Non-wood biomass is particularly attractive as a cellulose source because of the lower lignin content. However, optimal cellulose extraction conditions are required as lignin content varies between plant sources. Further, the use of organic acids in place of harsh mineral acids is of interest in “greening” the cellulose production process. This study sought to establish optimum parameters for the extraction of cellulose microfibers (CMFs) from hemp (Cannabis sativa) biomass, using maleic and formic acids. Hemp fibers were pre-treated in NaOH (4 wt%) and aqueous chlorite in acetate buffer before ultrasonic treatment to break down bundles. The CMFs produced were compared with those generated from sulfuric acid hydrolysis. Response surface methodology (RSM) was used to determine combinations of three processing conditions, including acid concentration (45–64%), hydrolysis time (30–90 min), and temperature (45–65 °C). A central composite design (RSM-CCD) model with 21 experimental runs was optimized using MODDE 13.1 software. The model suitably described the data (R² = 0.99; R²adj = 0.96). Microfibers with an average width of 6.91 µm, crystallinity range 40–75%, and good thermal stability were produced. Crystallinity was influenced by all three factors. The optimal crystallinity predicted by the model was 83.21%, which could be achieved using formic acid 62 wt% formic acid, 36 min hydrolysis time, and 47 °C hydrolysis temperature. These conditions resulted in a crystallinity degree of 82%. These data suggest formic acid can be used as an alternative to sulfuric acid for synthesis of cellulose microfibers from biodegradable hemp waste fibers.

Silver nanoparticles belong to a highly versatile group of nanomaterials with an appealing range of potential applications. In the realm of antimicrobial and antioxidant application, silver nanoparticles (AgNPs) exhibit auspicious capabilities. This research, for the very first time, endeavors to carry out biosynthesis of AgNPs coupled with fermentation using Medusomyces gisevii and Origanum vulgare L. (O. vulgare) plant species. Fermentation (F) via Medusomyces gisevii is responsible for chemical, physical, biological, and electrochemical processes. During in vitro study of antioxidant activity, fermented O. vulgare herb extract showed strong reductive activity as evaluated by the cupric reducing antioxidant capacity (CUPRAC), ferric reducing antioxidant power (FRAP), and 2,2′-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS•+) assay, with a value of 1.45 ± 0.048 mmol TE/g, 0.95 ± 0.04 mmol TE/g, and 0.59 ± 0.023 mmol TE/g, respectively. The highest antimicrobial activity was shown by Staphylococcus aureus in the inhibition zone, with values of 1.40 ± 0.12 mm of OrV and of 10.30 ± 0.04 mm and 11.54 ± 0.10 mm for OrV-AgNPs and OrV-F-AgNPs, respectively. Analysis of phenolic compounds revealed that the highest total amount of the apigenin, 87.78 µg/g, was detected in OrV-F-AgNPs and the lowest amount, 16.56 µg/g, in OrV-AgNPs. Moreover, in OrV-F-AgNPs, the collective amount of proanthocyanidins, hydroxycinnamic, and flavonoids was prominently high in all cases, i.e., 145.00 ± 0.02 mg EE/g DW, 2.86 ± 0.01 mg CAE/g DW, and 0.55 ± 0.01 mg RE/g DW, respectively, as compared to the original extract (102.1 ± 0.03 mg EE/g DW, 2.78 ± 0.02 mg CAE/g DW, and 0.47 ± 0.01 mg RE/g DW, respectively). During the characterization of biosynthesized nanoparticles by scanning electron microscopy (SEM), AgNPs demonstrated a uniform spherical shape with even distribution. The sample’s elemental composition was confirmed with a signal of 3.2 keV using energy-dispersive X-ray spectroscopy (EDS) analysis. Transmission electron microscopy (TEM) analysis showed silver nanoparticles that were round and spherical in shape in both stacked and congested form, with a size range of less than 30 nm. Thus, this green and sustainable synthesis of AgNPs, a blend of Medusomyces gisevii and O. vulgare herbal extract, has adequate potential for increased antimicrobial and antioxidant activity.

This review examines the emerging role of cellulose nanocrystals (CNCs) and nanofibers (CNFs) combined with green-synthesized metallic nanoparticles (Ag, ZnO, CuO) as sustainable antimicrobial and biomedical materials. The novelty of this work lies in consolidating evidence on how nanocellulose's high surface area, biocompatibility, and modifiable chemistry improve nanoparticle stability, reduce toxicity, and enhance antimicrobial effectiveness. By analyzing recent advancements in synthesis methods, antimicrobial mechanisms, and biomedical applications, the review shows that CNC/CNF-MNP composites demonstrate synergistic activity against pathogenic microbes and have promising results in wound healing, drug delivery, and antibiofilm treatments. The main conclusions indicate that these nanocomposites provide a viable alternative to traditional antimicrobials, especially in tackling the increasing problem of microbial resistance and supporting tissue regeneration. However, limited scalability, production costs, and inadequate clinical validation remain major barriers to their practical use. Overcoming these challenges through standardized green-synthesis protocols, improved composite engineering, and comprehensive in vivo studies will be crucial for advancing their application in healthcare.

Colorectal cancer (CRC) is a type of cancer that affects the colon and rectum, with overexpression of epidermal growth factor receptor (EGFR) and human epidermal growth factor receptor 2 (HER2) observed in up to 85% of colorectal cancer cases. Although CRC treatment has progressed with the introduction of targeted drugs, current approaches have primarily focused on a single blockage of EGFR or HER2 to combat colon cancer. However, monotherapies that target either the EGFR or HER2 receptor frequently have low efficacy due to mutations in downstream effectors such as Kirsten Rat Sarcoma 2 Viral Oncogene Homologue (KRAS) and the activation of compensatory signaling pathways that support tumor survival and proliferation. Hence, the discovery and development of a therapy with the capability to combat CRC by simultaneously inhibiting both EGFR and HER2 remain avenues for further exploration. This study introduces a novel machine learning (ML)-based stacking ensemble framework for rapidly and accurately identifying dual EGFR and HER2 inhibitors using SMILES notation. A benchmark data set comprising active and inactive compounds against EGFR and HER2 was curated from the ChEMBL database. Based on this data set, 40 baseline models were developed and optimized using a comprehensive set of well-known molecular descriptors and ML algorithms (Figure 1). These models generated probabilistic features integrated via a logistic regression model as a final estimate to construct the final stacking ensemble model. The model was applied to bioactive compounds identified through LC-MS/MS profiling of Ceratonia siliqua extract as well as to a subset of natural products from the LOTUS database for virtual screening. The cytotoxic potential of Ceratonia siliqua was experimentally validated using the MTT assay against HCT116 colorectal cancer cells and noncancerous Vero cells, where the extract exhibited an IC₅₀ value of 13.32 ± 1.09 μg/mL against HCT116 cells, indicating its significant anticancer potential. Additionally, molecular docking and in silico ADMET studies were conducted on the top three compounds from both the LOTUS database and the predicted candidate from the LC-MS/MS data set identified by the stacking model, alongside four FDA-approved anticancer drugs for comparative analysis. Among these, LTS0131923 demonstrated the highest binding affinity against HER2 (PDB ID: 7MN5), with a binding energy of −11.2 kcal/mol and an inhibition constant of 0.00626 μM, outperforming Tucatinib, a standard CRC treatment. This study reveals the potential of ML-driven approaches to accelerate the discovery of dual-target inhibitors for CRC therapy and highlights Ceratonia siliqua L. as a promising source of bioactive compounds for cancer treatment.

Diabetes mellitus (DM) is a growing global health challenge, particularly in low-resource settings where access to effective therapies remains limited. Dual inhibition of dipeptidyl peptidase IV (DPP-IV) and sodium–glucose co-transporter 2 (SGLT2) offers a synergistic therapeutic strategy by enhancing insulin secretion and promoting glucose excretion. This study developed an integrated in silico framework combining stacked ensemble machine learning, molecular docking, MD Simulations, and ADMET profiling to identify dual DPP-IV/SGLT2 inhibitors from M. oleifera metabolites. Baseline models were generated using 110 algorithm–descriptor combinations per target, and stacking significantly improved predictive accuracy, achieving Matthews Correlation Coefficients of 0.968 (training) and 0.937 (testing) for DPP-IV and 0.968 (training) and 0.861 (testing) for SGLT2. Validation against FDA-approved inhibitors confirmed the models’ reliability and generalisability. LC–MS/MS profiling of M. oleifera revealed several metabolites with high predicted activity, among which vitexin, homoorientin, lariciresinol 4-O-β-D-glucopyranoside, and N,α-L-rhamnopyranosyl vincosamide showed strong binding affinities and favourable pharmacokinetic properties. MD Simulations analyses further position N,α-L-rhamnopyranosyl vincosamide as a potential dual DPP-IV/SGLT2 hit inhibitor. The findings highlight M. oleifera as a promising source of multitarget antidiabetic compounds and demonstrate the potential of stacked ensemble learning in accelerating natural product-based drug discovery.

Pharmaceutical and microbial pollution in urban rivers is an emerging concern, particularly in developing regions with limited wastewater treatment capacity, posing risks to human health and ecosystems. This study evaluated the risk profiles of selected pharmaceutical compounds and bacterial indicators in the Sand River, South Africa, and computed their ecological risks, antimicrobial resistance (AMR), and human health risk assessment. Surface water samples were collected from three sites during the wet season and analyzed for target antibiotics and non-steroidal anti-inflammatory drugs (NSAIDs) using High-Performance Liquid Chromatography (HPLC) with a photodiode array (PDA) detector, while total coliforms (TCs) and Escherichia coli (E. coli) were enumerated using the Colilert system. Ciprofloxacin, sulfamethoxazole, and erythromycin were the most abundant pharmaceuticals, with maximum concentrations of 2.50 µg/L, 2.76 µg/L, and 2.53 µg/L, respectively. TC and E. coli levels exceeded regulatory thresholds, indicating severe microbial contamination. Risk quotient analysis identified ciprofloxacin, erythromycin, and trimethoprim as high-risk compounds for potential resistance selection (RQ ≥ 1), while ciprofloxacin and erythromycin posed significant ecological risks to fish. Although non-carcinogenic health risk assessment remained below concern (HI < 1), children showed higher exposure levels. These findings underscore the urgent need for improved pharmaceutical waste management and wastewater treatment infrastructure.