ObjectiveTo investigate the association between insulin resistance （IR） and cardiovascular metabolic indicators， including blood pressure， blood lipids， and serum uric acid， in a middle-aged population. MethodsBased on the baseline survey of the Hong Sifang cohort of China National Salt Industry Group， a total of 575 participants were included. According to the homeostasis model assessment of insulin resistance， the participants were divided into an IR group （n=116） and a non-IR group （n=459）. Differences between the two groups were compared using the t-test and chi-square test. Multivariate logistic regression analysis was performed to evaluate the associations between IR and abnormalities in metabolic indicators. ResultsA total of 575 participants were included， including 390 men and 185 women， with a mean age of （48.58 ± 5.14） years and a mean body mass index （BMI） of （24.16 ± 2.97） kg/m². Blood pressure， serum uric acid， and blood lipid levels were significantly higher in the IR group than in the non-IR group （P<0.001）. After adjusting for potential confounders including age， sex， and BMI， IR was independently associated with hypertension， dyslipidemia， and hyperuricemia， with odds ratios （OR） and 95% confidence intervals （CI） of 2.57 （1.61-4.09）， 3.81 （2.36-6.17）， and 2.68 （1.53-4.71）， respectively （all P<0.001）. ConclusionInsulin resistance is significantly associated with multiple cardiovascular metabolic risk factors in the middle-aged population. Monitoring IR may contribute to the early prevention and control of cardiovascular diseases.

An implantable cardiac monitor(ICM)is a subcutaneous electronic device for recording and remotely transmitting electrocardiographic events.New-generation ICM,like the Reveal LINQ,have extended arrhythmia monitoring duration and improved diagnostic yield through miniaturization,large storage capacity,and easy implantation.They provide comprehensive monitoring data for cardiovascular disease diagnosis and treatment,optimize individualized treatment plans,and enhance patient prognosis.In recent years,research on ICM in cardiovascular fields has deepened,and their clinical application scope has expanded.They show significant advantages and unique value and are widely used in early cardiovascular disease diagnosis and prognostic management.In the future,ICM are expected to play a bigger role in boosting diagnostic and prognostic capabilities for cardiovascular disease patients,offering stronger support for early disease detection,accurate diagnosis,and effective management.

Objective:To develop and validate machine learning models for predicting the cumulative live birth rate (CLBR) following in vitro fertilization (IVF) and to analyze key predictive features using SHAP values. Methods:This retrospective study included data from patients who underwent IVF-embryo transfer at the Department of Reproductive Medicine, Baoding Maternal and Child Health Hospital, between January 2017 and December 2022. Patients were categorized into two groups based on live birth outcome: the live birth group ( n=1 036) and the non-live birth group ( n=756). The dataset was randomly divided into a training set and a validation set in a ratio of 7∶3. Five algorithms were utilized for model development: logistic regression, random forest, extreme gradient boosting (XGBoost), support vector machine, and neural networks. Model performance was assessed using the area under the receiver operating characteristic (AUC) curve, F1 score, and calibration curves. Clinical decision curve analysis (DCA) was employed to evaluate the clinical utility of the models. SHAP values were used to interpret feature importance in the XGBoost model and enhance its explainability. Results:The XGBoost model demonstrated the best performance in predicting CLBR,with accuracy of 72.44%, AUC of 0.775, and F1 score of 0.654, accuracy and F1 score outperforming logistic regression (accuracy was 70.02%, F1 score was 0.585), random forest (accuracy was 71.69%, F1 score was 0.606), support vector machine (accuracy was 70.20%, F1 score was 0.607), and neural network (accuracy was 68.72%, F1 score was 0.560). The calibration curve of XGBoost closely aligned with the diagonal line, indicating that the predicted probabilities were very close to the actual outcomes, demonstrating good calibration. DCA indicated that the XGBoost model provided higher net benefits across a wide range of clinical decision thresholds. SHAP value analysis identified number of previous IVF failures, antral follicle count, anti-Müllerian hormone level, percentage of normal sperm morphology, and sperm DNA fragmentation index as key predictors of CLBR.Conclusion:The XGBoost model exhibits excellent predictive performance and calibration for CLBR, with SHAP values providing important insights into feature importance. This model has the potential to support the development of personalized treatment strategies in clinical practice. However, its generalizability needs to be validated using external datasets to ensure its applicability to diverse populations.

Objective:To develop and validate machine learning models for predicting the cumulative live birth rate (CLBR) following in vitro fertilization (IVF) and to analyze key predictive features using SHAP values. Methods:This retrospective study included data from patients who underwent IVF-embryo transfer at the Department of Reproductive Medicine, Baoding Maternal and Child Health Hospital, between January 2017 and December 2022. Patients were categorized into two groups based on live birth outcome: the live birth group ( n=1 036) and the non-live birth group ( n=756). The dataset was randomly divided into a training set and a validation set in a ratio of 7∶3. Five algorithms were utilized for model development: logistic regression, random forest, extreme gradient boosting (XGBoost), support vector machine, and neural networks. Model performance was assessed using the area under the receiver operating characteristic (AUC) curve, F1 score, and calibration curves. Clinical decision curve analysis (DCA) was employed to evaluate the clinical utility of the models. SHAP values were used to interpret feature importance in the XGBoost model and enhance its explainability. Results:The XGBoost model demonstrated the best performance in predicting CLBR,with accuracy of 72.44%, AUC of 0.775, and F1 score of 0.654, accuracy and F1 score outperforming logistic regression (accuracy was 70.02%, F1 score was 0.585), random forest (accuracy was 71.69%, F1 score was 0.606), support vector machine (accuracy was 70.20%, F1 score was 0.607), and neural network (accuracy was 68.72%, F1 score was 0.560). The calibration curve of XGBoost closely aligned with the diagonal line, indicating that the predicted probabilities were very close to the actual outcomes, demonstrating good calibration. DCA indicated that the XGBoost model provided higher net benefits across a wide range of clinical decision thresholds. SHAP value analysis identified number of previous IVF failures, antral follicle count, anti-Müllerian hormone level, percentage of normal sperm morphology, and sperm DNA fragmentation index as key predictors of CLBR.Conclusion:The XGBoost model exhibits excellent predictive performance and calibration for CLBR, with SHAP values providing important insights into feature importance. This model has the potential to support the development of personalized treatment strategies in clinical practice. However, its generalizability needs to be validated using external datasets to ensure its applicability to diverse populations.

Objective To investigate the status of population dynamics and distribution changes of Aedes aegypti in Guangdong Province.Methods Continuous monitoring was conducted from May 2018 to July 2024 in Wushi Town and Qishui Town,Leizhou City,Zhanjiang City,Guangdong Province.Additionally,a survey of the distribution of Ae.aegypti along the Leizhou Peninsula coast was carried out.Results The density of Ae.aegypti in Zhanjiang showed a gradual decline from 2018 to 2024.The last detection of adult Ae.aegypti in Wushi Town was in September 2021,and the last larva was found in October 2023.No Ae.aegypti was detected in Qishui Town during surveys from 2021 to 2024.A survey of 18 coastal villages in the Leizhou Peninsula revealed no detections of Ae.aegypti.Conclusions This study provides a basis for understanding the distribution and population density fluctuations of Ae.aegypti,assessing its invasion risk,and scientifically conducting relevant prevention and control efforts.

Numerous studies on the formation and consolidation of memory have shown that memory processes are characterized by phase-dependent and dynamic regulation. Memory retrieval, as the only representation of memory content and an active form of memory processing that induces memory reconsolidation, has attracted increasing attention in recent years. Although the molecular mechanisms specific to memory retrieval-induced reconsolidation have been gradually revealed, an understanding of the time-dependent regulatory mechanisms of this process is still lacking. In this study, we applied a transcriptome analysis of memory retrieval at different time points in the recent memory stage. Differential expression analysis and Short Time-series Expression Miner (STEM) depicting temporal gene expression patterns indicated that most differential gene expression occurred at 48 h, and the STEM cluster showing the greatest transcriptional upregulation at 48 h demonstrated the most significant difference. We then screened the differentially-expressed genes associated with that met the expression patterns of those cluster-identified genes that have been reported to be involved in learning and memory processes in addition to dipeptidyl peptidase 9 (DPP9). Further quantitative polymerase chain reaction verification and pharmacological intervention suggested that DPP9 is involved in 48-h fear memory retrieval and viral vector-mediated overexpression of DPP9 countered the 48-h retrieval-induced attenuation of fear memory. Taken together, our findings suggest that temporal gene expression patterns are induced by recent memory retrieval and provide hitherto undocumented evidence of the role of DPP9 in the retrieval-induced reconsolidation of fear memory.
Animals ; Fear/physiology* ; Male ; Dipeptidyl-Peptidases and Tripeptidyl-Peptidases/genetics* ; Memory Consolidation/physiology* ; Time Factors ; Mental Recall/drug effects* ; Mice ; Gene Expression Profiling

The molecular mechanism of verbascoside(OC1) in promoting acetylcholine(ACh) release in the pathogenesis of Alzheimer's disease(AD) was studied. Adrenal pheochromocytoma cells(PC12) of rats induced by β-amyloid protein(1-42)(Aβ_(1-42)) were used as AD models in vitro and were divided into control group, model group(Aβ_(1-42) 10 μmol·L~(-1)), OC1 treatment group(2 and 10 μg·mL~(-1)). The effect of OC1 on phosphorylated proteins in AD models was analyzed by whole protein phosphorylation quantitative omics, and the selectivity of OC1 for calcium channel subtypes was virtually screened in combination with computer-aided drug design. The fluorescence probe Fluo-3/AM was used to detect Ca~(2+) concentration in cells. Western blot analysis was performed to detect the effects of OC1 on the expression of phosphorylated calmodulin-dependent protein kinase Ⅱ(p-CaMKⅡ, Thr286) and synaptic vesicle-related proteins, and UPLC/Q Exactive MS was used to detect the effects of OC1 on ACh release in AD models. The effects of OC1 on acetylcholine esterase(AChE) activity in AD models were detected. The results showed that the differentially modified proteins in the model group and the OC1 treatment group were related to calcium channel activation at three levels: GO classification, KEGG pathway, and protein domain. The results of molecular docking revealed the dominant role of L-type calcium channels. Fluo-3/AM fluorescence intensity decreased under the presence of Ca~(2+) chelating agent ethylene glycol tetraacetic acid(EGTA), L-type calcium channel blocker verapamil, and N-type calcium channel blocker conotoxin, and the effect of verapamil was stronger than that of conotoxin. This confirmed that OC1 promoted extracellular Ca~(2+) influx mainly through its interaction with L-type calcium channel protein. In addition, proteomic analysis and Western blot results showed that the expression of p-CaMKⅡ and downstream vesicle-related proteins was up-regulated after OC1 treatment, indicating that OC1 acted on vesicle-related proteins by activating CaMKⅡ and participated in synaptic remodeling and transmitter release, thus affecting learning and memory. OC1 also decreased the activity of AChE and prolonged the action time of ACh in synaptic gaps.
Animals ; Rats ; Glucosides/administration & dosage* ; Acetylcholine/metabolism* ; Alzheimer Disease/genetics* ; PC12 Cells ; Phenols/chemistry* ; Neurotransmitter Agents/metabolism* ; Drugs, Chinese Herbal ; Calcium-Calmodulin-Dependent Protein Kinase Type 2/genetics* ; Humans ; Phosphorylation/drug effects* ; Calcium/metabolism* ; Polyphenols

An implantable cardiac monitor(ICM)is a subcutaneous electronic device for recording and remotely transmitting electrocardiographic events.New-generation ICM,like the Reveal LINQ,have extended arrhythmia monitoring duration and improved diagnostic yield through miniaturization,large storage capacity,and easy implantation.They provide comprehensive monitoring data for cardiovascular disease diagnosis and treatment,optimize individualized treatment plans,and enhance patient prognosis.In recent years,research on ICM in cardiovascular fields has deepened,and their clinical application scope has expanded.They show significant advantages and unique value and are widely used in early cardiovascular disease diagnosis and prognostic management.In the future,ICM are expected to play a bigger role in boosting diagnostic and prognostic capabilities for cardiovascular disease patients,offering stronger support for early disease detection,accurate diagnosis,and effective management.