Based on the thinking of programmatization and proceduralization， this study integrated traditional Chinese medicine （TCM） classic theories with modern knowledge expression technologies to construct a "formula-symptom" syndrome differentiation thinking model centered on "symptom clustering-main syndrome screening-formula adaptation"， and explored the standardization and intelligentization path of TCM syndrome differentiation and treatment. By establishing the mapping relationship model between formulas and syndromes including quantitative weight analysis of chief， deputy， assistant and envoy medicines， designing the logical hierarchical structure of formula-syndrome decision tree （application of three-level decision tree and fuzzy logic）， and formulating the procedural design of four diagnostic methods （structured collection， correlation model， and dynamic correction mechanism）， the standardization and visualization of the syndrome differentiation process are realized. This model can be transformed into the core data set for artificial intelligence training. Through ternary knowledge graph and machine learning algorithms， it can improve the repeatability of syndrome differentiation and the efficiency of diagnosis and treatment， and implement the strategy of "group model + individual modification" to balance the conflict between quantification and individualization. The core value of this model lies in promoting the objectification and precision development of TCM syndrome differentiation and treatment through the integration of traditional syndrome differentiation thinking and modern system science.

Objective: To investigate the epidemiological characteristics and spatial-temporal clustering of varicella in Changchun City from 2020 to 2024, so as to provide the evidence for formulating local varicella prevention and control measures. Methods: The individual case data of varicella were collected through the Surveillance and Reporting Management System of the Chinese Disease Prevention and Control Information System in Changchun City from 2020 to 2024. Descriptive epidemiological methods were used to analyze the population ,regional, and temporal distribution. Spatial autocorrelation and spatio-temporal scanning analyses were used to identify the spatial-temporal clustering characteristics. Results: A total of 8 850 varicella cases were reported in Changchun City from 2020 to 2024, with an average annual incidence of 19.64/105. There were 4 929 male cases and 3 921 female cases, with a male-to-female ratio of 1.26∶1. The age was mainly 0-<20 years (6 649 cases, 75.13%), and students were the predominant occupation (6 036 cases, 68.20%). The top three counties (cities, districts) with the highest number of cases were Chaoyang District (1 944 cases), Gongzhuling City (1 054 cases) and Nanguan District (987 cases), accounting for 45.03%. The peak incidence periods were from April to June and from October to December, with 2 166 and 4 226 cases, accounting for 24.47% and 47.75%, respectively. Spatial autocorrelation analysis showed that spatial clustering existed from 2020 to 2024. The high-high clustering areas were mainly some townships (streets) in Chaoyang District, Nanguan District, Changchun New District and Jingyue District. Spatio-temporal scanning analysis identified 6 high-risk clustering areas. The class Ⅰ clustering area was Nanhu Street in Chaoyang District, with the clustering period from September 2020 to February 2022. Conclusions Varicella cases in Changchun City were mainly males and students aged 0-<20 years from 2020 to 2024. The peak incidence was mainly in winter. Chaoyang District was a high-risk area, with obvious spatial-temporal clustering.

OBJECTIVE To conduct a clinical rapid evaluation of the marketed proprotein convertase subtilisin/kexin type 9 （PCSK9） inhibitors in China， including evolocumab， tafolecimab， recaticimab， ebronucimab， ongericimab and inclisiran. METHODS Based on the Rapid Guide for Drug Evaluation and Selection in Chinese Medical Institutions （second edition）， drug instructions， clinical diagnosis and treatment guidelines， and literature for six drugs were retrieved from CNKI， Wanfang Data， VIP， PubMed， Embase， Cochrane Library and related official websites. The clinical rapid evaluation was conducted from five aspects： pharmaceutical characteristics， effectiveness， safety， economy， and other attributes. RESULTS The pharmaceutical characteristics， effectiveness， safety， economy， other attributes， and total score of evolocumab scored 24， 27， 15.7， 10， 5.3， and 82 points， respectively. Tafolecimab scored 23.5， 23， 11.5， 9.97， 4.6， and 72.57 points， respectively. Recaticimab scored 20.5， 22， 15.5， 6.37， 3.5， and 67.87 points. Ebronucimab scored 20， 23， 11， 6.48， 3.5， and 63.98 points. Ongericimab scored 20.5， 23， 8.5， 4.83， 3.5， and 60.33 points. Inclisiran scored 25.5， 24， 13， 6.48， 5， and 73.98 points. CONCLUSIONS Evolocumab is the optimal choice for treating hypercholesterolemia and is recommended as the first-line option. Tafolecimab is the second-line option， and recaticimab is suitable for patients who are sensitive to drug adverse reactions. Inclisiran is suitable for patients with poor compliance. Ebronucimab and ongericimab are weakly recommended due to their later market introduction. Clinicians should make individualized drug selections based on factors such as patient risk level and compliance requirements.

Objective: To compare the effects of height-desk-chair matching on the viewing distance of primary school students before and after intervention, so as to provide scientific basis for the hygiene management of desks and chairs. Methods: From April to June 2025, a random cluster sampling method was used to select 141 third grade students from three classes equipped with adjustable desks and chairs in a primary school in Hefei City for a height-desk-chair matching intervention study. The height of students desks and chairs was adjusted according to the standard height and height range specified in the Functional Sizes and Technical Requirements of Chairs and Tables for Educational Institutions (GB/T 3976-2014), with an intervention period of one week. Before and after the intervention, eye use data were measured by using the electronic smart device "Cloud Clip", while collecting data on vision data viewing distance, time spent using eyes at close range and outdoor time, desk and chair height, and physical examination. Linear regression analysis was used to investigate the factors related to viewing distance before the intervention of height-desk-chair matching, and a paired t-test was used to analyze the difference in viewing distance before and after the intervention. A mixed effects model was used to explore the effect of height desk and chair adaptation intervention on viewing distance. Results: The compliance rates for desk and chair adjustments before and after the intervention were 1.4% and 18.4%, respectively, with a statistically significant difference ( χ 2=22.84, P <0.01). The viewing distance increased from (30.48±5.01) cm before intervention to (32.06±5.75) cm post intervention, with a statistically significant difference ( t=4.57, P <0.01). The proportion of students meeting the viewing distance standard increased from 33.3% to 51.1%. The linear mixedeffects model results indicated that the association between height appropriate desk and chair interventions and viewing distance was statistically significant, regardless of whether covariates such as time spent using eyes at close range and outdoor time were adjusted ( β=-1.58, 95%CI = -2.25 to -0.91; β=-1.14, 95%CI =-1.85 to -0.43, both P <0.05). Conclusion Height adjusted desks and chairs, which can effectively increase the viewing distance for primary school students, has positive implications for improving healthy eye care behaviors among children and adolescents.

In recent years， hyperuricemia （HUA） has shown a rapidly increasing incidence and tends to occur in increasingly young people， with a wide range of cardiac， renal， joint， and cancerous hazards and all-cause mortality associations. Western medicine treatment has limitations such as large liver and kidney damage， medication restriction， and easy recurrence. The intestine is the major extra-renal excretion pathway for uric acid （UA）， and the intestinal microecology can be regulated to promote UA degradation. It offers great potential to develop UA-lowering strategies that target the intestinal microecology， which are promising to provide safer and more effective therapeutic approaches. Traditional Chinese medicine （TCM） can treat HUA via multiple targets and multiple pathways from a holistic view， with low toxicity and side effects. Studies have shown that intestinal microecology is a crucial target for TCM in the treatment of HUA. However， its specific mechanism of action has not been fully elucidated. Focusing on the key role of intestinal microecology in HUA， this review explores the relationship between intestinal microecology and HUA in terms of intestinal flora， intestinal metabolites， intestinal UA transporters， and intestinal barriers. Furthermore， we summarize the research progress in TCM treatment of HUA by targeting the intestinal microecology， with the aim of providing references for the development of TCM intervention strategies for HUA and the direction of future research.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.