Background: Determination of postmortem interval (PMI) has always been a fundamental issue in forensic fields. Therefore, the purpose of this study was to assess usefulness of histopathological changes and collagen degradation in the prostate for estimation of PMI in adult male rats. Methods: Forty male albino rats were euthanized using CO2 chamber and then classified randomly into 8 groups; each group consisted of 5 rats. Rats were used for determining postmortem changes at 0, 6, 12, 24, 36, 48, 60 and 72 hs. Sections of prostate were fixed and stained for histopathology and quantitative analysis of collagen by Masson trichrome. Moreover, slices of 5µm mounted on positively charged slides were carried out for collagen III immunohistochemistry. Results: The prostate showed normal histology at 0 and 6 hs postmortem (PM). While, at 12 hs, most sections of the gland revealed no abnormalities except for mild focal widening between the acini. Starting from 24 hs, epithelial desquamation was seen in most acini. Seventy-two hours, obvious necrosis of prostatic acini appeared in most of samples. Degradation of collagen started and the spaces between acini became wide at 12 and 24 hs PM. Complete loss of stain reactivity was observed at both 60 and 72 hs. Moreover, a significant decrease in the amount of collagen's stained areas in the prostate started from 12 hs. Positive immunohistochemical reaction of collagen III was detected at 0, 6 and 12 hs PM. However, it was completely lost at 60 and 72 hs. Interestingly, the immuno-reactive area of collagen III was significantly increased at 6 hs, then a remarkable decline in immuno-reactive area was observed at 12 hs. Conclusion: Collagen type III proved to be a successful parameter with histopathology in determination of PM interval. However, further studies still needed to confirm the accuracy of these parameters.

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This paper presents a study for the effect of learning rate on an approach for texture classification and detection based on the neural network principle. This neural network consists of three layers, which are input, output, and hidden layers. The back propagation technique is considered. A computer algorithm is deduced and applied. In this work, the synthetic textures are generated. The results are taken for the modern computer of AT 486 type. The mathematical analysis is summarized in order to illustrate the effect of learning rate parameter on the exact discrimination during processing. This effect is studied through applications. The minimum consumed time for the computational time of classification in industry is correlated to correspond only the use of only 2 units in the hidden layer of a neural network for real images instead of 11 units.