Objective: To seek new method for the treatment of peripheral nerve injury. Methods: In rat model with sciatic nerve defect, chitosan collagen film was sutured into conduit to bridge 5 mm , 10 mm nerve defects. Rats t...Objective: To seek new method for the treatment of peripheral nerve injury. Methods: In rat model with sciatic nerve defect, chitosan collagen film was sutured into conduit to bridge 5 mm , 10 mm nerve defects. Rats that underwent end to end anastomosis were taken as controls. General observation, electrophysiological study, histological study and image analysis were performed at 4, 8, 12 weeks postoperatively. Results: In 5 mm nerve defects, the quality of nerve regeneration was similar to that of the control group. For 10 mm nerve defect, nerve regeneration was inferior to that of the control group. Chitosan collagen film obviously degraded at 12 weeks postoperatively. Conclusions: Chitosan collagen film conduit can be used to bridge peripheral nerve defect.展开更多
The generalized linear model is an indispensable tool for analyzing non-Gaussian response data, with both canonical and non-canonical link functions comprehensively used. When missing values are present, many existing...The generalized linear model is an indispensable tool for analyzing non-Gaussian response data, with both canonical and non-canonical link functions comprehensively used. When missing values are present, many existing methods in the literature heavily depend on an unverifiable assumption of the missing data mechanism, and they fail when the assumption is violated. This paper proposes a missing data mechanism that is as generally applicable as possible, which includes both ignorable and nonignorable missing data cases, as well as both scenarios of missing values in response and covariate.Under this general missing data mechanism, the authors adopt an approximate conditional likelihood method to estimate unknown parameters. The authors rigorously establish the regularity conditions under which the unknown parameters are identifiable under the approximate conditional likelihood approach. For parameters that are identifiable, the authors prove the asymptotic normality of the estimators obtained by maximizing the approximate conditional likelihood. Some simulation studies are conducted to evaluate finite sample performance of the proposed estimators as well as estimators from some existing methods. Finally, the authors present a biomarker analysis in prostate cancer study to illustrate the proposed method.展开更多
文摘Objective: To seek new method for the treatment of peripheral nerve injury. Methods: In rat model with sciatic nerve defect, chitosan collagen film was sutured into conduit to bridge 5 mm , 10 mm nerve defects. Rats that underwent end to end anastomosis were taken as controls. General observation, electrophysiological study, histological study and image analysis were performed at 4, 8, 12 weeks postoperatively. Results: In 5 mm nerve defects, the quality of nerve regeneration was similar to that of the control group. For 10 mm nerve defect, nerve regeneration was inferior to that of the control group. Chitosan collagen film obviously degraded at 12 weeks postoperatively. Conclusions: Chitosan collagen film conduit can be used to bridge peripheral nerve defect.
基金supported by the Chinese 111 Project B14019the US National Science Foundation under Grant Nos.DMS-1305474 and DMS-1612873the US National Institutes of Health Award UL1TR001412
文摘The generalized linear model is an indispensable tool for analyzing non-Gaussian response data, with both canonical and non-canonical link functions comprehensively used. When missing values are present, many existing methods in the literature heavily depend on an unverifiable assumption of the missing data mechanism, and they fail when the assumption is violated. This paper proposes a missing data mechanism that is as generally applicable as possible, which includes both ignorable and nonignorable missing data cases, as well as both scenarios of missing values in response and covariate.Under this general missing data mechanism, the authors adopt an approximate conditional likelihood method to estimate unknown parameters. The authors rigorously establish the regularity conditions under which the unknown parameters are identifiable under the approximate conditional likelihood approach. For parameters that are identifiable, the authors prove the asymptotic normality of the estimators obtained by maximizing the approximate conditional likelihood. Some simulation studies are conducted to evaluate finite sample performance of the proposed estimators as well as estimators from some existing methods. Finally, the authors present a biomarker analysis in prostate cancer study to illustrate the proposed method.
