ObjectiveTo construct a prediction model for high responsiveness to short-term aerobic training in patients with Parkinson's disease (PD) based on dual-task gait assessment, and to provide evidence for identifying individuals who are most likely to benefit from such interventions. MethodsA total of 42 patients with PD who visited the outpatient clinic of Tianjin Huanhu Hospital between August 29, 2023 and December 19, 2024 were enrolled. All participants completed a 4-week home-based aerobic cycling training program and underwent dual-task gait assessments pre- and post-training. Patients were classified into high-response group (n = 12) and low-response group (n = 30) according to whether the improvement in gait speed reached the minimal clinically important difference of 0.1 m/s. The clinical validity of this grouping was verified by comparing differences in cardiopulmonary exercise testing parameters and dual-task gait indicators between the two groups pre- and post-training. Based on baseline characteristics, Firth's penalized maximum likelihood logistic regression was applied to construct a predictive model for high responsiveness, and the discriminative performance of the model was assessed preliminarily. ResultsThe high-response group showed significantly greater improvement in gait speed, step length, cadence and the anaerobic threshold to maximal oxygen uptake ratio after training compared with the low-response group (P < 0.05), confirming the clinical validity of the grouping. The baseline anaerobic threshold to maximal oxygen uptake ratio (OR = 3.348), body mass index (OR = 2.229) and age (OR = 0.428) demonstrated certain discriminative ability in the present sample. The area under the receiver operating characteristic curve was 0.831, with an overall prediction accuracy of 81.0%, specificity of 83.3% and sensitivity of 75.0%. Robustness of the model was confirmed via internal validation using the Bootstrap method, which suggested that the results were informative, but further validation was still required. ConclusionThe anaerobic threshold/maximal oxygen uptake ratio, body mass index and age may predict the response to short-term aerobic training in patients with PD.

ObjectiveTo investigate the physical fitness status and associated factors among preschool children aged 3 to 6 years in Tongzhou District of Beijing, and to provide evidence for promoting physical development in this population. MethodsBased on the 2020 National Physical Fitness Surveillance data for Tongzhou District of Beijing, a total of 416 children were included for standardized physical tests, including morphological, physiological, and fitness indicators, and questionnaire surveys. Independent sample t-tests and ANOVA were used to compare differences in physical indicators across groups, while multivariate linear regression models were applied to analyze influencing factors. ResultsThe comprehensive fitness score of children aged 3 to 6 years old in Tongzhou District of Beijing was (79.98±6.97) points, with an overall physical fitness pass rate of 99.52%. The proportions of excellent, good, and pass grades were 37.50%, 39.90%, and 22.12%, respectively. The average heights for the 3-years-old, 4-years-old, 5-years-old, and 6-years-old groups in Tongzhou District were (102.13±4.24) cm, (107.92±4.66) cm, (115.43±4.70) cm, and (119.16±4.33) cm, respectively. The corresponding average body weights were (16.63±2.46) kg, (18.66±2.54) kg, (21.60±4.02) kg, and (23.46±4.64) kg, respectively. Among children aged 3 to 6 years, the average BMI was (15.95±1.79) kg‧m^-2, body fat percentage was (21.92±4.77)%, resting pulse rate was (95.24±9.70) beats per minute, grip strength was (5.74±2.07) kg, standing long jump distance was (78.12±19.95) cm, sit-and-reach flexibility was (12.25±4.65) cm, continuous two-foot jump time was (6.29±2.55) seconds, 15-meter obstacle shuttle run time was (7.94±1.30) seconds, and balance beam walking time was (5.11±2.25) seconds. Multiple linear regression analyses showed that higher composite fitness scores were significantly positively associated with female gender (b=1.72, 95%CI: 0.42-3.01), aged 4 years old and above (b=2.97, 95%CI: 1.35-4.59), mixed feeding (b=4.16, 95%CI: 1.60-6.72) or breastfeeding (b=3.23, 95%CI: 0.36-6.10) during infancy, taller paternal (b=0.16, 95%CI: 0.02-0.30) and maternal (b=0.25, 95%CI: 0.11-0.39) height (all P<0.05). ConclusionThe physical fitness of children aged 3 to 6 years in Tongzhou District of Beijing is generally favorable, with gender, age, feeding patterns, and genetic factors identified as key correlating factors. Comprehensive strategies are recommended to improve early childhood physical health.

OBJECTIVE: To evaluate the dynamic changes of glucocorticoid (GC) dose and the feasibility of GC discontinuation in rheumatoid arthritis (RA) patients under the background of Chinese medicine (CM). METHODS: This multicenter retrospective cohort study included 1,196 RA patients enrolled in the China Rheumatoid Arthritis Registry of Patients with Chinese Medicine (CERTAIN) from September 1, 2019 to December 4, 2023, who initiated GC therapy. Participants were divided into the Western medicine (WM) and integrative medicine (IM, combination of CM and WM) groups based on medication regimen. Follow-up was performed at least every 3 months to assess dynamic changes in GC dose. Changes in GC dose were analyzed by generalized estimator equation, the probability of GC discontinuation was assessed using Kaplan-Meier curve, and predictors of GC discontinuation were analyzed by Cox regression. Patients with <12 months of follow-up were excluded for the sensitivity analysis. RESULTS: Among 1,196 patients (85.4% female; median age 56.4 years), 880 (73.6%) received IM. Over a median 12-month follow-up, 34.3% (410 cases) discontinued GC, with significantly higher rates in the IM group (40.8% vs. 16.1% in WM; P<0.05). GC dose declined progressively, with IM patients demonstrating faster reductions (median 3.75 mg vs. 5.00 mg in WM at 12 months; P<0.05). Multivariate Cox analysis identified age <60 years [P<0.001, hazard ratios (HR)=2.142, 95% confidence interval (CI): 1.523-3.012], IM therapy (P=0.001, HR=2.175, 95% CI: 1.369-3.456), baseline GC dose ⩽7.5 mg (P=0.003, HR=1.637, 95% CI: 1.177-2.275), and absence of non-steroidal anti-inflammatory drugs use (P=0.001, HR=2.546, 95% CI: 1.432-4.527) as significant predictors of GC discontinuation. Sensitivity analysis (545 cases) confirmed these findings. CONCLUSIONS RA patients receiving CM face difficulties in following guideline-recommended GC discontinuation protocols. IM can promote GC discontinuation and is a promising strategy to reduce GC dependency in RA management. (Trial registration: ClinicalTrials.gov, No. NCT05219214).
Adult ; Aged ; Female ; Humans ; Male ; Middle Aged ; Arthritis, Rheumatoid/drug therapy* ; Glucocorticoids/therapeutic use* ; Medicine, Chinese Traditional ; Retrospective Studies

Gastrointestinal (GI) cancers are a leading cause of cancer morbidity and mortality worldwide. Despite advances in treatment, cancer relapse remains a significant challenge, necessitating novel therapeutic strategies. In this study, we engineered nanobody-based chimeric antigen receptor (CAR) natural killer (NK) cells targeting cadherin 17 (CDH17) for the treatment of GI tumors. In addition, to enhance the efficacy of CAR-NK cells, we also incorporated CV1, a CD47-SIRPα axis inhibitor, to evaluate the anti-tumor effect of this combination. We found that CDH17-CAR-NK cells effectively eliminated GI cancers cells in a CDH17-dependent manner. CDH17-CAR-NK cells also exhibit potent in vivo anti-tumor effects in cancer cell-derived xenograft and patient-derived xenograft mouse models. Additionally, the anti-tumor activity of CDH17-CAR-NK cells is synergistically enhanced by CD47-signal regulatory protein α (SIRPα) axis inhibitor CV1, likely through augmented macrophages activation and an increase in M1-phenotype macrophages in the tumor microenvironment. Collectively, our findings suggest that CDH17-targeting CAR-NK cells are a promising strategy for GI cancers. The combination of CDH17-CAR-NK cells with CV1 emerges as a potential combinatorial approach to overcome the limitations of CAR-NK therapy. Further investigations are warranted to speed up the clinical translation of these findings.

Apical microsurgery is accurate and minimally invasive, produces few complications, and has a success rate of more than 90%. However, due to the lack of awareness and understanding of apical microsurgery by dental general practitioners and even endodontists, many clinical problems remain to be overcome. The consensus has gathered well-known domestic experts to hold a series of special discussions and reached the consensus. This document specifies the indications, contraindications, preoperative preparations, operational procedures, complication prevention measures, and efficacy evaluation of apical microsurgery and is applicable to dentists who perform apical microsurgery after systematic training.
Microsurgery/standards* ; Humans ; Apicoectomy ; Contraindications, Procedure ; Tooth Apex/diagnostic imaging* ; Postoperative Complications/prevention & control* ; Consensus ; Treatment Outcome

Pulpotomy, which belongs to vital pulp therapy, has become a strategy for managing pulpitis in recent decades. This minimally invasive treatment reflects the recognition of preserving healthy dental pulp and optimizing long-term patient-centered outcomes. Pulpotomy is categorized into partial pulpotomy (PP), the removal of a partial segment of the coronal pulp tissue, and full pulpotomy (FP), the removal of whole coronal pulp, which is followed by applying the biomaterials onto the remaining pulp tissue and ultimately restoring the tooth. Procedural decisions for the amount of pulp tissue removal or retention depend on the diagnostic of pulp vitality, the overall treatment plan, the patient's general health status, and pulp inflammation reassessment during operation. This statement represents the consensus of an expert committee convened by the Society of Cariology and Endodontics, Chinese Stomatological Association. It addresses the current evidence to support the application of pulpotomy as a potential alternative to root canal treatment (RCT) on mature permanent teeth with pulpitis from a biological basis, the development of capping biomaterial, and the diagnostic considerations to evidence-based medicine. This expert statement intends to provide a clinical protocol of pulpotomy, which facilitates practitioners in choosing the optimal procedure and increasing their confidence in this rapidly evolving field.
Humans ; Calcium Compounds/therapeutic use* ; Consensus ; Dental Pulp ; Dentition, Permanent ; Oxides/therapeutic use* ; Pulpitis/therapy* ; Pulpotomy/standards*

Enamel demineralization, the formation of white spot lesions, is a common issue in clinical orthodontic treatment. The appearance of white spot lesions not only affects the texture and health of dental hard tissues but also impacts the health and aesthetics of teeth after orthodontic treatment. The prevention, diagnosis, and treatment of white spot lesions that occur throughout the orthodontic treatment process involve multiple dental specialties. This expert consensus will focus on providing guiding opinions on the management and prevention of white spot lesions during orthodontic treatment, advocating for proactive prevention, early detection, timely treatment, scientific follow-up, and multidisciplinary management of white spot lesions throughout the orthodontic process, thereby maintaining the dental health of patients during orthodontic treatment.
Humans ; Consensus ; Dental Caries/etiology* ; Dental Enamel/pathology* ; Tooth Demineralization/etiology* ; Tooth Remineralization

Intentional tooth replantation (ITR) is an advanced treatment modality and the procedure of last resort for preserving teeth with inaccessible endodontic or resorptive lesions. ITR is defined as the deliberate extraction of a tooth; evaluation of the root surface, endodontic manipulation, and repair; and placement of the tooth back into its original socket. Case reports, case series, cohort studies, and randomized controlled trials have demonstrated the efficacy of ITR in the retention of natural teeth that are untreatable or difficult to manage with root canal treatment or endodontic microsurgery. However, variations in clinical protocols for ITR exist due to the empirical nature of the original protocols and rapid advancements in the field of oral biology and dental materials. This heterogeneity in protocols may cause confusion among dental practitioners; therefore, guidelines and considerations for ITR should be explicated. This expert consensus discusses the biological foundation of ITR, the available clinical protocols and current status of ITR in treating teeth with refractory apical periodontitis or anatomical aberration, and the main complications of this treatment, aiming to refine the clinical management of ITR in accordance with the progress of basic research and clinical studies; the findings suggest that ITR may become a more consistent evidence-based option in dental treatment.
Humans ; Tooth Replantation/methods* ; Consensus ; Periapical Periodontitis/surgery*

Natural polyphenols are a group of components widely found in traditional Chinese medicines and have been demonstrated to delay or prevent the development of aging and age-related diseases in recent years. As far as we know, the studies of natural polyphenols in aging and aging-related diseases have never been extensively reviewed. In the present paper, we reviewed recent advances of natural polyphenols in aging and common age-related diseases and the current technological methods to improve the bioavailability of natural polyphenols. The results showed that natural polyphenols have the potential to prevent or treat aging and common age-related diseases through multiple mechanisms. Nanotechnology, structural modifications, and matrix processing could provide strong technical support for the development of natural polyphenols to prevent or treat aging and age-related diseases. In conclusion, natural polyphenols have important potential in the prevention and treatment of aging and age-related diseases.

OBJECTIVE: To construct machine learning prediction model for sepsis-associated encephalopathy (SAE), and analyze the application value of the model on early identification of SAE risk in elderly septic patients. METHODS: Patients aged over 60 years with a primary diagnosis of sepsis admitted to intensive care unit (ICU) from 2008 to 2023 were selected from Medical Information Mart for Intensive Care-IV 2.2 (MIMIC-IV 2.2). Demographic variables, disease severity scores, comorbidities, interventions, laboratory indicators, and hospitalization details were collected. Key factors associated with SAE were identified using univariate Logistic regression analysis. The data were randomly divided into training and validation sets in a 7 : 3 ratio. Multivariable Logistic regression analysis was conducted in the training set and visualized using a nomogram model for prediction of SAE. The discrimination of the model was evaluated in the validation set using the receiver operator characteristic curve (ROC curve), and its calibration was assessed using calibration curve. Furthermore, multiple machine learning algorithms, including multi-layer perceptron (MLP), support vector machine (SVM), naive bayes (NB), gradient boosting machine (GBM), random forest (RF), and extreme gradient boosting (XGB), were constructed in the training set. Their predictive performance was subsequently evaluated on the validation set. Taking the XGB model as an example, the interpretability of the model through the SHapley Additive exPlanations (SHAP) algorithm was enhanced to identify the key predictive factors and their contributions. RESULTS: A total of 2 204 septic patients were finally enrolled, of whom 840 developed SAE (38.1%). A total of 21 variables associated with SAE were screened through univariate Logistic regression analysis. Multivariable Logistic regression analysis showed that endotracheal intubation [odds ratio (OR) = 0.40, 95% confidence interval (95%CI) was 0.19-0.88, P < 0.001], oxygen therapy (OR = 0.76, 95%CI was 0.53-0.95, P = 0.023), tracheotomy (OR = 0.20, 95%CI was 0.07-0.53, P < 0.001), continuous renal replacement therapy (CRRT; OR = 0.32, 95%CI was 0.15-0.70, P < 0.001), cerebrovascular disease (OR = 0.31, 95%CI was 0.16-0.60, P < 0.001), rheumatic disease (OR = 0.44, 95%CI was 0.19-0.99, P < 0.001), male (OR = 0.68, 95%CI was 0.54-0.86, P = 0.001), and maximum anion gap (AG; OR = 0.95, 95%CI was 0.93-0.97, P < 0.001) were associated with an decreased probability of SAE, and age (OR = 1.05, 95%CI was 1.03-1.06, P < 0.001), acute physiology score III (APSIII; OR = 1.02, 95%CI was 1.01-1.02, P < 0.001), Oxford acute severity of illness score (OASIS; OR = 1.04, 95%CI was 1.03-1.06, P < 0.001), and length of hospital stay (OR = 1.01, 95%CI was 1.01-1.02, P < 0.001) were associated with an increased probability of SAE. A nomogram model was constructed based on these variables. In the validation set, ROC curve analysis showed that the model achieved an area under the ROC curve (AUC) of 0.723, and the calibration curve showed good consistency between the predicted probability of the model and the observed probability. Among the machine learning algorithms, including MLP, SVM, NB, GBM, RF, and XGB, the SVM model and RF model demonstrated relatively good predictive performance, with AUC of 0.748 and 0.739, respectively, and the sensitivity was both exceeding 85%. The predictive performance of the XGB model was explained through SHAP analysis, and the results indicated that APSIII score (SHAP value was 0.871), age (SHAP value was 0.521), and OASIS score (SHAP value was 0.443) were important factors affecting the predictive performance of the model. CONCLUSIONS The machine learning-based SAE prediction model exhibits good predictive capability and holds significant application value for the early identification of SAE risk in elderly septic patients.
Humans ; Machine Learning ; Aged ; Sepsis-Associated Encephalopathy ; Sepsis/complications* ; Intensive Care Units ; Logistic Models ; Middle Aged ; Male ; ROC Curve ; Female ; Bayes Theorem ; Nomograms ; Support Vector Machine ; Algorithms