Objective To systematically evaluate the risk prediction models for postoperative delirium in adults with cardiac surgery. Methods The SinoMed, CNKI, Wanfang, VIP, PubMed, EMbase, Web of Science, and Cochrane Library databases were searched to collect studies on risk prediction models for postoperative delirium in cardiac surgery published up to January 29, 2025. Two researchers screened the literature according to inclusion and exclusion criteria, used the PROBAST bias tool to assess the quality of the literature, and conducted a meta-analysis of common predictors in the model using Stata 17.0 software. Results A total of 21 articles were included, establishing 45 models with 28733 patients. Age, cardiopulmonary bypass time, history of diabetes, history of cerebrovascular disease, and gender were the top five common predictors. The area under the curve (AUC) of the 45 models ranged from 0.544 to 0.98. Fourteen out of the 21 studies had good applicability, while the applicability of the remaining seven was unclear; 20 studies had a high risk of bias. Meta-analysis showed that the incidence of postoperative delirium in adults with cardiac surgery was 18.6% [95%CI (15.7%, 21.6%)], and age [OR=1.045 (1.036, 1.054), P<0.001], history of cerebrovascular disease [OR=1.758 (1.459, 2.057), P<0.001], gender [OR=1.732 (1.430, 2.034), P<0.001], mini-mental state examination score [OR=3.930 (1.859, 8.309), P<0.001], and length of ICU stay [OR=5.586 (4.289, 6.883), P<0.001] were independent influencing factors for postoperative delirium after cardiac surgery. Conclusion The risk prediction models for postoperative delirium after cardiac surgery have good predictive performance, but there is a high overall risk of bias. In the future, large-sample, multicenter, high-quality prospective clinical studies should be conducted to construct the optimal risk prediction model for postoperative delirium in adults with cardiac surgery, aiming to identify and prevent the occurrence of postoperative delirium as early as possible.

Objective: To improve the efficiency of patient treatment in routine, emergency, and mass casualty scenarios (both peacetime and wartime), eliminate reliance on blood type compatibility, and promote the productization of platelet-rich plasma (PRP), this study investigated the optimal mixing ratio for developing a universal PRP formulation. Methods: PRP was prepared from donors with blood types O, A, B, and AB. Samples were mixed in varying proportions, and the optimal ratio was determined based on the trend of anti-A and anti-B antibody titers in the mixed PRP. Concentrations of the growth factors PDGF-AA, PDGF-BB, EGF, TGF-β1, and VEGF were measured by enzyme-linked immunosorbent assay (ELISA) before and after mixing. Results: A mixing ratio of A∶B∶AB=6∶2.5∶24 resulted in no detectable agglutination with either A or B cells in the microcolumn gel system, indicating the absence of immunological reactivity and confirming universal compatibility. Importantly, the levels of all measured growth factors remained unchanged following mixing, suggesting preservation of biological activity. Conclusion: These findings suggest that universal PRP can be prepared by neutralizing blood type antibodies through antigen-antibody reactions.

Objective To analyze the risk factors for delayed hemorrhage in patients with early gastric carcinoma(EGC)after endoscopic submucosal dissection(ESD),as well as to explore the predictive value of predictive models.Methods The clinical data of 386 EGC treated with ESD patients from July 2019 to March 2024 were retrospectively analyzed.They were followed up for a short period of time(24 h～30 d postoperatively)after ESD,and were divided into the group of occurrence(50 cases)and the group of non-occurrence(336 cases)according to the occurrence or non-occurrence of delayed hemorrhage.To understand the clinical data of the patients,multivariate Logistic regression was used to analyze the risk factors for delayed hemorrhage in patients with EGC after ESD,and plot receiver operating characteristic curve(ROC curve)to analyze the predicted value.Results The proportions of cardia gastric fundus,lesion diameter ≥ 3 cm,submucosa with fibrosis,coarse blood vessels,and having ulcer or scar in the occurrence group were significantly higher than those in the non-occurrence group,the differences were statistically significant(P＜0.05).The results of multivariate Logistic regression analysis(with an introduction level of 0.05)showed that the lesion location was cardia-gastric fundus(OR=1.088,95％CI:1.040～1.138),the diameter of the lesion was ≥3 cm(OR=1.095,95％CI:1.057～1.135),and fibrosis under the mucosa(OR=1.130,95％CI:1.061～1.203),thick blood vessels under the mucosa(OR=1.177,95％CI:1.116～1.241),and the presence of ulcer or scar(OR=1.082,95％CI:1.057～1.108)were the risk factors for the occurrence of delayed hemorrhage in patients with EGC after ESD(P＜0.05).A prediction model was established:Logit(P)=-9.238+Location of lesion as cardia-gastric fundus × 0.084+Diameter of lesion ≥ 3 cm× 0.091+Submucosal with fibrosis × 0.122+Submucosal with thick blood vessels × 0.163+Ulcer or scar×0.079(P＜0.05),the likelihood ratio test yielded x2=131.09,DF=8,P＜0.05,which indicated that the constructed prediction model was valid.Hosmer-Lemeshow goodness-of-fit test showed a good model fit(P＞0.05).The ROC curve showed an area under the curve(AUC)value of 0.929 at a cutoff value of 12.00,with a sensitivity of 88.00％(95％CI:0.757～0.955)and a specificity of 83.04％(95％CI:0.780～0.869).Conclusion Postoperative delayed hemorrhage in patients with EGC treated with ESD is associated with cardia-gastric fundus,lesion diameter ≥3 cm,submucosal fibrosis,thick blood vessels,and the presence of ulcer or scar,and the prediction value of the prediction model constructed accordingly is good.

Objective:To develop an artificial intelligence system using fully convolutional neural networks（FCN）to assist echocardiographers in the quantitative assessment of mitral regurgitation（MR）severity.Methods:From August 2021 to June 2024，echocardiographic images of 441 patients with MR were prospectively collected from Renmin Hospital of Wuhan University and the Central Hospital of Wuhan. After screening，a total of 269 patients（4 917 frames）were included in the study. Of these，3 644 frames（128 patients）of apical four-chamber color Doppler MR flow convergence images from Renmin Hospital of Wuhan University were selected as the training/validation set，while images from 121 patients（813 frames）were used as the internal test set. Additionally，images from 20 patients（460 frames）from the Central Hospital of Wuhan were selected as the external test set. The FCN algorithm was employed to capture features and segment the MR color region on the left atrial side，simultaneously outputting the regurgitant radius（r）for the calculation of the effective regurgitant orifice area and regurgitant volume. The severity of MR was then classified according to the 2017 guidelines of the American Society of Echocardiography. The segmentation and classification performance of the model was evaluated，and the measurement results of the AI system was compared with that of both senior and junior physicians.Results:In the internal test set，the accuracy of r identification for cases classified as Grade Ⅰ to Ⅳ was 0.48，0.81，0.86，and 0.87，respectively. In the external test set，the accuracy of r identification for cases classified as Grade Ⅰ to Ⅳ was 0.60，0.77，0.64，and 0.77，respectively. The average accuracy of MR classification in the internal and external test sets was 0.91 and 0.88，respectively.Conclusions:The FCN model is capable of segmenting the left atrial side regurgitant areas in apical four-chamber heart color Doppler images，aiding physicians in obtaining quantitative assessment parameters for MR，and assisting junior physicians in accurately assessing the severity of MR.

Objective To analyze the risk factors for delayed hemorrhage in patients with early gastric carcinoma(EGC)after endoscopic submucosal dissection(ESD),as well as to explore the predictive value of predictive models.Methods The clinical data of 386 EGC treated with ESD patients from July 2019 to March 2024 were retrospectively analyzed.They were followed up for a short period of time(24 h～30 d postoperatively)after ESD,and were divided into the group of occurrence(50 cases)and the group of non-occurrence(336 cases)according to the occurrence or non-occurrence of delayed hemorrhage.To understand the clinical data of the patients,multivariate Logistic regression was used to analyze the risk factors for delayed hemorrhage in patients with EGC after ESD,and plot receiver operating characteristic curve(ROC curve)to analyze the predicted value.Results The proportions of cardia gastric fundus,lesion diameter ≥ 3 cm,submucosa with fibrosis,coarse blood vessels,and having ulcer or scar in the occurrence group were significantly higher than those in the non-occurrence group,the differences were statistically significant(P＜0.05).The results of multivariate Logistic regression analysis(with an introduction level of 0.05)showed that the lesion location was cardia-gastric fundus(OR=1.088,95％CI:1.040～1.138),the diameter of the lesion was ≥3 cm(OR=1.095,95％CI:1.057～1.135),and fibrosis under the mucosa(OR=1.130,95％CI:1.061～1.203),thick blood vessels under the mucosa(OR=1.177,95％CI:1.116～1.241),and the presence of ulcer or scar(OR=1.082,95％CI:1.057～1.108)were the risk factors for the occurrence of delayed hemorrhage in patients with EGC after ESD(P＜0.05).A prediction model was established:Logit(P)=-9.238+Location of lesion as cardia-gastric fundus × 0.084+Diameter of lesion ≥ 3 cm× 0.091+Submucosal with fibrosis × 0.122+Submucosal with thick blood vessels × 0.163+Ulcer or scar×0.079(P＜0.05),the likelihood ratio test yielded x2=131.09,DF=8,P＜0.05,which indicated that the constructed prediction model was valid.Hosmer-Lemeshow goodness-of-fit test showed a good model fit(P＞0.05).The ROC curve showed an area under the curve(AUC)value of 0.929 at a cutoff value of 12.00,with a sensitivity of 88.00％(95％CI:0.757～0.955)and a specificity of 83.04％(95％CI:0.780～0.869).Conclusion Postoperative delayed hemorrhage in patients with EGC treated with ESD is associated with cardia-gastric fundus,lesion diameter ≥3 cm,submucosal fibrosis,thick blood vessels,and the presence of ulcer or scar,and the prediction value of the prediction model constructed accordingly is good.

Objective:To develop an artificial intelligence system using fully convolutional neural networks（FCN）to assist echocardiographers in the quantitative assessment of mitral regurgitation（MR）severity.Methods:From August 2021 to June 2024，echocardiographic images of 441 patients with MR were prospectively collected from Renmin Hospital of Wuhan University and the Central Hospital of Wuhan. After screening，a total of 269 patients（4 917 frames）were included in the study. Of these，3 644 frames（128 patients）of apical four-chamber color Doppler MR flow convergence images from Renmin Hospital of Wuhan University were selected as the training/validation set，while images from 121 patients（813 frames）were used as the internal test set. Additionally，images from 20 patients（460 frames）from the Central Hospital of Wuhan were selected as the external test set. The FCN algorithm was employed to capture features and segment the MR color region on the left atrial side，simultaneously outputting the regurgitant radius（r）for the calculation of the effective regurgitant orifice area and regurgitant volume. The severity of MR was then classified according to the 2017 guidelines of the American Society of Echocardiography. The segmentation and classification performance of the model was evaluated，and the measurement results of the AI system was compared with that of both senior and junior physicians.Results:In the internal test set，the accuracy of r identification for cases classified as Grade Ⅰ to Ⅳ was 0.48，0.81，0.86，and 0.87，respectively. In the external test set，the accuracy of r identification for cases classified as Grade Ⅰ to Ⅳ was 0.60，0.77，0.64，and 0.77，respectively. The average accuracy of MR classification in the internal and external test sets was 0.91 and 0.88，respectively.Conclusions:The FCN model is capable of segmenting the left atrial side regurgitant areas in apical four-chamber heart color Doppler images，aiding physicians in obtaining quantitative assessment parameters for MR，and assisting junior physicians in accurately assessing the severity of MR.

Healthy lifestyles and good living habits are effective strategies and important approaches to prevent chronic non-communicable diseases. With the development of evidence-based medicine, the evidence translation system has made some achievements in clinical practice. There is, however, no comprehensive, professional and efficient system for translating lifestyle evidence globally. Therefore, the Health Lifestyle Evidence (HLSE) Group of Lanzhou University constructed the HLSE Evidence Translation System (https://hlse.cn/), with the aim of synthesizing, evaluating, translating, and applying lifestyle-related evidence resources. This paper aims to introduce the functional module design of the evidence translation system, the system development process and testing, introduce the interface design and function demonstration, and explain the significance of constructing the HLSE.

To systematically construct a guideline to provide a methodological guide for researchers to develop patient decision aids.

Adaptive design, with advantages such as dynamically adjusting trial plans, reducing resource waste, and improving trial efficiency, has broken through the competitive situation of new drug development and gradually met the needs of clinical research. In recent years, the use of adaptive design in platform trials as an innovative research model has added impetus to new drug development. This article outlines the research progress, contents and characteristics, common design types, statistical analysis, and case interpretation of adaptive design, and introduces the concept, types, and applications of adaptive platform trials, with the hope of providing scientific reference for further exploration of clinical trials and new drug development.