gut barrier maintenance. Emerging evidence suggests that blood microbial components contribute to vascular inflammation, though methodological challenges remain. Multi-omics integration (metagenomics, metabolomics, host genomics) reveals interconnected metabolic networks linking microbial activity to atherosclerosis. Microbiota-targeted strategies, including dietary modulation, TMA lyase inhibitors, and probiotics, show promise for risk stratification and therapeutic intervention. The human microbiota regulates atherosclerosis through immunometabolic metabolites, offering promising biomarkers and therapeutic targets. However, clinical translation requires addressing interindividual variability, establishing causality, and standardizing methodologies. This review provides an integrated framework for leveraging microbiota–host interactions in precision cardiovascular medicine. Oral, gut, and blood microbiotas form an interconnected ecosystem that collectively contributes to atherosclerosis pathogenesis through niche-specific microbial communities and bidirectional cross-compartment signaling. Microbial metabolites, including TMAO, SCFAs, and secondary bile acids, serve as critical mediators linking microbial activity to host lipid metabolism, immune regulation, and endothelial dysfunction. Multi-omics integration (metagenomics, metabolomics, and host genomics) enables mechanistic insights into microbiota–host interactions and facilitates the discovery of composite biomarkers for improved cardiovascular risk stratification. Microbiota-targeted interventions, such as dietary modulation, TMA lyase inhibitors, probiotics, and prebiotics, offer promising therapeutic strategies for restoring metabolic homeostasis and mitigating atherosclerosis progression. Translating microbiota research into clinical practice requires addressing key challenges, including establishing causality, standardizing methodologies across studies, and accounting for interindividual variability to enable personalized cardiovascular care.

Colorectal cancer (CRC) remains a major cause of cancer-related mortality worldwide. Although curcumin has shown promising anticancer activity, its therapeutic application is limited by poor bioavailability, rapid metabolism, and low stability, necessitating the identification of derivatives with improved pharmacological properties. In this study, we employed an integrated network pharmacology and computational modeling framework to identify curcumin derivatives targeting CRC. SuperPred3 and CLC-Pred 2.0 identified 106 and 221 potential curcumin targets, respectively, while DisGeNET retrieved 5,474 CRC-associated genes. Overlapping these datasets yielded 68 common targets, with protein–protein interaction (PPI) network analysis and CytoHubba ranking identifying STAT3 as the top-ranked hub gene. Molecular docking-based screening of compounds from the Curcumin Chalcone Compound Database (CCCD) prioritized two lead derivatives, PubChem CID 122,378,841 and 135,494,223, which exhibited stronger binding affinities (− 8.294 and − 8.133 kcal/mol, respectively) than curcumin (− 7.169 kcal/mol). These compounds formed stable interactions with key STAT3 residues, including Ser613, Ser611, and Lys591. Extensive 200 ns molecular dynamics simulations, supported by principal component analysis (PCA) and free energy landscape (FEL) analyses, confirmed the structural stability of these complexes within the SH2-domain binding pocket of STAT3. MM/GBSA analysis further corroborated these findings, with 122,378,841–STAT3 and 135,494,223–STAT3 showing more favorable total binding free energies (− 45.91 ± 0.45 and − 44.76 ± 0.52 kcal/mol, respectively) than curcumin–STAT3 (− 38.31 ± 0.31 kcal/mol). In addition, density functional theory (DFT) analysis suggested that the selected derivatives possess greater kinetic stability and electrophilic character than curcumin, consistent with stronger interactions with STAT3. ADMET and physicochemical profiling further indicated favorable drug-likeness, predicted absorption, and pharmacokinetic properties of the derivatives. Collectively, these findings identify 122,378,841 and 135,494,223 as computationally prioritized STAT3-targeting curcumin derivatives in CRC and provide a mechanistically informed basis for further in vitro and in vivo validation of their biological activity, safety, and therapeutic relevance.

This work presents the solvothermal synthesis of a trimetallic CuCrFe-BDC Metal-Organic Framework (CuCrFe-BDC MOF), designed for highly efficient and stable adsorption of Methyl Violet 6B (MV-6B) dye from aqueous media. Detailed characterization via SEM, XRD, FTIR, BET, TGA, zeta potential, EDX, and elemental mapping was carried out. FTIR analysis verified all pivotal functionalgroup signatures, while XRD confirmed a highly crystalline phase exhibiting sharp, well-defined diffraction peaks. TGA demonstrated exceptional thermal resilience up to 600 degrees C. BET measurements revealed a remarkable specific surface area (626 m(2)/g) with pronounced microporosity, ideal for high-performance adsorption. The material was synthesized with an average nanoscale particle size of 87 nm, forming larger aggregates around 289 nm. Under optimized conditions of 0.01 g adsorbent dosage, 333 K, pH 8, and a 60 min contact time, the trimetallic CuCrFe-BDC MOF achieved over 91 % removal of MV-6B dye. Adsorption kinetics followed a pseudo-secondorder model (R-2 > 0.99), indicating chemisorption dominance. Equilibrium data conformed closely to the Langmuir isotherm (R-2 = 0.99), with a maximum monolayer adsorption capacity of 416 mg/g at 333 K. Thermodynamic analysis revealed an endothermic process, by positive enthalpy change (Delta H degrees = 17.79 kJ/mol ) that is spontaneous across the temperature range studied, as evidenced by consistently negative Delta G degrees values. The adsorption mechanism likely includes chemisorption combined with pi-pi stacking, pore-filling, electrostatic interactions, and hydrogen bonding. Importantly, the CuNiZnBDC MOF maintained its structural integrity and recyclability over seven adsorption-desorption cycles, retaining 65 % removal efficiency. These findings underline the potential of this robust, high-capacity MOF as an effective adsorbent for industrial wastewater applications.

The current study explores the synthesis, structural characterization, and pharmacological assessment of 9,9-dibutylfluorene-2-carboxylic acid, focusing on its potential as an inhibitor of dihydrofolate reductase (DHFR), a crucial enzyme in cancer treatment. SC-XRD confirmed its molecular structure, detailing essential bond lengths and angles, while Hirshfeld surface analysis identified significant intermolecular interactions primarily driven by H-bonding and van der Waals forces. Density Functional Theory (DFT) revealed stable electronic properties, providing deeper insight into the optimized geometric parameters. Molecular docking established a strong binding affinity to DHFR, indicating promising inhibitory effects. Furthermore, pharmacokinetic analysis suggested favorable drug-like properties, including high gastrointestinal absorption. Together, the findings present this compound as a fascinating candidate for future development as a fluorene-based anticancer agent.