OBJECTIVE To investigate a dynamic monitoring of drug consumption （DMDC） model based on the seasonal Mann-Kendall trend test， aiming to provide scientific evidence for the efficient and macroscopic monitoring of drug use. METHODS A monitoring list of key outpatient drugs was established based on the top 20% of drugs ranked by sales volume in the outpatient pharmacy in October 2024. A DMDC model based on the Mann-Kendall trend test was constructed using the monthly usage data of key outpatient drugs from November 2021 to October 2024， aiming to eliminate the impact of seasonal fluctuations and analyze the temporal trends in drug consumption. Taking mucolytic expectorants， triazole derivatives for dermatophytosis， and single-agent hydroxymethylglutaryl coenzyme A （HMG-CoA） reductase inhibitors as examples， the monitoring effectiveness of the DMDC model was demonstrated， and its performance was compared with that achieved by the traditional sequential growth rate ranking method. RESULTS A total of 215 drug varieties were included in the monitoring list， and DMDC models were successfully established for all of them. Among these， 119 showed a significant increasing trend （P＜0.05， S′＞0）. The model successfully monitored the monthly consumption of mucolytic expectorants， triazole derivatives for dermatophytosis， and single- agent HMG-CoA reductase inhibitors. The precision and recall rates of the DMDC model for identifying abnormal drug use were 60.7% and 85.0%， respectively， both significantly higher than those of the sequential growth rate ranking method （8.3% and 15.0%， respectively） （χ2＝20.114， P＜0.001； χ2＝19.600， P＜0.001）. CONCLUSIONS DMDC model based on the seasonal Mann-Kendall trend test can effectively identify long-term trends in drug consumption， eliminate seasonal interference， enhance monitoring accuracy and management efficiency， and is suitable for the dynamic monitoring of drug consumption.

ObjectiveTo establish and evaluate a hyperuricemia （HUA） mouse model with the damp-turbidity internal accumulation syndrome. MethodsThirty-six C57BL/6J mice were randomly divided into control group， chemically induced model group （Model）， and model combined with high-fat and fatigue group （Model+HF）. Twenty-four uricase gene knockout （UOX^-/-） mice were randomly divided into UOX^-/- group and UOX^-/-+HF group， with 12 mice in each group. The Model group and the Model+HF group established the HUA model by intragastric administration of potassium oxyazinate combined with adenine. The Model+HF group and the UOX^-/-+HF group adopted a high-fat diet combined with weight-bearing exhaustive swimming to establish the HUA model with the damp-turbidity internal accumulation syndrome. On day 1， 7， 14， 21， and 28 after modeling， the damp-turbidity internal accumulation syndrome score of each group was evaluated. On day 1 and day 28， a weight-bearing swimming exhaustion test was performed， and the exhaustive swimming time was recorded. On day 28， liver and kidney indexes were calculated， and hematoxylin-eosin （HE） staining was used to observe histopathological changes in liver and kidney tissues. Serum levels of uric acid （UA）， xanthine dehydrogenase （XDH）， xanthine oxidase （XOD） and urea nitrogen （BUN） were detected through biochemical assays. ELISA was used to detect serum levels of tumor necrosis factor-α （TNF-α） and interleukin-1β （IL-1β）. Immunohistochemistry was used to detect the protein expression of organic anion transporter 1 （OAT1） and glucose transporter 9 （GLUT9） in renal tissues. Quantitative real-time PCR （qRT-PCR） was used to detect the mRNA expressions of adenosine triphosphate binding transporter G2 （ABCG2）， hypoxia-inducible factor-1α （HIF-1α）， urate anion transporter 1 （URAT1）， and GLUT9 in renal tissues. ResultsOn day 14 of modeling， the damp-turbidity internal accumulation syndrome scores of mice in the Model+HF group and the UOX^-/-+HF group were elevated compared with the control group（P＜0.05）； on day 28， the scores significantly increased， and the exhaustive swimming time was shortened （P＜0.01）， indicating successful establishment of the damp-turbidity internal accumulation syndrome model. Compared with the control group， all other groups exhibited increased kidney index， serum levels of UA， XDH， XOD， BUN， TNF-α， and IL-1β， elevated renal mRNA expression of HIF-1α， URAT1， OAT1， and GLUT9， and decreased renal ABCG2 mRNA expression （P＜0.01 or P＜0.05）. Significant pathological damage in the kidney was observed in the Model+HF group and the UOX^-/-+HF group compared to the Model group and the UOX^-/- group， with decreased serum UA and increased BUN and TNF-α levels （P＜0.05 or P＜0.01）， as well as increased expression of OAT1 and GLUT9 in the renal tissue. ConclusionBoth chemical induction and gene knockout， combined with a high-fat diet and weight-bearing exhaustive swimming， can successfully establish mouse models of HUA with damp-turbidity internal accumulation syndrome. Chemical induction is simple to operate and cost-effective， suitable for short-term drug screening， while UOX gene knockout induces spontaneous elevation of serum uric acid levels without drug administration， which may better mimick human metabolic characteristics.

ObjectiveTo establish and evaluate a hyperuricemia （HUA） mouse model with the damp-turbidity internal accumulation syndrome. MethodsThirty-six C57BL/6J mice were randomly divided into control group， chemically induced model group （Model）， and model combined with high-fat and fatigue group （Model+HF）. Twenty-four uricase gene knockout （UOX^-/-） mice were randomly divided into UOX^-/- group and UOX^-/-+HF group， with 12 mice in each group. The Model group and the Model+HF group established the HUA model by intragastric administration of potassium oxyazinate combined with adenine. The Model+HF group and the UOX^-/-+HF group adopted a high-fat diet combined with weight-bearing exhaustive swimming to establish the HUA model with the damp-turbidity internal accumulation syndrome. On day 1， 7， 14， 21， and 28 after modeling， the damp-turbidity internal accumulation syndrome score of each group was evaluated. On day 1 and day 28， a weight-bearing swimming exhaustion test was performed， and the exhaustive swimming time was recorded. On day 28， liver and kidney indexes were calculated， and hematoxylin-eosin （HE） staining was used to observe histopathological changes in liver and kidney tissues. Serum levels of uric acid （UA）， xanthine dehydrogenase （XDH）， xanthine oxidase （XOD） and urea nitrogen （BUN） were detected through biochemical assays. ELISA was used to detect serum levels of tumor necrosis factor-α （TNF-α） and interleukin-1β （IL-1β）. Immunohistochemistry was used to detect the protein expression of organic anion transporter 1 （OAT1） and glucose transporter 9 （GLUT9） in renal tissues. Quantitative real-time PCR （qRT-PCR） was used to detect the mRNA expressions of adenosine triphosphate binding transporter G2 （ABCG2）， hypoxia-inducible factor-1α （HIF-1α）， urate anion transporter 1 （URAT1）， and GLUT9 in renal tissues. ResultsOn day 14 of modeling， the damp-turbidity internal accumulation syndrome scores of mice in the Model+HF group and the UOX^-/-+HF group were elevated compared with the control group（P＜0.05）； on day 28， the scores significantly increased， and the exhaustive swimming time was shortened （P＜0.01）， indicating successful establishment of the damp-turbidity internal accumulation syndrome model. Compared with the control group， all other groups exhibited increased kidney index， serum levels of UA， XDH， XOD， BUN， TNF-α， and IL-1β， elevated renal mRNA expression of HIF-1α， URAT1， OAT1， and GLUT9， and decreased renal ABCG2 mRNA expression （P＜0.01 or P＜0.05）. Significant pathological damage in the kidney was observed in the Model+HF group and the UOX^-/-+HF group compared to the Model group and the UOX^-/- group， with decreased serum UA and increased BUN and TNF-α levels （P＜0.05 or P＜0.01）， as well as increased expression of OAT1 and GLUT9 in the renal tissue. ConclusionBoth chemical induction and gene knockout， combined with a high-fat diet and weight-bearing exhaustive swimming， can successfully establish mouse models of HUA with damp-turbidity internal accumulation syndrome. Chemical induction is simple to operate and cost-effective， suitable for short-term drug screening， while UOX gene knockout induces spontaneous elevation of serum uric acid levels without drug administration， which may better mimick human metabolic characteristics.

OBJECTIVE To provide scientific evidence and practical references for refined antimicrobial stewardship in multi- branch medical institutions. METHODS Data of antibiotic use density （AUD） of inpatients as well as physician-level case mix index （CMI） were collected from the Liji Road main campus and Panlongcheng branch campus of our hospital from August 2023 to March 2026. The two-factor decomposition method was adopted to decompose total AUD variation into a level effect and structural effect. Grey relational analysis （GRA） was performed to quantify the correlation degree between ward CMI， physician CMI and physician AUD， so as to identify wards and physicians requiring targeted key intervention in different campuses. A refined antimicrobial management framework for multi-branch hospitals was constructed and implemented based on the above analytical results. An interrupted time series （ITS） model incorporating seasonal dummy variables was applied. The research period was divided into pre-intervention stage （August 2023 to March 2024） and post-intervention stage （April 2024 to March 2026）. The temporal changing trends of AUD in two campuses were compared to evaluate management efficacy. RESULTS Driving factors for AUD variation presented significant heterogeneity between the two campuses. AUD variation in the main campus was dominated by level effect， while AUD variation in the branch campus was jointly affected by structural effect and level effect. The grey relational degrees of ward CMI and physician CMI with physician AUD were 0.901 and 0.882， respectively. After refined management implementation， AUD decreased by 7.48 DDDs/（100 bed·days） and 20.54 DDDs/（100 bed·days） in the main campus and branch campus， respectively； the rising trends of AUD in both campuses reversed to declining trends after intervention. CONCLUSIONS Driving factors of AUD variation under the multi-branch hospital model show obvious inter-campus heterogeneity. CMI is highly correlated with AUD. The refined management system developed via AUD attribution decomposition and CMI correlation analysis matches the differentiated clinical characteristics of multi- branch hospitals and effectively improves the precision of antimicrobial stewardship.

ObjectiveTo construct a risk prediction model for carotid artery plaques in high-risk populations of stroke based on five machine learning methods. MethodsThe clinical information of the high-risk population of stroke was collected. Factor analysis and statistical analysis of syndrome elements were conducted on their traditional Chinese medicine （TCM） symptoms and tongue and pulse manifestations. On the basis of the results of factor analysis and variable screening， five machine learning methods-classification and regression tree （CART） decision tree， support vector machine （SVM）， back propagation（BP） neural network， logistic regression， and random forest-were used to construct the risk prediction model for carotid artery plaques. ResultsThe most common TCM syndrome elements in the high-risk population of stroke was Qi deficiency. The scores of Qi deficiency， Yin deficiency， and Yang deficiency in the population with carotid artery plaques were higher than those without carotid artery plaques （P<0.05）. The CART decision tree， SVM， logistic regression， BP neural network， and random forest models showed the areas under the receiver operating characteristic （ROC） curves of 0.71， 0.75， 0.76， 0.76， and 0.75， the accuracy rates of 68.94%， 69.27%， 69.44%， 70.10%， and 69.60%， the precision rates of 68.56%， 68.53%， 68.75%， 69.74%， and 69.42%， the recall rates of 68.91%， 67.93%， 67.92%， 68.10%， and 67.31%， and the F₁ values of 0.69， 0.68， 0.68， 0.68， and 0.68， respectively. ConclusionAmong the high-risk population of stroke， the most frequently distributed TCM syndrome element is Qi deficiency， with the rest mainly being fire heat， Yin deficiency， Yang deficiency， phlegm dampness， blood stasis， and Qi stagnation. Deficiency syndrome may be a major factor leading to carotid artery plaques in the high-risk population of stroke. The BP neural network model demonstrates better performance in predicting the risk of carotid artery plaques in the high-risk population of stroke. People with carotid artery plaques are more likely to present with symptoms such as a heavy head， dizziness， headache， and thready pulse. The primary community benefits more widely when the BP neural network model is adopted to predict the risk of carotid artery plaques in the high-risk population of stroke over 40 years old.

ObjectiveTo construct a risk prediction model for carotid artery plaques in high-risk populations of stroke based on five machine learning methods. MethodsThe clinical information of the high-risk population of stroke was collected. Factor analysis and statistical analysis of syndrome elements were conducted on their traditional Chinese medicine （TCM） symptoms and tongue and pulse manifestations. On the basis of the results of factor analysis and variable screening， five machine learning methods-classification and regression tree （CART） decision tree， support vector machine （SVM）， back propagation（BP） neural network， logistic regression， and random forest-were used to construct the risk prediction model for carotid artery plaques. ResultsThe most common TCM syndrome elements in the high-risk population of stroke was Qi deficiency. The scores of Qi deficiency， Yin deficiency， and Yang deficiency in the population with carotid artery plaques were higher than those without carotid artery plaques （P<0.05）. The CART decision tree， SVM， logistic regression， BP neural network， and random forest models showed the areas under the receiver operating characteristic （ROC） curves of 0.71， 0.75， 0.76， 0.76， and 0.75， the accuracy rates of 68.94%， 69.27%， 69.44%， 70.10%， and 69.60%， the precision rates of 68.56%， 68.53%， 68.75%， 69.74%， and 69.42%， the recall rates of 68.91%， 67.93%， 67.92%， 68.10%， and 67.31%， and the F₁ values of 0.69， 0.68， 0.68， 0.68， and 0.68， respectively. ConclusionAmong the high-risk population of stroke， the most frequently distributed TCM syndrome element is Qi deficiency， with the rest mainly being fire heat， Yin deficiency， Yang deficiency， phlegm dampness， blood stasis， and Qi stagnation. Deficiency syndrome may be a major factor leading to carotid artery plaques in the high-risk population of stroke. The BP neural network model demonstrates better performance in predicting the risk of carotid artery plaques in the high-risk population of stroke. People with carotid artery plaques are more likely to present with symptoms such as a heavy head， dizziness， headache， and thready pulse. The primary community benefits more widely when the BP neural network model is adopted to predict the risk of carotid artery plaques in the high-risk population of stroke over 40 years old.

Impaired glucose regulation （IGR） is an intermediate state between normal blood glucose and diabetes， falling under the category of splenic pure heat in traditional Chinese medicine （TCM）. The core pathogenesis is believed to be the dysfunction of the zang-fu （脏腑） organs， leading to metabolic abnormalities of subtle essence and the generation of "turbid pathogen". Its accumulation can block qi movement， damage the vessel collaterals， and promote the disease progression toward diabetes. The concept of "turbid pulse" is proposed as an early sign of IGR， characterized by full and heavy sensation with stagnated and astringent circulation， which can serve as an effective supplement to clinical screening. The differentiation and treatment approach in clinical practice is summarized as follows. If the ascending and descending mechanisms of qi movement in the middle jiao （焦） are disrupted， with mutual interference between the clear and the turbid， treatment should focus on harmonizing the ascending and the descending， and transforming dampness with aromatic medicinals. For spleen-stomach qi deficiency due to long-term restriction of the spleen and stomach's function of transportation and transformation， it is suggested to fortify spleen and harmonize stomach， and transport turbid with sweet-warm medicinals. For spleen deficiency and liver constraint induced by internal generation of damp-heat in the middle jiao， the treatment should focus on soothing the liver and opening constraint， and directing the turbid downward with sour-sweat medicinals. For solid accumulation and stagnation caused by liver qi constraint， it is recommended to unblock intestine and remove stagnation， and discharge turbid with bitter-cold medicinals. When the disease is transmitted to the lower jiao， and the kidneys are affected， the treatment should be regulating spleen and kidney， and rectifying turbid with salty-sour medicinals. In conclusion， it is emphasized to promote the transformation of turbid pathogen to restore metabolic homeostasis， providing methods for the clinical diagnosis and treatment of IGR.

Objective:To understand the molecular evolutionary characteristics of the recombinant strain GII.3[P12] of norovirus acute gastroenteritis outbreaks and aggregated outbreaks in China from 2022 to 2023.Methods:Epidemiological information, case information, clinical samples, as well as detection and genotyping information of norovirus outbreaks and aggregated outbreaks from 2022 to 2023 were collected; positive samples of the GII.3[P12] recombinant strain were subjected to nucleic acid extraction, whole-genome amplification, and sequence analysis; and homology simulation method were used to construct a three-dimensional structure and predict antigenic epitopes.Results:From January 2022 to December 2023, a total of 1 136 norovirus outbreaks and aggregated outbreaks were reported in China′s norovirus outbreak surveillance network, and genotyping result were successfully obtained for 942 outbreaks, with GII dominating, accounting for 76.0% (716/942), and the proportion of GI and mixed genotypes being 15.8% (149/942) and 8.2% (77/942). Norovirus outbreaks caused by GII were dominated by GII.3[P12] (22.5%, 161/716), while other major genotypes included GII.17[P17] (18.7%, 134/716), GII.4_Sydney 2012[P16] (11.6%, 83/716) and GII.6[P7] ( 11.3%, 81/716). 2022-2023 Outbreaks caused by GII.3[P12] were concentrated in February-March (54.0%, 87/161), with the main outbreaks occurring in nursery and primary schools (87.5%), the mode of transmission was mainly human-to-human (68.9%), and the main susceptible population was children aged 3-7 years (93.3%). In this study, the genome sequences of 25 GII.3[P12] recombinant strains were obtained, and according to the phylogenetic analysis, it was shown that the GII.3[P12] recombinant strains in China in 2022-2023 belonged to the Cluster IV cluster of sublineage b (7 strains) and sublineage c (18 strains). A total of 11 linear and 8 conformational epitopes were predicted by epitope prediction analysis, and the predicted linear and conformational epitopes had overlapping positions, and each conformational epitope was part of the predicted linear epitope with conserved potential antigen-binding and receptor-binding sites.Conclusions:The recombinant strain GII.3[P12] is one of the epidemic strains that will cause outbreaks and clusters of norovirus in China in 2022-2023, and its genome did not undergo significant mutation.

Objective:To examine the near-complete genomic analysis of norovirus (NoV) subtype GⅡ.17 [P17] outbreaks in Beijing Chaoyang District from 2014 to 2024.Methods:Data and specimens related to outbreaks of the NoV aggregation in Beijing′s Chaoyang District from 2014 to 2024 were collected. The NoV was identified using real-time fluorescence reverse transcription polymerase chain reaction (RT-PCR). Specimens with positive nucleic acid were amplified by standard PCR, whole genome sequencing and evolutionary analysis. Amino acid site variations were compared.Results:In Chaoyang District, from 2014 to 2024, a total of 637 aggregated outbreaks caused by the NoV infection were reported, of which 584 were successfully typed. The epidemic caused by the GⅡ.17 [P17] subtype accounted for 8.79% (56/637), which was the dominant epidemic gene subtype in 2014-2015, sporadic in 2016-2019, reappeared in 2022, and significantly increased in 2024 (27.27%, 24/88). Outbreaks caused by the GⅡ.17 [P17] subtype occurred mainly from October to December, with the main sites of occurrence in primary schools and kindergartens. This study yielded 53 near-complete genome sequences of the GⅡ.17 [P17] subtype from 46 incidents in Chaoyang District. The GⅡ.17 [P17] subtype sequences of Chaoyang District from 2014 to 2024 were segmented into three subgroups on the evolutionary tree, with sequences from 2014 to 2019, 2022 to April 2024, and May to December 2024 clustered into the d, e, and b subgroups, respectively. In the VP1 region′s P2 area, particularly at the HBGA binding site, subgroups b and e exhibited mutations in 22 and two sites, while subgroups b and e showed mutations in four and one sites, predominantly in the RdRp region.Conclusion:The outbreak caused by the NoV GⅡ.17 [P17] subtype in Chaoyang District from 2014 to 2024 continues, with a significant increase in 2024, and it becomes the dominant gene subtype from October to December. The sequence formation of the NoV GⅡ.17 [P17] subtype in Chaoyang District from January to April 2022 and from May to December 2024 shows two different evolutions, with specific mutation sites, requiring continuous monitoring of the NoV GⅡ.17 [P17] subtype.

Cholangiocarcinoma has an extremely poor prognosis, and the efficacy of existing treatment methods is limited. In the highly desmoplastic tumor microenvironment of cholangiocarcinoma, cancer-associated fibroblasts (CAFs) are the core regulators. Their significant heterogeneity and complex intercellular crosstalk network are not only key factors driving cholangiocarcinoma progression and drug resistance, but also highly promising therapeutic targets. This review focuses on the characteristics of CAFs in cholangiocarcinoma and the key crosstalk mechanisms between CAFs and tumor cells as well as immune cells, and summarizes the research progress and limitations of current therapeutic strategies targeting CAFs.