Traditional Chinese medicine （TCM） clinical trials face challenges such as low participant compliance， insufficient geographical coverage， and cost-effectiveness imbalances. Decentralized clinical trials （DCT）， enabled by digital technology for remote data collection and monitoring， offer a new direction for TCM clinical trial research. This article systematically reviews three novel clinical trial design models. Combining the holistic concept and indivi-dualized treatment characteristics of TCM， it analyzes the challenges currently faced in TCM DCT practice， including the digitization and standardization of TCM theory， data security， privacy protection and patient engagement difficu-lties， insufficient ethical review and regulatory system adaptation， inadequate personnel training， and a shortage of interdisciplinary talent. Addressing these challenges， the article proposes methodological recommendations for DCT implementation that align with the principles of TCM diagnosis and treatment. These recommendations include promoting the intelligentization and standardization of TCM practices， constructing a full-chain data security and privacy protection system， improving the ethical framework and clarifying regulatory responsibilities， and cultivating and building interdisciplinary talent and capabilities， which provide theoretical and technical references for establishing standardized DCT practices in TCM.

Traditional Chinese medicine （TCM） clinical trials face challenges such as low participant compliance， insufficient geographical coverage， and cost-effectiveness imbalances. Decentralized clinical trials （DCT）， enabled by digital technology for remote data collection and monitoring， offer a new direction for TCM clinical trial research. This article systematically reviews three novel clinical trial design models. Combining the holistic concept and indivi-dualized treatment characteristics of TCM， it analyzes the challenges currently faced in TCM DCT practice， including the digitization and standardization of TCM theory， data security， privacy protection and patient engagement difficu-lties， insufficient ethical review and regulatory system adaptation， inadequate personnel training， and a shortage of interdisciplinary talent. Addressing these challenges， the article proposes methodological recommendations for DCT implementation that align with the principles of TCM diagnosis and treatment. These recommendations include promoting the intelligentization and standardization of TCM practices， constructing a full-chain data security and privacy protection system， improving the ethical framework and clarifying regulatory responsibilities， and cultivating and building interdisciplinary talent and capabilities， which provide theoretical and technical references for establishing standardized DCT practices in TCM.

Implementation science (IS) aims to systematically analyze and address the real-world gaps from evidence to practice and the influencing factors of the context. It is necessary to carry out qualitative research to gather relevant implementation outcomes. Nevertheless, traditional qualitative analysis has issues such as consuming a great deal of time and energy, and it is unable to promptly provide the crucial data required for implementation science research. The Rapid Qualitative Analysis (RQA) method, through semi-structured interviews and the adoption of techniques such as immediate data condensation and matrix analysis, can effectively shorten the cycle of qualitative data collection and data processing. RQA can promptly identify social determinants of health such as structural barriers, facilitators, and the behavioral characteristics of target groups. It provides a real-time basis for public health decision-making, the interpretation of complex social phenomena, and the process and effectiveness evaluation of research projects. Although RQA is difficult to conduct in-depth theoretical analysis based on grounded theory, its efficiency and flexibility make it the preferred tool for large-scale and time-sensitive research. Thus, it has been widely applied in implementation science research. This paper sorts out the core concepts and commonly used technical methods of RQA, as well as the differences between RQA and traditional qualitative analysis. It also explores the applications of RQA in intervention optimization, process evaluation, and implementation outcome evaluation. By integrating specific cases, this paper clarifies its application value in the field of implementation science. In the future, it is advisable to explore the integration of RQA with technologies such as artificial intelligence and big data, in order to bridge the gap between the transformation of scientific research achievements into practice. Under circumstances of limited resources or tight time constraints, RQA can be used to efficiently conduct implementation science research, providing convenient and scientific methodological and technical support for accelerating evidence-based practice.

ObjectiveTo construct a scale for the diagnosis of chronic atrophic gastritis （CAG） with turbid toxin accumulating in the stomach. MethodsFirst， a research group was established to construct the scale framework. Relevant literature of CAG with syndrome of turbid toxin accumulating in the stomach was searched in CNKI， Wanfang Database （WF）， and VIP Database （CQVIP） from April 1， 2003 to April 1， 2023， and items were preliminarily selected after standardization of terms. Through clinical investigation， the discrete trend method， correlation coefficient method， Cronbach's coefficient method， and factor analysis method were used to screen symptom items， and the frequency method was used to screen signs， tongue coating， and pulse conditions. Three rounds of Delphi expert consultation were conducted to determine the items of the scale. The weight of each item was obtained by the analytic hierarchy process. ResultsA total of 49 articles were included， and 45 items were obtained after primary screening， including 28 symptoms， 2 signs， 10 tongue coatings， and 5 pulse conditions. After clinical investigation， 15 symptoms were retained， and 8 signs and pulse conditions of tongue coating were retained. The positive coefficients of experts in three rounds of Delphi expert consultation were 100%， 96.67%， and 100%， respectively. The expert authority coefficients were 0.86， 0.87， and 0.87， respectively， and the coordination coefficients were 0.18， 0.25， and 0.30. After core group discussion， Delphi method investigation， and AHP weight assignment， the diagnostic scale items of CAG with turbid toxin accumulating in stomach syndrome were finally established， namely， dark red or purplish tongue proper with yellow greasy （or dry） coating （30 points）， epigastric stuffiness and fullness or pain （15 points）， sticky and unsmooth defecation （10 points）， taste disturbance （sticky mouth， fetid breath， bitter taste， 7 points）， heartburn or acid regurgitation （6 points）， dizziness and clouding （5 points）， general heaviness and fatigue （5 points）， slippery， string‑slippery， or slippery‑rapid pulse （5 points）， dysuria （or yellow or deep yellow urine， 4 points）， poor appetite （4 points）， dull complexion （3 points）， sticky， greasy， and fetid secretions （3 points）， and poor sleep （3 points）. ConclusionBased on the establishment， screening， confirmation， and weighting of an item pool， combined with subjective and objective approaches as well as qualitative and quantitative methods， a diagnostic scale for CAG with the syndrome of turbid toxin accumulating in the stomach was successfully constructed.

Objective: To explore the association between the joint association of screen time on weekdays and weekends with self rated sleep quality among adolescents, providing scientific basis for improving adolescent sleep health. Methods: Using data from "Huantai Children s Cardiovascular Health Follow up Cohort" survey conducted during November to December 2023, a total of 1 197 adolescents aged 12-17 years with complete demographic characteristics, physical measurements, and questionnaire data were included. The dose response relationship between screen time and sleep quality was evaluated by restrictive cubic splines (RCS). Based on screen time on weekdays and weekends (normal: < 2 h/d; excessive: ≥ 2 h/d), adolescents were categorized into 4 groups. Multivariate Logistic regression models were used to analyze the association between the combined effect of screen time on weekdays and weekends and sleep quality. Results: The RCS analysis results showed that there was a linear dose response relationship between average daily screen time and poor sleep quality in adolescents (non linear P >0.05). The results of multiple Logistic regression analysis showed that adjusting for potential covariates,compared with adolescents having normal screen time on both weekdays and weekends, those with excessive screen time on both weekdays and weekends had increased risk of poor sleep quality ( OR=2.51, 95%CI=1.33-4.62, P <0.01). Sex stratified analysis revealed that girls with excessive screen time on both weekdays and weekends had increased odds of poor sleep quality ( OR=3.03, 95%CI=1.23-7.14, P =0.01), compared to girls who had normal screen time on both weekdays and weekends. Conclusions A linear dose response relationship exists between daily screen time and poor sleep quality in adolescents. Excessive screen time on both weekdays and weekends is positively associated with poor sleep quality, particularly more pronounced among female adolescents.

The aging microenvironment, as a key driver of tumorigenesis and progression, plays a critical role in tumor immune regulation through one of its core features-the senescence-associated secretory phenotype (SASP). SASP consists of a variety of interleukins, chemokines, proteases, and growth factors. It initially induces surrounding cells to enter a state of senescence through paracrine mechanisms, thereby creating a sustained inflammatory stimulus and signal amplification effect within the tissue microenvironment. Furthermore, these secreted factors activate key signaling pathways such as NF-κB, cGAS-STING, and mTOR, which regulate the expression of immune-related molecules (such as PD-L1) and promote the recruitment of immunosuppressive cells, including regulatory T cells and myeloid-derived suppressor cells. This process ultimately contributes to the formation of an immunosuppressive tumor microenvironment. Furthermore, the article explores potential anti-tumor immunotherapy strategies targeting SASP and its associated molecular mechanisms, including approaches to inhibit SASP secretion or eliminate senescent cells. Although these strategies have shown promise in certain tumor models, the high heterogeneity among tumor types may result in varied responses to SASP-targeted therapies. This highlights the need for further research into adaptive stratification and personalized treatment approaches. Targeting immune regulatory mechanisms in the aging microenvironment-particularly SASP-holds great potential for advancing future anti-tumor therapies.

Objective:To develop a predictive model for adverse pregnancy outcomes in patients with pregnancy-related acute kidney injury (Pr-AKI) using machine learning methods.Methods:This study was a single-center retrospective study. Patients with Pr-AKI in the First Affiliated Hospital of Xinjiang Medical University from January 2013 to December 2020 were included. Demographic characteristics, laboratory parameters, and fetal outcomes for comparative analysis between adverse pregnancy outcome group and favorable pregnancy outcome group were collected. Adverse pregnancy outcomes were defined as the occurrence of any one or more of the following events: stillbirth, perinatal death, preterm birth (reaching 28 weeks but less than 37 weeks), and low birth weight (< 2.5 kg). Conversely, an ideal pregnancy outcome was defined as the absence of any adverse pregnancy outcome events. The dataset was randomly divided into a training set (70%) and a validation set (30%). Logistic regression, decision tree, random forest, K-nearest neighbor, support vector machine, and lightweight gradient boosting algorithms were employed on the training set to develop predictive models for adverse pregnancy outcomes in patients with Pr-AKI. Receiver operating characteristic curves were plotted, and the area under the curves ( AUC) were calculated. Recall, precision, accuracy, and F1 scores were used to evaluate the predictive performance of each model. The optimal machine learning model was selected for subsequent analysis. Predictive model variables were screened and compressed by visualizing SHAP (SHapley additive exPlanations) with recursive feature regression. Furthermore, the efficacy of each model was evaluated through calibration curves and clinical decision curves. The optimal predictive model was selected for internal validation using the validation set, and data of in-hospital Pr-AKI patients (72 cases) in the hospital from January 2021 to June 2023 were collected for validation (time series validation set). Results:A total of 458 pregnancies in 441 patients were included in the present analysis, among which 277 cases (60.5%) resulted in adverse pregnancy outcomes. Utilizing the training set, 21 feature variables were selected for model construction. Among the 6 models, the random forest model performed the best ( AUC=0.860, recall=0.784, precision=0.813, F1-score=0.790, accuracy=0.806). With subsequent feature refinement proceeding, a total of 12 clinical indicators were selected to construct the model. Among them, proteinuria, systolic blood pressure, and the highest serum creatinine were the top three related factors, and the other related factors included: severe preeclampsia, baseline serum creatinine, serum albumin, diastolic blood pressure, aspartate aminotransferase, blood uric acid, white blood cell count, serum cystatin C, and cholesterol. Among various machine learning models, the random forest model demonstrated optimal net benefits and the widest clinical utility range, showing robust performance in both internal validation set ( AUC=0.80) and the time series validation set ( AUC=0.72). Conclusions:In this study, different machine learning algorithms are successfully applied to develop predictive models for adverse pregnancy outcomes in patients with Pr-AKI. The random forest model is translated into a clinically applicable tool, providing a reference for the convenient and rapid identification of adverse pregnancy outcomes in Pr-AKI patients.

Objective:To compare the efficacy and safety of flow diverter (FD) implantation versus stent-assisted coil embolization (SACE) in unruptured saccular aneurysms located at distal segments of the Willis circle.Methods:A retrospective study was performed; 119 patients with unruptured saccular aneurysms located at distal segments of the Willis circle who received FD implantation or SACE in Department of Neurosurgery, Guangdong Provincial People's Hospital, Southern Medical University and Department of Cerebrovascular Surgery, Neurosurgery Center, Southern Medical University from January 2018 to May 2025 were selected. According to different surgical methods, they were divided into a FD group ( n=60) and a SACE group ( n=59). These patients were subjected to propensity score matching (PSM) using 1:1 nearest neighbor matching method to eliminate confounding factors such as gender, age, aneurysm location, and aneurysm diameter (maximum diameter); and then, differences in neurological functional prognosis (good prognosis: modified Rankin scale scores of 0-2), complete aneurysm occlusion rate (O'Kelly-Marotta grade D or Raymond occlusion classification class I), and overall (perioperative and follow-up) complications were compared between the two groups. Results:After PSM, there were 43 patients in both FD group and SACE group. No significant difference was found in clinical and imaging follow-ups (12[6, 19] months vs. 10[6, 15] months, 10[5, 19] months vs. 9[2, 15] months) between the FD group and SACE group ( P>0.05). At the last imaging follow-up, the complete aneurysm occlusion rate in the FD group (65.1%[28/43]) was significantly lower than that in the SACE group (76.7%[33/43], P<0.05). At the last clinical follow-up, no significant difference was noted in good prognosis rate (95.3%[41/43] vs. 97.7%[42/43]) and overall complication rate (30.2%[13/43] vs. 18.6%[8/43]) between the FD group and SACE group ( P>0.05). Conclusion:Both FD implantation and SACE are safe and effective for unruptured saccular aneurysms located at distal segments of the Willis circle; the complete aneurysm occlusion rate within 1 year of FD implantation is relatively low.

Diabetic kidney disease(DKD),a prevalent complication of diabetes mellitus,remains a leading cause of end-stage renal disease.Recent research has identified lipophagy,a novel mechanism in DKD pathogenesis,drawing increasing attention in the field.This paper explores the biological connotation of the"internal heat leading to Zheng"pathogenesis based on lipophagy.The study proposes that lipophagy represents the microscopic biological correlation of liver-spleen coordination in regulating spleen transport and the ascending-descending dynamics of the middle jiao.Under persistent hyperglycemia,the suppression of lipophagic activity mirrors the traditional Chinese medicine(TCM)pathophysiological process described as"excessive fire consuming healthy qi,"whereas aberrant lipid accumulation in the kidney corresponds to the dynamic aggregation and dispersion of micro-zhengjia.Lipotoxicity,a key driver of DKD progression,is interpreted as the biological manifestation of accumulated turbidity transforming into toxicity,resulting in progressive impairment of renal essence and function.The dynamic process of lipophagy dysfunction under hyperglycemia,marked by renal microangiopathy,glomerular and tubular dysfunction,and renal fibrosis,closely mirrors the pathological evolution of"micro-zhengjia"and"internal heat leading to Zheng."Consequently,TCM strategies for DKD prevention and treatment should emphasize heat regulation,stage-specific interventions,liver-spleen harmonization,metabolic modulation,early collateral protection,and blood-activating approaches.

Objective To evaluate the therapeutic efficacy of stenting combined with medication versus medication alone in treating severe extracranial carotid artery stenosis.Methods This prospective,multicenter cohort study enrolled 337 patients with carotid artery stenosis ranging from 70%to 99%.Based on patient preference,they were divided into two groups:a stent combined with medication group(antiplatelet drugs+statins,n=169)and a medication-only group(n=168).The study assessed perioperative safety and the incidence of recurrent cerebrovascular events related to the target vessel within one year.Results The success rate of the stent combined with medication group was 100%.There was one case of hyperperfusion-related intracranial hemorrhage during the perioperative period(1/169)The patient recovered well after medical treatment.There was no statistically significant difference in perioperative complications between the two groups(P=0.318).During the 1-year follow-up,the stent combined with medication group(3/169)had a significantly lower incidence of recurrent ischemic stroke compared to the medication-only group(15/168),with a statistically significant difference(P=0.003).Univariate analysis showed that gender(χ2=4.372,P=0.037),stenosis rate≥85%(χ2=10.738,P=0.001),and non-stent treatment(χ2=7.171,P=0.007)were risk factors for recurrent stroke.However,the number of recurrent ischemic strokes in females was relatively small and after adjusting the chi-square test,the difference was not statistically significant(P=0.075).Logistic regression analysis revealed that stenosis rate≥85%(OR=115.925,95%CI:2.077-122.102,P=0.008)and non-stent treatment(OR=0.158,95%CI:0.044-0.564,P=0.004)were independent risk factors for recurrent cerebrovascular events within one year.Conclusions In patients with≥85%extracranial carotid artery stenosis,stenting combined with medication reduces recurrent ischemic cerebrovascular events within 1 year.