Biomedical subjects

D R Henry

Publications and source records attributed to D R Henry.

6 recordsLinked to original sources

PubMed2001

Factors that influence the decision to do an adequate evaluation of a patient with a positive stool for occult blood.

OBJECTIVE: The factors that influence the decision to do an adequate evaluation for a positive test for fecal occult blood in a middle-aged or elderly patient are largely unknown. Our study was undertaken to determine whether factors such as the number of positive Hemoccult II card windows, age, gender, family history of colon cancer, the patient's concern that he or she might have colon cancer, or history of rectal bleeding influence the evaluation performed. METHOD: A mass screening program for colon cancer was performed using unrehydrated Hemoccult II cards in the Boston area. RESULTS: Among the 23,593 Hemoccult II cards returned to Beth Israel Deaconess Medical Center, cards from 1,112 patients (4.7%) were found to be positive for one or more of the six possible card windows. Ninety percent, or 940 patients, over 40 yr of age had follow-up information available. As the number of positive windows increased from one to four, there was a significant trend (p < 0.001) for the adequacy of the evaluation to increase. Family history (p = 0.044) and a patient's worry that he or she might have colon cancer (p = 0.003) significantly improved a patient's chance for an adequate evaluation. CONCLUSIONS: Hemoccult testing is not followed by an adequate evaluation in a significant proportion of patients. Our study points out for the first time that the number of positive Hemoccult windows significantly influences the decision-making.

Adenocarcinoma↗

PubMed1987

Electronic factors and acridine frameshift mutagenicity--a pattern recognition study.

Using the ADAPT and CHEMLAB-II systems for structure-activity analysis, computer-calculated electronic properties of molecules were used to derive structure-activity relationships for predicting the mutagenicity of a set of substituted acridines in strain TA1537 of the Ames Salmonella assay. A collection of 40 acridines, with a variety of substituents, was examined. A set of 4 electronic descriptors was found which could be used to correctly classify all but two of the compounds as mutagenic or nonmutagenic. A negative correlation was found between the sum of the Hammett aromatic substituent parameters and the level of mutagenicity of the structures, expressed as log(number of revertants/plate + 1) at a 20-micrograms dose. This correlation, however, was not high enough to allow precise estimation of the mutagenicity values.

Acridines↗

PubMed1982

Structure-antitumor activity relationships of 9-anilinoacridines using pattern recognition.

A pattern-recognition analysis using the ADAPT system was performed on a set of 9-anilinoacridine antitumor agents, to determine whether computer-generated descriptors could be used to separate active from inactive compounds. A training set of 213 compounds was chosen by random computer selection from a list of 776 structures. Maximal increase in life span at the LD10 dosage, a response which is difficult to model using traditional Hansch analysis, was used as the measure of biological activity. A set of 18 molecular descriptors, including fragment, substructure environment, and physicochemical property descriptors (molar refraction, partial electronic charge) was identified which could correctly classify 94% of the compounds in the training set (97% of active and 85% of inactive compounds). Eight of the inactive compounds that were misclassified contained amino substituents, suggesting a role for ionization. The weight vector that was obtained from the training set was applied to a prediction set of 50 compounds that were not included in the original analysis and to a set of 69 structures drawn from the recent literature. The prediction set results, ranging from 73 to 86% correct, were lower than those of the training set, but they clearly indicate that pattern-recognition techniques can be useful in the screening of proposed or already existing agents and especially useful for the identification of active compounds.

Aminoacridines↗

PubMed1980

Pattern recognition of steroids using fragment molecular connectivity.

The use of pattern recognition methods to classify a set of steroids into five therapeutic categories was investigated. First-order fragment molecular connectivity values were determined for 10 positions on each molecule using a template-based method of position assignment. Learning set and test set classifications were performed. Although the numbers of compounds misclassified were comparable for all of the methods, the identities of the misclassified compounds varied depending on whether the classification method assumed a local or a global view of the data. The best classification results were comparable to those obtained by linear and quadratic discriminant analyses. For this set of compounds, it was concluded that pattern recognition methods offer no advantages over traditional discriminant analysis methods if classification alone is considered, especially since most discriminant analysis procedures utilize stepwise variable selection, which is not as common in pattern recognition analyses.

Chemical Phenomena↗

PubMed1979

Classification of drugs by discriminant analysis using fragment molecular connectivity values.

An investigation was made into the use of linear and quadratic discriminant analysis, along with K nearest-neighbor analysis, in the classification of a set of 51 compounds which were divided into five therapeutic categories. By superimposing each compound on a pattern structure, as first proposed by Cammarata, eight positions were assigned on the molecule. Each position was coded with the numerical value of a descriptor index. Relative molar refraction, which was the index used by Cammarata, was compared with a number of molecular connective indices. For each of the indices studied, it was found that only four of the eight positions contributed significantly to between-class differences. It was also found that first-order molecular connectivity, calculated as the sum of the contributions of each of the bonds joining a given position, resulted in consistently fewer misclassifications as compared with the other indices. Using first-order molecular connectivity, validation procedures were performed on the original set of compounds, on random samples drawn from this set, and on a set of ten compounds not included in the analysis. The results obtained were highly data dependent, but they, nevertheless, suggest that molecular connectivity indices should prove useful in structural classification procedures.

Analysis of Variance↗

PubMedDate not supplied

Computer-assisted studies of molecular structure and carcinogenic activity.

Both quantitative and qualitative studies of the structure-activity relationships (SAR) of carcinogenic compounds are discussed. Using the ADAPT software system, examples are given of (1) predicting the gas and liquid chromatographic retention behavior of polycyclic aromatic compounds, (2) predicting the carcinogenicity of N-nitrosamines, and (3) predicting the mutagenic behavior of pesticides and other compounds in short-term test systems. It is concluded that the complexity and lack of understanding of the carcinogenic response should not prevent scientists from searching for useful empirical relationships between molecular structure and carcinogenicity. Computer generated molecular descriptors and statistical and pattern recognition techniques can be useful tools in this research.

Animals↗