Antibiotic resistance, which renders existing antibiotics ineffective against bacterial infections, is among the top-most pressing global public health challenges. A promising strategy to combat bacterial infection without inducing the occurrence of drug resistance is by disrupting quorum sensing (QS)-a complex communication circuit that bacterial pathogens employ to regulate their virulence. Therefore, QS inhibitors have emerged in recent times as potential therapeutic agents against bacterial infections. S-Ribosylhomocysteinase (LuxS) is one particularly attractive target in the QS pathway, which synthesizes the signaling molecule that mediates interspecies bacterial communication called autoinducer-2 (AI-2). In this study, we used computational chemistry and drug discovery techniques, molecular docking, drug-likeness, toxicity prediction studies, and interaction profiling to identify bioactive phytochemicals from Annona muricata plant extract as potential anti-QS agents against LuxS. Screening a library of 123 natural acetogenins from A. muricata, we identified gigantetronenin and isoannonacin as promising LuxS inhibitors. The potential inhibitory activity of these compounds against LuxS suggests that they could be explored as QS inhibitors with broad-spectrum activity against bacterial pathogens. These findings highlight the potential of gigantetronenin and isoannonacin as novel therapeutic candidates for combating bacterial infections through QS inhibition.

α-Galactosidase enzymes have a variety of applications in numerous areas, including medicine, energy, food, feed, prebiotics, and paper pulp production. A single α-galactosidase gene was identified in the Geobacillus kaustophilus genome through the analysis of the carbohydrate-active enzymes (CAZY) database. The objective of this study was to clone, express heterologously, purify, and biochemically characterize the α-galactosidase enzyme encoded in the G. kaustophilus genome. In order to achieve this, a codon-optimized synthetic gene encoding the α-galactosidase enzyme was cloned into the pQE30 plasmid and expressed in Escherichia coli BL21 DE3 pLysS. The biochemical characterization of G. kaustophilus α-galactosidase revealed that the purified enzyme exhibited optimal activity at 40°C and pH 6.0 in citrate buffer, with demonstrable activity over a broad range of pH and temperature. Furthermore, the GKαGal enzyme exhibited a Vmax value of 41.09U/mg and a 0.248mM Km value towards 4-nitrophenyl-α-d-galactopyranoside (pNPGal). In essence, the produced α-galactosidase enzyme has potential applications in the degradation of lignocellulosic biomass, disaccharides production, and in medical contexts.

Leukemia continues to provide a significant therapeutic challenge due to relapse, medication resistance, and treatment-associated toxicity, which frequently hinder sustained disease management. Rhizomes of ginger (Zingiber officinale) possess bioactive phenolics, notably 6-gingerol and 6-shogaol derivatives, which have demonstrated antileukemia efficacy in preclinical models. This study rigorously assesses the evidence regarding ginger-derived preparations and isolated compounds in both acute and chronic leukemia models, focusing on recurring mechanisms and translational viability. In leukemia cell line investigations and sparse resistant-model data, ginger-related interventions are consistently linked to diminished viability and the induction of mitochondrial apoptosis, typically indicated by alterations in Bax/Bcl-2 ratios, PARP breakage, and caspase-related measurements. Numerous studies indicate redox modulation, often characterized by elevated intracellular reactive oxygen species in leukemic cells, coupled with diminished pro-survival signaling, such as PI3K/Akt, as indicated by decreased pAkt and survivin levels. The suggested immunomodulatory and anti-inflammatory effects, encompassing alterations in NK-cell activity and cytokines like TNF-α and IL-6, are inadequately substantiated within leukemia-specific immunological contexts. Interpretation is limited by the variability in extract composition and chemical characterisation, inconsistent dose and exposure circumstances, dependence on endpoint markers without causative manipulation, and a lack of leukemia-specific clinical data. Ginger-derived compounds exhibit multi-target biological activity that necessitates further exploration through standardized and chemically defined preparations, pharmacokinetic and pharmacodynamic characterization, clinically relevant exposure benchmarks, and meticulously designed leukemia-focused translational and early-phase clinical studies to elucidate safety, efficacy, and compatibility with current therapies.

Acute myeloid leukemia (AML) is a rapidly progressing blood cancer with poor survival rates, necessitating aggressive treatment strategies like chemotherapy. Doxorubicin (DOXO) is commonly used but is limited by severe side effects, including myeloablation, which involves the depletion of bone marrow cells leading to immunosuppression and heightened infection risk. This study explores the potential of omega-3 polyunsaturated fatty acids (n-3 PUFAs), specifically docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA), to enhance the efficacy of DOXO against AML cells while mitigating some of its toxicities. The results show that DHA and EPA increase the DOXO-induced apoptosis in KG1a cells and greater accumulation in the sub-G1 phase, suggesting enhanced cell death. TUNEL assays confirmed increased DNA fragmentation, whereas mRNA analysis revealed upregulation of apoptosis and cell cycle regulation genes. Importantly, DHA and EPA also reduced the hemolytic activity of DOXO, suggesting a protective effect against chemotherapy-associated side effects. These findings suggest that DHA and EPA could enhance the anti-leukemic impact of DOXO, potentially reducing the need for high-dose chemotherapy and alleviating risks like myeloablation, offering a promising adjunct strategy for AML treatment.

Ovarian Cancer is a leading cause of mortality among women globally, primarily due to lack of specific and sensitive early-stage diagnostic tools. This study aims to identify hub genes associated with recurrent, late-stage, and metastatic tumors as potential prognostic biomarkers and drug targets. Gene expression data from eight National Center for Biotechnology Information (NCBI)-Gene Expression Omnibus (GEO) datasets were categorized by recurrence, tumor-stage, and metastasis. Differential gene expression and enrichment analyses were performed. Hub genes were identified by protein-protein interaction networks and validated by the University of Alabama at Birmingham Cancer Data Analysis Portal (UALCAN), GEPIA2, pROC, and Kaplan-Meier plotter databases. Genetic alterations, immune cell infiltration, miRNA prediction, and drug-gene interactions were assessed using cBioPortal, CIBERSORTx, Encyclopedia of RNA Interactomes (ENCORI), and Drug-Gene Interaction Database (DGIdb), respectively. Eight hub genes (FN1, COL1A1, COL1A2, COL3A1, POSTN, LUM, IGF1, and CXCL8) were identified, with COL1A2 common across all tumor categories. Note that 19.6% of cases showed mutations in these genes, primarily COL3A1. Overexpression of most hub genes and reduced expression of CXCL8 correlated with worse survival outcomes. COL1A1 and FN1 showed strong diagnostic ability. Late-stage tumors showed elevated M2 macrophages and neutrophils. hsa-miR-29a-3p, hsa-miR-29b-3p, and hsa-miR-29c-3p were identified as the most interactive miRNAs. Ocriplasmin and pamidronate were identified as potential therapeutics. Our findings highlight the therapeutic relevance of these hub genes and identify them as potential drug targets and prognostic biomarkers in ovarian cancer.

We present a novel, cost-effective sensor for carcinoembryonic antigen (CEA) detection utilizing a pencil graphite electrode (PGE) in combination with electrochemical impedance spectroscopy (EIS), which offers high sensitivity and selectivity. Anti-CEA/AuNPs/PGE was successfully illustrated as a label-free impedimetric immunosensor for the detection of CEA. Through EIS, we observed distinct impedance changes upon CEA binding, enabling real-time detection with high reproducibility and low interference from non-target molecules. Due to its satisfying impedimetric response, this new immunosensor demonstrated that it can be used for high-performance detection of CEA with a wide linear range extending from 13.2 to 1×105pgmL-1, with correlation coefficients (R2) of 0.9923. The PGE's excellent conductive properties and surface stability allowed for the successful detection of CEA at low concentrations, demonstrating a detection limit of 4.4pgmL-1, which is competitive with existing, more costly alternatives. The sensor's robust performance in spiked artificial urine samples indicates its potential for practical application in point-of-care cancer diagnostics, especially in resource-limited environments. The developed electrochemical biosensor holds promise for accurately detecting CEA in urine samples, offering a precise technique that could find valuable application in clinical tumor detection.

Multidrug-resistant tumor cells pose significant challenges in cancer treatment. Alternative strategies such as targeted gene silencing and the use of compounds with minimal cytotoxicity toward normal cells are therefore of great interest. Antimicrobial peptides (AMPs) have demonstrated anticancer potential due to their physicochemical properties. In lung cancer, overexpression of AKT serine/threonine kinase 1 (AKT1) promotes abnormal tumor growth and progression. In this study, we synthesized chitosan-based nanoparticles (CSNPs) co-loaded with Pseudomonas aeruginosa peptide from strain P3 (PAP3) (an AMP) and siRNA targeting the AKT1 gene, and evaluated their anticancer activity at the cellular and molecular levels. Characterization of the CSNPs revealed a nanoscale structure, low polydispersity index, and moderate encapsulation efficiency for both peptide and siRNA. Evaluation using L929 cells confirmed PAP3's nontoxic profile, while a dose-dependent anticancer effect against A549 cells was observed. Delivery of encapsulated peptide, siRNA, and their combination increased cell death and induced morphological changes in A549 cells. Gene expression analysis showed upregulation of pro-apoptotic markers (Bax and Caspase-3) and downregulation of the anti-apoptotic marker Bcl2, indicating promising anticancer properties of the engineered compound. In conclusion, co-delivery of PAP3 and AKT1-targeting siRNA via CSNPs demonstrates potential for future anticancer therapies.

Accurate prediction of protein secondary structure is a critical step toward understanding protein function and facilitating structure‑based drug discovery. We present a template‑independent, single sequence method utilizing a shallow feed‑forward artificial neural network (ANN) with one hot (binary) amino acid encoding and a sliding window input. The network is trained and evaluated on two datasets: (i) a curated, nonhomologous Protein Data Bank (PDB) set with a strict maximum pairwise sequence identity, annotated with STRIDE; and (ii) a homologous human papillomavirus (HPV) set (L1, L2, E1-E7) whose labels are obtained from the Proteus predictor and used solely for a system specific, post hoc analysis. To improve transparency regarding generalization, we report the all‑vs‑all sequence identity distribution for the nonhomologous set (matrix and histogram). The model achieves competitive Q3 accuracy on the nonhomologous PDB benchmark and yields Q3‑agreement (Proteus) on the HPV case study. We explicitly frame the HPV evaluation as agreement with a labeling tool rather than accuracy versus experiment. Despite its simplicity and lack of evolutionary profiles, the ANN demonstrates robust sequence-only performance, offering a lightweight baseline that is easy to reproduce and deploy on the CPU. We discuss limitations (dataset size, lack of cross‑tool bake‑offs, absence of long‑range features) and delineate concrete avenues for future work.

Wearable Human Activity Recognition (HAR) models often degrade across users and sensor placements due to domain shifts. This paper presents the Multi-Sensor Adaptive Feature Alignment Network (MSAFAN), integrating Sensor-Specific Normalization Layer (SSNL), Hybrid Polynomial Feature Transformation (HPFT), Conditional Alignment Loss (CAL), and Entropy-Guided Pseudo-Labeling (EGPL) for class-wise adaptation and robust cross-sensor generalization. Evaluated on four benchmark datasets: BAR, DSADS, PAMAP2, and MM-DOS, the MSAFAN improves macro-F1 by 8.4% and accuracy by 10.3% while reducing computational cost by 26% over state-of-the-art UDA models. The framework achieves stable convergence, efficient adaptation, and scalable performance, confirming its suitability for real-time deployment in edge AI and wearable computing applications.

Cow dung is a low-cost lignocellulosic biomass generated in large quantities across India, yet remains underutilized and contributes to environmental pollution when improperly managed. In this study, cellulose was isolated from cow dung using two different approaches a green, microbially-assisted natural extraction process under dark conditions with mild chemical uses, and a chemically driven Soxhlet-assisted method employing alkali oxidative pretreatment. The physicochemical characteristics of the isolated cellulose were examined using the Van Soest compositional protocol, FTIR spectroscopy, UV-Vis analysis, CHNS elemental profiling, and SEM imaging. The Soxhlet route produced a cellulose yield of 3.65% ± 0.2%, with purity of 28.68% cellulose and 3.68% lignin, whereas the natural method resulted in a yield of 3.55% ± 0.3%, with purity of 26.31% cellulose and 8.9% lignin. Soxhlet extraction enabled more effective delignification and improved fiber defibrillation, while the natural method, despite of lower lignin removal rate, preserved the structural integrity of the cellulose and offered substantial sustainability advantages by reducing chemical consumption and energy requirements. These findings highlight cow dung as a viable renewable feedstock for cellulose-based biomaterials and demonstrate that low-resource, environmentally benign extraction strategies can support decentralized and rural circular bio-economy initiatives.