【Abstract】 Objective To introduce the cl inical appl ication of heterogeneity (cattle) acellular dermal matrix(ADM)in the repair of mucosa defect otolaryngology. Methods From October 2006 to March 2007, 12 cases of mucosa defect was repaired with heterogeneity ADM after the surgery. There were 10 males and 2 females, aged 18-76 years. Defect was caused by deflection of nasal septum in 1 case, melanoma of front and midst basal is (capillary hemangioma) in 1 case, nasal vestibule angioma (T2N2M0)in 1 case, cancer of hypopharynx (T2N1M0) in 1 case, cancer of amygdale in 3 cases (2 of T2N0M0 and 1 of T3N1M0),cervical segments esophageal carcinoma in 1 case, and cancer of larynx in 4 cases (3 of T2N0M0 and 1 of T3N1M0). Results All these 12 cases were followed up for 6 months. The results of endoscope showed that heterogeneity ADM mingled with mucosa within 3 months after operation and the function was recovered. Pharynx fistula occurred in 1 case of hypopharynx cancer afterthe operation. After treatment of dressing change and antibiotics for 10 days, the wound healed, but after 2 months tumor recurred. All the patients were treated by radiation treatment. One case of amygdala cancer recurred and transferred to the neck after 2 months of radiation treatment. But 1 case of hypopharynx cancer died of massive haemorrhage after radiation treatment for 3 months. Conclusion Heterogeneity ADM can be easily obtained and it is a new method to repair mucosa defect. Theoperative procedure is easy to perform and worthwhile to be appl ied to cl inical operation.
ObjectiveWhen using multi-center data to construct clinical prediction models, the independence assumption of data will be violated, and there is an obvious clustering effect among research objects. In order to fully consider the clustering effect, this study intends to compare the model performance of the random intercept logistic regression model (RI) and the fixed effects model (FEM) considering the clustering effect with the standard logistic regression model (SLR) and the random forest algorithm (RF) without considering the clustering effect under different scenarios. MethodsIn the process of forecasting model establishment, the prediction performance of different models at the center level was simulated when there were different degrees of clustering effects, including the difference of discrimination and calibration in different scenarios, and the change trend of this difference at different event rates was compared. ResultsAt the center level, different models, except RF, showed little difference in the discrimination of different scenarios under the clustering effect, and the mean of their C-index changed very little. When using multi-center highly clustered data for forecasting, the marginal forecasts (M.RI, SLR and RF) had calibrated intercepts slightly less than 0 compared with the conditional forecasts, which overestimated the average probability of prediction. RF performed well in intercept calibration under the condition of multi-center and large samples, which also reflected the advantage of machine learning algorithm for processing large sample data. When there were few multiple patients in the center, the FEM made conditional predictions, the calibrated intercept was greater than 0, and the predicted mean probability was underestimated. In addition, when the multi-center large sample data were used to develop the prediction model, the slopes of the three conditional forecasts (FEM, A.RI, C.RI) were well calibrated, while the calibrated slopes of the marginal forecasts (M.RI and SLR) were greater than 1, which led to the problem of underfitting, and the underfitting problem became more prominent with the increase in the central aggregation effect. In particular, when there were few centers and few patients, overfitting of the data could mask the difference in calibration performance between marginal and conditional forecasts. Finally, the lower the event rate the central clustering effect at the central level had a more pronounced impact on the forecasting performance of the different models. ConclusionThe highly clustered multi-center data are used to construct the model and apply it to the prediction in a specific environment. RI and FEM can be selected for conditional prediction when the number of centers is small or the difference between centers is large due to different incidence rates. When the number of hearts is large and the sample size is large, RI can be selected for conditional prediction or RF for edge prediction.
Objective To summarize and review the heterogeneity of bone marrow derived stem cells (BMDSCs) and its formation mechanism and significance, and to analyze the possible roles and mechanisms in intestinal epithel ial reconstruction. Methods The related l iterature about BMDSCs heterogeneity and its role in intestinal epithel ial repair was reviewed and analyzed. Results The heterogeneity of BMDSCs provided better explanations for its multi-potency. The probable mechanisms of BMDSCs to repair intestinal epithel ium included direct implantation into intestinal epithel ium, fusion between BMDSCs and intestinal stem cells, and promotion of injury microcirculation reconstruction. Conclusion BMDSCs have a bright future in gastrointestinal injury caused by inflammatory bowl disease and regeneration.
Through collecting and synthesizing the paper concerning the method of dealing with heterogeneity in the meta analysis, to introduce the multi-levels statistical models, such as meta regression and baseline risk effect model based on random effects, and random effects model based on hierarchical bayes, and to introduce their application of controlling the meta analysis heterogeneity. The multi-levels statistical model will decompose the single random error in the traditional model to data structure hierarchical. Its fitting effect can not only make the meta-analysis result more robust and reasonable, but also guide clinical issues through the interpretation of association variable.
Objective To summarize the advancement of breast cancer stem cells and genotyping and analyze the correlation between the two. Methods Relevant literatures about breast cancer stem cells and genotyping, which were published recently were collected and reviewed. Results Cancer stem cell origin theory was supported by researches of correlation between breast cancer stem cells and genotyping, which also explained the complexity of intrinsic subtypes and heterogeneity of breast cancer. Conclusions A new way can be detected to study the formation mechanism and biological characteristics of breast cancer at the cellular and molecular level by researches of correlation between breast cancer stem cells and genotyping, which are expected to provide new strategies and tools for diagnosis and treatment of breast cancer.
Compared with traditional head to head meta-analysis, network meta-analysis has more confounding factors and difficulties to handle. Due to the mutual transitivity of evidence in network meta-analysis, heterogeneity may be brought into indirect meta-analysis. Hence, effective differentiation and correct handling of heterogeneity are being current focus. In order to ensure the reliability of the results of network meta-analysis, the concept of homogeneity is proposed and a series of methods are developed for differentiation and handling of homogeneity. Based on the extension of Bucher methods, current methods for differentiation and handling of homogeneity has extended to ten quantitative measures (eg., node analysis method, hypothesis tests, and two-step method). However, because of the differences and the focus of fundamental methodological theories as well as the limitation of statistics power, no highly-effective method has been worked out. Therefore, the exploration of highly-effective, simple and high-resolved methods are still needed.
ObjectiveTo categorize and describe stroke-patients based on factors related to patient reported outcomes. MethodsA questionnaire survey was conducted among stroke-patients in nine hospitals and communities in Shanxi Province. The general information questionnaire and stroke-patient reported outcome manual (Stroke-PROM) were completed. Latent profile analysis was used to analyze the scores of Stroke-PROM, and the explicit variables of the model were the final scores of each dimension. ANOVA and correlation analysis were used to measure the correlation between the factors and subtypes. ResultsFour unique stroke-patient profiles emerged, including a low physiological and low social group (9%), a high physiological and middle social group (40%), a middle physiological and middle social group (26%), and a middle physiological and high social group (25%). There were significant differences in scores of four areas among patients with different subtypes (P<0.05). Moreover, there was a correlation between age, payment, exercise and subtypes (P<0.05). ConclusionThere are obvious grouping characteristics for stroke patients. It is necessary to focus on stroke patients who are advanced in age, have a self-funded status and lack exercise, and provide targeted nursing measures to improve their quality of life.
The assumption of fixed-effects model is based on that the true effect of the each trial is same. However, the assumption of random-effects model is based on that the true effect of included trials is normal distributed. The total variance is equal to the sum of within-trial variance and between-trial variance under the random-effects model. There are many estimators of the between-trial variance. The aim of this paper is to give a brief introduction of the estimators of between-trial variance in trial sequential analysis for random-effects model.
Many meta-analysis studies evaluate rates as parameter to assess the overall estimate of effects. However, none of these studies address systematic approaches for the meta-analysis of rates. This paper outlines the conditions, analysis and software operation procedures for the meta-analysis of rates. It also compares different operation procedures of three types of commonly-used R software (Comprehensive Meta-Analysis, Stata and MetaAnalyst) through real application examples. The biggest challenge for the meta-analysis of rates is to determine whether rates can be pooled, and how to evaluate heterogeneity between studies' outcomes needs further discussion.
Objective To summarize the research progress of distributional heterogeneity of the molecular pathology characteristics in breast cancer. Methods The related literatures about the distribution of the molecular pathology characteristics in breast cancer were reviewed. Results The breast cancer had the same heterogeneity as other cancers. At the same time, the molecular pathology characteristics, such as estrogen receptor (ER), progesterone receptor (PR), Ki-67, and human epidermal growth factor receptor-2 (HER-2), had the distributional heterogeneity. The distributional heterogeneity of molecular pathology characteristics in breast cancer could effect the pathologic diagnosis, the treatment, and the prognosis. Conclusion Although there are some new techniques which were used to investigate the heterogeneity of breast cancer, but each way has some problems. The more attention should be paid to the research about the distributional heterogeneity of the molecular pathology characteristics in breast cancer.