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Biomedical subjects

A V Kulkarni

Publications and source records attributed to A V Kulkarni.

4 recordsLinked to original sources
PubMed1991

Acetylcholinesterase inhibition by pitofenone: a spasmolytic compound.

Pitofenone, a spasmolytic compound, inhibited the acetylcholinesterase activity from bovine erythrocytes and from electric eel. It is a potent inhibitor of this enzyme from the two sources, with Ki values of 36 and 45 microM, respectively. Of the five compounds structurally related to pitofenone, only those containing a piperidine moiety show acetylcholinesterase inhibition. All these inhibitions are reversible, linear, and noncompetitive in nature. A qualitative correlation between the anticholinesterase and the corresponding antimuscarinic activity for some of these compounds was apparent. Good separation of these two effects would be a desirable feature for newer muscarinic antagonists.

Acetylcholinesterase↗
PubMed1979

Effectiveness of feature groups for automated pairwise leukocyte class discrimination.

Automated blood cell differentials using statistical classification techniques have been implemented in several commercial machines. The machine-derived features used to classify leukocytes resemble the descriptors used by humans performing visual classification, e.g. size, content, shape, color, and texture. However, because of our crude modeling of vision there is no universally accepted measure of characteristics such as shape, color, or texture. One expects, therefore, that features which are powerful discriminators for humans may perform poorly when quantified by machine, while other parameters may be more precisely measured automatically and so prove more useful in cell classification. This paper reports the results of a feature evaluation study for automated discrimination between all pairs of a large set of leukocyte classes consisting of both normal and abnormal types. To provide a framework for comparing automated feature ordering with the ranking attached by medical technologists, the machine-derived features were divided into six groups: size and content, mean and mode, cytoplasm/nucleus comparison, contrast and texture, color, and nuclear shape. A sequential procedure was used to select the best five-feature set from each group and the globally best five-feature set for each pairwise classification. The results illustrate the strengths and weaknesses of machine-derived descriptors for each type of decision.

Computers↗