Clinical application of full automatic animal experimental cabin of normobaric/hypobaric hypoxia and high carbon dioxide

To explore the feasibility of the full automatic animal experimental cabin to establish the animal models in normobaric/hypobaric hypoxic and high carbon dioxide environment. Methods： Sixty SPF-class male DS rats were divided into 2 groups, 20 for normobaric, hypoxic conditions and the other 40 for hypobaric, hypoxic conditions. For each group, the pulmonary arterial pressure and carotid arterial pressure indicators of rats were examined by using the physiological multi-detector, and the pulmonary vascular changes in the structure were observed. Results： The normobaric/hypobaric hypoxic with high carbon dioxide environment can promote the formation of pulmonary hypertension and accelerate changes in pulmonary vascular remodeling, and promote the right ventricular hypertrophy. Conclusion： Clinical applications showed that the animal experimental cabin has observed and controlled accurately. The result was safe, reliable and reproducible. The cabin can successfully establish the pulmonary hypertension model in normobaric/hypobaric hypoxic with high carbon dioxide environment, and in order to study the physiological mechanism of a variety of circulation and respiratory diseases caused by lack of oxygen, which provided an experimental technology platform for clinical research.

Approximate Conditional Likelihood for Generalized Linear Models with General Missing Data Mechanism

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.