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Women with hypertensive disorders of pregnancy (HDP) have a higher risk of developing chronic hypertension (CHT) postpartum, which can lead to increased cardiovascular events. Therefore, we aimed to develop and validate a nomogram to predict the probability of CHT in HDP women by analyzing traditional characteristics and pregnancy-related indices. A total of 688 HDP women who delivered at the three designated hospitals in China, during the period of January 2011 to June 2021, were randomly divided into 70% (n = 482) as the training set and the remaining 30% (n = 206) as the validation set. Predictors for CHT were extracted to establish a nomogram based on multivariate logistic analysis of the training set. The performance of the nomogram was evaluated by an internal validation. In total, 207 (30.1%) patients developed CHT after delivery. Maternal age, highest systolic blood pressure (SBP), highest diastolic blood pressure (DBP), peak alkaline phosphatase (ALP) levels, peak uric acid (UA) levels, and urine protein during pregnancy were independent predictors of the nomogram. Area under the curve (AUC) of the training set was 0.819 (95% CI: 0.778-0.860, p < 0.001) and 0.800 (95% CI: 0.739-0.862, p < 0.001) in the validation set. A good consistency between the nomogram model and standard diagnostic criteria was obtained (p > 0.05). Decision curve analysis (DCA) also showed a net benefit in the nomogram when the risk thresholds were 10%-90%. In conclusion, we developed a novel clinical nomogram to predict CHT risk in women with HDP, which was a useful and easy tool to identify high-risk individuals and performed well on internal validation.
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http://dx.doi.org/10.1111/jch.70094 | DOI Listing |
Interv Neuroradiol
September 2025
University Clinic for Neuroradiology, University Hospital Magdeburg, Magdeburg, Germany.
BackgroundAt present, nonvirtual neurovascular training can be performed using either an angiographic suite under fluoroscopic guidance (entailing radiation exposure) or direct optical visualization with a camera-based system. The angiographic approach offers high-fidelity visualization and catheter control but is constrained by the limited availability of such specialized facilities, whereas the camera-based approach can be implemented virtually anywhere yet lacks comparable realism in key procedural aspects. The objective of this work is to develop and evaluate a novel camera-based angiography training system (CBATS) that generates artificial angiograms and roadmaps, thereby combining the advantages of both imaging techniques while eliminating radiation exposure.
View Article and Find Full Text PDFClin Exp Immunol
September 2025
Orthopedic Center, Sunshine Union Hospital, High-tech Zone, Weifang City, Shandong Province, China.
Introduction: We attempted to perform a comprehensive bioinformatics analyses on osteoarthritis (OA) based on the NKT-related genes and explore the clinical related critical genes.
Methods: Differentially expressed genes (DEGs) and NKT-related genes from WGCNA were obtained using the dataset GSE114007, followed by intersection analysis to obtain NKT-related DEGs. Lasso regression, support vector machine and random forest were performed to screen feature genes, followed by verification with ROC curve, and nomogram model.
Clin Exp Dent Res
October 2025
Tasmanian School of Medicine, College of Health and Medicine, University of Tasmania, Hobart, Tasmania, Australia.
Objectives: Oral health is an important aspect of quality of life for older people, especially those with dementia. The impact of an active oral hygiene program on the oral microbiome was explored in a group of older participants (average age 84 years old) with dementia against a separate control group whose oral hygiene followed the status quo.
Materials And Methods: The oral cavity bacteriomes and mycobiomes were assessed from swabs of cheek, gum, and tongue surfaces.
J Pharm Anal
August 2025
Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China.
Current experimental and computational methods have limitations in accurately and efficiently classifying ion channels within vast protein spaces. Here we have developed a deep learning algorithm, GPT2 Ion Channel Classifier (GPT2-ICC), which effectively distinguishing ion channels from a test set containing approximately 239 times more non-ion-channel proteins. GPT2-ICC integrates representation learning with a large language model (LLM)-based classifier, enabling highly accurate identification of potential ion channels.
View Article and Find Full Text PDFJ Comput Soc Sci
September 2025
Chair of Research Methods in Developmental and Educational Sciences, Institute of Education, University of Zurich, Zurich, Switzerland.
School curricula guide the daily learning activities of millions of students. They embody the understanding of the education experts who designed them of how to organize the knowledge that students should acquire in a way that is optimal for learning. This can be viewed as a learning 'theory' which is, nevertheless, rarely put to the test.
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