Objective To study the expression of CD44V6 in human epidermal tumours and malignant melanoma. Methods The expression of CD44V6 was detected by an immunohistochemical technique in squamous cell carcinoma(SCC), basal cell carcinoma(BCC) and malignant melanoma(MM). Results CD44V6 was expressed on the membrane of tumour cells of 10 patients with SCC, and there was downregulation of CD44V6 expression which along with the decrease of differentiation of SCC tumour cells. Tumour cells of 8 patients with MM and 10 patients with BCC did not express CD44V6. Conclusion Our findings suggest that CD44V6 expression is correlated with the types of skin tumours.

To enhance the quality and efficiency of ozagrel by investigating the differences between the ozagrel polymorphs in bioavailability. Solid ozagrel in different polymorph forms were orally administered to SD rats. An HPLC method was established to determinate plasma level of ozagrel. The bioavailabilities of two polymorph forms were calculated and compared. The pharmacokinetic parameters of ozagrel, were as follows: Cmax was 32.72 ± 17.04 and 34.01 ± 19.13 mg · L(-1), respectively; AUC0-t was 61.14 ± 14.76 and 85.56 ± 18.08 mg · L(-1) · h, respectively; t½ was 1.53 ± 0.51 and 4.73 ± 3.00 h, respectively. There was no significant difference in pharmacokinetic parameters between form I and II polymorphs of ozagrel while the t½ of form II is longer, which indicates that the use of form II polymorph as pharmaceutical product may prolong the effective action time in clinics. This would help the polymorph quality control in drug production.

Four crystalline forms of clopidogrel bisulfate were characterized by analytical techniques. Aiming to research the absorption characteristics of clopidogrel bisulfate polymorphs after taken orally by rat, and to estimate the influence of crystal form to pharmacodynamic action, four crystalline forms of clopidogrel bisulfate were administered intragastrically to rats, and high performance liquid chromatography (HPLC) was used to measure the contents of clopidogrel bisulfate and its metabolite in rat plasma. The metabolite of clopidogrel bisulfate was detected in rat plasma. There were significant deviations among four crystalline forms in the areas under curve of the metabolite of clopidogrel bisulfate. We concluded that the different crystal forms of clopidogrel bisulfate showed different pharmacokinetic characteristics, which might affect pharmacodynamic action.

Objective To investigate the curative effect of photodynamic therapy on acnes and summarize the nursing measures.Methods Twenty-four acne patients were treated with photodynamic therapy from our hospital.The nursing experience was summarized.Result Nineteen had complete recovery,3 significant improvement,and 2 mild improvement,with a total effective rate of 91.7%.Conclusion Health education on acnes,keeping the patients away from strong sunlight and instructing them to clean skin in a right way and timely treatment of complications are critical for the improvement of curative effect.

Professor YANG Zhao-gang graduated from the Tianjin University of TCM in 1965. As a director doctor and a famous expert on the elongate needle therapy, he is engaged in medicine for more than 40 years. Through his untiring investigation on the acupuncture techniques, more than 10 specialized books have been published. He summarized some of basic acupuncture techniques and principle of acupoints combination on the elongated needle therapy, such as, 'selecting acupoints located on the higher place of the body, and combining the acupoints of Shangwan (CV 13), Zhongwan (CV 12) and Xiawan (CV 10)' 'dredging the pivot of meridian, restoring the Conception Vessel and regulating qi'. Its clinical effect is definite for treating the diseases of nervous, digestive and urinary systems. Basing on the traditional acupuncture techniques of the elongated needle, he explored and innovated some of new methods, such as pricking the auricular acupoints and shallow puncturing with the filiform needle. These efforts make the acupuncture treatment more secure, effective and reliable.
Acupuncture Points ; Acupuncture Therapy ; methods ; Adolescent ; China ; Female ; History, 20th Century ; History, 21st Century ; Humans ; Medicine, Chinese Traditional ; Middle Aged

Objective:Based on radiomics characteristics, different machine learning classification models are constructed to predict the gamma pass rate of dose verification in intensity-modulated radiotherapy for pelvic tumors, and to explore the best prediction model.Methods:The results of three-dimensional dose verification based on phantom measurement were retrospectively analyzed in 196 patients with pelvic tumor intensity-modulated radiotherapy plans. The gamma pass rate standard was 3%/2 mm and 10% dose threshold. Prediction models were constructed by extracting radiomic features based on dose documentation. Four machine learning algorithms, random forest, support vector machine, adaptive boosting, and gradient boosting decision tree were used to calculate the AUC value, sensitivity, and specificity respectively. The classification performance of the four prediction models was evaluated.Results:The sensitivity and specificity of the random forest, support vector machine, adaptive boosting, and gradient boosting decision tree models were 0.93, 0.85, 0.93, 0.96, 0.38, 0.69, 0.46, and 0.46, respectively. The AUC values were 0.81 and 0.82 for the random forest and adaptive boosting models, respectively, and 0.87 for the support vector machine and gradient boosting decision tree models.Conclusions:Machine learning method based on radiomics can be used to construct a prediction model of gamma pass rate for specific dosimetric verification of pelvic intensity-modulated radiotherapy. The classification performance of the support vector machine model and gradient boosting decision tree model is better than that of the random forest model and adaptive boosting model.

Objective:To explore the feasibility of a classification prediction model for gamma pass rates (GPRs) under different intensity-modulated radiation therapy techniques for pelvic tumors using a radiomics-based machine learning approach, and compare the classification performance of four integrated tree models.Methods:With a retrospective collection of 409 plans using different IMRT techniques, the three-dimensional dose validation results were adopted based on modality measurements, with a GPR criterion of 3%/2 mm and 10% dose threshold. Then prediction were built models by extracting radiomics features based on dose documentation. Four machine learning algorithms were used, namely random forest (RF), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM). Their classification performance was evaluated by calculating sensitivity, specificity, F1 score, and AUC value. Results:The RF, AdaBoost, XGBoost, and LightGBM models had sensitivities of 0.96, 0.82, 0.93, and 0.89, specificities of 0.38, 0.54, 0.62, and 0.62, F1 scores of 0.86, 0.81, 0.88, and 0.86, and AUC values of 0.81, 0.77, 0.85, and 0.83, respectively. XGBoost model showed the highest sensitivity, specificity, F1 score, and AUC value, outperforming the other three models. Conclusions:To build a GPR classification prediction model using a radiomics-based machine learning approach is feasible for plans using different intensity-modulated radiotherapy techniques for pelvic tumors, providing a basis for future multi-institutional collaborative research on GPR prediction.

Objective:To explore the feasibility and validity of constructing an intensity-modulated radiotherapy gamma pass rate prediction model after combining the SHAP values with the extreme gradient boosting tree (XGBoost) algorithm feature selection technique, and to deliver corresponding model interpretation.Methods:The dose validation results of 196 patients with pelvic tumors receiving fixed-field intensity-modulated radiotherapy using modality-based measurements with a gamma pass rate criterion of 3%/2 mm and 10% dose threshold in Hunan Provincial Tumor Hospital from November 2020 to November 2021 were retrospectively analyzed. Prediction models were constructed by extracting radiomic features based on dose files and using SHAP values combined with the XGBoost algorithm for feature filtering. Four machine learning classification models were constructed when the number of features was 50, 80, 110 and 140, respectively. The area under the receiver operating characteristic curve (AUC), recall rate and F1 score were calculated to assess the classification performance of the prediction models.Results:The AUC of prediction model constructed with 110 features selected based on the SHAP-valued features was 0.81, the recall rate was 0.93 and the F1 score was 0.82, which were all better than the other 3 models.Conclusion:For intensity-modulated radiotherapy of pelvic tumor, SHAP values can be used in combination with the XGBoost algorithm to select the optimal subset of radiomic features to construct predictive models of gamma pass rates, and deliver an interpretation of the model output by SHAP values, which may provide value in understanding the prediction by machine learning-dependent models.