AIM: To study the anti-inflammatory effect of the Antivirus Oral Liquid(AOL) in experimental rabbit conjunctivitis model. METHODS: Rabbit's staphococcus aureus conjunctivitis models and cantharides tincture conjunctivitis models were made to observe different groups of animals' symptom of eye stimulation,negative rate of bacteria and conjunctiva histopathology.And then AOL compared with the effect of amphemycin gutta or Shumu(gutta.) RESULTS: It showed that AOL possessed significant anti-inflammatory effect on bacterial conjunctivitis.The negative rate of bacteria was more than 90% after being given AOL for 4 days successively.Compared with the models group,the hyperemia and edemda were markedly relieved.The result showed that AOL possessed significant anti-inflammatory effect on irritable conjunctivitis.The hyperemia and edema were markedly relieved at the second day after being given AOL,and the inflammatory symptoms had almost disappeared after being given AOL for 4 days successively. CONCLUSION: AOL has a good effect on the therapy of staphylococcus aureus conjunctivitis and cantharides tincture.

OBJECTIVE:To provide reference for rational drug use and strengthen the management of adjuvant medication man-agement in the hospital. METHODS:1 080 and 860 discharged medical records of 12 Vitamins for injection were selected from our hospital during Jun.-Nov. 2014 (pre-special comment group,i.e. group A) and Jun.-Nov. 2015 (post-special comment group,i.e. group B),respectively. The application of 12 Vitamins for injection,hospitalization stay,drug cost and the incidence of ADR were compared before and after adjuvant medication special comment. RESULTS:After adjuvant medication special comments,the propor-tion of drug use of no indication,unsuitable solvent selection,irrational drug dosage,drug use of drug interaction and contraindica-tion,drug cost and the incidence of ADR in group B were all lower than in group A,with statistical significance(P<0.05). There was no statistical significance in hospitalization stay between 2 groups(P>0.05). CONCLUSIONS:Clinical pharmacists adopt adju-vant medication special comments to effectively standardize clinical application of 12 Vitamins for injection in our hospital.

Objective:To construct and validate a machine learning model for preoperative prediction of perineural invasion (PNI) status in intrahepatic cholangiocarcinoma (ICC).Methods:Clincial data of 329 patients, including 245 admitted to Zhengzhou University People's Hospital from January 2018 to June 2023 and 84 admitted to the Affiliated Cancer Hospital of Zhengzhou University from January 2013 to January 2020 were retrospectively analyzed. Patients were divided into a training set ( n=231) and a validation set ( n=98). Clinicopathological data including age, gender, hepatitis B virus (HBV) infection status were collected. Predictive variables were determined using least absolute shrinkage and selection operator (LASSO) regression analysis. Six machine learning algorithms including random forest (RF), logistic regression, and linear kernel-based support vector machine were selected to construct the preoperative prediction model for PNI in ICC. Performance metrics of the model were calculated using a confusion matrix, and the final model was selected. The model performance was evaluated in the validation set. Calibration curves were plotted to evaluate the final model, and a Pareto chart was used to visualize the importance of predictive variables. Results:LASSO regression identified nine predictive variables included in the prediction model, including carbohydrate antigen 19-9 (CA19-9), HBV infection status, alkaline phosphatase, alanine aminotransferase, prothrombin time, total bilirubin, albumin, neutrophil times gamma-glutamyl transferase to lymphocyte ratio, and tumor burden score. Among the trained six models, the area under the curve (AUC) of the RF model was 0.909, with a sensitivity of 0.842 and an accuracy of 0.870. Compared with the AUC of the RF model, the AUCs of the other 5 models were lower (all P<0.05). The AUC of the RF model for predicting PNI in ICC in validation set was 0.736. Calibration curves showed good fit of the RF model's prediction of PNI in ICC in both training and validation sets. The Pareto chart showed that CA19-9 was the most important predictive variable in the model, followed by HBV infection status. Conclusion:The machine learning model based on the RF algorithm has a high accuracy in preoperative prediction of PNI status in ICC.

Objective:To establish and validate a preoperative differentiateon model of intrahepatic cholangiocarcinoma (ICC) and combined hepatocellular carcinoma (CHC) based on the inflammatory markers and conventional clinical indicators.Methods:The clinical data of 116 patients with ICC or CHC admitted to Henan Provincial People's Hospital from January 2018 to March 2023 were retrospectively analyzed, including 74 males and 42 females, aged (58.5±9.4) years old. The data of 83 patients were used to establish the differentiation model as the training group, including 50 cases of ICC and 33 cases of CHC. The data of 33 patients were used to validate the model as the validation group, including 20 cases of ICC and 13 cases of CHC. The clinical data including the platelet-to-lymphocyte ratio (PLR), systemic immune inflammation index (SII), prognostic inflammatory index (PII), prognostic nutritional index (PNI), neutrophil-to-lymphocyte ratio (NLR) and lymphocyte-to-monocyte ratio (LMR) were collected and analyzed. The receiver operating characteristic (ROC) curve was used to analyze the best cut-off values of PLR, SII, PII, PNI, NLR and LMR. Univariate and multivariate logistic regression analysis were used to determine the differential factors between ICC and CHC. The R software was used to draw the nomogram, calculate the area under the curve (AUC) to evaluate the model accuracy, and draw the calibration chart and the decision curve to evaluate the predictive efficacy of the model.Results:Univariate logistic regression analysis showed that liver cirrhosis, history of hepatitis, alpha fetoprotein, carbohydrate antigen 199, gamma-glutamyltransferase (GGT), PLR, PNI and inflammation score (IS) could be used to differentiate ICC from CHC (all P<0.05). The indicators identified in univariate analysis were included in multivariate logistic regression analysis. The results showed that absence of liver cirrhosis, GGT>60 U/L, PNI>49.53, and IS<2 indicated the pathology of ICC (all P<0.05). Based on the above four factors, a nomogram model was established to differentiate the ICC and CHC. The AUC of ROC curve of the nomogram model in the training and validation groups were 0.851 (95% CI: 0.769-0.933) and 0.771 (95% CI: 0.594-0.949), respectively. The sensitivities were 0.760 and 0.750, and the specificities were 0.818 and 0.769, respectively. The calibration chart showed that the predicted curve fitted well to the reference line. The decision curve showed that the model has a clear positive net benefit. Conclusion:The nomogram model based on inflammatory markers showed a good differentiation performance of ICC and CHC, which could benefits the individualized treatment.

Objective:A predictive nomogram model for the prognosis of intrahepatic cholangiocarcinoma (ICC) patients after curative resection was constructed based on the albumin-bilirubin score and tumor burden score (ATS) grade, and the predictive performance of the nomogram model was evaluated.Methods:Retrospective analysis of clinical data was made, from ICC patients who underwent curative resection at Zhengzhou University People's Hospital and Zhengzhou University Cancer Hospital from January 2016 to January 2020. A total of 258 patients were included in the study, with 140 males and 118 females, with an average age of (56.5±9.5) years. The 258 ICC patients were randomly divided into a training set ( n=174) and a testing set ( n=84) in a 7∶3 ratio. Single-factor and multi-factor Cox regression analyses were performed to identify prognostic factors for ICC patients of the training set, and then a nomogram model was constructed. The performance of the nomogram model was evaluated by using the concordance index (C-index), calibration curve, and risky decision curve analysis. Results:In the training set, univariate Cox regression analysis indicated that albumin-bilirubin (ALBI), tumor burden score (TBS), carcinoembryonic antigen (CEA), tumor differentitation, lymphvascular invasion and ATS significantly influenced overall survival after radical resection for ICC (all P<0.05). Multifactorial Cox regression analysis revealed that ATS grade, CEA, tumor differentiation, lymphovascular invasion, and AJCC N stage are independent risk factors for the prognosis of ICC patients after curative resection (all P<0.05). Assessment of the postoperative survival prediction model based on multifactorial Cox regression yielded a C-index of 0.775(95% CI: 0.747-0.841) for the training set and 0.731(95% CI: 0.668-0.828) for the testing set. The calibration curves for both the training and testing sets indicated strong predictive capability of the model. Additionally, the risk decision curve also suggested high net benefit of the model. Conclusions:The preoperative ATS grade is an independent factor affecting the survival after ICC radical resection. The nomogram model constructed based on ATS grade demonstrates excellent predictive value for postoperative prognosis in ICC patients.

Objective:To explore the prognostic value of systemic immune-inflammatory index(SII)combined with tumor burden score (TBS) (hereinafter referred to as STS) in patients with intrahepatic cholangiocarcinoma (ICC) after radical resection, and to construct a nomogram model.Methods:The clinical data (including the degree of tumor differentiation, vascular cancer thrombus, and lymph node metastasis, etc.) of 258 ICC patients who received radical resection at People′s Hospital of Zhengzhou University (170 cases, training set) and Cancer Hospital of Zhengzhou University (88 cases, validation set) from January 1, 2016 to January 31, 2020 were retrospectively analyzed and graded by SII, TBS and STS. Multivariate Cox regression analysis were used to identify independent factors affecting the prognosis of patients with ICC. Kaplan-Meier survival curve and receiver operating characteristic curve (ROC) were drawn to evaluate the predictive efficiency of SII, TBS and STS in the overall survival of patients with ICC after radical resection. The nomogram prediction model was constructed and evaluate the performance of nomogram model using consistency index (C-index) and calibration curve.Results:Among 170 ICC patients in the training set, there were 106 cases of SII grade 1 and 64 cases of SII grade 2; 137 cases of TBS grade 1 and 33 cases of TBS grade 2; and 98 cases of STS grade 1, 47 cases of STS grade 2, and 25 cases of STS grade 3. Among 88 ICC patients in the validation set, there were 33 cases of SII grade 1 and 55 cases of SII grade 2; 66 cases of TBS grade 1 and 22 cases of TBS grade 2; and 30 case of STS grade 1, 39 cases of TBS grade 2, and 19 cases of TBS grade 3.The multivariate Cox regression analysis showed that tumor differentiation degree (highly differentiated vs. moderately differentiated HR=0.157, 95% confidence interval(95% CI) 0.045 to 0.546, highly differentiated vs. poorly differentiated HR=0.452, 95% CI 0.273 to 0.750), STS (grade 3 vs. grade 2 HR=1.966, 95% CI 1.148 to 3.469; grade 3 vs. grade 1 HR=1.405, 95% CI 0.890 to 2.216), vascular cancer thrombus ( HR=2.006, 95% CI 1.313 to 3.066), nerve invasion ( HR=1.865, 95% CI 1.221 to 2.850), and lymph node metastasis ( HR=1.802, 95% CI 1.121 to 2.896) were independent influencing factors of overall survival in ICC patients after radical resection (all P<0.05). The Kaplan-Meier survival curve showed that SII, TBS, and STS were independent influencing factors of overall survival in ICC patients (all P<0.05). The results of ROC analysis showed that the areas under the curve of SII, TBS and STS in predicting overall survival of ICC patients after radical resection were 0.566 (95% CI 0.479 to 0.652), 0.585 (95% CI 0.499 to 0.672), and 0.657 (95% CI 0.522 to 0.692), respectively. Tumor differentiation, vascular tumor thrombus, nerve invassion, lymph node metastasis, and STS were included to constract the nomogram model. The C-indexes of the training set and validation set based on the nomogram model were 0.792 (95% CI 0.699 to 0.825) and 0.776 (95% CI 0.716 to 0.833), respectively. The calibration curves of the survival rate of the training set and the validation set were close to the reference lines, and the nomogram model had better predictive ability in both the training set and the validation set. Conclusions:Preoperative STS grading is an effective and practical predictor of overall survival in ICC patients after radical section. Compared with SII and TBS alone, it has better predictive value for the prognosis of patients with ICC.

Blast injury of the chest injury is the most common wound in modern war trauma and terrorist attacks, and is also the most fatal type of whole body explosion injury. Most patients with severe blast injury of the chest die in the early stage before hospitalization or during transportation, so first aid is critically important. At present, there exist widespread problems such as non-standard treatment and large difference in curative effect, while there lacks clinical treatment standards for blast injury of the chest. According to the principles of scientificity, practicality and advancement, the Trauma Society of Chinese Medical Association has formulated the guidance of classification, pre-hospital first aid, in-hospital treatment and major injury management strategies for blast injury of the chest, aiming to provide reference for clinical diagnosis and treatment.

Objective:To clarify the clinical efficacy of Yiqi Huoxue recipe in the treatment of liver fibrosis of chronic viral hepatitis.Methods:An open, positive-drug, parallel-controlled study method was applied. A total of 207 cases of liver fibrosis with chronic hepatitis B and C diagnosed with liver biopsy and transient elastography were selected. According to the principle of syndrome differentiation in traditional Chinese medicine, self-made Yiqi Huoxue recipe ( n = 127) and Fuzheng Huayu capsule ( n = 80) were used for the treatment course of 24-48 weeks. Change score of TCM symptom, liver biochemistry, liver stiffness measurement (LSM), and noninvasive liver fibrosis index [aspartate transaminase to platelet ratio index (APRI), and fibrosis-4 score (FIB-4)] were compared between the two groups to evaluate the therapeutic effect of Yiqi Huoxue recipe on liver fibrosis. Results:Yiqi Huoxue recipe group and Fuzheng Huayu capsule group baseline LSM, APRI and FIB-4 was compared, and there was no statistically significant difference between them ( P > 0.05). Yiqi Huoxue recipe and Fuzheng Huayu capsule received patients had improved symptom scores to a certain extent. Hepatic facies, discomfort over liver area, and soreness and weakness of waist and knees ( P < 0.05) was significantly improved in Yiqi Huoxue recipe than Fuzheng Huayu capsule. Liver biochemical indicators (ALT, AST, GGT, ALP) had gradually relapsed with the extension of treatment duration and the normalization rate between the two groups after 24 to 48 weeks had reached 100% vs. 100%, 100% vs. 93.8%, 96.8% vs. 92.3% and 87.5% vs. 81.8%. After 12 weeks of treatment, APRI values ??of both groups had significantly reduced, and after 48 weeks of treatment, LSM values of both groups had significantly improved. Moreover, Yiqi Huoxue recipe FIB-4 score was significantly improved after 48 weeks of treatment, and the difference was statistically significant compared to Fuzheng Huayu capsule group ( P < 0.05). After treatment, LSM, APRI, and FIB-4 total effectiveness in the two groups were 80.0% vs. 63.6%, P = 0.046; 68.4% vs. 52.0%, P = 0.052; 68.4% vs. 62.0%, P = 0.437, respectively. LSM total effectiveness was significantly higher in Yiqi Huoxue recipe treated group than Fuzheng Huayu capsule group. Conclusion:Traditional Chinese medicine Yiqi Huoxue decoction can be used as an optimal treatment for liver fibrosis of chronic viral hepatitis.