The chronicity of infectious diseases is an important field in the collaborative research of traditional Chinese and Western medicine. The warm disease theory of pathogen invading nutrient and blood aspects in traditional Chinese medicine （TCM） takes the struggle between healthy Qi and pathogenic Qi and cementation of Yin as the core pathogenesis， providing a unique theoretical framework for explaining the common pathology of infectious chronic diseases. This theory originated from Yin-Yang interaction in the Internal Classic and was enriched with WU Youke's theory of intruding pathogen interacting and lingering in blood vessels and YE Tianshi's theory of long-term illness entering collaterals. Combining the theory with modern medical knowledge， our team has condensed the dynamic pathogenesis model of deficiency （nutrient and blood aspects） and excess （pathogen） interacting in the blood collaterals of Yin aspect， the core feature of which is the four-dimensional interactions of cause （pathogen characteristics）， location （three Yin locations of diseases）， nature （deficiency and excess）， and potential （transmission trend）. The common pathology of infectious chronic diseases is reflected in interactions. That is， the interactions between nutrient and blood deficiency （immune exhaustion and metabolic disorder） and pathogen excess （pathogen persistence and fibrous hyperplasia） in the liver collaterals （Jueyin）， kidney collaterals （Shaoyin）， lung collaterals （Taiyin） and other blood collaterals of Yin aspect form the pathological damage characterized by immune inflammatory response-continuous tissue damage with excessive repair. Taking the inheritance and innovative development of classics as the main line， this paper systematically discusses the scientific connotation of the theory of pathogen invading nutrient and blood aspects and the paths of inheritance and innovation and clarifies the original significance of this theory in the chronic development of infectious diseases. Furthermore， taking clinical diseases as an example， this paper reflects the guiding value of this classical theory in the modern diagnosis and treatment of infectious diseases with integrated traditional Chinese and Western medicine and the application potential of this theory in solving complex medical problems through the construction of the innovative paradigm of precise diagnosis and treatment with integrated traditional Chinese and Western medicine.

Objective To investigate the capacity of municipal and county-level Centers for Disease Control (CDC) in Shandong Province for γ radionuclides in food as well as gross α and gross β in drinking water, and to provide evidence for strengthening radiological laboratories. Method Data regarding radioactive detection capacity were collected from 16 prefecture-level cities and 136 county-level CDCs. Chi-square test and Fisher's exact tests were used to compare capacity differences across regions and between cities with and without nuclear power plants. Correlation analysis was used to evaluate the spatial equity of gross α and gross β detection capacity for drinking water. Result The detection capacity of gross α and gross β in drinking water varied significantly among counties (χ²=81.026, P<0.001），no significant difference was found between coastal and inland cities (χ²=2.021，P>0.155); Cities with nuclear power plants showed better detection capacity (χ²=20.253，P<0.01). The detection capacity for γ radionuclides in food was primarily concentrated in the prefecture-level cities that host nuclear power plants. Conclusion The overall detection capacity for radioactivity in drinking water was acceptable but spatially uneven. The capacity for monitoring radioactive contamination in food was generally weak. It is necessary to strengthen capacity building at the grassroots level.

AIM: To investigate the prevalence of pterygium in the southeast coastal area, analyze associated risk factors, and develop a multimodal machine learning predictive model.METHODS:A cross-sectional epidemiological study. A random sampling of the resident population of the Zhoushan Islands was performed. Data were collected through on-site ophthalmic examinations and epidemiological surveys. Univariate and multivariate analyses were performed to identify risk factors associated with pterygium. A multimodal artificial intelligence machine learning model was constructed to integrate and analyze both structured and unstructured data for predictive modeling. RESULTS:A total of 1 842 participants were enrolled in this study, including 926 males(50.27%)and 916 females(49.73%), with a mean age of 48.62±10.35 y. A total of 644 patients(34.96%), including 323 males(50.15%)and 321 females(49.84%)were diagnosed with pterygium, including 502 cases(77.95%)were unilateral, and 142 cases(22.05%)were bilateral. There were 1 198 cases without pterygium, including 603 males(50.33%)and 595 females(49.67%). Occupation, daily outdoor sunlight exposure duration, meibomian gland orifice obstruction, age, smoking, and alcohol consumption were identified as significant risk factors for pterygium. Occupation and daily outdoor sunlight exposure duration were the most potent risk factors(OR=6.125, P<0.001; OR=5.348, P<0.001, respectively), followed by age >40 y(OR=5.295, P<0.001)and meibomian gland orifice obstruction(OR=5.248, P<0.001). The multimodal machine learning model demonstrated good predictive performance, as evidenced by the receiver operating characteristic(ROC)curve, precision-recall(PR)curve, and confusion matrix, all indicating high predictive value, AUC=0.86, AP=0.77.CONCLUSION: Fishermen and farmers aged over 40 y with more than 4 h of daily sunlight exposure are at high risk for pterygium in the southeast coastal area. Meibomian gland dysfunction is correlated with corneal invasion of pterygium and may facilitate its progression; therefore, active health education and targeted intervention are recommended. The multimodal machine learning model provides accurate and reliable predictive capability for pterygium risk, which may facilitate risk stratification and early warning for pterygium in southeast coastal populations.

Mammalian male germline development depends on a specialized gonadal somatic microenvironment. However, itsin vitro reconstruction remains challenging. Major barriers include the generation of appropriate supporting-cell lineages, organized tissue architecture, and culture conditions that sustain germ-cell maturation. Yoshino et al. established a mouse pluripotent stem cell-derived testicular organoid model. This model reconstitutes the sex-determining region Y (Sry)-SRY-box transcription factor 9 (Sox9)‑mediated sex-determination program and generates testicular somatic cell-like cells (TesLCs). When assembled with primordial germ cell-like cells (PGCLCs), TesLCs formed seminiferous tubule-like structures and supported male germline progression toward prospermatogonia and germline stem cell-like cells (GSCLCs). After transplantation into infertile recipient testes, organoid-derived GSCLCs produced fertilization-competent sperm and generated fertile offspring. The major significance of this work extends beyond sperm production. It integrates sex determination, germ-cell fate transition, and testicular niche self-organization into an experimentally tractable organoid system. This commentary discusses the technical strategy, sex-determination mechanism, value of testicular niche reconstruction, and translational limitations of this study. It also highlights its implications for in vitro gametogenesis, male infertility modeling, and reproductive medicine.

Objective: To analyze the characteristics and key risk factors of abnormal blood disposal throughout the 2024-2025 blood collection and supply process based on nationwide multi-site monitoring data, develop a comprehensive prevention and control strategy across the entire chain, reduce preventable blood waste, and ensure clinical blood safety. Methods: According to the Guidelines for Haemovigilance (T/CSBT 001-2026), we extracted 74 cases of abnormal blood disposal events reported by the China Blood Transfusion Association′s Blood Safety Monitoring System from January 1 2024, to December 31 2025, and conducted descriptive statistical analysis on the distribution of events, types of deviations, root causes, and classifications of disposal manifestations. Results: A total of 664 adverse events related to blood collection and supply were reported over two years, with 74 cases involving discarded abnormal blood units, accounting for 11.1% of the total. Among these, blood component preparation (40.5%) and blood collection (37.8%) remained traditional high-risk stages. The proportion of blood waste occurring during storage, distribution, and transportation increased from 7.7% in 2024 to 34.3% in 2025. Deviations from standard operating procedures (SOP) by personnel were the primary cause, accounting for 74.3% of cases. Discarded blood units were categorized into four types: compromised closed blood bag systems (40 cases), mainly due to inadequate heat sealing, physical damage, or dropped bags; blood out of the cold chain (12 cases), primarily resulting from blood being left at work sites; imbalanced proportion of preservation solution (10 cases), mostly caused by blood collection volumes exceeding the standard capacity of blood bags; and other causes (12 cases). Conclusion: Human operational errors are the core cause of abnormal blood waste. The collection and preparation stages have long been at high-risk, and the risks in the storage and transportation stages have been increasing year by year. We should focus on standardizing personnel management, improving specialized operation norms such as heat sealing, transportation, and storage, establishing a full-process intelligent cold chain monitoring and multi-node visual verification mechanism, and systematically reducing the occurrence rate of abnormal blood waste through a closed-loop quality intervention system, thereby improving the utilization efficiency of voluntary blood donation resources.

ObjectiveTo investigate the effects of lectin-like oxidized low-density lipoprotein receptor-1 （LOX-1） gene G501C on brain structure and cognitive function in patients with white matter hyperintensities （WMH）. MethodsA total of 118 patients with WMH were enrolled. All participants underwent T1-weighted and T2-fluid-attenuated inversion recovery （T2-FLAIR） MRI to assess gray matter structure and WMH burden， and completed the mini-mental state examination （MMSE） and Montreal cognitive assessment （MoCA）. Partial correlation and mediation analyses were performed to explore the impact of LOX-1 polymorphism on cognitive function. ResultsParticipants were divided into GG+GC group （n = 35） and CC group （n = 83）. The GG+GC group showed lower MMSE and MoCA scores （MMSE： P=0.003； MOCA： P=0.015）， as well as greater WMH burden （all P0.001）， compared with the CC group. Voxel-based morphometry （VBM） analysis revealed reduced left thalamic volume in the GG+GC group， which was correlated with cognitive scores （all P0.05）. Subregional thalamic analysis further showed volume reductions in the lateral， ventral， medial， and pulvinar thalamic regions （all P0.05） in the GG+GC group， all positively associated with cognitive performance （all P0.05）. Mediation analyses indicated that volumes of the medial and pulvinar thalamic regions mediated the association between genotype and cognitive function （MMSE， MoCA）， and that WMH volume mediated the effect on MoCA scores. ConclusionThe LOX-1 G501C polymorphism may indirectly affect cognitive function by influencing specific thalamic subregional volumes and white matter damage， suggesting a potential mediating role of thalamic structures between genetic background and cognitive impairment.

Objective:To investigate the association between small vulnerable newborn (SVN) phenotypes and the risk of neurodevelopmental delay at the age of 1 year.Methods:A prospective cohort study was conducted. A total of 25 860 singleton infants from "The Born in Guangzhou Cohort Study" who completed the Gesell developmental scale assessment at 1 year of age between January 2013 and June 2025 were included. Maternal sociodemographic characteristics, and other information were collected using a self-administered questionnaire, and maternal pregnancy-related information and neonatal birth data were extracted from medical records. Global developmental delay (GDD) was defined as a developmental quotient below 86 in ≥3 domains of the Gesell developmental scale, which assesses the adaptive, gross motor, fine motor, language, and personal-social domains. The random forest algorithm was employed for missing data imputation. Based on prematurity, small for gestational age (SGA), and low birth weight (LBW), newborns were categorized into 6 phenotypes: preterm-SGA-LBW, preterm-appropriate for gestational age (AGA)-LBW, preterm-AGA-nonLBW, term-SGA-LBW, term-LBW-only or term-SGA-only, and term-AGA-nonLBW phenotype. Among these, the first 5 were classified as SVN phenotypes, and the last one served as the reference group. Inter-group comparisons were performed using analysis of variance (ANOVA), χ2 tests, or Kruskal-Wallis test, as appropriate.?? Multivariable robust Poisson regression models were applied to analyze the association of different SVN phenotypes with the risks of GDD and developmental delays in specific domains, with stratified analyses by sex. Results:Among the 25 860 infants, 13 719 (53.1%) were male and 12 141 (46.9%) were female. The gestational age at birth was 39.4 (38.6, 40.0) weeks. The overall detection rate of GDD at 1 year of age was 3.7% (962/25 860). The rates of delay across developmental domains, in descending order, language in 8 134 cases (31.5%), gross motor in 4 488 cases (17.4%), personal-social in 1 271 cases (4.9%), adaptive in 1 262 cases (4.9%), and fine motor in 621 cases (2.4%). Compared with the reference group, preterm-AGA-LBW, preterm-SGA-LBW, preterm-AGA-noneLBW, and term-SGA-LBW phenotypes were all associated with an increased risk of GDD, with the adjusted RR (95% CI) of 6.07(5.01-7.35), 4.81(3.11-7.46), 2.10(1.54-2.88) and 1.89(1.29-2.76) respectively.The preterm-AGA-noneLBW phenotype was all associated with an increased risk of delay in gross motor, language and personal-social functional domains (all P<0.05). The term-SGA-LBW phenotype was associated with an increased risk of delay in gross motor, fine motor and personal-social functional domains (all P<0.01). Whereas the term-LBW-only or term-SGA-only phenotype showed no statistically association with developmental delay in any functional domain (all P≥0.05). Conclusion:The combined classification based on gestational age and birth weight helps identify infants at high risk for neurodevelopmental delay at 1 year of age, suggesting that it may offer a reference for the rational allocation of clinical resources.

Objective To investigate the current distribution and predict the future suitable habitats of Haemaphysalis longicornis in Nanjing City, so as to provide insights into control and early warning of ticks and management of tick-borne diseases in Nanjing City. Methods The electronic map of Nanjing City was obtained from the National Platform for Common GeoSpatial Information Services. The distribution of H. longicornis and the longitude and latitude of distribution points from 2022 to 2024 were obtained from centers for disease control and prevention across each district in Nanjing City. Climatic and environmental variable data in Nanjing City were captured from the Worldclim database. Initially, 19 bioclimatic variables in this database were selected, including annual mean temperature, mean diurnal range, isothermality, temperature seasonality, maximum temperature of the warmest month, minimum temperature of the warmest month, temperature annual range, mean temperature of the wettest quarter, mean temperature of the driest quarter, mean temperature of the warmest quarter, mean temperature of the coldest quarter, annual precipitation, precipitation of the wettest month, precipitation of the driest month, precipitation seasonality, precipitation of the wettest quarter, precipitation of the driest quarter, precipitation of the warmest quarter, and precipitation of the coldest quarter. The elevation and normalized difference vegetation index were obtained from Data Sharing Platform of the Center for Resources and Environmental Sciences, Chinese Academy of Sciences. Then, the distribution points of H. longicornis, elevation, vegetation index and 19 bioclimatic variables were loaded into the software MaxEnt 3.4.4 to evaluate and screen out the variables with a contribution rate of 1% and higher. ArcGIS 10.8.1 software was used to extract the elevation, vegetation index and 19 bioclimatic variables of the distribution points of H. longicornis for a correlation analysis. If the absolute value of the correlation coefficient was 0.8 and higher, the variable with the higher contribution was retained. The 2050 dataset of the BCCCSM2-MR atmospheric circulation model in the coupled model intercomparison project phase 6 (CMIP6) were obtained from the Worldclim database as climate data for 2050. Screened H. longicornis species data and environmental and climate data were loaded into the maximum entropy (MaxEnt) model with the software MaxEnt 3.4.4 for training and validation, and then, all data generated from the model were imported into the software ArcGIS 10.8.1 to generate raster data and yield the map pertaining to the distribution of H. longicornis risk in Nanjing City. The accuracy of the model was evaluated with a receiver operating characteristic (ROC) curve, and the predictive effect of the model was assessed with area under the ROC curve (AUC). The suitable habitats of H. longicornis were classified in Nanjing City with the software ArcGIS 10.8.1, and the areas of distribution of suitable habitats in various categories were recorded to create the map of current H. longicornis suitable habitats classification in Nanjing City. The climatic and geographic information data in 2050 were employed as future environmental and climatic factors, and current environmental and climatic factors and current H. longicornis distribution data were additionally used to predict the future suitable habitats of H. longicornis in Nanjing City. In addition, the contributions of environmental and climatic factors to distribution of suitable habitats of H. longicornis was evaluated with the Jackknife method in Nanjing City. Results A total of 10 environmental and climatic variables were screened for analysis of the suitability of H. longicornis in Nanjing City based on correlation analyses and contributions of the MaxEnt model, including annual mean temperature, precipitation of the warmest quarter, vegetation index, precipitation of the wettest month, temperature annual range, annual precipitation, mean temperature of the warmest quarter, elevation, mean temperature of the wettest quarter, and maximum temperature of the warmest month, and annual mean temperature (34.8%), precipitation of the warmest quarter (17.3%), vegetation index (13.1%), and precipitation of the wettest month (10.8%) contributed relatively highly to the distribution of suitable habitats of H. longicornis in Nanjing City. The mean AUC of the ROC curve was 0.810 ± 0.055 for 10 repeated modeling results of the MaxEnt model, indicating high predictive performance of the model. The potential distribution areas of H. longicornis were predicted to be mainly located in Luhe District, Pukou District, Jiangning District, Lishui District, and Gaochun District in Nanjing City with the MaxEnt model. Under current climatic conditions, the area of potential suitable habitats of H. longicornis was 4 182.42 km² in Nanjing City, including 1 252.94 km² highly suitable habitats, which accounted for 19.00% of the total area of Nanjing City. Under the climate scenario in 2050, the area of potential suitable habitats of H. longicornis was projected to increase to 5 467.58 km² in Nanjing City, accounting for 82.95% of the total area of the city, and these habitats were mainly concentrated in Luhe District, Pukou District, Jiangning District, Lishui District, and Gaochun District. The areas of suitable habitats of H. longicornis at various categories were predicted to vary greatly in 2050, and the area of highly suitable habitats of H. longicornis was projected to increase to 2 378.82 km², accounting for 36.08% of the total area of Nanjing City. Based on jackknife tests and contributions of environmental and climatic variables, 6 dominant environmental and climatic factors were screened, including annual mean temperature (34.8% contribution), precipitation of the warmest quarter (17.3% contribution), vegetation index (13.1% contribution), precipitation of the wettest month (10.8% contribution), temperature annual range (5.4% contribution), and mean temperature of the warmest quarter (5.0% contribution), with cumulative contributions of 86.4%. Conclusion The distribution of H. longicornis is strongly associated with vegetation, temperature and precipitation in Nanjing City. Future climate change may lead to an expansion of the distribution area of H. longicornis in Nanjing City.

ObjectiveThis paper aims to find the identified and validated clinical biomarker data building upon a clinical study of early-phase phase Ⅱ and investigate the correlation analysis of Huanglian Jiedu Wan on syndrome improvement and clinical biomarkers in the treatment of "excess heat-toxicity" based on a machine learning model. Additionally， the effective prediction of clinical biomarker values for the main symptoms of the "excess heat-toxicity" syndrome was assessed. MethodsA total of 229 patients meeting the inclusion criteria for "excess heat-toxicity" syndrome were randomly divided into the Huanglian Jiedu Wan group and the placebo group. Syndrome score transition matrices were constructed for the Huanglian Jiedu Wan group and the placebo group based on three main symptoms of "excess heat-toxicity" syndrome， such as oral ulcers， sore throat， and gum swelling and pain. Data from the patients with these three syndromes were also integrated for an overall analysis. The corresponding syndrome score transition matrices were further constructed to visualize symptom change trends of the patients in the two groups via heatmaps. Based on the identified and validated clinical biomarkers related to inflammation， oxidative stress， and energy metabolism in the early phase， Spearman correlation analysis was employed to analyze and evaluate the associations between clinical biomarkers and syndrome improvement. Key clinical biomarkers reflecting the effect of Huanglian Jiedu Wan were screened through the comparison of differences between groups. An extreme gradient boosting （XGBoost） algorithm was used to develop a prediction model for main symptom classification， with classification performance evaluated through 10-fold cross-validation. Feature importance analysis was applied to identify variables with the greatest contribution to the prediction result. ResultsThe syndrome transition matrix results indicated that the Huanglian Jiedu Wan group showed a superior effect to the placebo group in improving oral ulcers， sore throat， and overall symptoms， with significant effects observed especially in sore throat and overall symptom analyses （P<0.01）. Spearman correlation analysis revealed that several clinical biomarkers positively correlated with "excess heat-toxicity" syndrome and its main symptom improvement， were also called "heat-related biomarkers"， including succinic acid， α-ketoglutaric acid， glycine， lactic acid， adenosine monophosphate （AMP）， tumor necrosis factor-α （TNF-α）， interferon-γ （IFN-γ）， interleukin-1β （IL-1β）， interleukin-4 （IL-4）， interleukin-6 （IL-6）， interleukin-8 （IL-8）， interleukin-10 （IL-10）， and so on. Conversely， clinical biomarkers negatively correlated with symptom severity， were also called "heat-clearing related biomarkers" after administration of Huanglian Jiedu Wan， including malic acid， fumaric acid， cis-aconitic acid， adrenocorticotropic hormone （ACTH）， IL-1β， IL-4， IL-8， succinic acid， and citric acid. The XGBoost classification model using all 52 biomarkers as variables achieved an average test accuracy of 0.754 and an average F1 score of 0.777. Feature importance analysis identified the scores of glutamic acid in saliva and IL-6 were the highest in all the variables， with importance scores of 0.081 and 0.080， respectively. After screening out 14 key variables and optimizing the parameters， model performance improved to an average accuracy of 0.758 and an F1 score of 0.798. Feature importance analysis further determined that the glutamic acid in saliva and IL-6 showed obvious changes after screening the variables， confirming the good syndrome prediction ability of the model constructed by these key clinical biomarkers. ConclusionThis study systematically elucidates the correlation between syndrome improvement and clinical biomarkers of Huanglian Jiedu Wan in the treatment of "excess heat-toxicity" syndrome. An XGBoost classification model based on key clinical biomarkers is successfully established， achieving effective prediction of the symptoms related to the "excess heat-toxicity" syndrome such as oral ulcers and sore throat and providing a new insight for objective identification of traditional Chinese medicine syndromes.

ObjectiveThis paper aims to find the identified and validated clinical biomarker data building upon a clinical study of early-phase phase Ⅱ and investigate the correlation analysis of Huanglian Jiedu Wan on syndrome improvement and clinical biomarkers in the treatment of "excess heat-toxicity" based on a machine learning model. Additionally， the effective prediction of clinical biomarker values for the main symptoms of the "excess heat-toxicity" syndrome was assessed. MethodsA total of 229 patients meeting the inclusion criteria for "excess heat-toxicity" syndrome were randomly divided into the Huanglian Jiedu Wan group and the placebo group. Syndrome score transition matrices were constructed for the Huanglian Jiedu Wan group and the placebo group based on three main symptoms of "excess heat-toxicity" syndrome， such as oral ulcers， sore throat， and gum swelling and pain. Data from the patients with these three syndromes were also integrated for an overall analysis. The corresponding syndrome score transition matrices were further constructed to visualize symptom change trends of the patients in the two groups via heatmaps. Based on the identified and validated clinical biomarkers related to inflammation， oxidative stress， and energy metabolism in the early phase， Spearman correlation analysis was employed to analyze and evaluate the associations between clinical biomarkers and syndrome improvement. Key clinical biomarkers reflecting the effect of Huanglian Jiedu Wan were screened through the comparison of differences between groups. An extreme gradient boosting （XGBoost） algorithm was used to develop a prediction model for main symptom classification， with classification performance evaluated through 10-fold cross-validation. Feature importance analysis was applied to identify variables with the greatest contribution to the prediction result. ResultsThe syndrome transition matrix results indicated that the Huanglian Jiedu Wan group showed a superior effect to the placebo group in improving oral ulcers， sore throat， and overall symptoms， with significant effects observed especially in sore throat and overall symptom analyses （P<0.01）. Spearman correlation analysis revealed that several clinical biomarkers positively correlated with "excess heat-toxicity" syndrome and its main symptom improvement， were also called "heat-related biomarkers"， including succinic acid， α-ketoglutaric acid， glycine， lactic acid， adenosine monophosphate （AMP）， tumor necrosis factor-α （TNF-α）， interferon-γ （IFN-γ）， interleukin-1β （IL-1β）， interleukin-4 （IL-4）， interleukin-6 （IL-6）， interleukin-8 （IL-8）， interleukin-10 （IL-10）， and so on. Conversely， clinical biomarkers negatively correlated with symptom severity， were also called "heat-clearing related biomarkers" after administration of Huanglian Jiedu Wan， including malic acid， fumaric acid， cis-aconitic acid， adrenocorticotropic hormone （ACTH）， IL-1β， IL-4， IL-8， succinic acid， and citric acid. The XGBoost classification model using all 52 biomarkers as variables achieved an average test accuracy of 0.754 and an average F1 score of 0.777. Feature importance analysis identified the scores of glutamic acid in saliva and IL-6 were the highest in all the variables， with importance scores of 0.081 and 0.080， respectively. After screening out 14 key variables and optimizing the parameters， model performance improved to an average accuracy of 0.758 and an F1 score of 0.798. Feature importance analysis further determined that the glutamic acid in saliva and IL-6 showed obvious changes after screening the variables， confirming the good syndrome prediction ability of the model constructed by these key clinical biomarkers. ConclusionThis study systematically elucidates the correlation between syndrome improvement and clinical biomarkers of Huanglian Jiedu Wan in the treatment of "excess heat-toxicity" syndrome. An XGBoost classification model based on key clinical biomarkers is successfully established， achieving effective prediction of the symptoms related to the "excess heat-toxicity" syndrome such as oral ulcers and sore throat and providing a new insight for objective identification of traditional Chinese medicine syndromes.