OBJECTIVE: To explore the improvement effect of electroacupuncture (EA) based on Xingnao Kaiqiao acupuncture (acupuncture for regaining consciousness and opening orifices) on cognitive impairment in mice with Parkinson's disease (PD), and to explore its regulatory mechanisms on the kinesin family member 5A (KIF5A)/mitochondrial Rho GTPase 1 (Miro1) pathway and mitophagy in prefrontal cortical neurons. METHODS: A total of 70 male C57BL/6J mice of clean grade were randomly divided into a normal group (12 mice), a sham operation group (12 mice), and a model pre-screening group (46 mice). Unilateral stereotaxic injection of 6-hydroxydopamine (6-OHDA) into the medial forebrain bundle was adopted to establish the PD model in the model pre-screening group. Twenty-four mice after successful modeling were randomly selected and divided into a model group and an EA group, 12 mice in each one. In the EA group, acupuncture was applied at "Shuigou" (GV26) and bilateral "Sanyinjiao" (SP6) and "Neiguan" (PC6), ipsilateral "Sanyinjiao" (SP6) and "Neiguan" (PC6) were connected to EA respectively, with disperse-dense wave, 5 Hz/20 Hz in frequency, 0.5 mA in current intensity, 20 min a time, 6 times a week for 30 days. Cognitive function was assessed by Y-maze and Morris water maze tests; morphology of prefrontal cortex was observed by H.E. staining; reactive oxygen species (ROS) level in prefrontal cortex was detected by fluorescence probe method; mitochondrial morphology and autophagosome ultrastructure were observed by transmission electron microscopy; the mRNA expression of tyrosine hydroxylase (TH) was detected by quantitative real-time PCR; the protein expression of TH, KIF5A, Miro1, p62, Parkin and PTEN induced kinase 1 (PINK1) was detected by Western blot. RESULTS: Compared with the sham operation group, both the model group and the EA group exhibited increased rotation number of per minute (P<0.001). Compared with the sham operation group, in the model group, the novel arm exploration time of Y-maze test was shortened (P<0.001), the escape latency of Morris water maze test was prolonged (P<0.05) and the platform crossing number of Morris water maze test was reduced (P<0.01); in the prefrontal cortex, the number of cellular vacuole and neurons with karyopyknosis was increased (P<0.001), and mitochondrial autophagosomes could be observed; in the prefrontal cortex, the relative expression of ROS was increased (P<0.001), the protein and mRNA expression of TH was decreased (P<0.001), the protein expression of Miro1, PINK1, Parkin was increased (P<0.001, P<0.01), the protein expression of KIF5A and p62 was decreased (P<0.001). Compared with the model group, in the EA group, the novel arm exploration time of Y-maze test was prolonged (P<0.01), the escape latency of Morris water maze test was shortened (P<0.05) and the platform crossing number of Morris water maze test was increased (P<0.05); in the prefrontal cortex, the number of cellular vacuole and neurons with karyopyknosis was decreased (P<0.001), and the number of mitochondrial autophagosomes reduced and the mitochondrial morphology was improved; in the prefrontal cortex, the relative expression of ROS was decreased (P<0.01), the protein and mRNA expression of TH was increased (P<0.001, P<0.01), the protein expression of Miro1, PINK1, Parkin was decreased (P<0.001, P<0.01, P<0.05), the protein expression of KIF5A and p62 was increased (P<0.01, P<0.05). CONCLUSION Xingnao Kaiqiao electroacupuncture effectively alleviates cognitive impairment and damage of neuronal function in PD mice, its mechanism may be related to the regulation of KIF5A/Miro1 pathway, hence reducing the mitophagy in prefrontal cortical neurons.
Animals ; Electroacupuncture ; Male ; Mice ; Parkinson Disease/physiopathology* ; Cognitive Dysfunction/psychology* ; Kinesins/genetics* ; Humans ; Mitophagy ; Mice, Inbred C57BL ; rho GTP-Binding Proteins/genetics* ; Mitochondria/genetics* ; Prefrontal Cortex/metabolism*

Objective:To investigate the prognostic factors for esophageal cancer after radical resection and the application value of machine learning prediction model.Methods:The retrospective cohort study was conducted. The clinicopatholigical data of 406 esophageal cancer patients who were admitted to Qilu Hospital of Shandong University from January 2018 to March 2022 were collected. There were 357 males and 49 females, aged (64±8)years. All patients underwent radical resection of esophageal cancer. The 406 patients were randomly divided into a training set of 285 cases and a validation set of 121 cases at a 7∶3 ratio based on a random number table. The training set was used to construct prediction model, and the validation set was used to validate prediction model. Patients were divided into high-risk group and low-risk group based on risk scores. Observation indicators: (1) follow-up of patients and analysis of influencing factors for prognosis; (2) construction and validation of machine learning prediction models. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test. Comparison of ordinal data between groups was conducted using the rank sum test. The Kaplan-Meier method was used to calculate survival rate and plot survival curve, and the Log-rank test was used for survival analysis. The Cox proportional hazard regression model was used for univariate and multivariate analyses. Independent influencing factors were included, and data processing, machine learning model construction, and visualization were performed using R packages including random survival forest (RSF), gradient boosting machine (GBM), least absolute shrinkage and selection operator Cox regression (LASSO-Cox), Cox proportional hazards model boosting (CoxBoost), survival support vector machine (survivalsvm), extreme gradient boosting (XGBoost), supervised principal component analysis (SuperPC), and Cox partial least squares regression (plsRcox). Receiver operating characteristic (ROC) curves were drawn, and sensitivity, specificity, and area under the curve (AUC) were calculated. The Delong test was used to assess the differences in AUC among different models in the training set, and the time-dependent ROC was used to compare the predictive performance of different models. Calibration curves were used to evaluate model accuracy, and decision curve analysis (DCA) was used to evaluate overall net benefit. Results:(1) Follow-up of patients and analysis of influencing factors for prognosis. All 406 patients were followed up postoperatively for 28(range, 6-36)months, with 1- and 3-year overall survival rate of 86.5% and 40.9%, respectively. The 285 patients in the training set were followed up postoperatively for 30(range, 6-36)months, with 1- and 3-year overall survival rate of 85.1% and 35.5%, respectively. The 121 patients in the validation set were followed up postoperatively for 25(range, 6-36)months, with 1- and 3-year overall survival rate of 87.0% and 43.2%, respectively. There was no significant difference in postoperative overall survival rate between the training set and the validation set ( χ2=3.20, P>0.05). Results of multivariate analysis showed that left thoracic surgical approach, preopera-tive neutrophil count, vascular invasion, perineural invasion, pathological T2-4 stage, pathological N2-3 stage, and postoperative pneumonia were independent risk factors affecting postoperative survival of 285 patients in the training set ( hazard ratio=1.466, 1.037, 1.482, 1.549, 5.268, 7.727, 22.202, 2.539, 2.686, 1.425, 95% confidence interval as 1.026-2.096, 1.003-1.073, 1.008-2.179, 1.105-2.170, 1.201-23.099, 1.833-32.576, 4.734-104.128, 1.577-4.087, 1.631-4.422, 1.018-1.994, P<0.05). (2) Construction and validation of machine learning prediction models. Independent risk factors affecting postoperative survival were included to construct RSF, GBM, LASSO-Cox, CoxBoost, survivalsvm, XGBoost, SuperPC, and plsRcox machine learning prediction models. Results of Delong test showed that there were significant differences in the AUC of RSF and GBM from the other six models ( P<0.05). Results of time-dependent ROC curve showed that all 8 machine learning predic-tion models had good discriminative ability in the training cohort, among which the RSF machine learning prediction model had the best predictive performance. Results of calibration curve showed that the RSF machine learning prediction model fitted well for predicting postoperative 1-, 2-, and 3-year overall survival in the training cohort, with high consistency with actual results. Results of decision curve analysis showed that within a threshold range of 0-0.80, the RSF machine learning prediction model provided a better overall net benefit. Further analysis showed that in the validation set, the AUC of RSF machine learning prediction model for postoperative 1-, 2-, and 3-year survival prediction were 0.786 (95% confidence interval as 0.609-0.962), 0.774 (95% confidence interval as 0.676-0.873), and 0.750 (95% confidence interval as 0.652-0.848), respectively. Results of calibration curve showed that the RSF machine learning prediction model fitted well for predicting postopera-tive 1-, 2-, and 3-year overall survival in the validation set, with high consistency with actual results. In the training set, the optimal cutoff value of the RSF machine learning prediction model risk score was 11.7. Patients with risk score ≥11.7 were classified as the high-risk group, and those with risk score <11.7 as the low-risk group. The median survival times of the two groups were 18.0 months and >36.0 months, respectively, showing a significant difference between them ( χ2=73.30, P<0.05). In the validation set, the optimal cutoff value of the RSF machine learning prediction model risk score was 11.7. Patients with risk score ≥11.7 were classified as the high-risk group, and those with risk score<11.7 as the low-risk group. The median survival times of the two groups were 17.0 months and>36.0 months for the high-risk and low-risk groups, respectively, showing a significant difference between them ( χ2=35.20, P<0.05). Conclusions:Left thoracic surgical approach, preoperative neutrophil count, vascular invasion, perineural invasion, pathological T2-4 stage, pathological N2-3 stage, and postoperative pneumonia are independent risk factors affecting survival of esophageal cancer patients after radical resection. The RSF machine learning prediction model constructed based on these factors can effectively distinguish the survival prognosis of high-risk and low-risk patients.

Objective:To investigate the prognostic factors for esophageal cancer after radical resection and the application value of machine learning prediction model.Methods:The retrospective cohort study was conducted. The clinicopatholigical data of 406 esophageal cancer patients who were admitted to Qilu Hospital of Shandong University from January 2018 to March 2022 were collected. There were 357 males and 49 females, aged (64±8)years. All patients underwent radical resection of esophageal cancer. The 406 patients were randomly divided into a training set of 285 cases and a validation set of 121 cases at a 7∶3 ratio based on a random number table. The training set was used to construct prediction model, and the validation set was used to validate prediction model. Patients were divided into high-risk group and low-risk group based on risk scores. Observation indicators: (1) follow-up of patients and analysis of influencing factors for prognosis; (2) construction and validation of machine learning prediction models. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test. Comparison of ordinal data between groups was conducted using the rank sum test. The Kaplan-Meier method was used to calculate survival rate and plot survival curve, and the Log-rank test was used for survival analysis. The Cox proportional hazard regression model was used for univariate and multivariate analyses. Independent influencing factors were included, and data processing, machine learning model construction, and visualization were performed using R packages including random survival forest (RSF), gradient boosting machine (GBM), least absolute shrinkage and selection operator Cox regression (LASSO-Cox), Cox proportional hazards model boosting (CoxBoost), survival support vector machine (survivalsvm), extreme gradient boosting (XGBoost), supervised principal component analysis (SuperPC), and Cox partial least squares regression (plsRcox). Receiver operating characteristic (ROC) curves were drawn, and sensitivity, specificity, and area under the curve (AUC) were calculated. The Delong test was used to assess the differences in AUC among different models in the training set, and the time-dependent ROC was used to compare the predictive performance of different models. Calibration curves were used to evaluate model accuracy, and decision curve analysis (DCA) was used to evaluate overall net benefit. Results:(1) Follow-up of patients and analysis of influencing factors for prognosis. All 406 patients were followed up postoperatively for 28(range, 6-36)months, with 1- and 3-year overall survival rate of 86.5% and 40.9%, respectively. The 285 patients in the training set were followed up postoperatively for 30(range, 6-36)months, with 1- and 3-year overall survival rate of 85.1% and 35.5%, respectively. The 121 patients in the validation set were followed up postoperatively for 25(range, 6-36)months, with 1- and 3-year overall survival rate of 87.0% and 43.2%, respectively. There was no significant difference in postoperative overall survival rate between the training set and the validation set ( χ2=3.20, P>0.05). Results of multivariate analysis showed that left thoracic surgical approach, preopera-tive neutrophil count, vascular invasion, perineural invasion, pathological T2-4 stage, pathological N2-3 stage, and postoperative pneumonia were independent risk factors affecting postoperative survival of 285 patients in the training set ( hazard ratio=1.466, 1.037, 1.482, 1.549, 5.268, 7.727, 22.202, 2.539, 2.686, 1.425, 95% confidence interval as 1.026-2.096, 1.003-1.073, 1.008-2.179, 1.105-2.170, 1.201-23.099, 1.833-32.576, 4.734-104.128, 1.577-4.087, 1.631-4.422, 1.018-1.994, P<0.05). (2) Construction and validation of machine learning prediction models. Independent risk factors affecting postoperative survival were included to construct RSF, GBM, LASSO-Cox, CoxBoost, survivalsvm, XGBoost, SuperPC, and plsRcox machine learning prediction models. Results of Delong test showed that there were significant differences in the AUC of RSF and GBM from the other six models ( P<0.05). Results of time-dependent ROC curve showed that all 8 machine learning predic-tion models had good discriminative ability in the training cohort, among which the RSF machine learning prediction model had the best predictive performance. Results of calibration curve showed that the RSF machine learning prediction model fitted well for predicting postoperative 1-, 2-, and 3-year overall survival in the training cohort, with high consistency with actual results. Results of decision curve analysis showed that within a threshold range of 0-0.80, the RSF machine learning prediction model provided a better overall net benefit. Further analysis showed that in the validation set, the AUC of RSF machine learning prediction model for postoperative 1-, 2-, and 3-year survival prediction were 0.786 (95% confidence interval as 0.609-0.962), 0.774 (95% confidence interval as 0.676-0.873), and 0.750 (95% confidence interval as 0.652-0.848), respectively. Results of calibration curve showed that the RSF machine learning prediction model fitted well for predicting postopera-tive 1-, 2-, and 3-year overall survival in the validation set, with high consistency with actual results. In the training set, the optimal cutoff value of the RSF machine learning prediction model risk score was 11.7. Patients with risk score ≥11.7 were classified as the high-risk group, and those with risk score <11.7 as the low-risk group. The median survival times of the two groups were 18.0 months and >36.0 months, respectively, showing a significant difference between them ( χ2=73.30, P<0.05). In the validation set, the optimal cutoff value of the RSF machine learning prediction model risk score was 11.7. Patients with risk score ≥11.7 were classified as the high-risk group, and those with risk score<11.7 as the low-risk group. The median survival times of the two groups were 17.0 months and>36.0 months for the high-risk and low-risk groups, respectively, showing a significant difference between them ( χ2=35.20, P<0.05). Conclusions:Left thoracic surgical approach, preoperative neutrophil count, vascular invasion, perineural invasion, pathological T2-4 stage, pathological N2-3 stage, and postoperative pneumonia are independent risk factors affecting survival of esophageal cancer patients after radical resection. The RSF machine learning prediction model constructed based on these factors can effectively distinguish the survival prognosis of high-risk and low-risk patients.

Objective:We aimed to develop a novel visualized model based on nomogram to predict postoperative overall survival.Methods:This was a multicenter, retrospective, observational cohort study, including participants with histologically confirmed gastric and colorectal cancer who underwent radical surgery from 11 medical centers in China from August 1, 2015 to June 30, 2018. Baseline characteristics, histopathological data and nutritional status, as assessed using Nutrition Risk Screening 2002 (NRS 2002) score and the scored Patient-Generated Subjective Global Assessment, were collected. The least absolute shrinkage and selection operator regression and Cox regression were used to identify variables to be included in the predictive model. Internal and external validations were performed.Results:There were 681 and 127 patients in the training and validation cohorts, respectively. A total of 188 deaths were observed over a median follow-up period of 59 (range: 58 to 60) months. Two independent predictors of NRS 2002 and Tumor-Node-Metastasis (TNM) stage were identified and incorporated into the prediction nomogram model together with the factor of age. The model's concordance index for 1-, 3- and 5-year overall survival was 0.696, 0.724, and 0.738 in the training cohort and 0.801, 0.812, and 0.793 in the validation cohort, respectively.Conclusions:In this study, a new nomogram prediction model based on NRS 2002 score was developed and validated for predicting the overall postoperative survival of patients with gastric colorectal cancer. This model has good differentiation, calibration and clinical practicability in predicting the long-term survival rate of patients with gastrointestinal cancer after radical surgery.

Objective To evaluate the diagnostic value of myocardial work related parameters in coronary ischemia patients with coronary artery disease(CAD)coronary ischemia using non-invasive left ventricular pressure strain loop(PSL),taking fraction flow reservation(FFR)as the gold standard.Methods From December 2020 to December 2021,53 clinically suspected CAD patients were prospectively enrolled.All patients underwent echocardiography,invasive coronary angiography and FFR measurement.According to the results of coronary angiography,patients were divided into myocardial ischemia group(n=24,FFR≤0.80)and non-myocardial ischemia group(n=29,FFR＞0.80).PSL was used for off-line analysis to obtain the global work index(GWI),global constructive work(GCW),global wasted work(GWW),global work efficiency(GWE),global positive work(GPW),and global systolic constructive work(GSCW)and other myocardial work parameters.The differences of parameter values between the two groups were compared.The diagnostic efficacy of work parameters in myocardial ischemia was analyzed by ROC curve.Results Compared with the non-myocardial ischemia group,GWI,GCW,GPW and GSCW were significantly decreased in the myocardial ischemia group at the 18-,16-,and 12-segment levels(P＜0.001).The ROC curve showed that the AUC results of GWI,GCW,GPW,GSCW at the 18-segment level were 0.803(95％CI 0.679-0.927),0.807(95％CI 0.687-0.928),0.822(95％CI 0.708-0.936),0.819(95％CI 0.703-0.935).The optimal cut-off value of GWI was 1 676.3 mmHg％,and the sensitivity,specificity and accuracy of predicting myocardial ischemia were 70.8％,86.2％and 79.2％,respectively.The optimal cut-off value of GCW was 1 999.4 mmHg％,and the sensitivity,specificity and accuracy of predicting myocardial ischemia were 75.0％,82.8％and 79.2％,respectively.Conclusions Analyzing myocardial work using PSL has good significance for screening suspected myocardial ischemia in CAD patients.

Objective:To investigate the effect of instantaneous flow rate on the consistency of diagnostic accuracy of severe degenerative mitral regurgitation (DMR) using proximal isovelocity surface area (PISA).Methods:From June 2019 to June 2021, 75 patients with DMR who underwent echocardiography in Department of Echocardiography of Zhongshan Hospital, Fudan University were prospectively enrolled. The instantaneous flow rate of DMR during the systolic phase was calculated using M-mode PISA(PISA M-mode), and a time-integrated curve was plotted. Regurgitant volume (RVol) and effective regurgitant orifice area (EROA) were calculated by traditional PISA (PISA max), pair PISA (PISA pair), and PISA M-mode, respectively. RVol acquired from cardiac magnetic resonance (CMR) volumetric method in 22 patients of the enrolled patients. The correlation and consistency of RVol acquired between the three PISA methods and CMR were compared. Agreement of diagnostic accuracy of severe mitral regurgitation (sMR) acquired between the three PISA methods and multi-parameter algorithm by American Society of Echocardiography (ASE) was analyzed using Cohen′s Kappa analysis. Results:The curve of instantaneous flow rate of DMR showed unimodal pattern with the peak at mid-late systolic phase. The correlation of RVol acquired between PISA methods and CMR was moderate for PISA max and PISA pair ( r=0.77, 0.80, both P<0.001), whereas PISA M-mode presented strong correlation with CMR ( r=0.87, P<0.001). RVol acquired from PISA max was larger than that of CMR[(69.1±37.1) ml vs (49.0±29.0)ml, P=0.002]. Both PISA max and PISA pair were shown moderate agreement of diagnostic accuracy of sMR with ASE multi-parameters algorithm (RVol: κ=0.496, 0.525, both P<0.001; EROA: κ=0.570, 0.578, both P<0.001), while PISA M-mode presented strong agreement (RVol: κ=0.867 and EROA: κ=0.802, both P<0.001). Conclusions:Based on the unimodal pattern of instantaneous flow rate in patients with DMR, PISA max may significantly overestimate RVol, exposing a significant proportion of patients with DMR to unnecessary MR surgery. PISA M-mode presents better correlation and consistency with CMR on the quantification of RVol compared with PISA max and PISA pair, and may improve the diagnostic accuracy of quantification of sMR using PISA.

Alanyl-glutamine dipeptide is an important component in parenteral nutrition, which can be decomposed into alanine and L-glutamine in vivo. It plays multiple functions including maintaining intestinal barrier, improving immunity, promoting protein synthesis, and regulating the production and release of inflammatory mediators. Substantial clinical evidences have demonstrated its favorable effectiveness and safety. Rational application of alanyl-glutamine dipeptide can reduce postoperative complications, shorten hospital stay and save medical costs. There are still controversies at home and abroad on the applicable population and dosage of alanyl-glutamine dipeptide. Chinese Society of Parenteral and Enteral Nutrition organized China's experts of related disciplines to compile international standards in accordance with the latest guidelines and consensus, so as to achieve the goal of standardized application and patient benefits.

Objective: To investigate the effects of heme oxygenase-1 (HO-1) knockdown on proliferation, invasion and migration of lung adenocarcinoma A549 cells and explore the mechanism. Methods: The expression levels of HO-1 mRNA in human bronchial epithelial cells (HBECs) and human lung cancer cell lines (A549, H1299, H358 and H1993) were detected by real-time quantitative polymerase chain reaction (RT-qPCR), and immunohistochemistry (IHC) was used to detect the expression level of HO-1 in human lung adenocarcinoma specimens. The HO-1 short hairpin RNA (shRNA) was transfected into A549 cells by RNA interference technique. HO-1 stably deleted A549 cells were selected (HO-1 shRNA group) and verified by RT-qPCR and western blot. HO-1 shRNA A549 cells and control shRNA A549 cells were treated with the inducer of autophagy Torin1 or its inhibitor Bafilomycin A1 (Baf A1), respectively. The expressions of autophagic markers LC3B and p62 were determined by western blot. The proliferation, invasion and migration abilities of each group of A549 cells were assessed by cell counting, Transwell and wound healing assays, respectively. Results: The expressions of HO-1 mRNA in lung cancer cell lines (A549, H1299, H358 and H1993) were significantly higher than that of HBECs, and HO-1 upregulated in human lung adenocarcinoma. The expression of p62 protein and the ratio of LC3B-Ⅱ/ LC3B-Ⅰ in no treatment group, Torin1 treatment group and Baf A1 treatment group were significantly higher than those of the corresponding control group (P<0.05). After 11 days of culture, the number of cells in HO-1 shRNA group were 41.8%, 30.4% and 14.0% of the corresponding control group, respectively. The number of lower chamber cells in HO-1 shRNA group were (35.7±2.1), (27.0±1.0) and (38.0±1.0)/field, respectively, which were lower than (66.0±9.2), (39.3±1.2) and (43.0±2.6)/field of the corresponding control group, respectively (P<0.05). The migration distances of HO-1 shRNA group were (7.47±0.91) mm, (4.23±0.82) mm and (5.42±0.24) mm, which were lower than (10.07±1.26) mm, (7.14±0.07) mm and (12.04±0.80) mm of the corresponding control groups, respectively (P<0.05). Conclusion Knockdown of HO-1 inhibits the proliferation, invasion and migration of A549 cells by impeding autophagy.

Objective To investigate the effect of Kuanxiong Lifei decoction on inflammatory factors in patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD) and turbid phlegm obstructing lung syndrome. Methods Two hundred patients with AECOPD and turbid phlegm obstructing lung syndrome diagnosed by traditional Chinese medicine (TCM) differentiation visiting four hospitals of Longhua Hospital Affiliated to Shanghai University of TCM, Shanghai Seventh People's Hospital, Punan Hospital of Shanghai Pudong New Area, Gongli Hospital of Shanghai Pudong New Area were selected from May 2017 to March 2018, and they were divided into a test group and a control group by a random number table, 100 cases per group. The patients in the two groups were treated with routine western medicine according to the guidelines, and in the test group, additionally Kuanxiong Lifei decoction (components: pinellia ternate 15 g, allium macrostemon 12 g, ephedra 9 g, trichosanthes 30 g, poria cocos 15 g, almond 12 g, lumbricus 12 g, citrus peels 12 g, peach kernel 12 g , roasted licorice 6 g) was used for 10 days, the decoction was uniformly made by Chinese Medicine Pharmacy of Longhua Hospital, 1 dose daily, 2 times a day orally taken, warm 200 mL each time, 0.5 hours before or after meal. The efficacy was evaluated after treatment for 10 days. The level changes of white blood cell count (WBC), neutrophils (N), C-reactive protein (CRP), interleukin-8 (IL-8), tumor necrosis factor-α (TNF-α) before and after treatment and the improvement of TCM syndrome scores and clinical efficacy were observed in two groups. Results After treatment, the levels of WBC, N, CRP, IL-8, TNF-α, TCM syndrome score of the patients in the two group were significantly decreased compared with those before treatment in the two groups (P < 0.05), and the above indexes in the test group were all significantly lower than those in the control group after treatment [WBC (×109/L): 6.58±1.41 vs.7.44±1.85, N: 0.58±0.08 vs. 0.64±0.08, CRP (mg/L): 7.3±1.8 vs. 9.6±1.7, IL-8 (ng/L): 23.5±6.2 vs. 27.8±9.8, TNF-α (ng/L): 9.45±2.18 vs. 10.25±1.67, TCM syndrome total score: 4.0 (3.0, 8.0) vs. 8.0 (5.0, 10.0), all P < 0.05]. The total effective rate of the test group was significantly higher than that of the control group [88% (88/100) vs. 84% (84/100), P < 0.05]. Conclusion Kuanxiong Lifei decoction can significantly reduce lung inflammatory factors, ameliorate overall symptoms and improve the prognosis of AECOPD patients with turbid phlegm obstructing lung syndrome.

Objective To investigate the expression of interferon-induced protein 44 (IFI44) gene in the leukocytes of the peripheral blood samples from patients with systemic lupus erythematosus (SLE), and to evaluate the relationship between the expression level and disease activity. Methods Mononuclear cells in the peripheral blood samples from 100 SLE patients were compared with those of 40 disease controls and 40 healthy donors (HD) and the expression of the IFI44 was evaluated by quantitative real-time PCR.Comparisons between groups were performed with ANOVA, and the correlation analysis between the level of expression was higher in SLE patients than disease controls and healthy donors (26.8±5.3, 7.4±2.7, 5.2±2.0,respectively) (P=0.0012, P=0.005), but no difference was found between disease controls and healthy donors. Mild disease activity and the SLE patients with stable disease (63.1±22.4, 28.0±7.2, 9.2±1.8, respectively)and 24 hours urine protein level (r=0.42, P=0.000). Conclusion IFI44 is demonstrated to be highly expressed in SLE patients. The level of IFI44 may be a promising candidate biomarker for identifying SLE activity.