OBJECTIVE To investigate the clinical characteristics and risk factors for batroxobin-related severe hypofibrinogenemia （HFIB） and construct a risk prediction model. METHODS A retrospective analysis was conducted on inpatients treated with batroxobin in the First Medical Center of a tertiary hospital from January 1， 2020， to December 31， 2024. Patients were categorized into non-severe HFIB group and severe HFIB group based on the severity of HFIB. Univariate and multivariate Logistic regression analyses were performed to identify the independent influencing factors for batroxobin-related severe HFIB. A nomogram was developed using the “rms” package in R 4.5 software. The predictive performance of the model was evaluated using the receiver operating characteristic curve. Calibration was assessed via the Bootstrap resampling method， and goodness-of-fit was evaluated with the Hosmer-Lemeshow test. RESULTS A total of 1 472 patients were included in this study. Of these， 1 445 developed HFIB， yi elding an incidence of 98.17%. Furthermore， 895 were classified as severe HFIB， accounting for 60.80% of the cohort. Multivariate Logistic regression analysis showed that increased age， high initial dose per 10 kg body weight， use of maintenance dose， and concomitant glucocorticoid use were independent risk factors for batroxobin-related severe HFIB， while high baseline fibrinogen （FIB） level was identified as a protective factor. The model demonstrated an area under the curve of 0.760 （95% CI： 0.735-0.785）. The mean absolute error of the calibration curve was 0.006. The P value of the Hosmer-Lemeshow test was 0.609. CONCLUSIONS Batroxobin can rapidly and significantly reduce FIB levels and carries a risk of inducing severe HFIB. Patients with advanced age， high initial dose per 10 kg body weight， use of maintenance dose and concomitant glucocorticoid use had a higher risk of batroxobin-related severe HFIB， while high baseline FIB level had a lower risk of batroxobin-related severe HFIB. The risk prediction model developed based on these factors can be used to predict the likelihood of batroxobin-related severe HFIB.

ObjectiveTo identify potential influencing factors of urate crystal deposition at ankle/foot in patients with hyperuricemia （HUA）， and to analyze the predictive value of inflammatory indicators for urate crystal deposition in patients with different traditional Chinese medicine （TCM） syndromes， so as to provide potential reference for clinical risk assessment and individualized TCM intervention. MethodsA retrospective study was carried out with the enrollment of 231 HUA patients from The Third Affiliated People's Hospital of Fujian University of Traditional Chinese Medicine between January 2021 and December 2024. The enrolled patients were further divided into a crystal deposition-positive group （143 cases） and a crystal deposition-negative group （88 cases） according to the results of dual-energy computed tomography （CT）. Sociodemographic data， living habits， serum uric acid levels， and inflammatory indicators of the enrolled patients were collcted， and TCM syndrome differentiation was performed. Furthermore， univariate analysis was used to compare inter-group differences in clinical characteristics. MMultivariate Logistic regression was applied to identify the influencing factors of urate crystal deposition. In addition， the receiver operating characteristic （ROC） curves were plotted to evaluate the predictive efficacy of inflammatory indicators for crystal deposition across different TCM syndromes. ResultsThere were statistically significant inter-group differences in the proportion of males， age， body mass index， proportion of mental labor， rate of low water intake， and rate of high-sugar beverage consumption （P<0.05），whereas no significant difference in low exercise intensity was found between the two groups. Furthermore， compared with the negative group， the positive group had higher serum uric acid level， neutrophil-to-lymphocyte ratio （NLR）， and platelet-to-lymphocyte ratio （PLR）， but lower systemic immune-inflammation index （SIRI） （P<0.05）. Regarding the distribution of TCM syndromes， the positive group was dominated by the dampness-heat accumulation syndrome （55/143，38.46%）， while the negative group was mainly characterized by the phlegm-turbidity obstruction syndrome （44/88，50.00%）. Multivariate Logistic regression analysis revealed that high-sugar beverage consumption， elevated NLR， and elevated PLR were risk factors for urate crystal deposition ［odd ratio （OR） = 8.002， 5.377， 1.034， respectively； 95% CI 1.572-40.732， 2.179-13.270， 1.013-1.054，all P<0.05］， while SIRI was a protective factor （OR = 0.869， 95% CI 0.778-0.971， P<0.05）. In the positive group， patients with the dampness-heat accumulation syndrome exhibited the highest NLR， while the lowest PLR and SIRI， showing statistically significant differences with those of other syndromes （all P<0.05）. In addition， ROC curve analysis indicated that for the dampness-heat accumulation syndrome， the combined "NLR + PLR" model had an area under the curve （AUC） of 0.901 （95% CI 0.850-0.951， P<0.01）， with a sensitivity of 89.1% and a specificity of 79.5%； for the blood stasis-heat obstruction syndrome， the combined "NLR + PLR" model had an AUC of 0.880 （95% CI 0.825-0.934， P<0.01）， with a sensitivity of 100.0% and a specificity of 67.3%； for the liver-kidney Yin-deficiency syndrome， the single PLR model had an AUC of 0.842 （95% CI 0.731-0.952， P<0.01）， with a sensitivity of 83.3% and a specificity of 84.0%. ConclusionUrate crystal deposition in HUA patients exhibits intimate associations with high-sugar beverage consumption as well as elevated NLR and PLR levels. Meanwhile， TCM syndrome differentiation has potential correlation with inflammatory characteristics. The inflammatory indicator-based prediction model constructed based on TCM syndromes exhibits good predictive value.

ObjectiveThe development trend and knowledge structure of the research on human use experience （HUE） of traditional Chinese medicine （TCM） were systematically reviewed， and the core challenges and future directions were identified. This study aims to provide reference for the construction of a scientific and feasible research and development framework and evidence transformation system. MethodsLiterature related to "human use experience" published from January 1， 2019 to July 31， 2025 was retrieved from the China National Knowledge Infrastructure （CNKI）， Wanfang， China Science and Technology Journal Database （VIP）， and PubMed databases. Bibliometric visualization was conducted using Excel， VOSviewer， and CiteSpace， followed by in-depth reading and thematic summarization of core literature. ResultsA total of 181 papers were included for bibliometric analysis， with 45 articles used for in-depth thematic mining. The analysis showed that the number of publications on HUE research has increased in a stepwise manner over the past five years. Yang Zhongqi （24 times） was the core of the author network， the journal with the highest number of publications was China Journal of Chinese Materia Medica， the institutions publishing the most articles were mainly research institutions， regulatory agencies， hospitals， and universities， high-frequency keywords included "new TCM drugs"， "real-world studies"， and "clinical comprehensive evaluation"， keyword clustering analysis formed three major clusters： Policy orientation， application fields， and methodological approaches. Thematic analysis reveals that HUE-based evaluation should be integrated throughout the research and development process， encompassing three dimensions： TCM theory， clinical value， and pharmaceutical fundamentals， with toxic herbs and compatibility contraindications being key foci. Data collection primarily relies on empirical data， while real-world data constitute the primary source for clinical research， with efficacy and safety as the shared core. Data management emphasizes quality control and statistical analysis； however， the management of bias and confounding remains a critical bottleneck in evidence transformation. In practice， HUE-based approaches have successfully supported the registration and evaluation of multiple categories of new TCM drugs. ConclusionThe research on HUE of TCM has formed a policy-driven pattern characterized by， rapid development and close link with regulatory practice. A technical framework covering the whole chain of research and development has been constructed with clinical value as the core， which provides methodological basis and strategy reference for the scientific transformation of HUE of TCM from "experience" to "evidence".

Ulcerative colitis （UC）， a chronic relapsing inflammatory bowel disease， involves multifaceted pathological mechanisms such as intestinal barrier dysfunction， immune dysregulation， and oxidative stress. Current therapeutic strategies remain limited in efficacy and safety. In recent years， the adenosine monophosphate-activated protein kinase （AMPK） signaling pathway has emerged as a pivotal therapeutic target for UC due to its central role in energy metabolism， inflammatory regulation， and intestinal homeostasis. This article systematically reviewed the mechanisms by which traditional Chinese medicine （TCM） prevented and treated UC through the regulation of the AMPK signaling pathway， with a focus on elucidating AMPK's multidimensional regulatory network in inflammatory signaling crosstalk， alleviating oxidative stress， restoring intestinal immune balance， repairing the intestinal barrier， and modulating gut microbiota. Leveraging its unique advantages of multi-target engagement and low toxicity， TCM demonstrates promising potential in UC treatment and has become a focal area of research. By systematically summarizing and synthesizing the existing literature on TCM-mediated AMPK pathway modulation in UC， this review aims to provide a theoretical foundation for advancing mechanistic research and clinical interventions in UC.

Objective: To understand the epidemiological characteristics of a pertussis outbreak in Guangzhou, so as to provide references for outbreak response and prevention strategies. Methods: From April 5 to June 9, 2024, case screening was conducted among 246 preschool children, 35 staff members, and one full time school nurse in a kindergarten in Guangzhou based on case definition. Field epidemiological investigation methods were employed to collect relevant information, and screening samples were collected from individuals involved in the outbreak. The clinical manifestations, epidemiological characteristics, and risk factors for transmission of the outbreak were analyzed, with rate comparisons performed using the χ 2 test. Results: There were a total of 15 confirmed cases of pertussis in the kindergarten. The main clinical manifestations included intermittent cough in 14 cases ( 93.33 %), sputum production in 5 cases (33.33%), fever in 2 cases (13.33%), paroxysmal spasmodic cough in 1 case (6.67%), and vomiting in 1 case (6.67%). There was no statistically significant difference in the reporting rates of interrupted cough symptoms between pertussis cases (93.33%) and non pertussis cases (92.86%)( χ 2=3.74, P >0.05). The cases were aged 4-5 years, including 5 males and 10 females. The interval between symptom onset and diagnosis ranged from 2 to 25 days, with a median of 10 days. The outbreak involved two classes, with attack rates of 48.28% and 3.45%, respectively. Laboratory testing confirmed 14 close contacts positive for Bordetella pertussisnucleic acid. Among close contacts, only one received prophylactic medication as required. Conclusion The outbreak is a pertussis outbreak in a kindergarten caused by Bordetella pertussis infection, demonstrating distinct temporal and spatial clustering characteristics.

OBJECTIVE To establish a high-risk medication list and preventive and therapeutic measures for drug-induced hypofibrinogenemia， and to provide a reference for the prevention and treatment of this condition. METHODS By integrating domestic and international case reports， retrospective case-control studies， and spontaneous adverse drug reaction reporting databases， 19 domestically marketed high-risk drugs for drug-induced hypofibrinogenemia were identified. Based on the clinical characteristics and mechanisms of these drugs， relevant risk factors were systematically reviewed， and existing treatment options were summarized， leading to the preliminary development of recommended preventive and therapeutic measures. A two-round Delphi consultation was conducted to evaluate， revise， and ultimately reach consensus on the preliminary findings， using a mean importance score of ≥3.5 points for indicators and a coefficient of variation ＜0.3 as screening criteria. RESULTS The coefficient of expert authority for both rounds of expert consultation was 0.904. In the first round， the Kendall coordination coefficients （Kendall’s W ） for the high-risk medication list and the proposed preventive and therapeutic measures were 0.390 and 0.223 （ P ＜0.05）， respectively. In the second round， the Kendall’s W were 0.227 and 0.200 （ P ＜0.05）， respectively. After two rounds of expert consultation and discussion， 11 high-risk drugs for drug-induced hypofibrinogenemia， represented by hemocoagulase and certain anti-infective agents， were ultimately identified， along with 5 preventive and therapeutic measures spanning the entire process of “pre-medication assessment， intra-medication monitoring， and bleeding event management”. CONCLUSIONS This study has established a scientific and reliable high-risk medication list， and corresponding preventive and therapeutic measures for drug-induced hypofibrinogenemia， providing a theoretical basis and practical support for the early identification， stratified management， and precise intervention of this condition.

OBJECTIVE To investigate the application and influencing factors of sodium-dependent glucose transporters 2 inhibitors（SGLT-2i） in patients with heart failure with preserved ejection fraction（HFpEF） in the real world. METHODS Data from 358 patients with HFpEF who were hospitalized at the First Affiliated Hospital of Chongqing Medical University from May 2023 to May 2024 were retrospectively collected. The patients were divided into the SGLT-2i group and the non-SGLT-2i group based on whether they were prescribed SGLT-2i upon discharge. Baseline characteristics， comorbidities， and differences in drug treatment were compared between the two groups. Based on univariate analysis， multivariate Logistic regression analysis was performed to identify independent influencing factors of SGLT-2i use in patients with HFpEF， followed by further stratified analysis. RESULTS Among 358 HFpEF patients， the overall utilization rate of SGLT-2i was 33.5%. Combined with type 2 diabetes [OR＝9.063，95%CI（4.924-16.679） ] ， atrial fibrillation [OR＝3.135，95%CI（1.590-6.178） ] ， coronary artery heart disease [OR＝1.888，95%CI（1.072-3.327） ] and the use of loop diuretics [OR＝3.822， 95%CI （1.588-9.200） ] were all independent influencing factors for the use of SGLT-2i in patients with HFpEF （ P ＜0.05）. The results of the stratified descriptive analysis were consistent with those of the multivariate analysis， showing a higher utilization rate of SGLT-2i among patients with concomitant T2DM，atrial fibrillation， coronary artery heart disease， and those receiving loop diuretics （ P ＜0.05）； whereas the utilization rate of SGLT-2i was comparable across patients with different levels of renal function （ P ＞0.05）. CONCLUSIONS In the real-world clinical practice， the utilization of SGLT-2i in patients with HFpEF remains suboptimal， and treatment coverage still needs to be improved. Their use of SGLT-2i is primarily influenced by the presence of type 2 diabetes， atrial fibrillation， coronary artery heart disease， and the use of loop diuretics.

ObjectiveTo investigate the metabolic alterations of serum short chain fatty acids （SCFAs） in pediatric patients with atopic dermatitis （AD） and their correlation with different clinical phenotypes using targeted metabolomics. MethodsThis study enrolled 87 AD patients and 67 healthy controls （HC）. Serum levels of eight SCFAs were quantified by ultra-high-performance liquid chromatography-mass spectrometry. The associations between SCFAs and AD were assessed using various statistical methods. ResultsCompared with the HC group， levels of acetic acid （AA）， propionic acid （PA）， and caproic acid （CA） （P=0.002，P=0.002，P=0.043） decreased in the AD group. Logistic regression analysis identified AA （OR=0.449， 95% CI： 0.289–0.698） and PA （OR = 0.487， 95% CI： 0.324–0.732） as protective factors against AD. The combination of AA and PA yielded an area under the curve （AUC） greater than 0.7， indicating good diagnostic efficacy. Age-stratified analysis revealed that AA reduction was predominant in childhood， whereas PA reduction was predominant in adolescence. Pathway enrichment analysis showed significant enrichment of fatty acid biosynthesis （FDR=0.341， P=0.003） and vitamin K metabolism （FDR=1， P=0.039） pathways. Furthermore， subgroup analyses based on disease severity， personal/family history of atopy， and sex revealed no significant differences in SCFAs levels among the groups. ConclusionDifferential serum SCFAs and their enriched metabolic pathways may be implicated in the pathogenesis of AD.

ObjectiveTo investigate the application value of a machine learning model based on preoperative clinical indicators in predicting the risk of hypoalbuminemia after partial hepatectomy. MethodsA retrospective analysis was performed for the clinical data of 700 patients who underwent partial hepatectomy in Nanfang Hospital， Southern Medical University， from January 2018 to January 2023， including demographic data， history of underlying diseases， tumor characteristics， preoperative laboratory markers， and perioperative indicators. The research data were divided into a training set and a test set at a ratio of 7∶3. The two-independent-samples t test was used for comparison of normally distributed continuous data between two groups； the two-independent-samples Wilcoxon rank-sum test was used for comparison of continuous data with skewed distribution between two groups； the chi-square test or the Fisher’s exact test was used for comparison of categorical data between two groups. The least absolute shrinkage and selection operator （LASSO） regression analysis was used to identify characteristic variables， and 7 machine learning algorithms were used to construct predictive models， i.e.， logistic regression， decision tree， artificial neural network， K-nearest neighbors （KNN）， support vector machine， eXtreme gradient boosting， and light gradient boosting machine. The receiver operating characteristic （ROC） curve and the area under the ROC curve （AUC） were used to assess the discriminatory ability of models， and the DeLong test was used for comparison of AUC. The calibration curve and decision curve analysis were used to assess the calibration and clinical practicability of models， and the models were compared with albumin-bilirubin （ALBI） score and Model for End-Stage Liver Disease （MELD） score. SHapley Additive exPlanations （SHAP） were used to interpret the key influencing factors for the optimal model. ResultsA total of 700 patients were finally enrolled， 283 （40.42%） developed hypoalbuminemia after surgery. The LASSO regression analysis identified 8 predictive factors of age， hepatitis B， fatty liver， blockade time， preoperative albumin （Alb）， time of operation， intraoperative blood loss， and preoperative aspartate aminotransferase （AST）. Among the 7 machine learning models， the KNN model showed the best overall predictive performance， with an AUC of 0.835 （95% confidence interval： 0.781 — 0.889）， a sensitivity of 84.0%， and a specificity of 65.5% in the test set. ALBI and MELD scores had an AUC of 0.652 and 0.524， respectively， and the KNN model had a better predictive performance than these two scores （Z=5.309 and 8.945， both P <0.001）. The calibration curve showed good consistency between predicted probabilities and actual incidence rates， and the decision curve analysis showed that the KNN model had net clinical benefit across a wide threshold range. The SHAP analysis showed that preoperative Alb， hepatitis B， time of operation， and age were the most significant influencing factors， and a synergistic effect was observed between hepatitis B and age/time of operation. ConclusionThe KNN machine learning model constructed based on preoperative clinical indicators can effectively predict the risk of hypoalbuminemia after partial hepatectomy and has a better performance than traditional scoring models， which provides a reference for the early identification of high-risk patients in clinical practice.

To formulate an expert consensus on the principles of preoperative hair removal and provide scientific guidance for standardized removal of hair before surgical procedures so as to reduce the incidence of surgical site infections.METHODS Led by the Hospital Management Institute of National Health Commission of the People's Republic of China,this consensus was reached with the joint efforts from the expects of relevant fields such as surgeries,interventional therapies,nursing,and infection prevention and control.The consensus facilitates the classification and evaluation of literatures by following the evidence grade formulated by Oxford Evidence-based Medicine Center and focuses on the association of preoperative hair removal with surgical site infection,it reaches the evidence grade of expert consensus and recommendation intensity by integrating with discussions on meetings and clinical experience of the expects from relevant fields.RESULTS A total of 6 items of consensus were reached by summarizing the latest evidence on the aspects including the indications for preoperative hair removal,tools,range,timing and places.CONCLUSION The consensus,to some extent,make supplements to and complete the exiting regulations and standards.It provides guidance for the medical institutions to carry out the preoperative hair removal.