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.

The disease-syndrome combination animal model in traditional Chinese medicine （TCM） andrology serves as an important bridge linking TCM theory with modern medical research， providing a key experimental platform for elucidating the 'syndrome-disease' correlation mechanism in male-specific diseases and for screening effective prescriptions. This article reviews recent progress in animal model research on common TCM andrological diseases， including prostatic diseases， sexual dysfunction， and male infertility， with a focus on analyzing the application， advantages， and disadvantages of various modeling strategies， such as immune induction， hormonal intervention， and multi-factor combination across different syndrome types. However， despite breakthroughs in model construction techniques， current research still faces several challenges， including insufficient standardization of syndrome differentiation and difficulties in quantifying TCM-specific indicators. Future studies need to optimize model evaluation systems by integrating modern technologies， in order to promote the standardization and internationalization of TCM andrology research.

ObjectiveTo analyze the correlation between the clinical symptoms， signs， syndrome characteristics and laboratory indicators of cough variant asthma （CVA） and deepen the understanding of the treatment of this disease based on the theory of "Fu Feng". MethodsAn observational study was conducted. A total of 207 CVA patients who visited the respiratory department of the Third Affiliated Hospital of Beijing University of Chinese Medicine from September 2022 to November 2023 were included. The information from the four diagnostic methods and the laboratory test results of patients were collected. Factor analysis was conducted on the information obtained through the four diagnostic methods in TCM， and the nature and location of CVA were extracted. Sample cluster analysis （Q clustering） and the K-means method were used for data clustering analysis to determine the syndrome types of CVA and analyze the syndrome characteristics and differences in laboratory indicators among different syndrome types. ResultsThe main symptom of CVA patients was cough， accompanied by symptoms such as itchy throat， foreign body sensation in the throat， dry throat， shortness of breath， dry mouth， chest tightness， hoarseness， bitter mouth， poor appetite， and skin itching. Factor analysis showed that the disease was located in the lung， involving the liver， spleen， and kidney. The pathological factors involved Yin deficiency， Yang deficiency， wind factor， dampness factor， Yin factor， and Qi stagnation. Cluster analysis revealed four syndrome types： Fengfu Yinshang syndrome， Shixie Neiyun syndrome， Tanyin Zufei syndrome， and Ganhuo Fanfei syndrome. Fengfu Yinshang syndrome accounted for the highest proportion， followed by Tanyin Zufei syndrome. There were no significant differences in eosinophil count and percentage， fractional nasal nitric oxide （FnNO） level， and pulmonary function indexes among the four syndromes. The levels of serum total IgE and fractional exhaled nitric oxide （FeNO） in patients with Fengfu Yinshang syndrome were significantly higher than those in patients with Shixie Neiyun syndrome and Tanyin Zufeisyndrome. ConclusionCough is the main symptom of CVA， accompanied by pharyngeal itching， foreign body sensation in the throat， dry throat， shortness of breath， dry mouth， and allergic manifestations. The disease involves the lung， liver， spleen， and kidneys. The essence of the pathogenesis lies in a latent dormant pathogen and a disorder of the pivot mechanism. The four common syndrome types are Fengfu Yinshang syndrome， Shixie Neiyun syndrome， Tanyin Zufei syndrome， and Ganhuo Fanfei syndrome. The TCM syndrome types are correlated with laboratory indexes. The serum total IgE and FeNO of patients with Fengfu Yinshang syndrome are worse.

General anesthesia, pivotal for surgical procedures, requires precise depth monitoring to mitigate risks ranging from intraoperative awareness to postoperative cognitive impairments. Traditional assessment methods, relying on physiological indicators or behavioral responses, fall short of accurately capturing the nuanced states of unconsciousness. This study introduces a machine learning-based approach to decode anesthesia depth, leveraging EEG data across different anesthesia states induced by propofol and esketamine in rats. Our findings demonstrate the model's robust predictive accuracy, underscored by a novel intra-subject dataset partitioning and a 5-fold cross-validation method. The research diverges from conventional monitoring by utilizing anesthetic infusion rates as objective indicators of anesthesia states, highlighting distinct EEG patterns and enhancing prediction accuracy. Moreover, the model's ability to generalize across individuals suggests its potential for broad clinical application, distinguishing between anesthetic agents and their depths. Despite relying on rat EEG data, which poses questions about real-world applicability, our approach marks a significant advance in anesthesia monitoring.
Animals ; Machine Learning ; Electroencephalography/methods* ; Ketamine/administration & dosage* ; Rats ; Male ; Propofol/administration & dosage* ; Rats, Sprague-Dawley ; Anesthesia, General/methods* ; Brain/physiology* ; Intraoperative Neurophysiological Monitoring/methods*

Objective To analyze the epidemiological characteristics of newly diagnosed occupational diseases in Guangdong Province from 2019 to 2023. Methods Data on newly diagnosed occupational diseases reported in Guangdong Province from 2019 to 2023 were collected from the national occupational disease network reporting system. The spectrum of occupational diseases and their distribution by region, industry, and population were analyzed. Results A total of 4 136 newly diagnosed occupational disease cases were reported in Guangdong Province from 2019 to 2023, showing an overall downward trend. Newly diagnosed cases were classified into eight categories and 53 types of occupational diseases. In terms of the number of cases, the top five categories were occupational diseases of the ear, nose, throat and oral cavity;occupational pneumoconiosis and other respiratory diseases; occupational diseases caused by physical factors; occupational chemical poisoning; and occupational tumors, accounting for 98.62% of all cases. The top ten specific disease types were occupational noise-induced deafness, occupational silicosis, occupational other pneumoconiosis, occupational chronic benzene poisoning, occupational heatstroke, occupational hand-arm vibration disease, occupational coal workers′ pneumoconiosis, occupational welders′ pneumoconiosis, occupational tumor (leukemia caused by benzene exposure), and occupational chronic n-hexane poisoning, accounting for 94.85% of all cases. Most of the cases were distributed in the Pearl River Delta region, accounting for 89.19%; as well as manufacturing industry, accounting for 84.89%. Male cases accounted for 87.02%. Most diagnoses occurred in individuals aged >40-60 years, accounting for 74.73%. Conclusion Newly diagnosed occupational diseases in Guangdong Province from 2019 to 2023 showed the following characteristics: concentration of categories and disease types, polarization of regional distribution, industry clustering, and population difference. The disease spectrum is evolving from a dual-disease predominance toward a multi-disease predominance.

Objective To investigate the effect and mechanism of remimazolam on circulatory function in septic shock rats.Methods Seventy-two SPF grade rats were randomly divided into the control group,the model group,the dexamethasone group,the low and high dose remimazolam groups and the high-dose remimazolam+Nrf2 inhibitor(ML385)group,with 12 rats in each group.The septic shock rat model was established by intravenous infusion of 10 mg/kg lipopolysaccharide(LPS).After 6 hours of modeling,the mean arterial pressure(MAP)and heart rate(HR)of rats were measured.Enzyme linked immunosorbent assay(ELISA)method was applied to measure serum levels of lactic acid(Lac),tumor necrosis factor-α(TNF-α),interleukin(IL)-1β,IL-6,nitric oxide(NO),and endothelin-1(ET-1).Hematoxylin-eosin(HE)staining was applied to observe morphological changes in vascular tissue.TUNEL staining was applied to observe the apoptosis of vascular endothelial cells.DHE fluorescent probe was used to detect the level of ROS in vascular tissue.The colorimetric method was applied to detect the contents of MDA and the activity of SOD in vascular tissue.Western blot assay was applied to detect the protein expression of nuclear factor E2-related factor 2(Nrf2)and glutathione peroxidase 4(GPX4)in vascular tissue.Results Compared with the control group,MAP,SOD activity in vascular tissue,Nrf2 and GPX4 protein levels were lower in the model group,while HR,serum Lac,NO,ET-1,TNF-α,IL-1β,IL-6 levels,endothelial cell apoptosis rate,ROS level in vascular tissue and MDA content were higher(P＜0.05).Compared with the model group,MAP,SOD activity in vascular tissue,Nrf2 and GPX4 protein levels were higher in the dexamethasone group and in the low and high dose remimazolam groups,while HR,serum Lac,NO,ET-1,TNF-α,IL-1β,IL-6 levels,endothelial cell apoptosis rate,ROS level in vascular tissue and MDA content were lower(P＜0.05).Nrf2 inhibitor ML385 greatly reduced the protective effect of remimazolam on septic shock rats(P＜0.05).Conclusion Remimazolam may improve circulatory function in septic shock rats by activating the Nrf2/GPX4 pathway,inhibiting inflammatory response and oxidative stress,reducing endothelial cell damage.

Objective:To integrate implicit templates with deep learning techniques, a novel neural network, the tooth-deformable deep implicit network (T-DDIN), was constructed to achieve high-precision shape completion of tooth defects in a personalized manner.Methods:A total of 550 intraoral scan models were collected from patients treated at the Department of Orthodontics and Department of Prosthodontics, West China Hospital of Stomatology, Sichuan University (500 for training and 50 for testing), between March 2022 and March 2024. T-DDIN reconstructed defective tooth morphology using an implicit template and a latent encoding prediction network. During model evaluation, Class Ⅱ cavity defects and occlusal wear defects were simulated in the test set. Morphological restoration was performed using both traditional computer aided design (CAD) methods and the T-DDIN deep learning approach. The two methods were compared based on three-dimensional deviation, occlusal adjustment volumes, cusp angle deviation, and restoration time.Results:The T-DDIN group demonstrated significantly lower three-dimensional deviation for Class Ⅱ cavity defects and occlusal wear restoration [(0.14±0.05) and (0.16±0.09) mm], occlusal adjustment volumes [(0.44±0.03) and (0.49±0.03) mm 3], and difference value of the tooth cusp angles (5.69°±1.90° and 6.04°±0.53°) compared to the traditional CAD group (both P<0.001). No significant differences were observed within the T-DDIN group between the two defect types in terms of three-dimensional deviation ( P=0.098) or occlusal adjustment volume ( P=0.154) or difference value of the tooth cusp angles ( P=0.196). However, in the traditional CAD group, three-dimensional deviation, occlusal adjustment volume and difference value of the tooth cusp angles was significantly higher in occlusal wear restorations than in Class Ⅱ cavity defects restorations ( P<0.001). The T-DDIN group, which involved Class Ⅱ cavity defects and occlusal wear, demonstrated significantly less recovery time of morphology (37.2±7.7) and (39.4±6.2) s compared to the traditional CAD group ( P<0.001). Conclusions:T-DDIN demonstrated superior stability and accuracy in morphological reconstruction for various types of dental defects while significantly reducing restoration time.

Objective To investigate the distribution and antimicrobial resistance profiles of clinically isolated Haemophilus influenzae and Moraxella catarrhalis in hospitals across China from 2015 to 2021,and provide evidence for rational use of antimicrobial agents.Methods Data of H.influenzae and M.catarrhalis strains isolated from 2015 to 2021 in CHINET program were collected for analysis,and antimicrobial susceptibility testing was performed by disc diffusion method or automated systems according to the uniform protocol of CHINET.The results were interpreted according to the CLSI breakpoints in 2022.Beta-lactamases was detected by using nitrocefin disk.Results From 2015 to 2021,a total of 43 642 strains of Haemophilus species were isolated,accounting for 2.91％of the total clinical isolates and 4.07％of Gram-negative bacteria in CHINET program.Among the 40 437 strains of H.influenzae,66.89％were isolated from children and 33.11％were isolated from adults.More than 90％of the H.influenzae strains were isolated from respiratory tract specimens.The prevalence of β-lactamase was 53.79％in H.influenzae strains.The H.influenzae strains isolated from children showed higher resistance rate than the strains isolated from adults.Overall,779 strains of H.influenzae did not produce β-lactamase but were resistant to ampicillin(BLNAR).Beta-lactamase-producing strains showed significantly higher resistance rates to these antimicrobial agents than the β-lactamase-nonproducing strains.Of the 16 191 M.catarrhalis strains,80.06％were isolated from children and 19.94％isolated from adults.M.catarrhalis strains were mostly susceptible to both amoxicillin-clavulanic acid and cefuroxime,evidenced by resistance rate lower than 2.0％.Conclusions The emergence of antibiotic-resistant H.influenzae due to β-lactamase production poses a challenge for clinical anti-infective treatment.Therefore,it is very important to implement antibiotic resistance surveillance for H.influenzae and guide rational antibiotic use.All local clinical microbiology laboratories should actively improve antibiotic susceptibility testing and strengthen antibiotic resistance surveillance for H.influenzae.

Objective:To compare the accuracy of one-stage clotting assay and chromogenic substrate assay for testing an extended half-life recombinant FⅧ and to explore standardized conversion models between methods.Methods:Observational study. FⅧ activity (FⅧ:C) in plasma samples with theoretical values of 1 000, 800, 600, 500, 400, and 300 IU/L was measured using both one-stage clotting assay (employing Siemens Actin FSL reagent, Werfen SynthASil reagent, Stago PTT-A reagent) and the chromogenic substrate assay from Hyphen Biomed. Differences in FⅧ:C measured by the various methods were compared using the SNK test. Recovery rates were calculated to evaluate the accuracy of each assay. Sample activity was verified using the thrombin generation assay (TGA). Correlations between activities determined by the different assay systems were assessed using linear regression analysis.Results:Observational study. FⅧ activity (FⅧ:C) in diluted plasma samples with theoretical values of 1 000, 800, 600, 500, 400, and 300 IU/L was measured using both one-stage clotting assay (employing Siemens Actin FSL reagent, Werfen SynthASil reagent, Stago PTT-A reagent) and the chromogenic substrate assay from Hyphen Biomed. Differences in FⅧ:C measured by the various methods were compared using the SNK test. Recovery rates were calculated to evaluate the accuracy of each assay. Sample activity was verified using the thrombin generation assay (TGA). Correlations between activities determined by the different assay systems were assessed using linear regression analysis.Conclusion:Some marked one-stage clotting assay system has limitations in the clinical detection of extended half-life recombinant FⅧ. While the chromogenic substrate assay provides more accurate results. The one-stage clotting assay values can undergo cross-assay correction for FⅧ:C using a standardized conversion coefficient, which can further elevate the accuracy of monitoring hemophilia treatment efficacy.