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A data base consisting of 61 heterocyclic amines formed during food preparation and their des-amino analogs were subjected to structure-activity analysis using the CASE method, a structural activity relational expert system. The program identified the major structural determinants associated with mutagenic activity or lack thereof. The structures identified as contributing to the probability of activity as well as those associated with mutagenic potency were highly predictive of molecules not in the learning set. The major structural determinant, the aromatic amino moiety, and quantum mechanical calculations revealed that the mutagenic potency associated with this functionality derived from their contribution to the energy of the Lowest Unoccupied Molecular Orbital (LUMO).

Solution-focused brief therapy (SFBT) is a strengths-based approach that was developed by American social workers and a team of interdisciplinary colleagues in the early 1980s. This article provides a review of SFBT in mainland China, showing the cross-cultural and transdisciplinary use of SFBT by diverse professionals within China. In particular, this article discusses how SFBT has spread from the United States to China and how the practice of SFBT has grown in mainland China. This is the first article in English to review the Chinese literature and further summarize the use of SFBT in mental health and health care fields within China. Practice implications for the future use of SFBT with Chinese people are also discussed.

To evaluate the role of electrophilicity in the induction of allergic contact dermatitis (ACD) in humans, we compared the structure-activity relationship (SAR) model of ACD with those of electrophilic and nonelectrophilic subsets of chemicals in the ACD database. For these analyses, electrophilicity was defined as the potential of a chemical to induce mutations in Salmonella. It was found that electrophilicity accounted for approximately 30-40% of ACD-inducing ability, and the remainder was associated with nonelectrophilic structures. The identification of these moieties opens the possibility for studying their role in ACD.

Models investigating relationships between chemical structures and biological activities are receiving increased recognition for the identification of chemicals with the potential for inducing adverse health effects. The relationships can be either qualitative (noted as SAR) or quantitative (noted as QSAR). The objective of the present study was to define an effective process for evaluating such models. The predictivity of SARI QSAR models derived from the U.S. National Toxicology Program Rodent Carcinogenicity Bioassay endeavor by CASE/MultiCASE was evaluated by several different approaches: leave-one-out tests, 1 0-fold crossvalidations and by the use of an independent test set. The goodness-of-fit for the data used in the model building, the predictivity for the chemicals not contained in the model, and the consistency of the predictions for a group of chemicals by different SAR/QSAR sub-models were examined systematically. Individual prediction indices generated by CASE/MultiCASE, arbitrary combinations thereof, as well as weighted combinations using Bayes' theorem, were utilized to derive predictions of carcinogenicity. Combinations derived using Bayes' theorem provided the most predictive model. The closeness between sub-models based on the leave-one-out procedure and the full model (all chemicals used for model building) makes it the most reliable process for the estimation of a model's predictivity. However, the similarity between the predictions of the leave-one-out models and the 10fold cross-validation models indicates that the latter process provides an acceptable approach.

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The structural basis of the induction of unscheduled DNA synthesis (UDS) in isolated rat hepatocytes was studied with CASE, an expert system. The analysis identified a number of structural determinants associated with the induction of UDS. These structures accounted for 97.3% of the activity of the chemicals in the database. Further analyses indicated that the concordance between prediction and experimentally determined UDS of molecules not in the learning set is > 82%. A comparison of predictions of UDS and mutagenicity in Salmonella indicated that there is a dichotomy between these activities. The basis of this lack of coincidence remains to be elucidated.

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