Objective To explore the characteristics of fine motor deficits in the elderly individuals with MCI due to AD through a new wearable inertial motion capture device,and then construct a prediction model for MCI.Methods A total of 260 elderly subjects were recruited in community from November,2022 to April,2023,and based on diagnosis,they were divided into a MCI group(134 cases)and a control group(126 cases).A new wearable inertial motion capture device,which was self-designed and developed based on MEMS inertial sensor,was used to capture the fine mo-tor movements of the hands,and the obtained data were analyzed with a computerized assessment system to make the quantitative evaluation of fine motor.LASSO learning algorithm and logistic regression analysis were employed to identify the predictive factors for MCI,and then a nomo-gram was constructed based on these factors.ROC curve was plotted to evaluate the predictive ability of the model by calculating its AUC value.DC A,CIC,and Bootstrap method were applied to evaluate and validate the clinical utility and stability of the model.Results The total score of MoCA(22.18±2.84 vs 27.60±1.10)and scores of the dimensions were significantly lower in the MCI group than the control group(all P＜0.01).In the five digital assessment tasks,the MCI group showed obviously poorer fine motor performance of both hands than the control group(P＜0.05,P＜0.01).ROC curve analysis showed that the AUC value of our nomogram model in predicting MCI was 0.762(95％CI:0.705-0.819).DCA,CIC,and Bootstrap methods demonstra-ted good and relatively stable discrimination,calibration,and clinical applicability of the model.Conclusion MEMS inertial sensor motion capture technology can make digital evaluation of fine motor.For the elderly,fine motor deficits are significantly associated with risk for MCI.Our no-mogram model based on fine motion parameters shows good predictive efficacy in assessing the risk of MCI.

Objective:To construct a hierarchical training course system for neonatal nurses based on core competencies, to provide a reference for meeting the training needs of neonatal nurses under the new situation.Methods:Through literature review, questionnaire survey on training needs, and focus group interviews, a preliminary hierarchical training curriculum system for neonatal nurses was developed. Two rounds of Delphi correspondence were conducted with 19 domestic experts to finalize the system.Results:The effective questionnaire recovery rates of the two rounds of expert consultation were 95.00% and 100.00%, and the expert authority coefficient was 0.916, the Kendall harmony coefficient of the first round of expert opinions was 0.351 ( P<0.001), and the Kendall harmony coefficient of the second round of expert opinions was 0.463 ( P<0.001). The hierarchical training course structure and course training content are formed, including N0: 3 first-level items, 9 second-level items, 80 third-level items, N1: 3 first-level items, 9 second-level items, 91 third-level items, N2: 3 first-level items, 9 second-level items, 86 third-level items, N3: 3 first-level items, 10 second-level items, 81 third-level items, N4: 3 first-level items, 10 second-level items, 76 third-level items. Conclusions:The hierarchical training course system for neonatal nurses based on the core competence of nurses is scientific and practical, which can provide a reference for the hierarchical training of neonatal nurses.

Objective To observe the value of the integrated model of MR high-resolution vascular wall imaging(HR-VWI)and attention mechanism for predicting stroke recurrence in symptomatic intracranial atherosclerotic stenosis(sICAS)patients.Methods A total of 363 patients with sICAS who underwent HR-VWI were enrolled and stratified into training set(n=254)and validation set(n=109)according to their origins.Employing a radiomics model that utilized HR-VWI T1 and contrast-enhanced sequences for feature extraction,image data were captured from relevant plaques.Subsequently,a Trans model was developed by integrating the Transformer attention mechanism.The predictive performance and clinical utility of conventional radiomics models and Trans models for forecasting stroke recurrence among patients with sICAS were evaluated.Results In training set and validation set,the area under the curve of Trans model for predicting stroke recurrence in sICAS patients was 0.992 and 0.988,respectively,both superior to that of T1 model,T1 enhanced model and dual sequence model(all P＜0.05).The calibration curve and decision curve analysis showed that Trans model had good predictive probability and clinical practicality.Conclusion The obtained integrated model of HR-VWI radiomics combined with attention mechanism had certain value for predicting stroke recurrence in patients with sICAS.

Objective To explore the characteristics of fine motor deficits in the elderly individuals with MCI due to AD through a new wearable inertial motion capture device,and then construct a prediction model for MCI.Methods A total of 260 elderly subjects were recruited in community from November,2022 to April,2023,and based on diagnosis,they were divided into a MCI group(134 cases)and a control group(126 cases).A new wearable inertial motion capture device,which was self-designed and developed based on MEMS inertial sensor,was used to capture the fine mo-tor movements of the hands,and the obtained data were analyzed with a computerized assessment system to make the quantitative evaluation of fine motor.LASSO learning algorithm and logistic regression analysis were employed to identify the predictive factors for MCI,and then a nomo-gram was constructed based on these factors.ROC curve was plotted to evaluate the predictive ability of the model by calculating its AUC value.DC A,CIC,and Bootstrap method were applied to evaluate and validate the clinical utility and stability of the model.Results The total score of MoCA(22.18±2.84 vs 27.60±1.10)and scores of the dimensions were significantly lower in the MCI group than the control group(all P＜0.01).In the five digital assessment tasks,the MCI group showed obviously poorer fine motor performance of both hands than the control group(P＜0.05,P＜0.01).ROC curve analysis showed that the AUC value of our nomogram model in predicting MCI was 0.762(95％CI:0.705-0.819).DCA,CIC,and Bootstrap methods demonstra-ted good and relatively stable discrimination,calibration,and clinical applicability of the model.Conclusion MEMS inertial sensor motion capture technology can make digital evaluation of fine motor.For the elderly,fine motor deficits are significantly associated with risk for MCI.Our no-mogram model based on fine motion parameters shows good predictive efficacy in assessing the risk of MCI.

Objective:To construct a hierarchical training course system for neonatal nurses based on core competencies, to provide a reference for meeting the training needs of neonatal nurses under the new situation.Methods:Through literature review, questionnaire survey on training needs, and focus group interviews, a preliminary hierarchical training curriculum system for neonatal nurses was developed. Two rounds of Delphi correspondence were conducted with 19 domestic experts to finalize the system.Results:The effective questionnaire recovery rates of the two rounds of expert consultation were 95.00% and 100.00%, and the expert authority coefficient was 0.916, the Kendall harmony coefficient of the first round of expert opinions was 0.351 ( P<0.001), and the Kendall harmony coefficient of the second round of expert opinions was 0.463 ( P<0.001). The hierarchical training course structure and course training content are formed, including N0: 3 first-level items, 9 second-level items, 80 third-level items, N1: 3 first-level items, 9 second-level items, 91 third-level items, N2: 3 first-level items, 9 second-level items, 86 third-level items, N3: 3 first-level items, 10 second-level items, 81 third-level items, N4: 3 first-level items, 10 second-level items, 76 third-level items. Conclusions:The hierarchical training course system for neonatal nurses based on the core competence of nurses is scientific and practical, which can provide a reference for the hierarchical training of neonatal nurses.

Objective:To explore the mechanism by which governance mechanisms within the county medical community affect organizational collaboration.Methods:Based on institutional logic theory,this study analyzes the bureaucratic logic,market logic,and community logic within the county medical community,clarifies the administrative mechanisms,competitive mechanisms,relational mechanisms,and other governance mechanisms arising from different logics,and constructs a framework for the role of governance mechanisms in organizational collaborative behavior.Using 235 township health centers within the county medical communities across the country as a sample,the study employs path analysis to empirically test the mechanism framework for county-township collaboration and township-village collaboration models.Results:In the county-township collaboration model,there are a total of 7 pathways.Administrative mechanism and competitive mechanism directly influence collaborative behavior,while organizational interests and need cognition play a partial mediating role;organizational cognition fully mediates between relational mechanism and collaborative behavior.In the town-village collaboration model,there are a total of 4 pathways.Administrative mechanism and competitive mechanism directly influence collaborative behavior,with organizational interest cognition partially mediating between competitive mechanism and collaborative behavior;organizational interest cognition fully mediates between relational mechanism and collaborative behavior.Conclusions:Administrative mechanism serve as the foundation for organizational collaborative behavior;competitive mechanism have a significant negative impact on organizational collaborative behavior;relational mechanism are an important safeguard for organizational collaboration;enhancing organizational interests can directly enhance collaborative behavior.

Objective To explore the correlation between serum circularRNA-HOMER1(circHOMER1),microRNA(miR)-23a-3p levels with clinical stages and oxidative stress in patients with diabetic retinopathy(DR).Methods From January 2023 to July 2024,75 DR patients treated in Handan Central Hospital were included as the DR group.According to the clinical staging of DR,they were divided into non proliferative DR(NPDR group,n=43)and proliferative DR(PDR group,n=32).In addition,75 patients with simple type 2 diabetes who came to Handan Central Hospital were included as non DR group.The levels of serum circHOMER1,miR-23a-3p,malondialdehyde(MDA),superoxide dismutase(SOD),and reduced glutathione(GSH)were detect-ed.Clinical data of the subjects were collected.The TargetScan website was used to predict the targeting relationship between circHOMER1 and miR-23a-3p.Pearson method was used to analyze the correlation between serum circHOMER1,miR-23a-3p and MDA,SOD,GSH.Univariate and multivariate Logistic regression were used to analyze the influencing factors of progression of DR in type 2 diabetes patients.Receiver operating characteristic(ROC)carve was used to analyze the predictive value of serum circHOMER1 and miR-23a-3p in the progression of DR in patients with type 2 diabetes.Results There was a targeted relationship between circHOMER1 and miR-23a-3p.The serum MDA(28.66±4.52ng/ml)and circHOMER1(1.24±0.16)levels in the DR group were higher than those in the non DR group(16.95±3.27ng/ml,1.02±0.11),while SOD(45.39±7.84U/L),GSH(135.82±21.23μg/mL)and miR-23a-3p(0.88±0.07)levels were lower than those in the non DR group(81.65±11.47U/L,207.44±25.95μg/mL,1.01±0.09),and differences were statistically significant(t=9.813～22.602,all P<0.001).The serum MDA(33.28±4.96ng/ml)and circHOMER1(1.36±0.20)levels in the PDR group were higher than those in the NPDR group(25.23±3.58ng/ml,1.15±0.17),while SOD(34.39±7.15U/L),GSH(113.50±20.17μg/ml)and miR-23a-3p(0.79±0.07)levels were lower than those in the NPDR group(53.27±8.44U/L,152.43±23.99μg/ml,0.94±0.08),and the differences were statistically significant(t=4.906～10.376,all P<0.001).Spearman analysis showed that serum MDA and circHOMER1 were positively correlated with the severity of DR(r=0.533,0.473,all P<0.001),while SOD,GSH,miR-23a-3p were negatively correlated with the severity of DR(r=-0.552,-0.515,-0.529,all P<0.001).Pearson analysis showed that serum circHOMER1 was negatively correlated with miR-23a-3p,SOD,GSH,and positively correlated with MDA(r=-0.475,-0.460,-0.455,0.462,all P<0.001).Serum miR-23a-3p was positively correlated with SOD and GSH,and negatively correlated with MDA(r=0.428,0.437,-0.439,all P<0.001).Logistic regression analysis showed that high MDA,low SOD,low GSH,high circHOMER1,low miR-23a-3p,high FPG and high HbA1c were the risk factors of progression of DR in type 2 diabetes patients(OR=0.214～3.556,all P<0.05).The area under curve(AUC)of serum circHOMER1 and miR-23a-3p alone and jointhy predicting the progression of DR in type 2 diabetes patients were 0.751,0.797 and 0.903 respectively.The combined prediction was higher than that of serum circHOMER1 and miR-23a-3p alone(Z=3.179,2.335,P=0.002,0.020).Conclusion Serum MDA and circHOMER1 levels are higher in DR patients,while serum SOD,GSH and miR-23a-3p levels are lower.Abnormal expression of circHOMER1 and miR-23a-3p in serum is associated with progression of DR and oxidative stress.Combined detection of circHOMER1 and miR-23a-3p in serum can predict the progression of DR in patients with type 2 diabetes.

Objective·To develop a nomogram prediction model and an online risk calculator,and to predict the continence of patients after robot-assisted laparoscopic radical prostatectomy(RARP).Methods·A total of 604 prostate cancer patients who underwent RARP and had preoperative prostate magnetic resonance imaging at the Shanghai General Hospital,Shanghai Jiao Tong University School of Medicine from September 2022 to December 2024 were analyzed and included.All patients were randomly resampled and divided into a training set(n=420)and a validation set(n=184)at a ratio of 7∶3.The patients'continence was followed up every month from the first month after the operation.The least absolute shrinkage and selection operator(LASSO)model was applied to screen the features.A Logistic multivariate regression analysis was used to establish a prediction model integrating the features selected from the LASSO analysis.The receiver operator characteristic(ROC)curve was drawn to predict the recovery of continence in patients after RARP,and the areas under the curve were compared by the DeLong test to evaluate the discrimination of the model.Calibration curves and decision curve analysis(DCA)were used to evaluate the calibration and clinical utility the model.Results·According to the postoperative continence follow-up data of the patients,the continence rate of the patients at 3 months after the operation was 58.28%(352/604).The length of the membranous urethra,the thickness of the right levator ani muscle,and blood loss were identified as independent predictors of early postoperative(3-month)incontinence by Logistic multivariate regression analysis of the training set.The area under the ROC curve was calculated as 0.976(0.954,0.998)for the training set and 0.977(0.945,1.000)for the validation set,demonstrating good discrimination of this model.No significant difference between the ROC curves of the training set and the validation set was confirmed by the DeLong test(P=0.949).A good goodness of fit of this model was demonstrated by the Hosmer-Lemeshow test(P=0.179).The clinical utility of the nomogram prediction model was indicated by the DCA plot.This nomogram prediction model was incorporated into an online calculator(https://yitingd.shinyapps.io/DynNomapp).Conclusion·This study developed and validated a nomogram prediction model that can effectively predict the early continence of patients after RARP.The length of the membranous urethra,the thickness of the right levator ani muscle,and the intraoperative blood loss are significant independent predictors of early postoperative incontinence.

Objective To evaluate the application of high-resolution magnetic resonance vessel wall imaging(HRMR-VWI)in identifying high-risk features of intracranial atherosclerotic plaques,and to analyze the correlation between plaque characteristics and stroke recurrence under varying degrees of stenosis.Methods The data from 368 patients with intracranial atherosclerotic stenosis(ICAS)across two centers were retrospectively analyzed.Based on the degree of stenosis,all patients were categorized into mild-to-moderate stenosis group(luminal stenosis<70%,n=155)and severe stenosis group(luminal stenosis≥70%,n=213).HRMR-VWI images and clinical information of the patients were collected and analyzed,and the culprit plaques were quantitatively analyzed.Univariate and multivariate logistic regression analyses were employed to identify the risk factors for stroke recurrence,and the predictive performance was evaluated using the area under the curve(AUC)of the receiver operating characteristic(ROC)curve.Results Higher normalized wall index(NWI)[odds ratio(OR)=1.082,95%confidence interval(CI)1.050-1.118,P<0.05]and the presence of intraplaque hemorrhage(IPH)(OR=1.843,95%CI 1.120-3.036,P<0.05)were risk factors for stroke recurrence in all patients.And these two factors were also significant in the mild-to-moderate stenosis group(NWI:OR=1.088,95%CI 1.009-1.186,P<0.05;IPH:OR=4.049,95%CI 1.227-16.065,P<0.05).A predictive model for stroke recurrence was constructed using the combination of IPH and NWI,with the best performance in the mild-to-moderate stenosis group(AUC=0.813,95%CI 0.723-0.906).Conclusion In patients with luminal stenosis<70%,the increase of NWI and the presence of IPH have been validated as significant and effective indicators for predicting stroke recurrence,demonstrating notable predictive performance.In contrast,among patients with luminal stenosis≥70%,the utility of plaque characteristics in predicting stroke recurrence is relatively lower,indicating that the correlation between plaque characteristics and stroke recurrence varies across different degrees of stenosis.

This consensus was developed by the Orthodontic Society of the Chinese Stomatological Association to provide a systematic, scientific, and practical guideline for informed consent in orthodontic care. Orthodontic treatment is typically lengthy, highly individualized, and involves multiple factors such as growth and development, occlusal function, and facial esthetics. Rapid technological advances and diverse risk profiles make the traditional reliance on orthodontist experience or institutional templates insufficient to ensure patients′ full understanding and autonomous decision-making. To address this, the expert panel conducted extensive reviews of domestic and international guidelines, analyzed representative dispute cases, and performed multicenter patient-clinician surveys. Using a multi-round Delphi method, the group established a standardized informed consent framework covering the initial consultation, treatment, and retention phases. The consensus emphasizes that informed consent is not only a fundamental legal and ethical requirement but also a key step in building trust, improving patient compliance, and enhancing treatment satisfaction. Orthodontists should clearly and comprehensively explain treatment plans, potential risks, uncertainties, and associated costs, while respecting the autonomy of patients or guardians, and maintain continuous communication and dynamic evaluation throughout the treatment process. The release of this consensus provides unified and authoritative guidance for clinical orthodontics, helping to standardize informed consent, enhance its transparency, safeguard patient rights, reduce medical risks, and promote high-quality, sustainable development of orthodontic practice.