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Deep intronic MSH2 variant confirms Muir-Torre subtype of Lynch syndrome
No full textWhole-genome sequencing can uncover clinically significant noncoding variants missed by standard germline testing, as demonstrated in this report in a patient with Muir–Torre syndrome, a subtype of Lynch syndrome. In this case, despite a convincing clinical phenotype and immunohistochemical loss of MSH2/MSH6 in 1 of the patient’s tumors, conventional gene panel testing failed to detect a germline pathogenic variant. Whole-genome sequencing identified a deep intronic MSH2 variant, and tumor sequencing revealed somatic MSH2 mutations (second hits) across multiple tumors, confirming mismatch repair deficiency and establishing a Muir-Torre syndrome diagnosis. This report underscores the limitations of routine genetic testing and highlights the clinical utility of whole-genome sequencing in identifying pathogenic variants in noncoding regions. It also emphasizes the role of dermatologists in recognizing cutaneous markers of hereditary cancer syndromes and the importance of interdisciplinary evaluation in guiding both patient care and familial risk assessment
Exploring the interaction between klotho and TyG index in cardiovascular risk stratification: A metabolic-inflammatory network analysis with mediation and machine learning insights
No full textBackground: This study explores the relationship of metabolic-inflammatory network and cardiovascular disease (CVD), offering new insights into the roles of Klotho and the Triglyceride-Glucose (TyG) index in CVD pathogenesis. Methods: Data from 5402 adults (mean age: 58.04 ± 10.83 years; 50.96 % female) from the NHANES in 2007–2016 database were analyzed. We proposed a prediction model for CVD risk incorporating Klotho protein, TyG index, and their interaction. The predictive value of these factors was evaluated using machine learning techniques, including random forest analysis and CHAID decision tree modeling. Results: The study found no association between serum alpha-Klotho levels and CVD risk. However, the TyG index was demonstrated to be a significant predictor of CVD risk, particularly when lifestyle and socio-economic factors were not accounted for. TyG values were associated with an increased risk of metabolic syndrome and CVD (Model 1 OR: 1.234; Model 2 OR: 1.268). There was a significant interaction between Klotho-TyG was observed (coefficient − 2.608 × 106). In addition, the random forest model achieved an accuracy of 66.63 % with high specificity and precision, and in the CHAID model an error of 27 %. Conclusions: This study underscores the TyG index as a key biomarker for CVD risk, with the Klotho-TyG interaction improving risk stratification, and supporting early screening, treatment, and personalized interventions for more effective CVD management
Acceptance of Lung Cancer Screening and Associated Factors in Hong Kong: A Population‐Based Study
No full textABSTRACT Introduction Low‐dose computed tomography (LDCT) enables early detection of lung cancer and reduces mortality, yet public willingness to undergo screening remains suboptimal. This study aimed to assess willingness and its associated factors among high‐risk individuals in Hong Kong. Methods A territory‐wide cross‐sectional survey was conducted among adults aged 54 years or above, and those aged 45–54 years with at least one lung cancer risk factor (e.g., smoking, secondhand smoke exposure, or family history) in Hong Kong. Data were collected via self‐administered questionnaires, which included socio‐demographic information, risk exposure, awareness and experience of LDCT, and constructs from the Health Belief Model (HBM). Logistic regression was performed to identify factors associated with willingness to undergo LDCT screening. Results A total of 1100 participants were included in the analysis. Among them, 57.3% expressed willingness to undergo LDCT within the next year. Multivariable logistic regression showed that higher self‐efficacy was the strongest factor of willingness, followed by greater perceived benefits and stronger cues to. Additional significant factors included being a current or former smoker, secondhand smoke exposure, age > 65 years, and being responsible for cooking at home. In contrast, unmarried individuals were significantly less likely to be willing to undergo LDCT (aOR = 0.678; 95% CI: 0.486–0.946; p = 0.022). Conclusion Willingness to undergo LDCT screening was suboptimal among high‐risk individuals in Hong Kong. Key facilitators included higher self‐efficacy, perceived benefits, and cues to action—central domains of the Health Belief Model. Targeted strategies that strengthen these domains may improve screening uptake
An automatic, rapid and continuous impervious surface mapping framework based on historical land cover datasets
No full textLong time series impervious surface mapping (ISM) is important for understanding urban expansion, environmental impacts, and urban planning. There are some historical global ISM products, such as GAIA and NUACI datasets, whereas they may not meet the user’s diverse application needs in the aspects of mapping timeliness, temporal resolution, and spatial resolution. Therefore, this study proposes an automatic, rapid, and continuous impervious surface mapping and updating framework based on historical land cover datasets without using other labeled data, to improve the updating speed and spatio-temporal resolution of impervious surface maps. The main process is divided into three steps: (1) Multi-temporal samples for classification were obtained by using GAIA dataset, FROM-GLC dataset and the unsupervised continuous change detection (CCD) algorithm; (2) Quarterly long time series ISM results (ISMs) were obtained by using multi-temporal samples and quarterly features; (3) The final results were obtained by using the post-processing operations in the obtained quarterly long time series ISMs to improve the mapping accuracy. The proposed framework is applied to eight cities around the world, and the total overall accuracy (OA) and Kappa of long time series ISMs with post-processing in the eight cities are 92.64% and 0.8525, respectively, improving the OA and Kappa of those without post-processing by 1.41% and 0.0281, respectively, and those of GAIA dataset by 4.57% and 0.0914, respectively, which proved the effectiveness of the proposed method. This study also analyzed the spatial patterns of impervious surface expansion in eight cities and identified different spatial patterns of expansion that existed among the cities, while capturing the abrupt change in the spatial patterns of expansion in Rosario and Novosibirsk after the second quarter of 2021. The proposed framework achieved rapid mapping and updating of impervious surface without any labeled samples, and has the potential to map the global impervious surface continuously
Application of a novel deep learning method for electricity theft detection based on explainable artificial intelligence
No full textTo address the challenges of weak feature representation, difficult extraction, and insufficient classification accuracy in electricity consumption time-series data for smart grid security monitoring, this paper proposes a temporal convolutional network (TCN) model based on Bayesian optimization. This study innovatively combines deep learning’s feature extraction capability with Bayesian optimization’s parameter tuning strategy, effectively improving traditional TCN models. The proposed Bayesian optimization-enhanced TCN (BO-TCN) integrates the TCN’s powerful temporal feature extraction with BO’s efficient hyperparameter search to achieve optimal model configuration for electricity theft detection. To address the critical issue that deep learning performance is sensitive to hyperparameter settings, Bayesian optimization constructs a Gaussian process model to approximate the objective function and uses acquisition functions for efficient hyperparameter selection, enabling automatic optimization of TCN hyperparameters. This method significantly improves parameter optimization efficiency while maintaining prediction performance. Experimental results on the SGCC dataset demonstrate that the optimized TCN model exhibits excellent generalization ability in electricity theft detection, achieving 98.04% classification accuracy—significantly outperforming LSTM, CNN, and baseline models. The effectiveness of the BO-TCN is verified through comprehensive ablation experiments and post-hoc explainable artificial intelligence analysis using SHAP and LIME techniques. Compared with the NTCN (eliminating the 1 × 1 convolutional residual block), the BO-TCN improves accuracy by ∼1.23%; compared with the ANTCN (eliminating all residual structures), BO-TCN’s accuracy improves by ∼2.27%
Augmenting medical visual question answering with mixup, label smoothing, and layer-wise relevance propagation eXplainable Artificial Intelligence
No full textThe growing volume of medical data presents significant opportunities for advancing Medical Visual Question Answering (MVQA) systems. However, an imbalance in the number and distribution of image and Question–Answer (QA) pairs poses challenges for developing robust models. This study proposes improving existing MVQA datasets using data augmentation techniques specifically Mixup and Label Smoothing—to address this issue. The performance of MVQA models trained on these enhanced datasets is evaluated using quantitative metrics, as well as Layer-wise Relevance Propagation for eXplainable artificial intelligence (LRP XAI). Results indicate that models trained on the augmented datasets outperform those trained on the baseline datasets, showing significant gains in both accuracy and Bilingual Evaluation Understudy (BLEU) score. Furthermore, LRP XAI visualizations highlight key image and text regions that contribute to accurate answer predictions, thereby improving model interpretability and trust. This work underscores the importance of dataset augmentation and explainability in advancing MVQA research and it is available in https://doi.org/10.5281/zenodo.15910714
Porcelio e la crociata contro i Turchi nel carme De die mundi
No full textAll'interno della raccolta encomiastica intitolata De felicitate temporum Pii II, il carme De die mundi costituisce un adattamento in versi del discorso pronunciato da Pio II alla dieta di Mantova (1459), in cui, com'è noto, il pontefice esortava i principi cristiani a intraprendere una crociata contro gli Ottomani dopo la caduta di Costantinopoli. L'articolo ricostruisce innanzitutto la tradizione manoscritta dell'opera di Porcelio, identificando e descrivendo i testimoni chiave, tra cui il manoscritto parzialmente autografo Vat. Lat. 1670 e l'elegante copia di dedica Reg. Lat. 1991. Offre poi un'edizione critica del De die mundi basata sul manoscritto Reg. Lat. 1991, col corredo di una traduzione italiana e di note esplicative. L'analisi mostra come Porcelio mescoli modelli classici con temi cristiani per creare un potente manifesto ideologico
Understanding the Role of Trust, Perceived Risk, and Habit in Organization Members' Generative AI Use
No full textThe rapid advancement of generative artificial intelligence (AI) presents organizations with unique opportunities and challenges. While companies are increasingly adopting this technology to enhance productivity and gain a competitive edge, understanding the key factors influencing organization members' use of generative AI remains critical. This study investigates the determinants of generative AI usage within organizations, focusing on the interplay of trust, perceived risk, and habit. This study addresses this gap by examining how organization members' perceptions of trust and risk associated with generative AI influence their usage decisions. Furthermore, it clarifies the role of generative AI usage habit in organization members' usage decisions. The research model was tested using data collected from 214 organization members, and partial least squares (PLS) was utilized for the analysis. While trust in generative AI did not significantly affect generative AI use, perceived risk was found to negatively affect both generative AI use and trust. This study confirmed the significant role of habit in facilitating organization members' use of generative AI. The findings of this study provide several theoretical and practical implications that encourage organization members' use of generative AI by inducing positive experiences and repeated use of the technology
The Changes in Housing Prices and Fertility Levels in Chinese Cities—From the Perspectives of the Incentive Effect and the Crowding-Out Effect
No full textQuantitative analysis of the impact of housing prices on fertility levels in Chinese cities is an important step for promoting high-quality economic development. Achieving balanced urban population growth in China will also contribute to sustained and healthy social and economic development. This paper presents empirical research based on panel data from 284 prefecture-level and higher cities in China, covering the period from 2010 to 2022. The research examines both incentive effect and Crowding-out effect. It uses regression models, mediating effect models, and threshold effect models. The study reaches the following conclusions: First, the increase in housing prices initially leads to a decline in fertility rates, followed by an increase. This suggests a positive “U”-shaped relationship. The declining part of the curve is significantly larger than the increasing part, and the results have passed rigorous robustness and endogeneity tests. Second, from the income perspective, in cities where housing prices have risen, labor, capital, and technology have grown significantly, helping to mitigate the decline in fertility. The contribution of the mediating effect of technology expenditure is 9.45%, making it an effective variable. Third, from the consumption perspective, in cities with high housing prices, as per capita GDP rises, the negative impact of housing prices on fertility strengthens. Therefore, this paper also provides four key recommendations for urban development to reduce the negative effect of housing prices on fertility levels
Pharmacological and nonpharmacological management of dysmenorrhea among senior high school girls in two districts in the Central Region of Ghana
No full textBackground: Dysmenorrhea is a common menstrual health problem among adolescents, often managed through pharmacological and non-pharmacological strategies. However, the factors that predict adolescents’ choice between these management approaches remain poorly understood, particularly in settings where traditional and modern practices coexist. This study examined the predictors of pharmacological and non-pharmacological management practices among school-going female adolescents in Ghana. Methods: A cross-sectional survey was conducted among 490 female students aged 13–24 years in 2 senior high schools within the Effutu and Agona West Municipalities. Participants were recruited using simple random sampling. For minors (aged 13–17 years), written informed consent was obtained from Legally Authorized Representatives and written assent from participants; adult participants (aged 18–24 years) provided written informed consent directly. Exclusion criteria included diagnosed gynecological disorders, inability to provide informed assent or parental/guardian consent, and cognitive impairments. Data were collected using a structured questionnaire and analyzed with chi-square tests and logistic regression. Results: The prevalence of dysmenorrhea was 82.4%. Pharmacological management was significantly associated with dysmenorrhea experience (χ 2 = 11.51, p = 0.003). Pain intensity emerged as a strong predictor, with adolescents reporting moderate (Exp( B ) = 1.7, p = 0.03) and severe pain (Exp( B ) = 2.42, p < 0.01) more likely to use medications. Non-pharmacological practices were significantly associated with program of study (χ 2 = 28.75, p = 0.001), while demographic factors showed no significant associations. Conclusion: Pain severity drives pharmacological management, whereas educational exposure influences non-pharmacological choices. Comprehensive menstrual health education is needed across academic programs to promote effective, evidence-based management of dysmenorrhea among adolescents in Ghana
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