Polygonatum sibiricum， as a traditional Chinese medicine with both medicinal and edible properties， has attracted considerable attention due to its functions of nourishing Yin and moistening the lungs， tonifying the spleen and benefiting Qi， and nourishing the kidneys and filling essence. Recent studies have demonstrated that Polygonatum sibiricum plays a significant role in regulating the immune system， effectively enhancing and improving the morphology and function of immune organs， stimulating the proliferation and activation of immune cells， and regulating the secretion and release of immune factors， thereby enhancing the immune function of the body and improving various immune-related diseases. Although a large number of studies have explored the pharmacological effects and mechanisms of P. sibiricum， there has been no systematic review and summary of its immune regulatory activity and mechanisms. Therefore， this article comprehensively reviews the research achievements of P. sibiricum polysaccharides and saponins in the field of immune regulation in recent years， and further sorts out the immune regulatory mechanisms of P. sibiricum in multiple aspects： including increasing the organ index of the spleen and thymus， increasing the number and activity of tumor-suppressive bone marrow hematopoietic stem cells， improving intestinal flora imbalance， regulating the quantity and proportion of T lymphocyte subsets， increasing the level of immunoglobulin， promoting the proliferation of macrophages， enhancing the activity of natural killer cells， increasing the number of white blood cells， and promoting the maturation of dendritic cells， providing a solid theoretical basis and scientific evidence for the research and application of P. sibiricum， and promoting its development and application in traditional Chinese medicine immune enhancers and various functional products.

Objective: To explore the bidirectional associations among body mass index Z scores (BMI Z scores) and weight stigma with depressive symptoms in adolescents, thereby providing evidence for targeted intervention strategies. Methods: A stratified cluster random sampling method was employed to select 18 301 adolescents aged 12-18 years from all 12 prefectures (103 counties) in the Inner Mongolia Autonomous Region, and two waves of longitudinal surveys were conducted in September 2023 (T1) and September 2024 (T2) among the adolescents. Weight stigma was assessed by using a self developed questionnaire, depressive symptom was measured with the Center for Epidemiologic Studies Depression Scale (CES-D), and BMI Z scores were calculated according to the World Health Organization standards. Pearson correlation analysis was used to examine associations among variables, and cross lagged panel models were constructed to investigate the dynamic bidirectional relationships among the three variables. Results: Adolescents BMI Z scores and weight stigma with depressive symptoms all exhibited autoregressive stability across the two time points (autoregressive paths, all P <0.01). Cross lagged model comparisons indicated that the bidirectional path model achieved the best fit ( χ 2=12.65, RMSEA =0.017, CFI =1.000; △ χ 2=193.39, P <0.01), supporting dynamic bidirectional associations among the three variables. After adjusting for gender, age, subjective social status and only child status, T1 BMI Z scores among adolescents positively predicted T2 weight stigma ( β =0.061), and T1 weight stigma positively predicted T2 depressive symptoms ( β =0.608); in the reverse direction, T1 depressive symptoms predicted T2 weight stigma ( β =0.003), and T1 weight stigma predicted T2 BMI Z scores ( β =0.081) (all P <0.01). Conclusions There is a bidirectional cross lagged relationship among adolescents BMI Z scores and weight stigma with depressive symptoms, suggesting that weight stigma may serve as a key psychological variable linking obesity and depressive symptoms. Greater attention should be paid to the potential threat of weight stigma to adolescents mental health, with intervention strategies expanded from a solely physiological focus to encompass psychosocial dimensions.

Objective: To explore the relationship of physical activity (PA) and sedentary behavior (SB) with cardiorespiratory fitness (CRF) among middle schoold students in Tibet, so as to provide empirical references for improving the cardiorespiratory fitness and health levels of adolescents in Tibet. Methods: From August to December 2020, 1 225 junior and senior high school students were selected from 2 middle schools in Lhasa, Tibet Autonomous Region, using the stratified cluster random sampling method. Triaxial accelerometers were used to evaluate PA and SB behaviors, and the 20 meter shuttle run was employed to assess CRF among the middle school students. Isochronous substitution modeling was used to analyze the associations of SB, low intensity physical activity (LPA), and moderate vigorous physical activity (MVPA) with CRF, and the saturation threshold effect in the dose response relationship between MVPA and CRF was analyzed through restricted cubic spline and two stage linear regression. Results: After adjusting for covariates such as gender, body mass index and sleep quality score, isotemporal substitution analysis showed that among junior high school students aged 13-15, replacing 30 minutes of SB ( B =1.73) or LPA ( B =2.38) with MVPA were positively associated with CRF (both P <0.05). Among senior high school students aged 16-18, replacing SB ( B =0.99) or LPA ( B =1.38) with MVPA were also positively associated with CRF (both P <0.05). Restricted cubic spline and two piecewise linear regression analyses indicated that only middle school girls aged 13-18 exhibited a saturation threshold effect between MVPA and CRF (logarithmic likelihood ratio test=0.03), with the optimal CRF improvement observed at 60 minutes of MVPA per day ( B=0.13, P ＜ 0.01). Conclusions Reducing SB and LPA while increasing MVPA can improve CRF in Tibetan middle school students. To maximize CRF improvement, middle school girls should engage in at least 60 minutes of MVPA daily.

Fecal microbiota transplantation (FMT) technology originated in China during the Eastern Jin Dynasty and has rapidly developed over the past two decades, becoming a primary method for studying the causal relationship between gut microbiota and the occurrence and progression of diseases. At the same time, the therapeutic effects of FMT in the field of gastrointestinal diseases have gained widespread recognition and are gradually expanding into other disease areas. The FMT procedure is relatively complex, and there is currently no standardized method; its success is influenced by various factors, including the donor, recipient, processing of the fecal material, and the method of implantation. Given the increasingly recognized relationship between gut microbiota and various diseases, FMT has become a research hotspot in both scientific studies and clinical applications, achieving a series of significant advancements. To help researchers better understand this technology, this paper will outline the development history of FMT, summarize common operational methods in research and clinical settings, review its application progress, and look forward to future development directions.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

OBJECTIVE To construct three-class （insufficient， normal， excessive） and two-class （insufficient， normal） models for predicting plasma concentration of valproic acid （VPA）， and compare the performance of these two models， with the aim of providing a reference for formulating clinical medication strategies. METHODS The clinical data of 480 patients who received VPA treatment and underwent blood concentration test at the Xi’an International Medical Center Hospital were collected from November 2022 to September 2024 （a total of 695 sets of data）. In this study， predictive models were constructed for target variables of three-class and two-class models. Feature ranking and selection were carried out using XGBoost scores. Twelve different machine learning algorithms were used for training and validation， and the performance of the models was evaluated using three indexes: accuracy， F1 score， and the area under the working characteristic curve of the subject （AUC）. RESULTS XGBoost feature importance scores revealed that in the three-class model， the importance ranking of kidney disease and electrolyte disorders was higher. However， in the two-class model， the importance ranking of these features significantly decreased， suggesting a close association with the excessive blood concentration of VPA. In the three-class model， Random Forest method performed best， with F1 score of 0.704 0 and AUC of 0.519 3 on the test set； while in the two-class model， CatBoost method performed optimally， with F1 score of 0.785 7 and AUC of 0.819 5 on the test set. CONCLUSIONS The constructed three-class model has the ability to predict excessive VPA blood concentration， but its prediction and model generalization abilities are poor； the constructed two-class model can only perform classification prediction for insufficient and normal blood concentration cases， but its model performance is stronger.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.