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Biomedical subjects

D Bahler

Publications and source records attributed to D Bahler.

5 recordsLinked to original sources
PubMed2001

T cell antigen receptor vaccines for active therapy of T cell malignancies.

T cell lymphoproliferative disorders continue to be serious management problems, and so alternative therapeutic modalities are continuously being explored. One such strategy involves immunotherapy using the T cell receptor (TCR) as a target. Specifically we are attempting to develop a T cell receptor idiotype (TCR-Id) vaccine because the TCR-Id can serve as a tumor-specific antigen. In this article we will briefly review the rationale for TCR-Id vaccines, the preclinical models as developed in our laboratory, and a discussion of our current plans for a vaccine trial in mycosis fungoides.

Adenoviridae↗
PubMed1996

Recurrent Epstein-Barr virus-associated lesions in organ transplant recipients.

Posttransplant lymphoproliferative disorders (PTLD) are related to Epstein-Barr virus (EBV) and range from lymphoid hyperplasias to lymphomas. The authors report 11 transplant recipients with recurrent EBV-associated lesions. Four patients presented with EBV-positive mononucleosis-like lymphadenitis. One had recurrence of a similar lesion and the other three developed polymorphic PTLDs. Matched clonal studies in one patient showed clonal lymphoid and EB viral populations in the recurrent lesion, but not in the initial lesion. Six patients presented with polymorphic PTLDs. Five later developed histologically dissimilar tumors that resembled non-Hodgkin's lymphoma (two B-cell and one T-cell origin), Hodgkin's disease (one patient), or smooth muscle tumor (one patient). Matched clonal studies were available from one patient and showed that the primary and recurrent lesions were clonally distinct. The sixth patient had recurrence of histologically and clonally identical polymorphic PTLD. One patient presented with monomorphic PTLD and developed recurrence of a clonally identical tumor after a 6-month remission. This study shows that a few patients with EBV-associated lesions have clinical recurrence, which may be either a relapse of the original process or a new EBV-associated lesion. In some patients, the new lesion appeared to represent a more fully developed malignancy that did the antecedent lesion.

Adolescent↗
PubMed1993

The induction of rules for predicting chemical carcinogenesis in rodents.

This paper presents results from an ongoing effort in applying a variety of induction-based methods to the problem of predicting the biological activity of noncongeneric (structurally dissimilar) chemicals. It describes initial experiments, the long-term goal of which is to assist toxicologists, cancer researchers, regulators, and others to predict the toxic effects of chemical compounds. We describe a series of experiments in tree and rule induction from a set of example chemicals whose carcinogenicity has been determined from long-term animal studies, and compare the resulting classification accuracy with eight published human and computer predictions for a common set of 44 test chemicals. The accuracy of our system is comparable to the most accurate human expert prediction yet published, and exceeds that of any of the computer-based predictions in the literature. The induced rules provide confirmation of current expert heuristic knowledge in this domain. These early results show that an inductive approach has excellent potential in predictive toxicology.

Animals↗
PubMedDate not supplied

Symbolic, neural, and Bayesian machine learning models for predicting carcinogenicity of chemical compounds.

Experimental programs have been underway for several years to determine the environmental effects of chemical compounds, mixtures, and the like. Among these programs is the National Toxicology Program (NTP) on rodent carcinogenicity. Because these experiments are costly and time-consuming, the rate at which test articles (i.e., chemicals) can be tested is limited. The ability to predict the outcome of the analysis at various points in the process would facilitate informed decisions about the allocation of testing resources. To assist human experts in organizing an empirical testing regime, and to try to shed light on mechanisms of toxicity, we constructed toxicity models using various machine learning and data mining methods, both existing and those of our own devising. These models took the form of decision trees, rule sets, neural networks, rules extracted from trained neural networks, and Bayesian classifiers. As a training set, we used recent results from rodent carcinogenicity bioassays conducted by the NTP on 226 test articles. We performed 10-way cross-validation on each of our models to approximate their expected error rates on unseen data. The data set consists of physical-chemical parameters of test articles, alerting chemical substructures, salmonella mutagenicity assay results, subchronic histopathology data, and information on route, strain, and sex/species for 744 individual experiments. These results contribute to the ongoing process of evaluating and interpreting the data collected from chemical toxicity studies.

Animals↗