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Biomedical subjects

G L Yang

Publications and source records attributed to G L Yang.

5 recordsLinked to original sources

Cell survival probability under ionizing radiation.

The survival probability of a living cell exposed to ionizing radiation in an experimental setup is derived. The survival of a cell depends on the severity of the radiation damage and efficiency of the cellular repair. The formula of the survival probability is expressed as a function of dose, nonlinear rate of lesion induction, nonlinear rate of cellular repair, and a key experimental parameter--the holding time. The result is an extension of the Markovian dose-response model developed by Yang and Swenberg.

Animals

A stochastic two-stage carcinogenesis model: a new approach to computing the probability of observing tumor in animal bioassays.

A new definition of probability of observing tumor in animal bioassay is developed. It is derived from a two-stage stochastic model for carcinogenesis with time-dependent birth and death rates for cell proliferation. The model takes into account the method of collecting data on preneoplastic and neoplastic lesions. The new definition is appropriate for analyzing the presence or absence of tumors in animal bioassays.

Animals

A stochastic model for analyzing prevalence surveys of hepatitis A antibody.

Epidemiologic surveys of the age-specific prevalence of antibody to hepatitis A virus (anti-HAV) provide information on the spread of infection such as the infection rate and age-dependent characteristics. However, the data on prevalence are confounded with the mortality and diminished immunity of surveyed individuals. Through modeling, the age-specific prevalence of an individual can be separated from these confounding factors. A Markov chain is used to model the process of acquisition of anti-HAV by an individual and to derive the age-specific prevalence. Data from Frösner et al. [Am. J. Epidemiol. 110:63-69 (1979)] are used for illustration and estimation of parameters. The model offers an explanation of the well-known phenomenon of a decline in prevalence in older age. In addition to hepatitis, the framework of the model can be adapted to analyzing seroepidemiologic surveys of other diseases.

Age Factors

[Distribution and ultrastructural localization of carcino-embryonic antigen (CEA) in signet-ring cells of gastric cancer].

The distribution and ultrastructural localization of CEA in signet-ring cells of 15 gastric cancer specimens were observed by PAP and immunoelectron microscopic methods. The mechanism of abnormal distribution of CEA in the signet-ring cell and its biological significance are discussed. The results showed that the CEA positive rate in signet-ring cells was 100% with the polarity lost in distribution. Under the light microscope, the CEA stain patterns were of two types-cytoplasmic and membranous types. The former was predominant. Under the electron microscope, most of the CEA was distributed on the cell membrane and cytoplasm. CEA was found in intracellular membranous structure of the cancer cells, especially in protein synthesis and transport organellae (RER, Golgi Complex etc). The synthesis of CEA in cancer cells increased, yet its elimination was somewhat hampered. The result was that the RER became extended and were full of CEA (+) material. In the free signet-ring cell, there was a small and short contact plane. The tight junction was severed as the cell junction was reduced. The antigenic determinant of CEA was glycoprotein. The abnormal distribution of CEA in signet-ring cells might be the morphologic reflection of the glycosylation of surface glycoprotein of tumor cells. These abnormal changes would lead to mal-functions and biologic misbehavior in the cells. It may even lead to disturbances in connection and recognition, loss of contact inhibition and decrease in the adhesion between tumor cells and therefore may easily give rise to infiltration and metastasis.

Adenocarcinoma, Mucinous

A quantitative index for evaluating patient care with longitudinal data.

This paper describes a patient-outcome based index of the quality of health care useful to health services researchers and planners. This index is applicable in any health care situation where longitudinal data are available from patients who can be classified into mutually exclusive stages of severity by functional status, psychological well-being or diagnosis and followed over a period of time. The rationale of the index is presented, along with an illustrative example based on a study on long-term care. The procedure for generating weights for the index is briefly described.

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