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In the Philippines, community health workers (CHWs) face psychosocial difficulties as they deliver health services to communities with limited access to healthcare, yet they receive no support for their well-being. To maintain their psychological strengths, we adapted the Locus-of-Hope Enhancement Program (LEAP), a positive intervention that strengthens hope of CHWs from urban-poor communities in Metro Manila. Using a double-blind, parallel randomized alternative-treatments experiment, LEAP's effects on locus-of-hope and other psychological resources across three time-points were tested in Manila. Group contrast estimates indicate improvements in external-peer and family locus-of-hope, and psychological well-being. LEAP boosted external-peer and family locus-of-hope after 1 month via peer and family hope at posttreatment. LEAP also increased psychological well-being, civic engagement attitudes, and behaviors posttreatment, and civic engagement attitudes at follow-up through peer locus-of-hope at posttreatment. Findings suggest how LEAP empowers CHWs from urban-poor communities by fortifying hope, thus sustaining their role in community health. Trial Registration: Open Science Framework (OSF) Registries (https://osf.io/egtju).

Nutrient imbalances, soil salinity, and shrinking arable land threaten global food security, driving demand for sustainable biofertilizer alternatives to chemical inputs. Aquatic ecosystem-derived biofertilizers such as Magnetospirillum gryphiswaldense (MSR-1) are promising sustainable substitutes and show strong agricultural potential due to their stress tolerance, adaptability, and plant growth-promoting traits. This study investigated the ability of MSR-1 to enhance the growth and productivity of tomato and paddy under normal, iron-deficient, and saline conditions. MSR-1 was cultured in modified Magnetospirillum Growth Medium (MSGM) under microaerophilic conditions, with SEM confirming its spiral gram-negative morphology and successful, non-destructive colonization on tomato and paddy roots and leaves. Moreover, HR-LCMS profiling of root exudates identified chemoattractant compounds such as quinic acid, tryptophan, quercetin, glucosinolates, and strigolactones, promoting bacterial attachment. Further, Magnetospirillum liquid biofertilizer (MLB) was formulated from MSR-1 cultures (1.5 × 108 cells/mL) and applied at 20-100% concentrations (25 mL/pot). Among the treatments, 20% MLB gave the best results under normal conditions, whereas 60% MLB was more effective under iron-deficient and saline stress conditions. In tomato, 20% MLB increased shoot length (73.5 cm), chlorophyll content (4.5 mg/g), and fruit yield (1066.95 g/plant). Under stress, 60% MLB improved fruit yield (760-800 g/plant) and boosted antioxidant enzymes (SOD 75 U/mg; CAT 15.5 U/mg). In paddy, 20% MLB enhanced shoot and root length (66.0 and 15.13 cm), while 60% MLB under stress increased growth, carbohydrates, proteins, amino acids, phenols, and antioxidant enzymes (SOD/CAT 49.63/19.83 U/mg). Overall, MSR-1 offers a sustainable, effective biofertilizer option for managing soil salinity and iron deficiency.

Childbirth is a crucial experience that impacts women's lives, and the choice between vaginal delivery and cesarean section (C-section) is a critical decision in obstetrics. Maternal satisfaction is influenced by multiple factors since the childbirth experience is a composite of physical, emotional, and social components. Understanding patient evaluation is crucial for providing patient-centered care and improving maternity and neonatal care services. This systematic review aimed to compare patient satisfaction between vaginal and cesarean delivery patients and identify influencing factors. We performed a thorough search of databases for studies published between 2000 and 2024 on patient-reported satisfaction with vaginal delivery vs. C-section interventions. Eligible studies were assessed for methodological quality and relevance. The findings indicated that most women were satisfied with their delivery experience, with vaginal delivery leading to higher satisfaction than C-sections. Factors influencing satisfaction include maternal education, domicile, planned delivery care, healthcare professional gender, complications, partners' education, pain control measures, Apgar scores, and injuries. However, satisfaction levels were not significantly different across other maternal demographic factors or pregnancy-related characteristics. Certain features, such as planned pregnancy and excellent prenatal care, improved satisfaction with both vaginal and cesarean deliveries. The presence of a supporting companion during birth significantly boosted satisfaction levels, especially in primary care settings. Inadequate communication is associated with decreased maternal satisfaction. Therefore, healthcare practitioners should prioritize patient-centered care, good communication, and support. Targeted interventions are recommended, considering factors that influence the delivery of maternal and child care services.

Given the increasing demand for efficient technologies in heavy-oil recovery and upgrading, methods that can reduce viscosity and improve oil mobility are of considerable interest. Heavy oils are difficult to produce and process because of their high viscosity and density, as well as their elevated contents of asphaltenes, heteroatoms, and heavy metals. Although thermal and chemical recovery methods can reduce viscosity during production, the oil may regain viscosity after reaching the surface, often requiring diluents or additional upgrading steps. In this context, nanocatalyst-assisted in-situ upgrading has attracted attention as a route to simultaneously improve oil mobility and quality. In this study, a data-driven framework was developed to predict viscosity reduction during NiO nanoparticle-assisted in-situ upgrading. Predictive performance was evaluated using fivefold out-of-fold cross-validation (OOF-CV) to obtain a more reliable estimate of generalization. Conventional models (MLP, RBF, and ANFIS) were compared with modern tree-based ensembles, including Random Forest and Gradient Boosted Decision Trees (GBDT). GBDT delivered the best cross-validated performance, with a pooled OOF [Formula: see text] of 0.925 and a normalized RMSE of 0.1628, demonstrating superior predictive capability over the other examined models. For design-of-experiments (DOE)-based analysis of main effects and two-factor interactions, an MLP surrogate was retained to enable efficient response evaluation across the design space; therefore, DOE findings are interpreted as qualitative, surrogate-dependent trends rather than the most accurate pointwise predictions. Permutation-based importance analysis identified upgrading time as the most consistently influential variable, whereas the secondary driver depended on model family, with temperature contributing more strongly in tree-based ensembles and acidity/catalyst-related descriptors being more prominent in neural/surrogate models. Pressure showed a comparatively smaller contribution within the investigated range. Independent laboratory tests supported the overall predictive trends; however, deviations under some conditions indicate inter-source heterogeneity in the compiled literature data and limited representation of extreme operating regimes. Overall, the proposed framework can serve as a screening and decision-support tool for prioritizing operating conditions and guiding future experimental design in heavy-oil in-situ upgrading studies. The findings of this study can support faster screening of operating conditions, improve understanding of viscosity-reduction behavior, and assist the design of data-driven decision tools for catalytic upgrading of heavy crude oils.

The rapid spread of the competing weed L. arvensis poses a major threat to wheat production; therefore, modern risk assessment methods are necessary for its management. This study developed and compared machine learning models (Random Forest [RF], Boosted Regression Trees [BRT], and Maximum Entropy [MaxEnt]) to evaluate habitat suitability for L. arvensis, a dominant weed in the wheat cropping systems of Pakistan's semi-arid regions. For this purpose, weed data from 402 wheat fields, along with 20 environmental factors, including topography, climate, soil characteristics, anthropogenic factors, and proximity metrics, were analysed. Soil texture (silt and clay), soil chemistry (EC, OM, TDS), and rainfall patterns were identified through a partial least squares (PLS) algorithm as major factors affecting the species distribution. The ROC-AUC results showed that MaxEnt (AUC = 0.93) and RF (AUC = 0.92) performed slightly better than BRT (AUC = 0.86). All models identified the eastern and southeastern regions as the main areas of highly suitable habitat. Although these models are reliable, their predictions may be affected by changes in environmental factors in cropland. These results demonstrate that machine learning methods are effective for mapping weed distribution and provide a scientific foundation for sustainable weed management in these regions.

Live Newcastle disease (ND) vaccines face major limitations: spray vaccination causes adverse vaccinal reactions in day-old chicks, while in ovo vaccination results in high pathogenicity in embryos. We hypothesized that specific host responses contribute to the pathogenicity of live ND vaccines. In this study, lentogenic NDV La Sota vaccination in ovo significantly reduced the hatchability and post-hatch survival, accompanied by robust upregulation of inflammatory and cell death-related genes, notably the receptor-interacting serine/threonine-protein kinase 2 (RIPK2) in chicken lungs. Given the absence of RIPK3 in chickens and the structural homology of chicken RIPK2 to mammalian RIPK3 (sharing the conserved DFG motif), the RIPK3 inhibitor GSK840 targeting the DFG motif was tested as a potential modulator of La Sota pathogenicity. Co-administration of GSK840 with La Sota in ovo markedly improved the hatchability, post-hatch survival and antibody response. Furthermore, GSK840 pre-treatment at 18 days of embryonation prevented tracheal pathology, particularly the loss of cilium and goblet cells, and boosted antibody responses in day-old chicks receiving spray vaccination. Through biotin-conjugated GSK840 pull-down and mass spectrometry, multiple myosins (~200kDa) were identified as its potential targets in chicken trachea, rather than RIPK2. Collectively, our results demonstrate that GSK840 improves the safety and immunogenicity of La Sota in ovo and spray vaccination and that myosins are potential targets of GSK840. These findings present novel insights into the pathogenicity of live ND vaccines and provide important implications for optimization of live ND vaccination.

Delayed healing of post-operative anal fistula wounds is driven by a hostile inflammatory microenvironment. While autologous concentrated growth factors (ACGF) show regenerative potential, the underlying immunomolecular mechanisms remain unclear. This study investigates if ACGF accelerates contaminated wound healing by modulating macrophage polarization via the AKT/mTOR pathway. In vitro, RAW264.7 macrophages were treated with ACGF to assess phenotypic switching and AKT/mTOR activation. The paracrine effects of ACGF-primed macrophages on fibroblasts were evaluated using a co-culture system with AKT-siRNA validation. In vivo, a rat fecal-contaminated wound model was established. Therapeutic efficacy, with or without the AKT inhibitor Triciribine, was assessed via laser speckle imaging, histology, and immunofluorescence. ACGF effectively reprogrammed macrophages from a pro-inflammatory M1 phenotype toward a reparative M2 phenotype, significantly upregulating CD206 and Arg-1 expression while activating AKT/mTOR signaling. Mechanistically, ACGF-primed macrophages significantly boosted RSF proliferation, migration, and collagen synthesis, effects that were substantially abrogated by AKT silencing. In vivo, ACGF treatment markedly accelerated wound closure, enhanced microvascular perfusion, and increased the density of CD31 + and Ki67 + cells. These regenerative benefits were accompanied by a significant shift toward M2 infiltration and a reduction in pro-inflammatory cytokines, all of which were reversed by pharmacological inhibition of AKT. ACGF promotes the healing of contaminated wounds by orchestrating an AKT/mTOR-dependent macrophage M2 polarization. This shift initiates a beneficial paracrine relay that enhances fibroblast activity and neoangiogenesis, effectively restoring the regenerative niche. Our findings establish ACGF as a potent immunomodulatory biomaterial and offer a promising therapeutic strategy for complex perianal wounds and chronic recalcitrant defects.

Exchange rate forecasting is an important and well-studied problem in finance. However, most existing approaches rely only on historical price data and overlook the role of public sentiment, which can be a strong signal during periods of political and economic instability. This paper presents a hybrid forecasting framework for the USD/PKR currency pair that combines historical exchange rate time series with daily sentiment scores extracted from Urdu-language tweets. From January 2021 to January 2025, a total of 172,002 tweets were gathered from the X (formerly Twitter) platform with the help of 1,079 trending hashtags. As a result of pre-processing and language filtering, 45,048 tweets containing Urdu language were left for sentiment analysis and are contextually relevant. Four methods of sentiment annotation were implemented and compared: Gemini 1.5 Flash, a fine-tuned version of GPT-3.5 Turbo, GPT-4o, and XGBoost trained on FastText embeddings. The SI generated by each model was then matched with the exchange rate data of USD/PKR from the State Bank of Pakistan and fed to three models i.e. Long Short-Term Memory (LSTM), Xtreme Gradient Boosting (XGBoost) and two-stage hybrid LSTM+XGBoost. The hybrid model with GPT-4o based sentiment performed the best with Root Mean Squared Error (RMSE) of 0.0831 and Mean Absolute Percentage Error (MAPE) of 0.03%. The results are better than that of the LSTM baseline trained with historical data alone and similar studies related to the USD/PKR forecasting. The findings show that public opinion in the Urdu social media can be a valuable tool to predict the evolution of exchange rates and that hybrid architectures are more appropriate than standalone models to exploit public opinion in Urdu social media for the purpose of predicting the exchange rates.

Osteoarthritis (OA) is a common age-associated joint disorder driven not only by mechanical wear but also by progressive intracellular stress, metabolic imbalance, and chronic inflammation that culminate in cartilage degeneration and functional disability. Increasing evidence identifies mitochondrial dysfunction and endoplasmic reticulum stress (ERS) as central pathological hubs regulating chondrocyte survival, extracellular matrix (ECM) integrity, and inflammatory signaling. Mitochondrial impairment promotes excessive reactive oxygen species (ROS) generation, defective ATP production, disturbed mitochondrial dynamics, and inadequate mitophagy, collectively accelerating ECM catabolism and chondrocyte apoptosis. In parallel, ERS activates the unfolded protein response (UPR) to restore proteostasis through the PERK, IRE1α, and ATF6 pathways; however, sustained UPR activation shifts from adaptive signaling to maladaptive outcomes, amplifying inflammation, oxidative injury, and cell death in OA cartilage. Notably, emerging data highlight bidirectional crosstalk between mitochondria and ER, particularly via mitochondria-associated membranes (MAMs), as a key driver of Ca²⁺ dysregulation, inflammasome activation, and degenerative joint remodeling. Therapeutic strategies targeting these stress pathways including mitochondrial antioxidants, NAD⁺-boosting agents, mitophagy modulators, chemical chaperones, and selective UPR regulators have demonstrated potential to attenuate cartilage destruction and restore joint homeostasis. This review synthesizes current mechanistic insights into mitochondrial ERS signaling in OA and critically evaluates evolving disease-modifying interventions aimed at intracellular stress reprogramming. Finally, we discuss translational challenges and future directions for developing precision therapies that exploit organelle stress pathways to improve long-term joint health.

Liver cancer, a leading global cause of death, requires early and accurate diagnosis for better treatment outcomes and reduced mortality rates. This retrospective cohort study presents a machine learning-based predictive framework utilizing a combined panel of minimally invasive circulating biomarkers for early diagnosis and multi-grade prediction of liver cancer. This curated dataset is composed of urine/blood biomarkers like β-hCG, PD-L1 and Alpha-Fetoprotein. A hybrid and heterogeneous ensemble learning-based prediction technique has been proposed for the thorough analysis of collected data about the subject and associated biomarkers from highly diverse and broad age groups. The model's heterogeneity involved applying multiple distinct learning algorithms to the same training dataset, with the outcomes of each classifier used to expand the feature space and guide decision-making in subsequent stages. The proposed ensemble model is characterized by an iterative methodology incorporating both bagging and boosting techniques, ensuring adaptability and sustained enhancement, thereby surmounting limitations inherent in conventional static ensembles. The best hyperparameters using evolutionary algorithm achieved an accuracy of 93.33% and an F1-score of 94.29% on the test samples. The developed model accurately predicts liver cancer risk and grade, outperforming conventional methods. This AI model minimizes invasive procedures and healthcare costs, enhancing overall public health. The proposed minimally invasive biomarker-based framework may be particularly useful in resource-limited clinical settings where advanced imaging infrastructure and invasive diagnostic procedures are not readily accessible.

Clinical decision support systems (CDSS) have emerged as a vital means to boost decision-making capabilities of clinicians, but still exhibit low clinical adoption rates. Systematically exploring the evolution, status, topic trends and variations of CDSS is expected to guide the development of next-generation CDSS. This study investigated scientific publications pertaining to CDSS over a 30-year period. Publications about primary topic (clinical decision support system) and 24 sub-topics grouped into application purposes, technical features and medical resources in PubMed and IEEE from 1 January 1995 to 31 December 2024 were collected. A total of 34,100 publications across 4315 journals were identified, where the number of publications belonged to Q1, Q2, Q3, and Q4 journals was 9721, 5074, 2667 and 1328 respectively, and 15,310 publications were not indexed by Journal Citation Reports in the publication year. This study found that data processing techniques from 2007 to 2024 profoundly steered CDSS toward data centric paradigms, whereas its clinical applications were significantly neglected from thereon. Uncannily, with the rising attention to "knowledge graph", its two important knowledge resources, "medical knowledge" and "clinical guidelines", have simultaneously declined in attention. Similarly, although interpretability has gradually gained visibility, focus on clinical reasoning/thinking/decision (RTD) theory has paradoxically declined. Concerning data processing techniques has significantly upwardly diverged with declined focus on medical related concerning. Such a focus shift has reduced contemporary CDSS to a technical showcase of data manipulation, a trend that should be reversed to enhance subsequent clinical utility.

DNA fix needs BRCA1 and BRCA2. These genes stop bad cell growth. They sit on links 17 and 13. Bad changes in some genes raise the risk of womb cancer. This is true for a type called serous. These faults also boost the chance for womb and breast bad growth. BRCA1 faults lead to more severe womb tumors than BRCA2 faults. Drugs like tamoxifen increase this risk more. More breast trouble adds risk, too. The risk of womb cancer in a lifetime is still small - not quite 3% for BRCA1 folk and just 1 or 2% for BRCA2 folk. Yet, BRCA1 faults might bring worse sorts of womb growth. These have bad cell traits and weak fix tools. Now, rules do not say all BRCA folk must have their wombs removed or get womb checks. They say to talk hard about risks. Think of drug use, weight, and past breast issues. New tools look at many cell parts at once. This helps grasp how BRCA links to bad growth. It lets us pick just the right cures, like PARP drugs. But past studies differ. We need wide, long studies with many groups of folks. This will show the real womb cancer risk. So, BRCA faults do play a small but key part in a bad womb growth type. But they mainly boost breast and egg trouble.

Many medicinal plants produce secondary metabolites, which are main sources of drugs and therapeutic products. Besides the complex biosynthetic pathways and their regulation by multiple genes, environmental factors like light play crucial roles in their accumulation, thus adding a layer of complexity. With an upsurge in market demand for secondary products throughout the world, a paradigm shift is observed towards the deployment of in vitro technologies for large-scale production of low-volume, and high-value secondary metabolites. Elicitation of plant cells is an alternative and viable strategy to augment secondary metabolites used in therapeutics. Different sources of light like fluorescent light, light-emitting diodes (LED), coloured LEDs, ultra violet (UV-A, B, C) light, a combination of lights, and temperature perceived by multisensory photoreceptors orchestrate in improving targeted secondary metabolites. This review provides an overview of the role of signaling pathways in secondary product biosynthesis and the need to explore widely light combinations as a vital source and future line of research for tailoring artificial light sources to boost the accumulation of secondary metabolites.

As one of the key technologies in image processing, multi-threshold image segmentation has been widely applied in various image analysis tasks. However, how to improve computational efficiency while ensuring segmentation accuracy remains a current research challenge. To address this, this paper proposes a hybrid strategy-based Improved Dung Beetle Optimization (IDBO) algorithm and applies it to Kapur multi-threshold image segmentation, then hybrid strategy Improved Dung Beetle Optimization algorithm based 2D Kapur entropy image Segmentation (IDBOKS) method is obtained. The proposed algorithm first initializes the population with the SPM-DE mechanism, which integrates the SPM sequence and differential evolution (DE) strategy to ensure a uniform distribution of initial solutions in the search space and enhance population diversity. Second, a rolling dung beetle group information sharing strategy is introduced, a dynamic scaling factor is employed for simulating dung beetle cooperative behavior to improve information exchange among individuals and boost search efficiency. Finally, a global quadratic interpolation mechanism is adopted to optimize the position update process to further enhance the algorithm's ability to escape local optima and accelerate convergence speed. To validate the effectiveness of the proposed algorithm, FSIM, SSIM, and PSNR are selected as evaluation metrics, and comparative experiments are conducted against several recent swarm intelligence-based image segmentation algorithms. The experimental results show that the proposed IDBOKS demonstrates superior segmentation performance and stronger robustness when dealing with complex images.

This study develops an integrated machine learning-experimental framework to predict the compressive strength (CS) of concrete incorporating ternary industrial wastes glass powder, marble powder, and iron ore slag. For this purpose, a dataset comprising 366 mix ratios and corresponding CS values was compiled from various sources for analysis. Advanced machine learning (ML) algorithms, including extreme gradient boosting (XGB), gradient boosting, and random forest (RF), were employed alongside hybrid techniques such as XGB-GBR and XGB-RF to evaluate the influence of these materials on strength. Based on the outcomes of the analysis, the hybrid XGB-GBR model demonstrates the highest balanced performance for both training (R2 = 0.911) and testing (R2 = 0.869) data sets. For validating the ML modeling and developing an interactive graphical user interface (GUI), experimental evaluation of CS and scanning electron microscopy was conducted. Additionally, feature importance modeling and optimization identified curing age and coarse aggregate as the most influential factors that would impact the model prediction. The contribution of this research lies in the combined modeling and experimental evaluation of a ternary waste concrete system, along with the development of a GUI. This deployable GUI will enhance the industrial applicability of ML-based concrete optimization by reducing material costs, minimizing trial batching, and supporting sustainable mix design practices.

We aimed to develop and validate machine-learning models to predict antenatal care (ANC) dropout and describe maternal and neonatal outcomes by ANC dropout status in a low-resource setting in Northern Sidama, Ethiopia. Retrospective cohort study. Health facilities across four districts in Northern Sidama, Ethiopia. 3855 pregnant women aged 15-49 years who initiated ANC between January 2021 and January 2025 were included, whereas women with incomplete records or those who relocated before delivery were excluded. The primary outcome was ANC dropout, defined as attending <8 recommended ANC visits. Secondary outcomes included maternal and delivery-related outcomes. Among 3855 pregnant women, 71.4% (n = 2,753) did not complete the recommended eight ANC visits. The extreme gradient boosting (XGBoost) model achieved the strongest predictive performance, with an accuracy of 87%, area under the receiver operating characteristic curve of 0.91, sensitivity of 85% and specificity of 88%. The model identified late ANC initiation (>16 weeks), rural residence, low maternal education, distance to health facilities (>5 km), multiparity and lack of reliable transportation as the main contributors to predicted ANC dropout. Women who did not complete ANC showed higher proportions of maternal complications, including pre-eclampsia (12%) and antepartum haemorrhage (8%), and were less likely to deliver in health facilities (41% vs. 87%). Neonatal adverse outcomes, such as low birth weight and need for resuscitation, were also more frequent among women who did not complete ANC. All outcome comparisons are descriptive and do not imply causal relationships. Machine-learning models, particularly XGBoost, effectively predicted ANC dropout and identified women with a high predicted probability of dropout. We also described differences in maternal and neonatal outcomes by ANC dropout status. These findings support the potential use of predictive tools to guide early identification and targeted maternal health interventions in resource-limited settings.

To explore the application scenarios of maximum a posteriori Bayesian estimation (MAP-BE) and eXtreme Gradient Boosting (XGBoost) in the prediction of polymyxin B (PMB) exposure. Two sets of simulations based on the population pharmacokinetic (PopPK) model developed for PMB were used for the development and testing of Bayesian and XGBoost models in four scenarios. The predictive performances of MAP-BE and XGBoost for the area under the concentration-time curve (AUC) over 12-h intervals at steady state were evaluated using root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R 2). Both MAP-BE and XGBoost accurately estimate the AUC0-12h under a single sampling strategy using a dense point model (median [range] (mg&#xb7;h/L): 3.32 [0.78-7.39] vs. 3.30 [0.95-7.04] for RMSE, 2.41 [0.54-5.53] vs. 2.61 [0.72-5.46] for MAE for MAP-BE and XGBoost, respectively). A single 6-h sampling strategy achieved the best prediction with negligible bias (RMSE<1&#xa0;mg h/L, MAE<1&#xa0;mg h/L, R 2 > 0.99). XGBoost was more accurate and efficient than MAP-BE for fitting single-trough concentrations. The performance of the 12-h XGBoost model allowed for temporal fluctuations in the 6-h range. This study provides evidence for the application scenario of MAP-BE and XGBoost for predicting the AUC of PMB, which assists in selecting better approaches when predicting drug exposure with available therapeutic drug monitoring information to guide the adjustment of dosing regimens in the clinic.

&#x3b1;-L-Rhamnosidases are valuable biocatalysts for the synthesis of bioactive flavonoids, however, their industrial application is often restricted by limited catalytic efficiency and suboptimal thermal stability. This study reports the rational engineering of an &#x3b1;-L-rhamnosidase (DthRha) to address these limitations and enhance its performance for flavonoid production. A library of mutants was constructed via substrate-binding pocket modeling and site-directed mutagenesis. Among them, the R783A variant exhibited a 3.56-fold increase in enzymatic activity relative to the wild-type enzyme (WT), along with remarkable thermal stability, retaining over 92% of its initial activity after 2&#x202f;h incubation at 60-90&#x202f;&#xb0;C&#xff0c; whereas the WT rapidly lost activity above 60&#x202f;&#xb0;C. Kinetic assays revealed a 1.46-fold increase in kcat/Km over the WT, coupled with enhanced substrate specificity. Molecular docking and MD simulations suggested that the enhanced catalytic performance of R783A may arise from favorable steric conformation and a more accessible catalytic tunnel. The practical applicability of the R783A was demonstrated at a 50&#x202f;mL laboratory scale, affording prunin and isoquercitrin in >98% yield with space-time yields of 3.58 and 1.92&#x202f;g/L/h, respectively. These findings highlight the R783A mutant as a robust and thermally stable biocatalyst with great potential for the sustainable production of bioactive flavonoids.

Mass transfer limitations and inefficient nonradical oxidant generation often constrain the practical performance of Fenton-like catalysts for micropollutant removal. Here, we develop a synergistic architectural strategy to construct carbon-supported cobalt single-atom catalysts (Co-INC) via ammonium iodide-assisted chemical vapor deposition, integrating atomically dispersed Co-N4 sites with carbon-iodine (C-I) coordination motifs to simultaneously enhance reactant transport and singlet oxygen (1O2) production. Benefiting from in situ NH3-mediated etching, the architecture enhances active-site accessibility while achieving a high Co density of 0.165 mmol g-1, facilitating peroxymonosulfate (PMS) diffusion. Additionally, iodine incorporation creates an asymmetric Co-N4/C-I coordination environment that upshifts Co d-band center to -0.54 eV, strengthening PMS adsorption (-2.54 eV). This configuration promotes interfacial electron transfer and reduces the rate-determining energy barrier to -0.84 eV, substantially boosting 1O2 generation and achieving a steady-state concentration of 0.295 mM. Furthermore, the optimized pore architecture alleviates mass-transfer constraints, enabling efficient 1O2 utilization and a 2.3-fold increase in the ciprofloxacin mass-transfer coefficient. Consequently, Co-INC exhibits an exceptional normalized rate constant of 507 min-1&#xb7;M-1 for ciprofloxacin degradation, outperforming pristine Co-NC by 10.3 times and surpassing most PMS-based catalysts. This study demonstrates that coupling active-site coordination with mass-transfer enhancement is pivotal for maximizing nonradical oxidation pathways in water treatment.