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Biomedical subjects

T Bednarski

Publications and source records attributed to T Bednarski.

3 recordsLinked to original sources

Differential effect of IFN-tau on proliferation and distribution of lymphocyte subsets in one-way mixed lymphocyte reaction in cows and heifers.

IFN-tau is a signaling protein secreted by the bovine conceptus during the peri-implantation period and responsible for pregnancy recognition. Its main role is the prevention of pulsatile release of luteolytic PGF2alpha, but it also exerts immunomodulatory activities characteristic for other type I interferons. The aim of the study was to examine the effect of IFN-tau on the proliferation and distribution of peripheral blood lymphocyte subsets during one-way mixed lymphocyte reaction (MLR) in cows and heifers. IFN-tau inhibited the proliferative response of lymphocytes in MLR both in cows and heifers in a dose-dependent manner, but cow lymphocytes were less susceptible than those ones from heifers. It was also showed that IFN-tau differentially changed lymphocyte subsets distribution in MLR in cows and heifers. In cows, the relative percentage of CD8(+) cells after MRL in the presence of IFN-tau was significantly lower than in heifers. Differential effect of rIFN-tau on proliferation and lymphocyte subsets distribution in a one-way MRL in cows and heifers indicated that the age of the mother is an important factor in immunomodulatory effect towards developing bovine embryo.

Analysis of Variance↗

A robust method for proportional hazards regression.

In this paper we give an informal introduction to a robust method for survival analysis which is based on a modification of the usual partial likelihood estimator (PLE). Large sample results lead us to expect reduced bias for this robust estimator compared with the PLE whenever there are even slight violations of the model. In this paper we investigate three types of violation: (a) varying dependency structure of survival time and covariates over the sample; (b) omission of influential covariates, and (c) errors in the covariates. The simulations presented support the above expectation. Analyses of data sets from cancer epidemiology and from a clinical trial in lung cancer illustrate that a better fit and additional insights may be gained using robust estimators.

Algorithms↗