PubMed · 7903783
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D Chadwick. 1994-01-08. Gabapentin.. https://doi.org/10.1016/s0140-6736(94)90820-6
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Computational neural networks have been developed to classify and quantify nine organic vapors. The neural network analyses used data that consisted of the change in fluorescence from a sensor array that consisted of 19 fiber optics with immobilized dye in polymer matrices. Plots of change in fluorescence intensity versus time were measured as pulses of analyte were presented to the sensor array. Descriptors were calculated from the intensity vs time plots, and they were used to build neural network models that accurately classified and quantified each of the nine analytes. Most of the data were used to train the neural networks (training set members), some were used to assist termination of training (cross-validation set members), and some were used to validate the models (prediction set members). Classification rates approaching 100% were achieved for the training set data, and 90% of the members in the prediction set were correctly classified. In addition, 97% of the prediction set observations were assigned a correct relative concentration.
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Accumulation of intracellular sorbitol, the reduced product of glucose, catalyzed by aldose reductase (AR) (EC 1.1.1.21), is thought to be the cause of the development of diabetic complications. Our attention is focused on finding compounds which inhibit AR without significantly inhibiting aldehyde reductase (ALR) (EC 1.1.1.2). The uracil or 2,4-dioxoimidazolidine skeleton having the benzothiazolyl or 4-chloro-3-nitrophenyl group as an aryl part indicated not only extremely high AR inhibitory activity but also AR selectivity. The ratio of IC50(ALR)/IC50(AR) of 3-[(5-chlorobenzothiazol-2-yl)methyl]-1,2,3,4-tetrahydro-2,4- dioxopyrimidine-1-acetic acid (47d) was more than 17 500. The uracil skeleton with the benzothiazolyl moiety seemed to be the best combination for selective AR inhibition.