ObjectiveTo explore the correlation between the occurrence of cardiovascular events in rheumatoid arthritis（RA） and traditional Chinese medicine（TCM） syndrome. MethodsThe cross-sectional study selected 6713 RA patients from 122 centres nationwide， in which general information such as name， gender， age， height， body weight， and course of disease were collected by completing a questionnaire； patients were classified into eight types of syndrome according to the information of their four examinations，i.e. wind-dampness obstruction syndrome， cold-dampness obstruction syndrome， dampness-heat obstruction syndrome， phlegm-stasis obstruction syndrome， stasis-blood obstructing collateral syndrome， qi-blood deficiency syndrome， liver-kidney insufficiency syndrome， and qi-yin deficiency syndrome. According to the occurrence of cardiovascular events， they were divided into the occurrence group and the non-occurrence group， and the condition assessment data and laboratory examination indexes were recorded. The test of difference between groups was used to analyse the possible risk factors for the occurrence of RA cardiovascular events， and binary logistic regression was used to analyse the correlation between TCM syndromes and RA cardiovascular events. ResultsA total of 6713 RA patients were included， including 256 cases in occurrence group and 6457 in non-occurrence group. There was no statistically significant difference between groups in terms of height， gender， insomnia， appetite， white blood cell（WBC）， hemoglobin（HGB）， platelets（PLT）， rheumatoid factor（RF）， anti-cyclic peptide containing citrulline（CCP）， alanine aminotransferase（ALT）， aspartate aminotransferase（AST）， γ-glutamyl transpeptidase（GGT）， urea creatinine（CREA）， and glucose（GLU）（P＞0.05）. The TCM syndromes between groups showed significant statistic differences（P＜0.05）. Patients in occurrence group had longer disease duration， heavier body weight， and older age； more severe conditions such as disease activity（DAS-28）， number of painful joints（TJC）， number of swollen joints（SJC）， health questionnaire scores（HAQ）， visual analog scores（VAS）， restlessness， and fatigue； higher blood sedimentation rate（ESR）， low-density lipoprotein（LDL-C）， triglyceride（TG）， total cholesterol（TC）， D-Dimer， and lower high-density lipoprotein（HDL-C）（P＜0.05）. The distribution of syndrome types showed that dampness-heat obstruction syndrome accounted for the largest proportion of patients in both groups and was higher in RA cardiovascular events. Logistic regression analysis showed that the occurrence of RA cardiovascular events was strongly associated with dampness-heat obstruction syndrome［OR=5.937， 95%CI （4.434， 7.949）， P＜0.001］. ConclusionThe occurrence of RA cardiovascular events were associated with TCM syndromes， and the probability of cardiovascular events in the RA patients with dampness-heat obstruction syndrome was 5.937 times higher than patients with other TCM syndromes.

We propose a strategy to reduce unnecessary prostate biopsies in Chinese patients with total prostate-specific antigen (tPSA) >10 ng ml -1 and Prostate Imaging Reporting and Data System (PI-RADS) scores between 1 and 3. Clinical data derived from 517 patients of The First Affiliated Hospital of USTC (Hefei, China) from January 2020 to December 2023 who met the screening criteria for the study were retrospectively collected. Independent predictors were identified via univariate and multivariate logistic regression analysis. The diagnostic capacity of clinical variables was evaluated using the receiver operating characteristic (ROC) curves and area under the curve (AUC). A prostate biopsy strategy was developed via risk stratification. Of the 517 patients, 17/348 (4.9%) with PI-RADS 1-2 were diagnosed with clinically significant prostate cancer (csPCa), and 27/169 (16.0%) patients with PI-RADS 3 were diagnosed with csPCa. The appropriate prostate-specific antigen density (PSAD) cut-off values were 0.45 ng ml -2 for PI-RADS 1-2 patients and 0.3 ng ml -2 for PI-RADS 3 patients. The appropriate prostate volume (PV) cut-off values were 40 ml for PI-RADS 1-2 patients and 50 ml for PI-RADS 3 patients. The prostate biopsy strategy based on PSAD and PV developed in this study can reduce unnecessary prostate biopsies in patients with tPSA >10 ng ml -1 and PI-RADS 1-3. In the study, 66.5% (344/517) patients did not need to undergo prostate biopsy, at the expense of missing only 1.7% (6/344) patients with csPCa.
Humans ; Male ; Prostatic Neoplasms/diagnostic imaging* ; Prostate-Specific Antigen/blood* ; Aged ; Middle Aged ; Retrospective Studies ; Prostate/diagnostic imaging* ; Unnecessary Procedures/statistics & numerical data* ; Biopsy/statistics & numerical data* ; China ; ROC Curve

Objective:To construct a machine learning model based on localization CT-based radiomics, dosiomics and clinical features for predicting radiotherapy response in non-small cell lung cancer (NSCLC) and validate its application value.Methods:A retrospective case series study was conducted. A total of 138 NSCLC patients who received radiotherapy at the Affiliated Hospital of Jining Medical University from January 2016 to December 2022 were selected. The efficacy was evaluated according to the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1, and the patients were stratified according to the objective remission (complete remission+partial remission). Random stratified sampling was used to divide the 138 patients into a training group (96 cases) and an internal validation group (42 cases) at a ratio of 7∶3. Additionally, 33 patients who received radiotherapy at Jining Cancer Hospital from January 2019 to December 2022 were included as the external validation group. Based on the pre-radiotherapy data of the radiotherapy planning system, PyRadiomics software package was used to extract 107 radiomics features and 107 dosiomics features for each patient. Pearson correlation analysis and LASSO regression analysis were used for dimensionality reduction screening; the final selected features were weighted and integrated to generate radiomics-dosiomics scores (RDS), which were then input into logistic regression (LR), support vector machine (SVM), extremely randomized forest (Extra Trees), K-nearest neighbor algorithm (KNN), lightweight gradient boosting machine (Light GBM), and multi-layer perceptron (MLP) machine learning algorithms to construct 6 radiomics-dosiomics models (RDM) for predicting the objective remission. RECIST 1.1 standard was used to evaluate objective remission as the gold standard, receiver operating characteristic (ROC) curve of 6 RDM for predicting objective remission was plotted, and the optimal algorithm for RDM was selected. Univariate and multivariate logistic regression were performed on demographic characteristics, hematological indicators and radiotherapy parameters of the training group to screen independent risk factors for NSCLC patients who received radiotherapy but did not achieve objective remission. These factors were input into the optimal machine learning algorithm to construct a clinical model (CM). Combined with features from RDS and CM, the clinical feature-radiomics-dosiomics combined model (CRDM) was established, and the nomogram of the model for predicting objective remission in NSCLC patients with radiotherapy was drawn. ROC curves were used to evaluate the efficacy of CM, RDM and CRDM in predicting the objective remission in NSCLC patients with radiotherapy in the training group, internal validation group and external validation group.Results:Four radiomics features (including grayscale variance, low grayscale long-range operation emphasis, low grayscale area emphasis, and small area low grayscale area emphasis, all of which were texture features) and 6 dosiomics features [including 1 first-order feature (robust mean absolute deviation), 4 texture features (grayscale non-uniformity, large area emphasis, large area high grayscale emphasis, contrast) and 1 shape feature (shortest axis length)] were selected. ROC curve analysis showed that the area under the curve (AUC) of the RDM constructed using SVM algorithm for judging the objective remission in the training group and the internal validation group was 0.907 (95% CI: 0.836-0.977) and 0.822 (95% CI: 0.685-0.959), which were higher than RDM constructed using other algorithms, and the sensitivity (96.2% and 91.7%), specificity (78.6% and 76.7%) and accuracy (83.3% and 81.0%) at the optimal cut-off values were all higher. Considering the stability and generalization ability of the model, SVM algorithm was ultimately used to construct RDM, CM and CRDM uniformly. Based on training group data, univariate and multivariate logistic regression analysis showed that elevated platelet-to-lymphocyte ratio (PLR) ( OR = 1.001, 95% CI: 1.000-1.003, P = 0.035) and increased target volume of radiotherapy plan ( OR = 1.001, 95% CI: 1.000-1.001, P = 0.008) were independent risk factors for failure to achieve objective remission. ROC curve analysis showed that in the training group and the internal validation group, the AUC of CRDM predicting objective remission were 0.914 (95% CI: 0.856-0.972) and 0.864 (95% CI: 0.754-0.974), respectively, which were better than CM [AUC were 0.735 (95% CI: 0.612-0.857) and 0.697 (95% CI: 0.507-0.888)] and RDM, respectively. In the external validation group, the AUC of CRDM, CM and RDM were 0.778 (95% CI: 0.500-1.000), 0.667 (95% CI: 0.434-0.899) and 0.741 (95% CI: 0.463-1.000), respectively. Conclusions:The CRDM constructed by combining radiomics, dosiomics and clinical features can comprehensively and accurately evaluate the radiotherapy response of NSCLC patients, and may have important clinical application value in achieving precision medicine and optimizing treatment strategies.

Objective:To construct a machine learning model based on localization CT-based radiomics, dosiomics and clinical features for predicting radiotherapy response in non-small cell lung cancer (NSCLC) and validate its application value.Methods:A retrospective case series study was conducted. A total of 138 NSCLC patients who received radiotherapy at the Affiliated Hospital of Jining Medical University from January 2016 to December 2022 were selected. The efficacy was evaluated according to the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1, and the patients were stratified according to the objective remission (complete remission+partial remission). Random stratified sampling was used to divide the 138 patients into a training group (96 cases) and an internal validation group (42 cases) at a ratio of 7∶3. Additionally, 33 patients who received radiotherapy at Jining Cancer Hospital from January 2019 to December 2022 were included as the external validation group. Based on the pre-radiotherapy data of the radiotherapy planning system, PyRadiomics software package was used to extract 107 radiomics features and 107 dosiomics features for each patient. Pearson correlation analysis and LASSO regression analysis were used for dimensionality reduction screening; the final selected features were weighted and integrated to generate radiomics-dosiomics scores (RDS), which were then input into logistic regression (LR), support vector machine (SVM), extremely randomized forest (Extra Trees), K-nearest neighbor algorithm (KNN), lightweight gradient boosting machine (Light GBM), and multi-layer perceptron (MLP) machine learning algorithms to construct 6 radiomics-dosiomics models (RDM) for predicting the objective remission. RECIST 1.1 standard was used to evaluate objective remission as the gold standard, receiver operating characteristic (ROC) curve of 6 RDM for predicting objective remission was plotted, and the optimal algorithm for RDM was selected. Univariate and multivariate logistic regression were performed on demographic characteristics, hematological indicators and radiotherapy parameters of the training group to screen independent risk factors for NSCLC patients who received radiotherapy but did not achieve objective remission. These factors were input into the optimal machine learning algorithm to construct a clinical model (CM). Combined with features from RDS and CM, the clinical feature-radiomics-dosiomics combined model (CRDM) was established, and the nomogram of the model for predicting objective remission in NSCLC patients with radiotherapy was drawn. ROC curves were used to evaluate the efficacy of CM, RDM and CRDM in predicting the objective remission in NSCLC patients with radiotherapy in the training group, internal validation group and external validation group.Results:Four radiomics features (including grayscale variance, low grayscale long-range operation emphasis, low grayscale area emphasis, and small area low grayscale area emphasis, all of which were texture features) and 6 dosiomics features [including 1 first-order feature (robust mean absolute deviation), 4 texture features (grayscale non-uniformity, large area emphasis, large area high grayscale emphasis, contrast) and 1 shape feature (shortest axis length)] were selected. ROC curve analysis showed that the area under the curve (AUC) of the RDM constructed using SVM algorithm for judging the objective remission in the training group and the internal validation group was 0.907 (95% CI: 0.836-0.977) and 0.822 (95% CI: 0.685-0.959), which were higher than RDM constructed using other algorithms, and the sensitivity (96.2% and 91.7%), specificity (78.6% and 76.7%) and accuracy (83.3% and 81.0%) at the optimal cut-off values were all higher. Considering the stability and generalization ability of the model, SVM algorithm was ultimately used to construct RDM, CM and CRDM uniformly. Based on training group data, univariate and multivariate logistic regression analysis showed that elevated platelet-to-lymphocyte ratio (PLR) ( OR = 1.001, 95% CI: 1.000-1.003, P = 0.035) and increased target volume of radiotherapy plan ( OR = 1.001, 95% CI: 1.000-1.001, P = 0.008) were independent risk factors for failure to achieve objective remission. ROC curve analysis showed that in the training group and the internal validation group, the AUC of CRDM predicting objective remission were 0.914 (95% CI: 0.856-0.972) and 0.864 (95% CI: 0.754-0.974), respectively, which were better than CM [AUC were 0.735 (95% CI: 0.612-0.857) and 0.697 (95% CI: 0.507-0.888)] and RDM, respectively. In the external validation group, the AUC of CRDM, CM and RDM were 0.778 (95% CI: 0.500-1.000), 0.667 (95% CI: 0.434-0.899) and 0.741 (95% CI: 0.463-1.000), respectively. Conclusions:The CRDM constructed by combining radiomics, dosiomics and clinical features can comprehensively and accurately evaluate the radiotherapy response of NSCLC patients, and may have important clinical application value in achieving precision medicine and optimizing treatment strategies.

Objective:To investigate the value of radiomics nomogram based on standardized pre-treatment chest enhanced CT in predicting the mutation status of epidermal growth factor receptor (EGFR) for patients with lung adenocarcinoma.Methods:A retrospective analysis was conducted on pre-treatment chest enhanced CT images and clinical data of 262 patients from the affiliated hospital of Jining Medical University with pathologically proven primary lung adenocarcinoma who received EGFR gene testing, including EGFR wild type ( n=122) and mutant type ( n=140). The patients were divided into training group ( n=183) and testing group ( n=79) according to a ratio of 7∶3 by stratified sampling method. Standardized pre-processed the images, delineated the ROI and extracted the radiomics features. Least absolute shrinkage and selection operator (LASSO) algorithm was used to reduce the dimension and select key features. The standardized radiomics model, clinical model and the combined model were established by Logistic Regression (LR) machine learning method. Calculated the Rad-score and drew the nomogram. ROC curve and Delong were used to evaluate and compare the predictive performance of different models. Results:23 standardized enhanced CT radiomics features and 4 clinical features were selected. The predictive performance of standardized radiomics model was better than that of non-standardized radiomics model [area under curve (AUC): 0.863 vs. 0.805, t=2.19, P<0.05]. The AUCs of the combined model and standardized radiomics model were higher than that of the clinical model (training group: 0.885, 0.863 vs. 0.774, t=3.57, 2.17, P<0.05; testing group: 0.873, 0.829 vs. 0.763, t=2.19, 2.02, P<0.05). The radiomics nomogram was built based on Rad-score, age, sex, smoking history and BMI. Conclusions:The combined model and standardized radiomics model could effectively predict the mutation status of EGFR gene in lung adenocarcinoma patients before treatment, providing valuable clinical insights.

Objective To investigate the clinical efficacy of Liuwei Anshen Capsules combined with Amlodipine+Benazepril Tablets in the treatment of senile essential hypertension(EH)patients complicated with anxiety and depressive disorders and its intervention effect on the expression of N-methyl-D-aspartate receptor 2B subunit(NR2B)protein.Methods A retrospective study was conducted on 138 senile EH patients complicated with anxiety and depressive disorders of accumulation of excess phlegm-heat syndrome treated in the Geriatric Department of Sinopharm Gezhouba Central Hospital from December 2020 to December 2022.The patients were divided into an observation group and a control group according to the treatment schemes,with 69 cases in each group.The control group was treated with Amlodipine+Benazepril Tablets and routine psychotherapy,and the observation group was treated with Liuwei Anshen Capsules orally on the basis of treatment for the control group.The course of treatment lasted for one month.The changes of blood pressure indicators of systolic blood pressure(SBP)and diastolic blood pressure(DBP),Hamilton Anxiety Scale(HAMA)score,17-Item Hamilton Depression Scale(HAMD-17)score,NR2B protein expression,and the levels of monoamine neurotransmitters of dopamine(DA),epinephrine(NE),and 5-hydroxytryptamine(5-HT)in the two groups were observed before and after treatment.After treatment,the clinical efficacy and total incidence of adverse reactions were compared between the two groups.Results(1)After one month of treatment,the total effective rate of the observation group was 95.65%(66/69),and that of the control group was 75.36%(52/69).The intergroup comparison(tested by chi-square test)showed that the curative effect of the observation group was significantly superior to that of the control group(P＜0.01).(2)After treatment,the SBP and DBP of the two groups were significantly decreased compared with those before treatment(P＜0.05),and the decrease of SBP and DBP in the observation group was significantly superior to that in the control group(P＜0.01).(3)After treatment,the HAMA score and HAMD-17 score of the two groups were significantly decreased compared with those before treatment(P＜0.05),and the decrease of HAMA score and HAMD-17 score in the observation group was significantly superior to that in the control group(P＜0.01).(4)After treatment,the expression level of NR2B protein in the two groups was significantly decreased compared with that before treatment(P＜0.05),and the decrease in the observation group was superior to that in the control group(P＜0.01).(5)After treatment,the levels of serum DA,NE and 5-HT in the two groups were significantly decreased in comparison with those before treatment(P＜0.05),and the decrease of serum DA,NE and 5-HT levels in the observation group was significantly superior to that in the control group(P＜0.01).(6)The total incidence of adverse reactions in the observation group was 2.90%(2/69)and that in the control group was 5.80%(4/69).There was no significant difference between the two groups(P＞0.05).Conclusion Liuwei Anshen Capsules combined with Amlodipine+Benazepril Tablets can effectively alleviate the bad mood,decrease blood pressure,inhibit the overexpression of NR2B protein,and correct the disorder of monoamine neurotransmitters in senile EH patients complicated with anxiety and depressive disorders of accumulation of excess phlegm-heat syndrome.The combined treatment does not cause serious adverse reactions,which is safe and effective.

Shenling Baizhu San is a classic prescription for replenishing Qi to invigorate the spleen and dispelling dampness to check diarrhea, which mainly treats the syndrome of spleen deficiency and heavy dampness. With the pharmacological effects of regulating immune system, improving lung function and gastrointestinal function, and resisting oxygen, tumor, and inflammation, Shenling Baizhu San is commonly used in modern clinical practice to treat chronic obstructive pulmonary disease, pulmonary fibrosis, bronchial asthma, irritable bowel syndrome, ulcerative colitis, chronic diarrhea, and diabetic, etc. This paper summarized the chemical constituents, pharmacological effects, and clinical application of Shenling Baizhu San in recent years, and predictively analyzed the quality markers of Shenling Baizhu San according to the "five principles" of Q-marker. The Q-markers of Shenling Baizhu San involved ginsenoside Rg_1, ginsenoside Re, ginsenoside Rb_1, pachymic acid, dehydrotumulosic acid, batatasin Ⅰ, batatasin Ⅲ, diosgenin, liensinine, neferine, luteolin, quercetin, glycerol trioleate, β-sitosterol, platycodin D, glycyrrhizic acid, glycyrrhetinic acid, liquiritin, pipecolinic acid, atractylenolide Ⅰ, atractylenolide Ⅲ, and bornyl acetate, which provided references for the quality control and follow-up research of Shenling Baizhu San.
Humans ; Ginsenosides/therapeutic use* ; Drugs, Chinese Herbal/pharmacology* ; Colitis, Ulcerative/drug therapy* ; Diarrhea/drug therapy*

WRKY, a class of conserved transcription factors in plants, plays important roles in plant growth, development and secondary metabolism. In the present study, 65 WRKY members were identified from de novo transcriptome sequencing data of three different tissues (root, stems and leaves) of Baphicacanthus cusia. BcWRKY proteins contained from 221 to 706 amino acids and the isoelectric point is from 4.68 to 9.68. Molecular weights range from 25 711.8 to 75 475 Da. The main secondary structures of BcWRKYs protein are random coil. A subcellular localization prediction indicated that the putative BcWRKY proteins were enriched in the nuclear region. Phylogenetic analysis showed that BcWRKYs could be categorized into three groups and five subgroups (Group IIa, Group IIb, Group IIc, Group IId and Group IIe) in Group II. Structural analysis found that all BcWRKY proteins contained a highly conserved motif WRKYGQK. Finally, the transcriptional profiles of ten BcWRKY genes highly expressed in root, stem and leaf tissues under abscisic acid (ABA), methyl jasmonate (MeJA), or salicylic acid (SA) treatment were systematically investigated using qRT-PCR analysis. Results showed that a total of ten BcWRKY genes were differentially expressed in response to ABA, MeJA, and SA treatment. This work would be provided a basis for further elucidating the molecular mechanism of WRKY transcription factors in the biosynthesis of indole alkaloids in B. cusia.

Objective: To evaluate the performance of point-of-care testing for cervical cancer and precancerous lesions screening. Methods: In September 2020, 197 and 273 women were selected by using simple random sampling method from "self-sampling" cohort and "physician-sampling" cohort established in Xiangyuan county, Shanxi Province, China, respectively. Cervical exfoliated cells were collected by women themselves or gynecologists. All samples were detected by POCT and women with positive result were directly referred for colposcopy. Subsequently, all the samples were detected by careHPV and PCR test. Colposcopy and punch biopsy were performed for women with POCT negative but careHPV or PCR test positive at another visit. Using histopathological diagnosis as the gold standard, we calculated sensitivity, specificity and drew the receiver operating characteristic (ROC) curves. The accuracy of POCT was analyzed and compared to that of careHPV and conventional PCR test in cervical cancer and precancerous lesions screening. Results: The median (Q₁ , Q₃) age of 470 women was 51 (45, 57) years old. Based on self-sampling, the sensitivity and specificity of POCT for CIN2+ were 100.00% (95%CI: 56.56%-100.00%) and 28.95% (95%CI: 22.97%-35.76%), respectively. Compared with POCT, POCT HPV16/18 test had similar sensitivity and higher specificity of 89.47% (95%CI: 84.30%-93.08%). Self-sampling POCT HPV16/18 test had an AUC of 0.947 (95%CI:0.910-0.985), which was higher than that of careHPV and PCR test. Physician-sampling POCT test had 100.00% sensitivity (95%CI: 64.57%-100.00%) and 55.85% specificity (95%CI: 49.83%-61.70%) for detecting CIN2+. POCT HPV16/18 test had lower sensitivity (71.43%, 95%CI: 35.90%-91.76%) and higher specificity (92.45%, 95%CI: 88.63%-95.06%). POCT HPV16/18 test generally showed similar AUC on both self-collected samples and clinician-collected samples (0.947 vs 0.819, P=0.217). Conclusion: POCT HPV16/18 test is an effective method with relatively high sensitivity and specificity for cervical cancer screening.
Cervical Intraepithelial Neoplasia/diagnosis* ; Colposcopy ; Early Detection of Cancer/methods* ; Female ; Human papillomavirus 16/genetics* ; Human papillomavirus 18 ; Humans ; Mass Screening/methods* ; Papillomaviridae ; Papillomavirus Infections/diagnosis* ; Point-of-Care Testing ; Pregnancy ; Sensitivity and Specificity ; Uterine Cervical Neoplasms

OBJECTIVE To explore the curative effect and mechanism of Yiqi Huoxue decoction in the treatment of coronary heart disease with Qi deficiency and blood stasis syndrome. METHODS The patients with coronary heart dis?ease of Qi deficiency and blood stasis syndrome were treated with Yiqi Huoxue decoction for 3 months, and the changes of cardiac function were observed. 61 serum samples (including 29 cases of disease group and 32 cases of Yiqi Huoxue expression group) were analyzed by non labeled proteomics. The disease group was used as the control group, and the protein with expression level difference of more than 1.2 folds (P<0.05) was screened. The molecular function, biologi?cal pathway and protein interaction of the different proteins were analyzed by bioinformatics, so as to identify the molecu?lar and biological pathway of Yiqi Huoxue decoction in the treatment of coronary heart disease with Qi deficiency and blood stasis syndrome. RESULTS Clinical treatment found that Yiqi Huoxue decoction can improve TCM syndrome score and left ventricular ejection fraction, regulate blood glucose and blood lipid levels, prolong thrombin time, and improve heart function. The results of proteomic quantitative analysis showed that there were 69 proteins with different expression levels in the disease group. Bioinformatics analysis results showed that Yiqi Huoxue decoction may regulate ApoA1, alpha-2 and other proteins to act on HDL assembly, platelet degradation, PI3K Akt signaling pathway, and then play a therapeutic role in coronary heart disease with Qi deficiency and blood stasis syndrome. CONCLUSION Yiqi Huoxue decoction can effectively improved the heart function decline caused by Qi deficiency and blood stasis syn?drome of coronary heart disease. It mainly act on energy metabolism and platelet activation pathway by activating HDL assembly and platelet degradation signal pathway proteins. This study can provide reference for the follow-up treatment mechanism of Qi deficiency and blood stasis syndrome of coronary heart disease.