Background: Salivary proteomics is a non-invasive, low-cost, and real-time diagnostic approach increasingly applied in cancer research. Salivary biomarkers hold particular promise for the early identification of gastric cancer (GC). This study aimed to detect salivary proteins altered in GC and evaluate their potential as novel non-invasive biomarkers.Methods: We analyzed salivary proteomes from GC patients (group 1: n = 12; group 2: n = 13) and healthy controls (n = 11) using isobaric tags for relative and absolute quantitation (iTRAQ). Differentially expressed proteins (DEPs) were identified and functionally annotated using gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and protein–protein interaction (PPI) networks. Candidate proteins were further validated by parallel reaction monitoring (PRM), and prognostic significance was assessed through Kaplan–Meier (KM) survival analysis.Results: A total of 671 proteins with unique peptide segments were identified. Among them, 124 and 102 proteins were significantly differentially expressed in GC groups 1 and 2, respectively, compared with controls. Fifty-six overlapping DEPs were detected between the two GC groups, including 24 upregulated and 32 downregulated proteins. Functional enrichment and PRM validation highlighted four key proteins (S100A8, S100A9, CST4, CST5) with consistent differential expression. Interestingly, CST4 and CST5 were downregulated in saliva but upregulated in GC tissue and blood.Conclusion: Our findings demonstrate that salivary proteins, particularly S100A8, S100A9, CST4, and CST5, hold significant potential as non-invasive biomarkers for gastric cancer detection. These results provide new insights into saliva-based diagnostics and highlight the importance of cross-comparison with tissue and blood expression profiles.

In the era of artificial intelligence (AI) and Industry 4.0, pilot-scale platforms for small molecule chemical drugs are undergoing a transformative digital evolution. These platforms serve as a critical link between early-stage laboratory research and full-scale pharmaceutical manufacturing, ensuring process feasibility, scalability, and regulatory compliance. This review offers a comprehensive and forward-looking analysis of the structure, function, and strategic importance of pilot-scale systems within the modern pharmaceutical landscape. Focusing on the integration of AI and intelligent automation, the study highlights innovations such as AI-driven process optimization, predictive maintenance, data integration, digital twin technologies, and continuous manufacturing. These technologies are reshaping conventional production paradigms by enhancing efficiency, improving quality control, and reducing environmental impact. The convergence of computational pharmaceutics and green chemistry is also examined as a key driver of sustainable and intelligent drug development. Moreover, the review addresses the industry’s transition toward Industry 5.0, characterized by human-machine collaboration, data-centric innovation, and an emphasis on sustainability. Persistent challenges such as equipment standardization gaps, data-sharing limitations, and outdated infrastructure are critically discussed. Drawing from industrial case studies, academic research, and best practices, this paper explores both the opportunities and constraints associated with AI-enabled pilot platforms. Ultimately, the review aims to inform future strategies in digital pharmaceutical manufacturing by underscoring the importance of technological innovation, regulatory alignment, and collaborative ecosystems in advancing the development, efficiency, and sustainability of small molecule drug production.

Background/Objective: This study evaluated the antidiabetic and antihypercholesterolemic potential of the botanical metabolite troxerutin (TRX) and compared it with that of metformin in high-fat diet-fed streptozotocin-induced diabetic male Wistar rats. Methods: The rats (n = 48) were divided into six groups. Diabetes was induced in the treatment groups, and different doses of troxerutin (TRX)—25 mg/kg/day (TRX25-D), 50 mg/kg/day (TRX50-D), and 75 mg/kg/day (TRX75-D)—or the standard drug (10 mg/kg/day; MET10-D) were administered for a period of 7 weeks, compared to the negative (non-diabetic control, NDC) and positive (diabetic control, DC) control groups. At the end of the trial period, serum was collected to determine the lipid profile (high-density lipoprotein, low-density lipoprotein, and very-low-density lipoprotein (VLDL)) and the concentrations of hepatic (aspartate aminotransferase and alanine aminotransferase), renal (urea and creatinine), and oxidative stress (catalase and malondialdehyde) markers. Adipose tissue, skeletal muscle, and liver tissue samples were collected to determine mRNA expression, of pro-inflammatory cytokines [tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6)] and genes involved in lipid metabolism [peroxisome proliferator-activated receptor α (PPARα), peroxisome proliferator-activated receptor γ (PPARγ), fatty acid synthase (FAS), and sterol regulatory element-binding protein-1c (SREBP-1c)]. Results: The results showed a significant decrease (p < 0.05) in total cholesterol (TC), triglycerides (TGs), VLDL, and LDL levels, along with hepatic, renal, and stress markers, in the rats treated with a higher concentration of troxerutin (TRX75-D) compared to diabetic control rats. Moreover, troxerutin significantly (p < 0.05) upregulated the expression of PPARα and PPARγ, while the expression of FAS, SREBP-1c, TNF-α, and IL-6 genes were significantly (p < 0.05) downregulated simultaneously in the adipose tissue, skeletal muscles, and liver in a dose-dependent manner, compared to diabetic ct control rats. Conclusion: Troxerutin has considerable antidiabetic and antihypercholesterolemic potential and thus could be safely used as an alternative therapeutic compound to the standard antidiabetic drug metformin.

The biocompatibility of the Sprague Dawley (SD) rat osteoblasts, which were cultured on the surfaces of nano-hydroxyapatite/polyetheretherketone (n-HA/PEEK) composites were investigated in this work. The osteoblasts of 24- hour old SD rats were cultured and identified by modified enzymatic digestion in vitro. The morphology and proliferation of cells were observed in CCK-8 regent staining, inverted microscopes, and by scanning electron microscopy (SEM) respectively. The results show that n-HA/PEEK composites have good biocompatibility with SD osteoblasts and that they can promote the growth of the cells that were cultured on the surfaces of the composites. The content of HA in n- HA/PEEK composites plays an important role in cell proliferation.

Despite the transition to a post-pandemic phase, SARS-CoV-2 continues to generate genetically diverse variants that challenge existing therapeutic strategies. The 3-chymotrypsin-like protease (3CLpro) is a highly conserved viral enzyme essential for replication, making it an attractive target for variant-agnostic antiviral intervention. Nanobodies offer distinct advantages over conventional antibodies, including small size, high stability, and strong antigen-binding potential, but their rational design remains technically challenging. We developed a scaffold-based, AI-assisted pipeline for nanobody design that integrates large language model-driven generation of complementarity-determining region (CDR) sequences with structure-guided grafting onto a conserved humanized nanobody scaffold. Nanobody structures were predicted using AlphaFold2, and binding performance was evaluated through molecular docking against 3CLpro from multiple SARS-CoV-2 variants. Physicochemical properties and structural stability were assessed using established computational tools. Among the candidates generated, Nb01 consistently showed favorable predicted binding to 3CLpro across major SARS-CoV-2 variants. Compared with benchmark nanobodies (S43, bn03, R14, and 3-2A2-4), Nb01 showed stronger predicted binding performance for Alpha, Gamma, Omicron BA.2, BA.4/5, BF.7, and XBB variants, while maintaining competitive predicted performance across other lineages. Nb01 also exhibited favorable physicochemical properties, including low predicted toxicity, acceptable stability, and a profile consistent with potential biophysical suitability. Nb01 represents a promising computationally identified nanobody candidate targeting the conserved SARS-CoV-2 3CLpro. This scaffold-based, AI-assisted design strategy provides a systematic and generalizable computational pipeline for the development of antiviral nanobody candidates with potential broad variant coverage. Further experimental validation will be required to confirm the binding properties and therapeutic relevance of Nb01.

Background: Wastewater surveillance has emerged as a cost-effective and powerful approach for monitoring infectious diseases at the community level, as demonstrated during the COVID-19 pandemic. However, translating wastewater viral signals into reliable epidemic forecasts remains challenging because measurements are noisy, environmental conditions vary over time, and disease transmission is inherently non-linear. Mechanistic models such as the Susceptible-Exposed-Infectious-Recovered-Virus (SEIR-V) framework can bridge wastewater signals with infection dynamics, but their performance depends heavily on accurate parameter estimation, and conventional least-squares optimization is prone to convergence to local optima. Methods: We developed a Genetic Algorithm-optimized SEIR-V (GA-SEIR-V) model that applies evolutionary optimization for robust multi-parameter estimation. To improve biological realism, the model incorporates a temperature-dependent viral decay rate represented by a sinusoidal function fitted to regional climate data, enabling dynamic simulation of seasonal viral persistence in wastewater. The framework was calibrated and evaluated using wastewater viral load, clinical case, and temperature data from California (September 2020-November 2022), Greater Boston (September 2020-May 2021), and Switzerland (January 2022-December 2022). Results: Using a unified evaluation protocol with 30 independent genetic algorithm runs, we explicitly separated in-sample calibration from out-of-sample prediction. During calibration, GA-SEIR-V reproduced the reported model fit for California and consistently outperformed a carefully re-tuned least-squares baseline on large, heterogeneous datasets, demonstrating greater robustness and more reliable parameter estimation where gradient-based optimization became trapped in local optima. In contrast, long-term prediction beyond the calibration period remained difficult for all methods, particularly across multiple epidemic waves, resulting in limited out-of-sample forecasting accuracy. Conclusion: GA-SEIR-V combines evolutionary optimization with temperature-aware viral decay modeling to improve the robustness and identifiability of SEIR-V parameter estimation, particularly for complex wastewater datasets. By explicitly distinguishing calibration from prediction, the study shows that excellent in-sample fitting does not necessarily translate into reliable long-term forecasting. The proposed framework is therefore most suitable for robust parameter estimation and short-term epidemic tracking, while providing a foundation for future improvements toward more accurate long-range prediction.