As a distinctive resource of Chinese civilization， traditional Chinese medicine （TCM） technology transfer faces significant opportunities under the background of digital and intelligent transformation， while also being constrained by unique challenges such as the complexity of its theoretical system， lengthy industrial chains， and multidimensional policy restrictions， resulting in a "high-value-high-threshold" paradox. At present， TCM technology transfer is deeply trapped in a "threefold reluctance" dilemma， i.e.， unwillingness to transfer， inability to transfer， and lack of capacity to transfer. Specifically， the disconnection between scientific research evaluation systems and market demand leads to low conversion rates of research achievements， unclear ownership and compliance risks suppress innovation incentives， and the absence of professional services intensifies supply-demand mismatches. This article systematically analyzes the specific characteristics of TCM technology transfer and proposes a breakthrough pathway centered on full-chain digital and intelligent transformation. By integrating technologies such as intelligent sorting systems， blockchain-based traceability， and AI diagnostic models， the TCM ecosystem spanning "cultivation-production-service" can be reconstructed. In terms of standardization， promoting the progression from "experience-based data conversion" to "data standardization" and further to "intelligent standardization" is advocated to resolve quality control challenges. For example， a "three-no-one-full" certification system can strengthen quality trust. Policy coordination should focus on optimizing mechanisms for the transformation of scientific and technological achievements， while exploring intellectual property securitization and risk-sharing models to stimulate research momentum. In terms of internationalization， reliance on the Belt and Road Initiative platform to promote the export of geo-authentic medicinal material brands and standards is recommended to build a dual-driven model of "technology plus culture". Looking ahead， through the construction of national-level databases， the cultivation of interdisciplinary talent， and the mutual recognition of international standards， a new paradigm of "scientific intelligent manufacturing" can be formed， providing systematic solutions for the modernization of TCM and global health governance.

Human umbilical cord mesenchymal stem cell （hUC-MSC） as a cell-based therapeutic strategy have demonstrated significant application potential in the field of intervention for neurodegenerative disease （NDD） due to their advantages such as self-renewal， multi-directional differentiation， and low immunogenicity. hUC-MSC effectively intervenes in the pathological features and neurological functions of various disease models such as Alzheimer disease， Parkinson’s disease， amyotrophic lateral sclerosis， and multiple sclerosis primarily through multiple mechanisms such as homing and differentiation， mediating paracrine actions and releasing exosomes， as well as immune regulation and anti-inflammation. Some clinical studies have also preliminarily verified their safety and effectiveness. Currently， its research still faces challenges such as immune rejection reactions requiring further observation， long-term safety needing evaluation， mechanisms of action not being fully elucidated， and slow progress in clinical trials. Future research needs to establish pharmaceutical standards for hUC-MSC， deepen their pharmacological mechanisms and clinical trials， ultimately providing new and effective drug treatment options for patients with NDD.

Objective:To investigate the predictive value of computed tomography(CT) based radiomics model for the prognosis of patients with pancreatic ductal adenocarcinoma(PDAC).Methods:The retrospective cohort study was conducted. The clinicopathological data of 206 PDAC patients who were admitted to Zhongshan Hospital of Fudan University from August 2018 to December 2020 were collected. There were 115 males and 91 females, aged (64±9)years. All 206 pati-ents underwent enhanced CT examination. Based on radom number table, the 206 patients were randomly divided into a training set of 165 cases and a validation set of 41 cases with a ratio of 4∶1. The training set was used to construct the prediction model, and the test set was used to validate the performance of the prediction model. Observation indicators: (1) follow-up; (2) analysis of prognostic factors of PDAC patients in the training set; (3) construction and evaluation of prediction model for prognosis of PDAC patients. Comparison of measurement data with normal distribution between groups was conducted using the t test. Comparison of measurement data with skewed distribution between groups was conducted using the Wilcoxon W test. Comparison of count data between groups was conducted using the chi-square test or corrected chi-square test. The Kaplan-Meier method was used to calculate the survival rate and Log-rank test was used for survival analysis. Univariate and multivariate analyses were conducted using the COX regression model. The PyCharm software was used for the least absolute shrinkage and selection operator method (LASSO)-COX regression analysis. The receiver operating characteristic curve was plotted to evaluate the performance of radiomics model. Results:(1)Follow-up. Of the 206 patients,205 cases were followed up for 17.1(range, 12.0?40.1)months. The postoperative 1-, 2-, 3-year survival rates were 80.10%, 29.61% and 4.85%. (2) Analysis of prognostic factors for PDAC patients in the training dataset. Results of multivariate analysis showed that pathological N stage was an independent influencing factor for prognosis of PDAC patients in the training set ( hazard ratio=1.476, 95% confidence interval as 1.054?2.067, P<0.05). (3) Construction and evaluation of prediction model for prognosis of PDAC patients. A total of 1 595 radiomics features were finally extracted from the 206 patients. By intra-group feature selection and dimensionality reduction using LASSO-COX regression model, 10 radiomics features were obtained. Combined with 10 radiomics features and 11 clinical features, using the LASSO-COX regression analysis, 15 features were finally extracted to construct the CT based radiomics model for predicting prognosis of PDAC. The areas under receiver operating characteristic curve of the prediction model in predicting 2-year and 3-year overall survival rates of PDAC patients in the training set were 0.834 (95% confidence interval as 0.777?0.891) and 0.883 (95% confidence interval as 0.834?0.932), respectively. The area under curve of the prediction model for patients in the validation set was 0.606 (95% confidence interval as 0.456?0.756) and 0.625 (95% confidence interval as 0.477?0.773). Conclusion:The prediction model constructed on CT based radiomics features and clinical features for predicting the prognosis of PDAC patients shows a promising prediction efficiency.

Objective:To investigate the trajectory of frailty in elderly patients on maintenance hemodialysis(MHD)following SARS-CoV-2 infection and its associated risk factors.Methods:This prospective cohort study focused on elderly patients who underwent baseline frailty assessment(T0)during hemodialysis treatment at Beijing Friendship Hospital for over 3 months between December 1st, 2022, and December 31th, 2022, and were diagnosed with SARS-CoV-2 infection.The Fried Frailty Phenotype was evaluated at 1 month(T1), 3 months(T2), and 6 months(T3)post-infection.Frailty trajectory after infection was analyzed using repeated measurement ANOVA.Patients were divided into stable/improvement or exacerbation groups based on their frailty status at T0 and T3, with logistic regression analysis employed to identify risk factors for different frailty trajectories.Results:A total of 130 elderly maintenance hemodialysis patients, with a median age of 66 years(range: 63-71 years)and 62 males(47.7%), were included in the study.Six months after the infection, a majority of surviving patients saw their frailty scores return to baseline levels.Specifically, 72 patients(55.4%)either maintained or improved to robust or pre-frail states, while 9 patients(6.9%)progressed to a pre-frail state, 18 patients(13.8%)progressed to a frail state, and 31 patients(23.8%)remained in a frail state.Results from multivariate logistic regression analysis indicated that low grip strength( OR: 6.30, 95% CI: 1.48-26.73)and all-cause hospitalization( OR: 5.01, 95% CI: 1.19-21.03)were identified as risk factors for non-frail patients transitioning to frailty( P<0.05). Conclusions:The majority of elderly maintenance hemodialysis patients who survived SARS-CoV-2 infection returned to their baseline level of frailty or showed improvement within 6 months.Non-frail patients with low grip strength or those who were hospitalized were more likely to deteriorate towards frailty.

Objective:To investigate the trajectory of frailty in elderly patients on maintenance hemodialysis(MHD)following SARS-CoV-2 infection and its associated risk factors.Methods:This prospective cohort study focused on elderly patients who underwent baseline frailty assessment(T0)during hemodialysis treatment at Beijing Friendship Hospital for over 3 months between December 1st, 2022, and December 31th, 2022, and were diagnosed with SARS-CoV-2 infection.The Fried Frailty Phenotype was evaluated at 1 month(T1), 3 months(T2), and 6 months(T3)post-infection.Frailty trajectory after infection was analyzed using repeated measurement ANOVA.Patients were divided into stable/improvement or exacerbation groups based on their frailty status at T0 and T3, with logistic regression analysis employed to identify risk factors for different frailty trajectories.Results:A total of 130 elderly maintenance hemodialysis patients, with a median age of 66 years(range: 63-71 years)and 62 males(47.7%), were included in the study.Six months after the infection, a majority of surviving patients saw their frailty scores return to baseline levels.Specifically, 72 patients(55.4%)either maintained or improved to robust or pre-frail states, while 9 patients(6.9%)progressed to a pre-frail state, 18 patients(13.8%)progressed to a frail state, and 31 patients(23.8%)remained in a frail state.Results from multivariate logistic regression analysis indicated that low grip strength( OR: 6.30, 95% CI: 1.48-26.73)and all-cause hospitalization( OR: 5.01, 95% CI: 1.19-21.03)were identified as risk factors for non-frail patients transitioning to frailty( P<0.05). Conclusions:The majority of elderly maintenance hemodialysis patients who survived SARS-CoV-2 infection returned to their baseline level of frailty or showed improvement within 6 months.Non-frail patients with low grip strength or those who were hospitalized were more likely to deteriorate towards frailty.

Objective:To investigate the predictive value of computed tomography(CT) based radiomics model for the prognosis of patients with pancreatic ductal adenocarcinoma(PDAC).Methods:The retrospective cohort study was conducted. The clinicopathological data of 206 PDAC patients who were admitted to Zhongshan Hospital of Fudan University from August 2018 to December 2020 were collected. There were 115 males and 91 females, aged (64±9)years. All 206 pati-ents underwent enhanced CT examination. Based on radom number table, the 206 patients were randomly divided into a training set of 165 cases and a validation set of 41 cases with a ratio of 4∶1. The training set was used to construct the prediction model, and the test set was used to validate the performance of the prediction model. Observation indicators: (1) follow-up; (2) analysis of prognostic factors of PDAC patients in the training set; (3) construction and evaluation of prediction model for prognosis of PDAC patients. Comparison of measurement data with normal distribution between groups was conducted using the t test. Comparison of measurement data with skewed distribution between groups was conducted using the Wilcoxon W test. Comparison of count data between groups was conducted using the chi-square test or corrected chi-square test. The Kaplan-Meier method was used to calculate the survival rate and Log-rank test was used for survival analysis. Univariate and multivariate analyses were conducted using the COX regression model. The PyCharm software was used for the least absolute shrinkage and selection operator method (LASSO)-COX regression analysis. The receiver operating characteristic curve was plotted to evaluate the performance of radiomics model. Results:(1)Follow-up. Of the 206 patients,205 cases were followed up for 17.1(range, 12.0?40.1)months. The postoperative 1-, 2-, 3-year survival rates were 80.10%, 29.61% and 4.85%. (2) Analysis of prognostic factors for PDAC patients in the training dataset. Results of multivariate analysis showed that pathological N stage was an independent influencing factor for prognosis of PDAC patients in the training set ( hazard ratio=1.476, 95% confidence interval as 1.054?2.067, P<0.05). (3) Construction and evaluation of prediction model for prognosis of PDAC patients. A total of 1 595 radiomics features were finally extracted from the 206 patients. By intra-group feature selection and dimensionality reduction using LASSO-COX regression model, 10 radiomics features were obtained. Combined with 10 radiomics features and 11 clinical features, using the LASSO-COX regression analysis, 15 features were finally extracted to construct the CT based radiomics model for predicting prognosis of PDAC. The areas under receiver operating characteristic curve of the prediction model in predicting 2-year and 3-year overall survival rates of PDAC patients in the training set were 0.834 (95% confidence interval as 0.777?0.891) and 0.883 (95% confidence interval as 0.834?0.932), respectively. The area under curve of the prediction model for patients in the validation set was 0.606 (95% confidence interval as 0.456?0.756) and 0.625 (95% confidence interval as 0.477?0.773). Conclusion:The prediction model constructed on CT based radiomics features and clinical features for predicting the prognosis of PDAC patients shows a promising prediction efficiency.

Cognitive impairment(CI)is a clinical syndrome characterized by progressive decline in advanced cognitive functions such as memory,thinking,and judgment.Its etiology and pathogenesis are complex,and there is currently a lack of specific drug interventions.Metformin,as a first-line hypoglycemic drug for type 2 diabetes,not only lowers blood glucose levels but also improves CI.This article reviews and summarizes the pharmacological effects and mechanisms of metformin in improving Alzheimer's disease,diabetes cognitive impairment,cognitive impairment after chemotherapy,in order to provide novel insights and approaches for the treatment of CI-related diseases.Studies have shown that the mechanism by which MET intervenes in CI mainly includes regulating β-amyloid protein and tau protein metabolism,reducing insulin resistance,inhibiting neuroinflammation,improving synaptic plasticity,improving mitochondrial dysfunction,regulating gut microbiota and lipid metabolism,etc.Future research needs to be conducted through interdisciplinary collaboration,fully integrating multiple omics data,and combining advanced technologies to further reveal their mechanisms of effect.

Aim To explore the impact and mechanism of proprotein convertase subtilisin kexin 9(PCSK9)on the progression of abdominal aortic aneurysm(AAA).Methods 6～8 week old ApoE-/-mice were selected to estab-lish the AAA model.Angiotensin Ⅱ(Ang Ⅱ)was continuously infused through subcutaneous implantation of a micro-os-motic pump.The mice were fed with high-fat diet and killed after 28 days.The expression of PCSK9 in abdominal aor-tic smooth muscle cells was detected by immunohistochemistry and immunofluorescence in normal abdominal aortic blood vessels and AAA samples in human and mice.Primary cultured murine vascular smooth muscle cells(mVSMC)of C57BL/6 mice were treated with different concentrations of AngⅡ for 24 h,and the expression of PCSK9 mRNA and pro-tein was detected.PCSK9 overexpression and knockdown cell models were established,and mitochondrial reactive oxygen species(mtROS),mitochondrial membrane potential(MMP),mitochondrial permeability transition pore(MPTP)open-ing,and Z-DNA binding protein 1(ZBP1)protein expression were detected.Bioinformatics was used to analyze the dif-ferential expression of multiple single-cell sequencing datasets to obtain the key differentially expressed genes,and to study their expression and role in AAA.Results Immunohistochemistry and immunofluorescence results showed that PCSK9 expression in human and mouse AAA increased(P＜0.01),and co-localized with smooth muscle.Ang Ⅱ promoted PCSK9 expression in mVSMC in a concentration-dependent manner,the 2.0 μmol/L Ang Ⅱ group showed a 2.9-fold and 1.1-fold increase in the expression of PCSK9 mRNA and protein,respectively(P＜0.01),with the most significant effect observed.After successfully constructing PCSK9 overexpression and PCSK9 interference mVSMC models,PCSK9 overex-pression led to an increase in intracellular mtROS,a decrease in MMP,an increase in MPTP opening,and a decrease in cellular activity(P＜0.01);PCSK9 knockdown could reduce Ang Ⅱ induced increase in mtROS,decrease in MMP and MPTP opening;compared with the siNC+Ang Ⅱ group,the siPCSK9+Ang Ⅱ group showed a decrease in mtROS and an in-crease in the fluorescence brightness of MMP and MPTP(P＜0.05).Bioinformatics analysis revealed that ZBP1 was a core differentially expressed gene in AAA.Immunohistochemistry and immunofluorescence results showed that ZBP1 ex-pression in human and mouse AAA tissues increased,and co-localized with smooth muscle.Western blot results showed that PCSK9 overexpression or treatment with 2.0 μmol/L Ang Ⅱ could increase ZBP1 protein expression(P＜0.01),while PCSK9 knockdown could alleviate the increased ZBP1 expression caused by AngⅡ(P＜0.05).Conclusion PCSK9 may induce mitochondrial damage in smooth muscle cells,activate downstream molecule ZBP1 to cause cell damage,and promote the development of AAA.

Objective:To investigate the predictive value of the Japanese nutritional risk index(NRI)for one-year all-cause mortality risk among elderly maintenance hemodialysis (MHD)patients.Additionally, it seeks to compare the predictive abilities of NRI with those of the geriatric nutritional risk index (GNRI)and the mini nutritional assessment short-form(MNA-SF).Methods:This research was conducted as a prospective cohort study.Elderly patients(aged ≥60 years)who underwent hemodialysis treatment at Beijing Friendship Hospital of Capital Medical University for more than three months between July and October 2019 were selected for inclusion.The NRI score was utilized to evaluate the nutritional status of the participants, with the maximum point of the Jordan index designated as the cut-off value, thereby categorizing patients into high-risk and low-risk groups.The follow-up period concluded in August 2020, with all-cause mortality serving as the primary outcome measure.Kaplan-Meier methods were employed to construct survival curves, and the Cox proportional hazards model was applied to analyze the association between NRI and all-cause mortality.Furthermore, area under the curve(AUC) of receiver operating characteristic(ROC)curves were utilized to compare the predictive values of the three nutritional assessment methods(NRI, GNRI, and MNA-SF)regarding mortality risk.Results:A total of 150 patients were included in the study, with a median age of 69(64.75) years.The cohort comprised 73 males(48.7%)and 77 females(51.3%). Based on the NRI, patients were categorized into a low-risk group(NRI<5; n=81, 54.0%)and a high-risk group(NRI ≥ 5; n=69, 46.0%). Of the 150 patients, 147 completed the follow-up.During the follow-up period, 15 patients died, with 13 from the high-risk group and 2 from the low-risk group.The Kaplan-Meier survival curve indicated that the 1-year cumulative survival rate for patients in the high-risk group was significantly lower than the low-risk group (log-rank χ2=11.71, P<0.001). Furthermore, multivariate Cox regression analysis revealed a significant association between NRI and 1-year all-cause mortality in elderly patients undergoing MHD ( HR=3.779, 95% CI: 1.036-13.783, P=0.044). Additionally, NRI demonstrated a high predictive value for 1-year all-cause mortality risk in elderly MHD patients, with an AUC of 0.755(95% CI: 0.654-0.855), a sensitivity of 86.7%, and a specificity of 59.1%.Its predictive capability was slightly superior to that of the GNRI(AUC=0.691, 95% CI: 0.548-0.835)and the MNA-SF(AUC=0.634, 95% CI: 0.475-0.793), although no statistically significant differences were observed( Z=0.880, 1.177, P=0.379, 0.239). Conclusions:The NRI score demonstrates effective predictive capability for one-year all-cause mortality risk in elderly MHD patients and may serve as a more suitable nutritional assessment method for this population.