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PrivChain-AI leveraging blockchain and federated learning for private financial reporting and access control
Abstract Financial institutions are currently faced with suffering never experienced before as they strive to guarantee the privacy of data and address the demands of regulation to report and cooperate in machine learning. This paper proposes PrivChain-AI, a novel blockchain-based federated learning system designed to facilitate secure and privacy-preserving financial reporting and access control. The proposed framework will integrate three key components: differential privacy, homomorphic encryption, and smart contract-based governance, enabling cooperative model training across financial institutions while preventing the leakage of sensitive information. PrivChain-AI is a hierarchical design that incorporates permissioned consensus protocols and utilises zero-knowledge proof verification to authenticate transactions. It has been demonstrated that the performance is higher than that of the actual financial data, with an outcome of 94.7% accuracy in fraud recognition at the cost of e-differentiation privacy, where ϵ = 1.0. It is 40% faster in terms of communication overhead and ensures regulatory compliance, as it features immutable audit trails. The analysis of performances reveals that a privacy preservation metric improves by 78%, and access control granularity is improved by 62% compared to the current state-of-the-art approaches. The PrivChain-AI paradigm introduced provides a new analytical model for safe, collaborative finance, meeting the highest standards and ensuring compliance with relevant regulatory jurisdictions.
Analysis of risk factors for postoperative complications in hypospadias and construction and validation of a nomogram predictive model
Background The incidence of postoperative complications in children with hypospadias is notably high; however, research on predictive models for these complications remains limited. This study aims to analyze the factors associated with postoperative complications following hypospadias surgery and to develop a nomogram predictive model for such complications. Methods This study included 553 hypospadias patients who underwent surgery at Zunyi Medical University Affiliated Hospital from 1/1/2016–1/1/2023.The patients were randomly divided into training (n = 389) and validation (n = 164) cohorts in a 7:3 ratio. Univariate and multivariate logistic regression analyses were performed on the training cohort to identify risk factors for postoperative complications, which were then used to develop a nomogram prediction model. Finally, the internal validation cohort was used to assess the model’s prognostic accuracy and clinical utility. All statistical analyses were performed using R software(Version 4.2.2). Results Multivariate logistic regression analysis of the training cohort identified six independent risk factors for postoperative complications in hypospadias surgery: age (OR=1.02, 95% CI: 1.01–1.03, P < 0.001), surgeon’s experience (OR=0.44, 95% CI: 0.26–0.75, P = 0.0026), glans width (OR=0.70, 95% CI: 0.59–0.82, P < 0.001), length of reconstructed urethra (OR=1.03, 95% CI: 1.01–1.06, P = 0.003), hypospadias classification (OR=4.26, 95% CI: 1.96–9.26, P < 0.001), and urethral stent retention time (OR=0.25, 95% CI: 0.12–0.52, P < 0.001). Based on these factors, a nomogram was constructed. The area under the curve of the nomogram model was 0.800, and after internal validation it was 0.821, indicating good discriminative ability. Furthermore, the model exhibited excellent calibration and high clinical utility. Conclusion This study developed and validated a nomogram prediction model for postoperative complications in hypospadias surgery based on six factors: age, surgeon’s experience, glans width, length of reconstructed urethra, hypospadias classification, and urethral stent retention time. It provides a scientific basis for implementing personalized medicine in hypospadias patients in the future.
Prevalence and experiences of exclusive breastfeeding among working mothers at a tertiary hospital in Eastern Uganda
Abstract Exclusive breastfeeding (EBF) is vital for infant health, yet working mothers often struggle to maintain it. In Uganda, particularly at Mbale Regional Referral Hospital (MRRH), evidence on EBF prevalence and the experiences of employed mothers remains limited. This study examined the prevalence of EBF and explored the experiences of working mothers seeking care at MRRH. A cross-sectional mixed-methods design was used. Quantitatively, 221 working mothers with infants aged six months and below completed structured questionnaires. Qualitatively, 10 purposively selected mothers participated in in-depth interviews to provide detailed insights into their EBF experiences. Descriptive statistics summarized quantitative data, while thematic analysis identified patterns within qualitative narratives. EBF prevalence was 68.3%. Four themes emerged: Diverse breastfeeding practices, variable knowledge of EBF, and supportive factors and challenges/barriers to EBF. Mothers described adapting feeding schedules and responding to infant cues to sustain EBF. Knowledge varied, with some emphasizing nutritional and bonding benefits. Support from family members and proximity to the infant facilitated EBF, while work demands, short maternity leave, and inadequate breastfeeding-friendly facilities were major challenges. EBF among working mothers remains sub-optimal. Strengthened workplace policies and broader support systems are essential to enhance EBF continuity among employed women.
Impact of Metformin therapy on miR-9, miR-223, and miR-132 and inflammasome-related gen expression in obese and non-obes PCOS patients: A comparative study with healthy controls
Introduction Polycystic Ovary Syndrome (PCOS) is a common endocrine and metabolic disorder characterized by chronic inflammation, insulin resistance, and hormonal imbalances, often leading to infertility and metabolic dysfunction. Metformin, an insulin-sensitizing agent, has shown potential to improve these conditions. This study investigated the impact of metformin on inflammasome-regulating microRNAs ( miR-9, miR-223, miR-132 ) and related genes ( IL-1β, IL-18, caspase-1, NLRP3 ) in obese and non-obese PCOS patients compared to healthy controls. Materials and methods In this case-control study, 100 women aged 18–35 were divided into 50 PCOS patients and 50 controls, stratified by BMI (>25 kg/m² and <25 kg/m²). Blood samples were analyzed pre- and post-12 weeks of metformin treatment (500 mg twice daily) for serum hormone levels (FSH, LH, TSH) by ELISA kit, miRNA and mRNA expression by qPCR, and follicle count by transvaginal ultrasound were evaluated. Results The results demonstrated a significantly lower expression of miR-9 in PCOS patients (BMI >25 kg/m²) compared to healthy controls (mean ± SD: 0.54 ± 0.07 vs. 1.00 ± 0.11; P < 0.001). Following metformin treatment, miR-223 expression was significantly upregulated (from 0.88 ± 0.06 to 1.21 ± 0.08; P = 0.002). Similarly, the expression levels of IL-1β (2.01 ± 0.31 vs. 1.31 ± 0.23) and NLRP3 (2.12 ± 0.27 vs. 1.38 ± 0.22) decreased significantly post-treatment (P < 0.01). No significant change was observed in miR-132 expression. Overall, metformin modulated the expression profiles of inflammasome-related genes and miRNAs, particularly in obese patients with PCOS. Conclusion The findings suggest that metformin modulates inflammation in PCOS by altering microRNA and inflammasome-related gene expression, thereby reducing inflammatory markers such as IL-1β and miR-9, while enhancing miR-132 and miR-223, which may contribute to improved metabolic and inflammatory profiles. These results support the use of metformin as a BMI-tailored therapeutic strategy for PCOS, warranting further research to confirm its long-term effects and mechanisms.
Biocidal, antioxidant, and anti-inflammatory activities of essential oils extracted from Angelica spp. cultivated on contaminated soils
The Nurses’ Innovative Behavior Inventory (NIBI): A development and validation study
Introduction Innovative behavior is essential in nurses, driving continuous improvement and operational efficiency, significantly enhancing patient outcomes and overall healthcare quality. This study aims to evaluate the psychometric properties of a specific, theory-driven inventory for measuring nurses’ innovative behavior in Iran. Materials and methods A methodological study was conducted from November 2022 to April 2024. The conceptualization phase involved a qualitative study and a comprehensive literature review to define the concept of nurses’ innovative behavior and identify its key dimensions. The subsequent psychometric evaluation assessed face validity, content validity, construct validity, and structural validity (using exploratory and confirmatory factor analyses) on a sample of 572 clinical nurses. Reliability was evaluated through internal consistency and the test-retest methods. Additionally, responsiveness and interpretability were examined according to the COSMIN checklist. Results The construct validity of a five-factor structure (nurses’ competencies, idea validation, clinical idea implementation, promoting innovation, and generating care ideas), identified during the conceptualization phase, was confirmed. Confirmatory factor analysis yielded a χ2/df ratio of 1.88 for the NIBI five-factor structure. The goodness of fit indices indicated suitable values (CFI = 0.916, AGFI = 0.817, IFI = 0.917, PCFI = 0.709, and RMSEA = 0.057), with all factor loadings greater than 0.5 and statistically significant. Both convergent and divergent validities were demonstrated. The Cronbach’s alpha and omega coefficients ranged from 0.74 to 0.88 and 0.75 to 0.88, respectively. Additionally, the ICC for the entire inventory was 0.975 (CI 0.95–0.98, P < 0.001). Conclusion The findings revealed that the Nurses’ Innovative Behavior Inventory (NIBI) is both valid and reliable, making it a suitable tool for assessing and evaluating innovative behavior in nurses.
The efficacy of adjuvant chemotherapy after total mesorectal excision without selective lateral lymph node dissection for locally advanced rectal cancer
Visual prediction of outcomes in patients undergoing intravenous thrombolysis
Background This research presents a novel visual predictive model aimed at the early identification of patients at elevated risk of poor prognosis following intravenous thrombolysis, assessed six months post-acute ischemic stroke. Methods A retrospective cohort of patients who underwent intravenous thrombolysis at advanced stroke centers was analyzed. The latest Least Absolute Shrinkage and Selection Operator (LASSO) regression technique was employed to select relevant variables and develop nomograms. The model’s performance was evaluated through receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis, culminating in an assessment of the model’s reliability. Results We identified five principal predictors that are significantly associated with a 6-month adverse prognosis in patients undergoing intravenous thrombolysis. These predictors include door-to-needle time (DNT), homocysteine (HCY) levels, lactate dehydrogenase (LDH) levels, the post-thrombolysis National Institutes of Health Stroke Scale (NIHSS) score (P-NIHSS), and the monocyte to high-density lipoprotein cholesterol (MHR) ratio. The nomogram’s AUC-ROC was 0.914 (95% CI: 0.899–0.939) for the training cohort and 0.892 (95% CI: 0.852–0.932) for the validation cohort. Conclusion This straightforward visual prediction model effectively identifies factors linked to poor prognosis 6 months post-intravenous thrombolytic therapy for acute ischemic stroke, aiding early treatment and resource allocation.
The transcriptomic response of Staphylococcus equorum KS1030 to Lincomycin stress reveals transporters associated with horizontal gene transfer
Retraction: Choosing the most appropriate minimally invasive approach to treat gynecologic cancers in the context of an enhanced recovery program: Insights from a comprehensive cancer center
Automated project scheduling from UML sequence diagrams using OCR and critical path analysis
The effect of the modifiable areal unit problem on ecological model inference: A graphical simulation study for disease mapping in Australia
Statistical disease mapping is a valuable public health tool, as it identifies spatial patterns of disease occurrence. However, the Modifiable Areal Unit Problem (MAUP) poses challenges to disease mapping, as the aggregation of geographic units can impact statistical inferences. The effect of the MAUP depends on contextual factors, for example the geographic structure, aggregation level, choice of model, and the underlying data-generating process. We conducted a comprehensive simulation study to understand the role of these factors on the MAUP in the context of Australian disease mapping. We aggregated and rezoned disease count data at a fine geographic scale before fitting spatial and non-spatial regression models to assess the impact of the MAUP on coefficients. To aid the exploration of simulation results, we developed an interactive Shiny application that enables detailed and interactive exploration of the simulation results. This study highlights the need for disease mapping researchers to analyse sensitivity with rezoning and aggregation tools.
Stochastic growth marks in Crocodylus niloticus
HydroPredictor a hybrid machine learning model for addressing data scarcity in groundwater prediction
Mapping evolving care needs in older adults with disabilities: a qualitative study
Evolving trends in dupilumab use for chronic rhinosinusitis with nasal polyps (CRSwNP): a 4-year cohort analysis
Abstract Chronic rhinosinusitis with nasal polyps (CRSwNP) affects up to 4.5% of the population and is frequently driven by type 2 inflammation. Dupilumab, targeting IL‑4 and IL‑13 pathways, was approved in 2019 as the first biologic for CRSwNP in Germany. This single-center, retrospective study analyzed 174 patients initiating dupilumab between 2019 and 2023. Patients were grouped by year and assessed for trends in demographics, surgical history, and treatment response. Only adherent patients with ≥ 6 months of follow-up were included. Age and baseline disease severity were stable across cohorts. However, the proportion of patients with > 3 prior sinus surgeries declined from 28.3% in 2019/20 to 13.0% in 2023, suggesting earlier biologic initiation over time. All patients met at least three EPOS 2020 criteria. Significant and clinically meaningful improvements were observed in all cohorts for Nasal Polyp Score (NPS), SNOT‑22, VAS, and olfactory testing, with changes exceeding established minimal clinically important differences (MCIDs). No cohort showed superior outcomes. Tese findings highlight consistent real-world efficacy of dupilumab and evolving prescribing patterns that favor earlier intervention, while reflecting continued cautious patient selection in the context of high treatment costs.