Objective: To evaluate the effect of community comprehensive management model intervention among patients with dyslipidemia, so as to provide the reference for optimizing community management strategies and improving the target achievement rate for blood lipids among this population. Methods: From May to June 2023, a multi-stage stratified random sampling method was employed to select patients with dyslipidemia from primary healthcare institutions in Jiaxing City, Zhejiang Province. Eligible participants were randomly assigned to either a control group or an intervention group. The control group received routine management, while the intervention group was subjected to a community comprehensive management model in addition to the routine care. Both groups were followed up for 24 months. Data on demographic characteristics, lifestyle behaviors, physical examination indices, and blood biochemical indicators were collected at baseline and after the intervention through questionnaires, physical examinations, and laboratory tests. Changes in obesity rate, central obesity rate, target achievement rates for blood lipids, blood pressure, and blood glucose, as well as lifestyle modifications, were analyzed. Differences between the two groups before and after the intervention were assessed using generalized estimating equations (GEE). Results: The control group consisted of 560 patients, including 303 females (54.11%) and 430 individuals aged ≥65 years (76.79%). The intervention group also included 560 patients, with 300 females (53.57%) and 431 individuals aged ≥65 years (76.96%). Before the intervention, no statistically significant differences were observed between the two groups in terms of gender, age, educational level, history of chronic diseases, and atherosclerotic cardiovascular disease risk stratification (all P>0.05). After 24 months of intervention, interaction effects between group and time were observed for obesity rate, central obesity rate, target achievement rate for blood lipids, target achievement rate for blood glucose, composite target achievement rate, physical activity rate, and medication adherence (all P<0.05). Specifically, the intervention group demonstrated lower rates of obesity and central obesity, and higher target achievement rate of blood lipids, target achievement rate of blood glucose, composite target achievement rate, physical activity rate, and medication adherence compared to the control group. Conclusion The community comprehensive management model contributed to improvements in multiple metabolic parameters (including body weight, waist circumference, blood lipids, and blood glucose) among patients with dyslipidemia, and was associated with increased physical activity rate and medication adherence.

ObjectiveThis paper aims to clarify the material basis of Gualou Niubangtang and establish a quantitative analysis method for its main constituents， providing a reference for the overall quality control of this preparation. MethodsThe constituents in the formula were systematically characterized based on ultra-performance liquid chromatography-quadrupole time-of-flight tandem mass spectrometry （UPLC-Q-TOF-MS/MS）. Identification was performed by matching with the UNIFI 9.6 software and utilizing database platforms such as PubChem， ChemicalBook， and ChemSpider， combined with relevant literature reports. A quantitative analysis method for the seven main constituents in Gualou Niubangtang was established by using high performance liquid chromatography （HPLC）. ResultsUPLC-Q-TOF-MS/MS analysis identified 155 constituents， including 69 flavonoids， 36 terpenoids， 23 phenylpropanoids， 8 phenylethanoid glycosides， and 19 other types of constituents. In the established quantitative analysis method， the seven main constituents showed good linearity within their respective linear ranges. The precision， repeatability， stability， and spike recovery all met the required standards. The results showed that the content ranges of geniposide， liquiritin， hesperidin， arctiin， baicalin， oroxylin A-7-O-β-D-glucuronide， and wogonoside in 15 batches of Gualou Niubangtang were 13.67-21.25， 1.20-7.64， 5.45-7.45， 22.97-33.51， 29.95-39.07， 2.58-4.80， and 6.56-9.31 mg·g^-1， respectively. ConclusionThis study successfully characterizes and attributes multi-category constituents in Gualou Niubangtang， clarifying that its material basis is primarily composed of flavonoids， terpenoids， phenylethanoid glycosides， and phenylpropanoids. Furthermore， it enables the quantification of seven constituents within the formula. This work lays a foundation for research on the quality control， action mechanism， and clinical application of this formula.

ObjectiveTo evaluate the pregnancy outcomes of assisted reproductive technology （ART） in women with a history of thyroid cancer who retained fertility intentions after completing cancer treatment. MethodsA retrospective analysis was performed on 61 patients with a history of thyroid cancer who underwent in vitro fertilization/intracytoplasmic sperm microinjection and embryo transfer （IVF/ICSI-ET）. These patients were included as the case group. A total of 122 non-cancer patients who received ART during the same period were selected as the control group using 1∶2 matching based on age and oocyte retrieval time. Baseline characteristics， outcomes of the first ART cycle， and cumulative pregnancy outcomes were compared between the two groups. ResultsThere was no significant difference in the basic data， the total amount of gonadotropin （Gn） and the days of use between the case group and the control group （P>0.05）. However， the case group had significantly fewer retrieved oocytes， mature oocytes （MII）， lower fertilization and cleavage rates， and fewer transferable and high-quality embryos， as well as fewer embryos transferred during the first cycle （P < 0.05）. However， there was no significant difference in the rate of first embryo implantation and first clinical pregnancy between the two groups （P>0.05）. In the analysis of cumulative outcomes， the two groups did not show statistically significant differences in the cumulative pregnancy rate， clinical pregnancy rate per transfer cycle， the number of oocyte retrieval cycles required per live birth， the number of embryo transfer cycles required per live birth， and the number of embryos used for each live birth （P>0.05）. However， the cumulative live birth rate was significantly lower in the case group compared to the control group （P=0.005）. ConclusionAfter treatment for thyroid cancer， when ART is used to help pregnant women， the pregnancy outcome is comparable to that of women without tumors. Individualized reproductive management and timely fertility preservation strategies are recommended to optimize reproductive outcomes in this population.

[摘 要] 目的：探讨环状RNA CEMIP（circCEMIP）对膀胱癌UMUC-3细胞增殖、迁移和侵袭的影响及其分子机制。方法：通过TCGA数据库分析circCEMIP在膀胱癌组织中的表达水平，分析其表达与膀胱癌患者临床分期及生存期的关系。采用qPCR法检测circCEMIP在膀胱癌细胞5637、UMUC-3、MGH-U3、J82和T24中的表达。利用RNA干扰技术，分别将si-circCEMIP及其阴性对照（si-NC）、anti-miR-335及其阴性对照（anti-miR-NC）转染UMUC-3细胞，记为si-circCEMIP组、si-NC组、si-circCEMIP + anti-miR-335组和si-circCEMIP + anti-miR-NC组。采用克隆形成实验、划痕愈合实验和Transwell实验分别检测circCEMIP和miR-335表达对UMUC-3细胞增殖、迁移和侵袭能力的影响，双萤光素酶报告基因实验验证circCEMIP与miR-335的靶向关系，WB法检测细胞中VEGF-C信号通路相关蛋白的表达。构建UMUC-3细胞裸鼠皮下移植瘤模型，观察敲低circCEMIP对移植瘤生长的影响。结果：膀胱癌组织中circCEMIP呈高表达（P < 0.01），其表达水平与膀胱癌的临床分期正相关（P < 0.01），circCEMIP高表达患者生存率较低（P < 0.01）。circCEMIP在膀胱癌5637、UMUC-3、MGH-U3、J82和T24细胞中呈高表达（均P < 0.01）。敲低circCEMIP显著降低UMUC-3细胞的增殖、迁移和侵袭能力（均P < 0.01）。circCEMIP可靶向结合miR-335（P < 0.01），敲低circCEMIP能显著上调miR-335表达（P < 0.01）。抑制miR-335表达能逆转敲低circCEMIP对UMUC-3细胞增殖、迁移和侵袭的抑制作用（均P < 0.01）。敲低circCEMIP能明显下调VEGF-C信号通路相关蛋白VEGF-C、MMP-2、MMP-9和β-catenin表达（均P < 0.01），抑制miR-335表达能部分逆转敲低circCEMIP对该通路相关蛋白表达的抑制作用（均P < 0.01）。体内实验证实，敲低circCEMIP能够抑制裸鼠膀胱癌移植瘤的生长（P < 0.01）。结论：敲低circCEMIP通过上调miR-335表达抑制膀胱癌UMUC-3细胞的增殖、迁移和侵袭。

Objective To estimate the lifetime attributable risk (LAR) of thyroid cancer due to radiation in individuals undergoing chest computed tomography (CT) examinations. Methods Scanning parameters were retrospectively collected from DICOM files of 660 individuals who underwent chest CT scans between 2022 and 2024. Individuals were stratified by age and sex. Size-specific dose estimates were calculated using a formula based on the volume CT dose index for each individual. The radiation doses received by the thyroid were estimated. The cancer risk prediction model from the US National Academy of Sciences report on the Biological Effects of Ionizing Radiation was referenced to predict the LAR of thyroid cancer. Results The LAR of thyroid cancer for males/females was 25.170/100 000 and 140.177/100 000 in the ≥ 0 and<5 years of age group, 21.779/100 000 and 102.498/100 000 in the ≥5 and <10 years of age group, 22.987/100 000 and 128.934/100 000 in the ≥10 and <15 years of age group, and 4.979/100 000 and 12.490/100 000 in the ≥15 years of age group. A Spearman correlation analysis showed that age was highly correlated with volume CT dose index, size-specific dose estimates, radiation dose received by the thyroid, and thyroid cancer LAR, with correlation coefficients of 0.887, 0.737, 0.737, and −0.41 (P<0.01), respectively. Sex was correlated with radiation dose received by the thyroid and thyroid cancer LAR, with correlation coefficients of 0.179 and 0.441 (P<0.01), respectively. Conclusion Chest CT scan leads to an increased LAR of thyroid cancer. Appropriate protective measures for the thyroid should be considered during chest CT scan to reduce the impact of radiation on the thyroid.

Objective: To develop a depression recognition model by integrating the spirit-expression diagnostic framework of traditional Chinese medicine (TCM) with machine learning algorithms. The proposed model seeks to establish a TCM-informed tool for early depression screening, thereby bridging traditional diagnostic principles with modern computational approaches. Methods: The study included patients with depression who visited the Shanghai Pudong New Area Mental Health Center from October 1, 2022 to October 1, 2023, as well as students and teachers from Shanghai University of Traditional Chinese Medicine during the same period as the healthy control group. Videos of 3 – 10 s were captured using a Xiaomi Pad 5, and the TCM spirit and expressions were determined by TCM experts (at least 3 out of 5 experts agreed to determine the category of TCM spirit and expressions). Basic information, facial images, and interview information were collected through a portable TCM intelligent analysis and diagnosis device, and facial diagnosis features were extracted using the Open CV computer vision library technology. Statistical analysis methods such as parametric and non-parametric tests were used to analyze the baseline data, TCM spirit and expression features, and facial diagnosis feature parameters of the two groups, to compare the differences in TCM spirit and expression and facial features. Five machine learning algorithms, including extreme gradient boosting (XGBoost), decision tree (DT), Bernoulli naive Bayes (BernoulliNB), support vector machine (SVM), and k-nearest neighbor (KNN) classification, were used to construct a depression recognition model based on the fusion of TCM spirit and expression features. The performance of the model was evaluated using metrics such as accuracy, precision, and the area under the receiver operating characteristic (ROC) curve (AUC). The model results were explained using the Shapley Additive exPlanations (SHAP). Results: A total of 93 depression patients and 87 healthy individuals were ultimately included in this study. There was no statistically significant difference in the baseline characteristics between the two groups (P > 0.05). The differences in the characteristics of the spirit and expressions in TCM and facial features between the two groups were shown as follows. (i) Quantispirit facial analysis revealed that depression patients exhibited significantly reduced facial spirit and luminance compared with healthy controls (P < 0.05), with characteristic features such as sad expressions, facial erythema, and changes in the lip color ranging from erythematous to cyanotic. (ii) Depressed patients exhibited significantly lower values in facial complexion L, lip L, and a values, and gloss index, but higher values in facial complexion a and b, lip b, low gloss index, and matte index (all P < 0.05). (iii) The results of multiple models show that the XGBoost-based depression recognition model, integrating the TCM “spirit-expression” diagnostic framework, achieved an accuracy of 98.61% and significantly outperformed four benchmark algorithms—DT, BernoulliNB, SVM, and KNN (P < 0.01). (iv) The SHAP visualization results show that in the recognition model constructed by the XGBoost algorithm, the complexion b value, categories of facial spirit, high gloss index, low gloss index, categories of facial expression and texture features have significant contribution to the model. Conclusion This study demonstrates that integrating TCM spirit-expression diagnostic features with machine learning enables the construction of a high-precision depression detection model, offering a novel paradigm for objective depression diagnosis.

ObjectiveTo investigate the role and underlying mechanism of the Wnt/β-catenin signaling pathway in the process of Xiaojinwan （XJW）-containing serum interfering with endothelial-mesenchymal transition （EMT） in human breast cancer MDA-MB-231 cells. MethodsXJW-containing serum was prepared by intragastric administration of XJW （4 g·kg^-1） to SD rats. MDA-MB-231 cells were cultured in vitro. β-Catenin overexpression and knockdown plasmids were constructed and transfected into MDA-MB-231 cells. The optimal XJW-containing serum （4 g·kg^-1） according to previous studies was used for β-catenin intervention and overexpression experiments. The groups for intervention/overexpression experiments included intervention/overexpression blank group， β-catenin intervention/overexpression group， intervention/overexpression blank + XJW group， and β-catenin intervention/overexpression + XJW group. Cell morphology was observed under a microscope. Flow cytometry was used to detect and record apoptosis rates. Western blot was performed to measure apoptosis-related proteins ［B-cell lymphoma 2 （Bcl-2）， Bcl-2-associated X protein （Bax）， and active-Caspase-3］ and EMT-related proteins （E-cadherin， N-cadherin， and vimentin）. Real-time polymerase chain reaction （Real-time PCR） was used to detect β-catenin mRNA expression levels in different groups. ResultsIntervention experiment results showed that compared with the intervention blank group， the β-catenin intervention group and the intervention blank + XJW group showed increased apoptosis rates （P<0.05， P<0.01）， elevated protein levels of Bax， active Caspase-3， and E-cadherin （P<0.05， P<0.01）， and decreased protein levels of Bcl-2 and Vimentin （P<0.05， P<0.01）. β-catenin mRNA expression was reduced in the β-catenin intervention group （P<0.05， 0.63 ± 0.15） and the intervention blank + XJW group （P<0.05， 0.79 ± 0.33）. Compared with the β-catenin intervention group， the β-catenin intervention + XJW group exhibited an increased apoptosis rate （P<0.01）， elevated Bax and active Caspase-3 protein levels （P<0.05， P<0.01）， and decreased Bcl-2， N-cadherin， and Vimentin protein levels （P<0.05）. β-catenin mRNA expression was further reduced in the β-catenin intervention + XJW group （P<0.01， 0.22 ± 0.08）. Compared with the intervention blank + XJW group， the β-catenin intervention + XJW group showed an increased apoptosis rate （P<0.01）， elevated Bax， active Caspase-3， and E-cadherin protein levels （P<0.05， P<0.01）， and decreased Bcl-2， N-cadherin， and vimentin protein levels （P<0.05， P<0.01）. β-catenin mRNA expression was also reduced （P<0.05）. Overexpression experiment results showed that compared with the overexpression blank group， the β-catenin overexpression group exhibited a reduced apoptosis rate （P<0.01）， while the overexpression blank + XJW group showed an increased apoptosis rate （P<0.05）. In the β-catenin overexpression and β-catenin overexpression + XJW groups， Bax and E-cadherin protein levels decreased （P<0.05， P<0.01）， while Bcl-2， N-cadherin， and vimentin levels increased （P<0.05）. In the overexpression blank + XJW group， Bax and E-cadherin levels increased （P<0.05， P<0.01）， while Bcl-2， N-cadherin， and vimentin levels decreased （P<0.05， P<0.01）. β-catenin mRNA expression increased in the β-catenin overexpression group （P<0.01， 7.26 ± 1.03） but decreased in the overexpression blank + XJW group （P<0.05， 0.73 ± 0.14）. Compared with the β-catenin overexpression group， the β-catenin overexpression + XJW group showed an increased apoptosis rate （P<0.01）， elevated Bax and E-cadherin levels （P<0.05）， and decreased N-cadherin and vimentin levels （P<0.05）. β-catenin mRNA expression was reduced （P<0.05， 4.65 ± 1.02）. Compared with the overexpression blank + XJW group， the β-catenin overexpression + XJW group exhibited a reduced apoptosis rate （P<0.01）， decreased Bax and E-cadherin levels （P<0.01）， and increased Bcl-2， N-cadherin， and Vimentin levels （P<0.05）. β-catenin mRNA expression increased （P<0.01）. ConclusionXJW-containing serum inhibits breast cancer cell proliferation by suppressing EMT， which is associated with the regulation of the Wnt/β-catenin pathway.

ObjectiveTo compare the performance of different automatic evaluation methods for detecting pattern hallucinations generated by large language models （LLMs） in traditional Chinese medicine （TCM） syndrome differentiation and to identify a strategy with higher overall discriminative performance against expert manual judgment. MethodsA standardized TCM syndrome-differentiation dataset containing 598 cases was constructed from case reports published in Chinese core journals indexed by Peking University Core Journals or Chinese Science Citation Database. Each of the 598 cases was submitted to five Chinese LLMs， with each model generating one syndrome-pattern output per case， yielding 2990 outputs in total. The 598 outputs generated by Qwen3-Next-80B-A3B-Thinking were independently annotated for syndrome-pattern hallucinations by two licensed TCM physicians with intermediate or higher professional titles. Disagreements were adjudicated by a third licensed TCM physician with a senior associate professional title and more than 10 years of clinical experience. The resulting consensus annotations served as the reference labels for training and validating the automated evaluation method. Based on these reference labels， three categories of automated evaluation methods were developed and compared following a progressive strategy from text-level semantic similarity， to syndrome-element structural consistency， and finally multi-feature fusion. Method A employed sentence-embedding models based on semantic similarity between pattern texts. Method B adopted a rule-based threshold method using weighted Jaccard coefficients of disease-location and disease-nature syndrome elements. Method C utilized a machine-learning classifier integrating syndrome-element matching scores， missing and redundant syndrome-element counts， and multiple semantic-similarity features. Five-fold cross-validation was used to evaluate the discriminative performance of each method against manual judgment. The optimal method was then applied to uniformly assess the pattern hallucination rates of five LLMs. ResultsIn method A， text2vec-large-chinese showed best overall performance， with an area under the receiver operating characteristic curve （AUROC）， area under the precision-recall curve （PR-AUC）， F1-score， sensitivity， and specificity of 0.849， 0.884， 0.794， 0.801， and 0.691， respectively. In method B， the corresponding metrics of the rule-based syndrome-element Jaccard threshold method were 0.848， 0.851， 0.817， 0.845， and 0.684， respectively； those of the LLM-based syndrome-element Jaccard method were 0.872， 0.906， 0.795， 0.764， and 0.780， respectively. In method C， the multilayer perceptron （MLP） classifier showed the best overall performance， with AUROC， PR-AUC， F1-score， sensitivity， and specificity of 0.933， 0.951， 0.871， 0.857， and 0.846， respectively. Under the unified evaluation framework combining DeepSeek-R1 syndrome-element extraction and MLP classifier， the estimated pattern hallucination rates of the five LLMs ranged from 56.0% to 77.4%. ConclusionAmong the three automatic evaluation methods， Method C integrating syndrome-element discrepancy features and text semantic features using an MLP classifier showed the highest overall discriminative performance against manual judgment， and is more suitable for evaluating syndrome hallucinations in LLM-based TCM syndrome differentiation tasks.

Objective: To examine the association between health behaviors and depressive symptoms among junior and senior high school students, so as to provide evidence for combined behavioral interventions targeting adolescent mental health. Methods: From September to November 2023, a stratified cluster random sampling method was used to recruit 104 834 junior and senior high school students from all 13 prefecture level cities (124 districts/counties) in Heilongjiang Province, China. Multivariable Logistic regression models, as well as multiplicative and additive interaction analyses were employed to analyze the associations between health behaviors and depressive symptoms, and Chi-square tests for trend were conducted to explore ,the changing tendency in the risk of depressive symptoms with increasing numbers of health behaviors among junior and senior high school students. Results: Healthy diet ( OR =0.66, 95% CI =0.63-0.68), healthy physical activity ( OR =0.47, 95% CI =0.45-0.49), healthy outdoor activity ( OR =0.55, 95% CI =0.52-0.57), healthy sleep ( OR =0.72, 95% CI =0.69-0.75), and healthy screen use ( OR =0.79, 95% CI =0.76-0.82) were independently associated with lower rates of depressive symptoms (all P <0.01). The risk of depressive symptoms decreased progressively with an increasing cumulative number of health behaviors ( χ 2 trend =996.73, P <0.01). Except for the interaction between healthy diet and healthy sleep, significant additive interactions were observed among the remaining health behaviors( RERI =0.07-0.23,all P <0.05). The probability of depressive symptoms was lower among girls with 2-3 healthy behaviors than among boys ( Z =2.51, 2.65), lower among urban students with 2-3 healthy behaviors than among suburban county students ( Z =-2.36, -2.04), and lower among senior high school students with 2-4 healthy behaviors than among junior high school students ( Z =2.17, 2.10, 2.04) (all P <0.05). Conclusions Health behaviors are significantly associated with depressive symptoms among junior and senior high school students. The probability of depressive symptoms is lower among girls, urban students, and senior high school students who engage in multiple healthy behaviors. Implementing combined interventions targeting multiple health behaviors in these populations may yield greater benefits.

Objective To investigate the predictive value of D-dimer for in-hospital mortality and heart failure with reduced ejection fraction (HFrEF) in patients with acute myocardial infarction (AMI). Methods A total of 688 patients with AMI who were hospitalized at Zhongshan Hospital, Fudan University, from January 2020 to December 2023 were retrospectively enrolled. Demographic and clinical characteristics, laboratory findings, echocardiographic parameters, and in-hospital outcomes were collected. Patients were divided into four groups according to D-dimer quartiles, and trend tests were used to compare changes in each indicator with increasing D-dimer levels. The primary endpoints were in-hospital mortality and HFrEF. Multivariable logistic regression analysis was performed to assess the independent associations of D-dimer with in-hospital mortality and HFrEF. Receiver operating characteristic (ROC) curve analysis was used to evaluate its predictive performance. Results Differences and trend tests among four groups for age, creatinine, peak cardiac troponin T, N-terminal pro-brain natriuretic peptide (NT-proBNP), left ventricular ejection fraction, HFrEF incidence, and in-hospital mortality were statistically significant (P＜0.05). Multivariable logistic regression analysis showed that elevated D-dimer was associated with an increased risk of in-hospital mortality in patients with AMI (OR＝1.251, 95%CI 1.093–1.432, P＝0.001), and HFrEF (OR＝1.075, 95%CI 1.028–1.124, P＝0.002). ROC curve analysis showed that the area under the curve (AUC) of D-dimer for predicting in-hospital mortality and HFrEF were 0.871 and 0.716, respectively. At a cutoff value of 0.47 mg/L, the sensitivity and specificity of D-dimer in predicting HFrEF were 81.9% and 52.1%, respectively; at a cutoff value of 2.18 mg/L, the sensitivity and specificity of D-dimer in predicting in-hospital mortality were 63.6% and 93.8%, respectively. Restricted cubic spline analysis demonstrated significant non-linear associations between D-dimer and the risks of in-hospital mortality and HFrEF (P＜0.001). Conclusions Elevated D-dimer level is significantly associated with increased risks of in-hospital mortality and HFrEF in patients with AMI and shows acceptable predictive performance. As a simple and readily available laboratory biomarker, D-dimer may provide useful information for early risk stratification and clinical management in patients with AMI.