BACKGROUND:Adipose-derived stem cells have anti-aging effects,but whether adipose-derived stem cells from donors of different ages are different needs further study. OBJECTIVE:To compare the biological properties of adipose-derived stem cells in old and young mice. METHODS:Adipose-derived stem cells were extracted from adipose tissue of C57BL mice aged 8 and 14 weeks,respectively.The differences of cell cycle,apoptosis,and proliferation of adipose-derived stem cells in old and young mice were compared.The expression levels of aging-related P21 and P27 genes and proteins of adipose-derived stem cells in old and young mice were detected by quantitative PCR and western blot assay. RESULTS AND CONCLUSION:Compared with old mouse adipose-derived stem cells,young mouse adipose-derived stem cells were more active,more regular in morphology,less apoptosis,faster proliferation,and lower in expression of age-related P21 and P27 genes and proteins.It has been proven that adipose-derived stem cells from young mice have better anti-aging effects.

ObjectiveThis study aims to develop and validate a novel multimodal predictive model， termed criss-cross network（CCNet）-directed graph neural network（DGNN）（CGN）， for accurate assessment of pulmonary nodule progression in high-risk individuals for lung cancer， by integrating longitudinal chest computed tomography（CT） imaging with both traditional Chinese and western clinical evaluation data. MethodsA cohort of 4 432 patients with pulmonary nodules was retrospectively analyzed. A twin CCNet was employed to extract spatiotemporal representations from paired sequential CT scans. Structured clinical assessment and imaging-derived features were encoded via a multilayer perceptron， and a similarity-based alignment strategy was adopted to harmonize multimodal imaging features across temporal dimensions. Subsequently， a DGNN was constructed to integrate heterogeneous features， where nodes represented modality-specific embeddings and edges denoted inter-modal information flow. Finally， model optimization was performed using a joint loss function combining cross-entropy and cosine similarity loss， facilitating robust classification of nodule progression status. ResultsThe proposed CGN model demonstrated superior predictive performance on the held-out test set， achieving an area under the receiver operating characteristic curve（AUC） of 0.830， accuracy of 0.843， sensitivity of 0.657， specificity of 0.712， Cohen's Kappa of 0.417， and F₁ score of 0.544. Compared with unimodal baselines， the CGN model yielded a 36%-48% relative improvement in AUC. Ablation studies revealed a 2%-22% increase in AUC when compared to simplified architectures lacking key components， substantiating the efficacy of the proposed multimodal fusion strategy and modular design. Incorporation of traditional Chinese medicine （TCM）-specific symptomatology led to an additional 5% improvement in AUC， underscoring the complementary value of integrating TCM and western clinical data. Through gradient-weighted activation mapping visualization analysis， it was found that the model's attention predominantly focused on nodule regions and effectively captured dynamic associations between clinical data and imaging-derived features. ConclusionThe CGN model， by synergistically combining cross-attention encoding with directed graph-based feature integration， enables effective alignment and fusion of heterogeneous multimodal data. The incorporation of both TCM and western clinical information facilitates complementary feature enrichment， thereby enhancing predictive accuracy for pulmonary nodule progression. This approach holds significant potential for supporting intelligent risk stratification and personalized surveillance strategies in lung cancer prevention.

ObjectiveTo construct a comprehensive evaluation indicator system for clinical practice guidelines of traditional Chinese medicine （TCM） that is scientific， systematic， and reflects the characteristics of TCM. MethodsA systematic search was conducted in Chinese and English databases， including CNKI， Wanfang， VIP， SinoMed， PubMed， Embase， and Cochrane Library， to include literature on domestic and international guideline evaluation tools and TCM-related research. Document analysis and CiteSpace were utilized for keyword co-occurrence and clustering analysis. ResultsA total of 65 relevant studies were included， from which seven core thematic domains were identified. Based on the research objectives， a two-step construction strategy was adopted： first， an external evaluation framework was established by referencing international tools to cover methodological rigor and procedural standardization； second， an internal evaluation framework was developed to reflect the distinctive features of TCM clinical practice， including syndrome differentiation and efficacy feedback. Through expert consensus， the indicator system was refined， resulting in a dual-layered structure comprising 8 primary indicators， 22 secondary indicators， and 62 evaluation criteria. ConclusionThe comprehensive evaluation system for TCM clinical practice guidelines， based on bibliometric analysis and core element extraction， integrates both theoretical integrity and practical applicability. This study provides a preliminary research foundation for further optimization， validation， and development of a refined comprehensive evaluation system.

ObjectiveTo systematically sort out the knowledge framework and conceptual logic relationship of "disease-syndrome-treatment-prescription-medicine" in the existing literature on traditional Chinese medicine（TCM） treatment of diabetic peripheral neuropathy（DPN）， to construct of the knowledge map of TCM treatment of DPN， and to promote the explicitation of the implicit knowledge in the literature on the treatment of DPN with TCM. MethodTaking the literature of China National Knowledge Infrastructure about TCM treatment of DPN as the main data source， TCM-related concepts and entities were constructed by manual citation， and the corresponding relationships between the entities were established. Structured data were formed by processing with Python 3.7， and the knowledge graph was constructed based on Neo4j 3.5.34 graph database. ResultThe resulting knowledge graph with TCM diagnosis and treatment logic， defined 12 node labels such as prescriptions， Chinese medicines and syndrome types at the schema layer， as well as 4 types of relationships， such as inclusion， correspondence， selection and composition. It could support the query and discovery of nodes（syndrome elements， syndrome types and treatment methods）， as well as the relationship between each node. ConclusionBased on the literature data， this study constructed a knowledge map for TCM treatment of DPN， which brought together various methods of TCM treatment of DPN， including internal and external treatment. The whole chain knowledge structure of syndrome differentiation and classification for DPN treatment is formed from syndrome element analysis， syndrome type composition to treatment method selection， which can provide new ideas and methods for literature data to serve clinical and scientific research work， as well as reference for visualization of TCM literature knowledge， intellectualization of TCM knowledge services and the standardization of TCM diagnosis and treatment.

This study used the Appraisal of Guidelines Research & Evaluation-Health Systems （AGREE-HS） to demonstratively compare 34 global coronavirus disease-2019 （COVID-19） health systems guidance documents （HSGs） and 6 World Health Organization （WHO） standard HSGs. The comparison involved topic， participants， methods， recommendations， and implementability， with the aim of exploring the characteristics of emergency HSGs. The results showed that the emergency HSGs had an overall average score of 49%， with topic having the highest score， recommendations having the second highest score， and participants having the lowest score. The standard HSGs had an overall average score of 79%， with high scores in all items. The emergency HSGs had lower scores in participants， methods， recommendations， and implementability than the standard HSGs （P<0.001）， while the COVID-19 emergency HSGs developed by the WHO had higher score in topic than the standard HSGs （P<0.05）. Compared with those released by countries， the COVID-19 emergency HSG developed by the WHO showed superiority in all items and overall scores （P=0.000 2）. This indicates that emergency HSGs， represented by the COVID-19 emergency HSG， place equal emphasis on topic and recommendations as standard HSGs but have low requirements in terms of expert participation， evidence support， and comprehensive consideration in the time- and resource-limited context. They have the characteristics of prominent topics， clear purposes， orientation to demand， keeping up with the latest evidence， flexible adjustment， and timeliness， emphasizing immediate implementation effects， weakening long-term effects， and focusing on comprehensive benefits. Additionally， developers， types， and report completeness are important influencing factors.

The scientific rigor and efficacy of methodologies employed in drafting emergency health systems guidance documents （HSGs） are paramount in guaranteeing the quality， reliability， and applicability of HSGs. According to the Appraisal of Guidelines for Research and Evaluation- Health Systems （AGREE-HS）， we demonstratively assessed both global coronavirus disease-2019 （COVID-19） emergency HSGs and World Health Organization （WHO） standard HSGs to uncover the core attributes of methods employed in the development of emergency HSGs. Our evaluation findings revealed that across the five assessment items of AGREE-HS， methods in the 34 emergency HSGs evaluated ranked third， trailing behind topic and recommendations. Notably， criterion 2 （the best available and most contextually relevant evidence is considered） received the highest score， whereas criterion 5 （evidence of cost and cost-effectiveness of the potential options is described） scored the lowest. Compared with the WHO standard HSGs， the COVID-19 emergency HSGs exhibited low scores in methods （P<0.05）， which was reflected in nine criteria （P<0.05）， especially in criteria 1 （systematic and transparent methods are used to identify and review the evidence） and 9 （systematic and transparent methods are used to agree upon the final recommendations）. Among the COVID-19 emergency HSGs， that developed by the WHO achieved higher scores in eight out of all nine criteria， excluding criterion 8 （P<0.05）. The clinically relevant emergency HSGs had higher scores in the criteria 3 （the evidence base is current） and 8 （the rationale behind the recommendations is clear） than other types of emergency HSGs. Collectively， the methodology for developing emergency HSGs， represented by the COVID-19 emergency HSG， underscores evidence orientation and integrates expert consensus. It is characterized by adaptable evidence synthesis strategies， streamlined evidence review protocols， and contextual relevance， all of which are influenced by external， internal， and implementation-specific factors.

ObjectiveTo explore the feasibility and rationality of applying relevant diagnostic and treatment programmes from textbooks as evidence in traditional Chinese medicine （TCM） diagnosis and treatment guidelines， using allergic rhinitis as an example. MethodsTextbooks published from October 1949 to December 2022， as well as TCM diagnosis and treatment guidelines published until December 2022 on allergic rhinitis were searched， and the contents of diagnosis and treatment related to allergic rhinitis were extracted. The similarities and differences between textbooks in different periods， between textbooks in different versions， and between textbooks and guidelines were compared and analyzed. ResultsA total of 12 national planning textbooks on TCM otolaryngology and 4 Chinese medicine diagnosis and treatment guidelines on allergic rhinitis from 1975 to 2021 were included. The evolution of diagnostic and treatment programmes was shown by the textbooks in different periods. Since 2003， syndrome of latent heat in lung channel has been added in the published textbook， and TCM featured therapies， such as nasal packing， intranasal spray were enriched， as well as the contents related to prevention and prognosis. The main syndromes included lung qi deficiency and cold， spleen qi deficiency， kidney yang deficiency， and latent heat in lung meridian. The most common recommended prescriptions were Wenfei Zhiliu Pill （温肺止流丹）， Yupingfeng Powder （玉屏风散） and Cangerzi Powder （苍耳子散）； Buzhong Yiqi Decoction （补中益气汤）， Shenling Baizhu Powder （参苓白术散）； Shenqi Pill （肾气丸） or Zhenwu Decoction （真武汤）； Xinyi Qingfei Decoction （辛夷清肺饮）. A comparison of different versions of textbooks showed that the classification of syndromes was consistent， while the formulas were slightly different. The comparison between different versions of the current textbooks and the guidelines showed that the recommended prescriptions of the textbooks had more Cangerzi Powder， Xiaoqinglong Decoction （小青龙汤）， Zhenwu Decoction， etc.， while TCM featured therapies such as nose blowing， smelling， nasal plugging， nasal washing were enriched， and information on formula modification， prevention and adjustment， prognosis were also added， which can supplement the corresponding evidence for the guidelines. ConclusionTextbooks could serve as a strong supplement for the evidence-based development of TCM clinical guidelines. However， there is still a need for further research on the quality assessment system of textbook evidence to improve the credibility of its applicability of the guidelines evidence.

ObjectiveTo investigate the identification of kidney Yang deficiency syndrome of patients with osteoporosis（OP）， and to form the clinical syndrome identification rules of traditional Chinese medicine（TCM）. MethodBasic information， etiology， clinical symptoms and other characteristics of 982 OP patients were included， and statistical tests were used to screen the variables associated with kidney Yang deficiency syndrome. Taking the decision tree as the base model， bootstrap aggregation algorithm（Bagging algorithm） was utilized to establish the classification model of kidney Yang deficiency syndrome in OP， generating numerous rules and removing redundancy. Combining least absolute shrinkage and selection operator（LASSO） regression to screen key rules and integrate them to construct an identification model， achieving the identification of kidney Yang deficiency syndrome in OP patients. ResultEighteen key identification rules were screened out， and of these， where 11 rules with regression coefficients>0 correlated positively with the kidney Yang deficiency syndrome， the rule with the highest coefficient was chilliness（present）&feverish sensation over the palm and sole（absent）. The other 7 rules with regression coefficients<0 correlated negatively with the syndrome， the rule with the lowest coefficient was reddish tongue（present）&diarrhea（absent）&deficiency of endowment（absent）. According to the regression coefficients of each key rule， variables with importance>0.2 were ranked as chilliness， reddish tongue， feverish sensation over the palm and sole， cold limbs， clear urine， diarrhea， deficiency of endowment， prolonged illness. The results of the partial dependence analysis of the identification model showed that compared to OP patients without chilliness， those with chilliness（present） had a 0.266 8 higher probability of being identified as having kidney Yang deficiency syndrome， indicating that this variable had the highest impact on identification of the syndrome. Similarly， compared to OP patients without reddish tongue， those with reddish tongue had a 0.141 9 lower probability of being identified as having kidney Yang deficiency syndrome， indicating that this variable had the highest impact on identifying non-kidney Yang deficiency syndrome. The accuracy， sensitivity， specificity and area under receiver operating characteristic curve（AUC） of the established kidney Yang deficiency syndrome identification model in the test set were 0.865 9， 0.853 7， 0.872 0 and 0.931 5， respectively. ConclusionA precise identification model of OP kidney Yang deficiency syndrome is conducted basing on the rule ensemble method of Bagging combining LASSO regression， and the screened key rules can explain the identification process of kidney Yang deficiency syndrome. In this research， according to the regression coefficients of rules， the importance and partial dependence of variables， combined with the thinking of TCM， the influence of patient characteristics on the identification of syndromes is described， so as to reveal the primary and secondary syndromes of identification and assist the clinical identification of kidney Yang deficiency syndrome.

ObjectiveTo construct a quantitative differentiation model of traditional Chinese medicine （TCM） syndromes by taking primary osteoporosis （POP） with kidney yang deficiency syndrome as an example， and to provide methodological reference for the standardization of syndrome differentiation. MethodsHigh-frequency clinical features of POP were screened by descriptive statistical analysis， and strong association features of POP were obtained by association rule algorithm. On this basis， a latent structure （latent tree） model was established through latent structure analysis， and the implicit and explicit variables （features） related to POP with kidney yang deficiency syndrome were comprehensively clustered， and the clustering results were interpreted by the indexes of mutual information and cumulative information coverage， to explore the primary and secondary symptoms， and to deduce the categories of POP with kidney yang deficiency syndrome based on the probability of the features appearing in the various latent categories. Based on the categories， the clinical feature scores and identification thresholds were calculated， and the syndrome differentiation model of POP with kidney yang deficiency was initially constructed by combining the comprehensive judgment rules. Finally， the results of TCM professionals' judgment were used as the gold standard to further evaluate the effectiveness of the model in assisting the syndrome differentiation. ResultsThe 32 features strongly associated with POP were obtained， and the Bayes information critedon score of the further constructed latent tree model was -15291.93. Based on the mutual information and the cumulative information coverage， the main symptoms of POP with kidney yang deficiency syndrome were bone weakness， fatigue， pale tongue， clear urine， frequent nocturnal urination， cold limbs， thin pulse， white coating， and secondary symptoms were weakness， loss of libido， loose stool， frequent urination， lumbar and knee weakness， and fear of cold. From the probability of the occurrence of each clinical feature in different latent categories of POP with kidney yang deficiency syndrome， the state was introduced as S0 category （none/mild kidney yang syndrome）/ S1 category （moderate kidney yang syndrome）/ S2 category （severe kidney yang syndrome）. Optimizing the preliminary rules of state identification and refining the state of S1 category， the results showed that among 970 patients with POP， there were 520 patients having no/mild kidney yang deficiency syndrome， 224 patients with moderate to mild kidney yang deficiency syndrome， 81 patients with moderate to severe kidney yang deficiency syndrome， and 145 patients with severe kidney yang deficiency syndrome. During the evaluation and validation process， the correct rate of the model assessment index was 0.8835， while the sensitivity was 0.7181， and the specificity was 0.9437. ConclusionCombined with the latent structure analysis of the association rule， the syndrome differentiation model for POP with kidney yang deficiency could be constructed， and the model shows a good quantitative identification effect， which can provide methodological supports for clinicians to improve the efficiency and accuracy of TCM diagnosis.

ObjectiveTo achieve high-dimensional prediction of class imbalanced of adverse drug reaction（ADR） of traditional Chinese medicine（TCM） and to classify and identify risk factors affecting the occurrence of ADR based on the post-marketing safety data of TCM monitored centrally in real world hospitals. MethodThe ensemble clustering resampling combined with regularized Group Lasso regression was used to perform high-dimensional balancing of ADR class-imbalanced data， and then to integrate the balanced datasets to achieve ADR prediction and the risk factor identification by category. ResultA practical example study of the proposed method on a monitoring data of TCM injection performed that the accuracy of the ADR prediction， the prediction sensitivity， the prediction specificity and the area under receiver operating characteristic curve（AUC） were all above 0.8 on the test set. Meanwhile， 40 risk factors affecting the occurrence of ADR were screened out from total 600 high-dimensional variables. And the effect of risk factors on the occurrence of ADR was identified by classification weighting. The important risk factors were classified as follows：past history， medication information， name of combined drugs， disease status， number of combined drugs and personal data. ConclusionIn the real world data of rare ADR with a large amount of clinical variables， this paper realized accurate ADR prediction on high-dimensional and class imbalanced condition， and classified and identified the key risk factors and their clinical significance of categories， so as to provide risk early warning for clinical rational drug use and combined drug use， as well as scientific basis for reevaluation of safety of post-marketing TCM.