Abstract In 2013 QoI-resistant isolates of the rice blast fungus (MoO) emerged suddenly and spread in Hyogo Prefecture. To estimate factors associated with their spread and subsequent survival, the population structure of MoO isolates was monitored from 2013 through 2023 using DNA fingerprinting with Pot2 . Most of the QoI-resistant isolates collected in 2013 were classified into haplotype HY1, irrespective of areas of collection in Hyogo Prefecture. This result suggests that the sudden emergence and spread of the QoI-resistant isolates in 2013 were caused by almost the single haplotype HY1 probably through seed transmission. After discontinuance of the QoI fungicide application in 2014, however, the QoI-resistant isolates decreased over the course of several years, suggesting that genetic alternations conferring the QoI resistance may have caused a decrease in fitness. Conversely, QoI-sensitive isolates increased after 2015, but predominant haplotypes changed across years of collection. We suggest that it could be possible to re-use the QoI-fungicide if the disappearance of the resistant strains is confirmed by careful monitoring of QoI resistance throughout the prefecture, especially in and around fields for seed production.

Abstract Objectives This study aimed to investigate ethnic differences in the trends of diabetes prevalence and self-management behaviors between the Han majority and the Na Xi ethnic minority in rural southwestern China. Design Data were collected from two waves of cross-sectional health interviews and examination surveys administered 10 years apart among individuals aged ≥ 35 years in rural China. Fasting blood sugar levels were recorded for each participant. Results From 2013 to 2023, the prevalence and treatment rate of diabetes increased significantly from 6.1% and 75.3% to 8.8% and 96.5%, respectively, among Han majority participants from 3.3% and 67.2% to 12.5% and 94.1%, respectively, among Na Xi ethnic minority participants (P < 0.01), with the greatest relative increase occurring among Na Xi ethnic minority participants. Although the overall awareness and control rates of diabetes did not differ between 2013 and 2023 for both nationalities (P > 0.05), a significant increasing trend in the control rate was observed among diabetic patients with poor access to medical services for both nationalities (P < 0.01). The overall rate of compliance with antidiabetic drugs or insulin injection during the 12 months prior to the survey increased significantly in both the Han majority (71.9% vs. 97.9%) and the Na Xi ethnic minority (67.2% vs. 95.4%, P < 0.01), whereas the increasing rate of having taken measures to control diabetes during the two weeks prior to the survey and regular blood glucose self-monitoring in the prior 12 months was found only among Na Xi ethnic minority participants (P < 0.01). Conclusions The prevalence and treatment of diabetes have increased substantially, and significant changes in diabetes self-management behaviors have occurred in rural southwestern China over the ten years studied, with striking ethnic differences in temporal trends for the observed rates. Our findings highlight the importance of addressing ethnicity in future interventions to further control diabetes and improve self-management behaviors among diabetic patients.

Abstract The swift development of Large Language Models (LLMs) is changing software engineering models, especially those that need to process many rules, as insurance policy management systems do. The current paper proposes an original LLM-based test optimization model designed to fit the Insurance Management Policy Engines, which can resolve the long-standing issues of high quality, effective and scalable testing. The insurance systems that use traditional test strategies tend to encounter problems of an exponential increase in the number of the combinations of policy rules which result in overlapping test cases and poor risk coverage. We introduce an AI-assisted style of testing, based on the idea of using generative LLMs to reason about policy requirements, test artifacts, and pattern-based system behaviour in order to use the reasoning to formulate an optimized test suite. Through the application of risk-based testing concepts and adaptive prioritization, the framework is dynamic in the way it finds high impact test scenarios, minimizes test redundancy, and improves automation in regression cycles. Our solution works with the current test automation systems, and allows flawless coordination of test generation, execution and assessment. The experimental outcomes of industry-grade insurance engines show that the detection rates of defects have become much better, the testing effort has decreased and the release cycles have been significantly faster. This study gives prominence to the potential of LLMs to revolutionize the quality engineering process in mission-critical insurance platforms, which creates the AI testing paradigm balancing comprehensive coverage with operational effectiveness.

Abstract Objective : This study aimed to establish and validate a machine learning based prognostic model to predict the prognostic risk of childhood Wilms tumor. Methods : Data of 1958 children with Wilms tumor (data from 2000-2022) were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. The data were divided into a training set (70%) and a validation set (30%). Prognostic factors were analyzed by Cox regression. Five machine learning algorithms were used to construct models, which were evaluated by C-index, receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA). Meanwhile, a nomogram was drawn to visualize the model. Results : The results of the multivariate Cox regression analysis showed that age, lymph node density (LND), tumor stage, and place of residence were independent risk factors related to the prognosis. Among the fitted machine learning models, the LASSO-Cox model demonstrated relatively stable and accurate predictive performance. The C-index of the training set was 0.755 (95% CI: 0.704-0.806), and the C-index of the validation set was 0.752 (95% CI: 0.660-0.844). Conclusion : This study has identified the key prognostic indicators for childhood Wilms tumor, which can assist surgeons in accurately identifying the high-risk population with poor prognosis of Wilms tumor.

Abstract Purpose Lung cancer remains the leading cause of cancer-related death worldwide, underscoring the need for reliable biomarkers for early detection. This study evaluated the promoter methylation and expression of MAX, MTURN, HLA-B, and CAV1 in tumor and tumor-free tissues and validated their methylation status in plasma cfDNA from patients with NSCLC and SCLC. It further assessed the potential of this multi-gene methylation panel as a non-invasive surrogate biomarker for early lung cancer detection and improved therapeutic outcomes. Methods In this study, we profiled promoter methylation in LC tissues and evaluated matched plasma-derived circulating cell-free DNA (cfDNA) as a “tissue-mirror” biomarker. mRNA expression of MAX, MTURN, HLA-B and CAV1 was quantified in tumor tissue (TT) from non-small cell (NSCLC) and small cell (SCLC) lung cancers versus tumor-free tissue (TF), and promoter methylation in tissue gDNA was assessed by methylation-specific PCR (MSP). The same loci were interrogated in plasma cfDNA from NSCLC and SCLC cases relative to healthy controls. Results In TT, methylation frequencies for MAX, MTURN and HLA-B were significantly elevated 68.45%, 56.94%, and 75.97%—compared with TF (20.42%, 15.28%, and 29.57%; p < 0.05 to p < 0.001). Correspondingly, TT showed down-regulation of gene expression (p < 0.01) with strong inverse associations to hypermethylation; CAV1 was the exception, demonstrating up-regulation with hypomethylation in NSCLC TT. Plasma cfDNA recapitulated tissue patterns, exhibiting significant hypermethylation in LC versus controls (p ≤ 0.05 to p ≤ 0.001) and yielding positive predictive values of 59.33–77.47% and negative predictive values of 52.47–67.33%. Conclusion Collectively, these findings indicate that promoter methylation of this four-gene panel in plasma can aid LC diagnosis and functions as a minimally invasive, tissue-reflective biomarker.

Abstract In essence, IoT technology has been developed at a rapid rate and we can now create smart monitoring systems in complicated locations such as oceans, with the use of identical connected gadgets. According to the environmental protection requirements, the marine ecosystems need constant observation in terms of climate analysis, disasters forecasting, and marine resources management. Historical methods of monitoring the ocean actually involve the use of ships and satellites, although they certainly lack in capability to cover extensive ranges and retrieve information within a short period. According to these issues, the paper will propose an Internet of Things wireless system in relation to smart ocean monitoring. According to the suggested system, underwater sensors, wireless communication, edge computing, and cloud platforms are incorporated in as far as real-time monitoring of the marine environment is concerned. It is only a system design with the various layers, which are sensor layer, communication layer, edge processing layer, and cloud intelligence layer stacked over each other. According to the research requirements, mathematical models are created on sensor data gathering, movement of underwater signals, energy consumption by the network, communication delay and reliability of the system. Moreover, an intelligent ocean data collection algorithm is suggested to be able to make the data transmission to be more efficient and to minimise on the communication overhead as such. Moreover, the outcomes of the simulation reveal that the proposed architecture enhances further network throughput, ratio of packet delivery, and conserves energy as compared to the conventional underwater monitoring systems in itself. In essence, this platform provides the same scalable and dependable configuration that the next-generation smart ocean monitoring applications require.

Abstract The study aims to explore the complex relationship between Corporate Social Responsibility (CSR), Innovation, and Firm Value within the context of an emerging market, specifically Vietnam. The empirical analysis uses a panel dataset of 128 Vietnamese listed firms from 2016 to 2023. The study employs Partial Least Squares Structural Equation Modeling (PLS-SEM) with a fixed-effects transformation to analyze the direct and indirect effects of CSR on firm value. The results show that the relationship between CSR and Firm value is U-shaped. Besides, the innovation is found to be a significant mediator in this process, and a positive moderator that amplifies the financial returns of CSR activities, particularly in firms with high innovative capabilities. The study contributes to the literature by providing a more nuanced understanding of the CSR-Firm value nexus, especially by exploring the U-shaped relationship and the dual roles of innovation (mediator and moderator) in an emerging market context. It offers valuable insights for managers and policymakers on how to strategically integrate CSR and innovation to enhance firm value.

Abstract Objective To compare recurrence patterns and identify risk factors after nonoperative management of acute appendicitis (AA) and periappendiceal abscess (PAA) in children. Methods In this retrospective cohort study, children (< 18 years) with AA or PAA (defined by imaging) and treated nonoperatively (Jan 2020–Jul 2024) were included. The primary outcome was clinical recurrence. Risk factors were identified using multivariable logistic regression and Kaplan–Meier analysis. Results Among 409 patients (median follow-up 38.8 months), recurrence was higher in the PAA group than in the AA group (26.5% vs. 14.9%), with earlier recurrence (HR 2.09, 95% CI 1.34–3.26; p < 0.001). In the PAA group, the presence of an appendicolith (adjusted odds ratio [aOR] 2.98, 95% CI 1.19–7.84) and a higher abscess-to-weight ratio at admission (aOR 1.12 per unit, 95% CI 1.01–1.26) were independent risk factors for recurrence, whereas a longer length of hospital stay was protective (aOR 0.89 per day, 95% CI 0.80–0.97). In the AA group, a negative pretreatment ultrasound finding was protective (OR 0.21, 95% CI 0.04–0.97). Surgical pathology of recurrent cases showed a predominance of gangrenous appendicitis in the PAA group versus suppurative appendicitis in the AA group (p = 0.032). Conclusion Following nonoperative management, PAA is associated with a high risk of early recurrence, driven by appendicolith and greater infectious burden, which justifies enhanced early surveillance and consideration of interval appendectomy. Recurrence risk in AA is lower and more protracted, particularly when ultrasound is negative. These findings support a risk-stratified approach to management.

Abstract Reproductive genetic carrier screening (RGCS) for cystic fibrosis, spinal muscular atrophy and fragile X syndrome was added to Australia’s Medicare Benefits Scheme in November 2023. We analysed national uptake of publicly funded RGCS and compared testing volumes with birth numbers from November 2023 to August 2025. During this period, 206,321 patient tests and 7,554 partner tests were performed. In 2024, 112,644 patient tests were billed, equivalent to 38.5% of registered births. Partner testing accounted for 3.7% of patient tests, consistent with expected carrier frequencies for autosomal recessive conditions. These findings demonstrate rapid and sustained uptake of publicly funded three-gene RGCS.

Abstract Background Sepsis-induced myocardial injury (SIMI) is a frequent and life-threatening complication among critically ill patients with sepsis. While lactate is a classic marker of tissue hypoperfusion and metabolic stress, haptoglobin (Hp) serves as a vital scavenger of toxic cell-free hemoglobin, reflecting the host's antioxidative defense capacity. Consequently, the haptoglobin-to-lactate ratio (HLR) may represent a novel composite index capturing the balance between physiological reserve and pathological insult. However, the prognostic value of HLR has not been systematically evaluated in the high-risk population of patients with SIMI.This study aimed to examine the association between the HLR and both short- and long-term all-cause mortality in patients with SIMI, and to assess its prognostic value in this high-risk population. Methods This retrospective cohort study was conducted using the Medical Information Mart for Intensive Care IV (MIMIC-IV, version 3.1) database. Adult patients who met Sepsis-3 criteria and were admitted to the intensive care unit (ICU) were included. SIMI was defined as an elevated serum troponin T (> 0.01 ng/mL) within the first 24 hours of ICU admission in the absence of acute coronary syndrome or other non-septic causes. The HLR was calculated as Hp (mg/dL) divided by serum lactate (mmol/L) on the first ICU day and analyzed both as a continuous variable and in quartiles. Baseline characteristics were compared across HLR quartiles. Survival differences were estimated using Kaplan–Meier curves. Cox proportional hazards regression models were applied to assess the independent association between HLR and 28-day and 90-day all-cause mortality. Non-linear relationships were explored using restricted cubic splines (RCS). Subgroup analyses were performed to test the consistency of associations across clinical strata. Results A total of 609 patients with SIMI were included. Patients in higher HLR quartiles had lower SOFA scores and significantly lower lactate levels compared with those in lower quartiles. Kaplan–Meier analysis demonstrated significantly improved survival with increasing HLR quartiles (log-rank P < 0.001). In multivariable Cox regression models, a higher HLR was independently associated with reduced 28-day all-cause mortality (Q4 vs. Q1: adjusted hazard ratio [HR] 0.63, 95% confidence interval [CI] 0.43–0.95, P = 0.025) and 90-day all-cause mortality (Q4 vs. Q1: adjusted HR 0.67, 95% CI 0.46–0.96, P = 0.027). RCS analysis revealed an L-shaped non-linear relationship between HLR and mortality risk, with optimal breakpoints identified at 7.864 for 28-day mortality and 2.125 for 90-day mortality. Subgroup analysis indicated that the prognostic value of HLR was particularly pronounced in elderly patients aged ≥ 65 years ( P for interaction = 0.035). Conclusions The HLR is a novel and reliable biomarker for predicting short- and long-term mortality in septic patients with myocardial injury. This simple, inflammation–metabolism–based index may serve as a convenient tool for risk stratification and prognostic assessment in critical care settings.