Use of Artificial Neural Networks and PCA to Predict Results of Infertility Treatment in the ICSI Method.

BACKGROUND: Intracytoplasmic sperm injection (ICSI) or microinjection is one of the most commonly used assisted reproductive technologies (ART) in the treatment of patients with infertility problems. At each stage of this treatment cycle, many dependent and independent variables may affect the results, according to which, estimating the accuracy of fertility rate for physicians will be difficult. OBJECTIVE: This study aims to evaluate the efficiency of artificial neural networks (ANN) and principal component analysis (PCA) to predict results of infertility treatment in the ICSI method. MATERIAL AND METHODS: In the present research that is an analytical study, multilayer perceptron (MLP) artificial neural networks were designed and evaluated to predict results of infertility treatment using the ICSI method. In addition, the PCA method was used before the process of training the neural network for extracting information from data and improving the efficiency of generated models. The network has 11 to 17 inputs and 2 outputs. RESULTS: The area under ROC curve (AUC) values were derived from modeling the results of the ICSI technique for the test data and the total data. The AUC for total data vary from 0.7670 to 0.9796 for two neurons, 0.9394 to 0.9990 for three neurons and 0.9540 to 0.9906 for four neurons in hidden layers. CONCLUSION: The proposed MLP neural network can model the specialist performance in predicting treatment results with a high degree of accuracy and reliability.

Evidence of active transport involvement in morphine transport via MDCKII and MDCK-PGP cell lines.

Several transporters appear to be important in transporting various drugs. Many patients, who receive morphine as analgesic medication, also receive other medications with potency of changing morphine transport by affecting P-glycoprotein (P-GP) and oatp2 transport system. This could influence morphine pharmacokinetics and pharmacodynamics. The aim of present study was to elucidate the transport mechanisms involved in transporting morphine via MDCKII and MDCK-PGP cells. Morphine permeability was examined in the presence of various compounds with ability in inhibiting different transport systems including: digoxin, probenecid and d- glucose. The effect of morphine concentration changes on its transport was also examined. Morphine concentration was measured using HPLC with electrochemical detector. Morphine permeability via a MDCK II cells was greater than sucrose permeability, and reduced when a P-GP expressed cell line was used. Its permeability was increased significantly in the presence of a strong P-GP inhibitor. Morphine permeability decreased significantly in the presence of digoxin but not in the presence of d-glucose or probenecid. These results showed that morphine was a P-GP substrate, and digoxin related transporters such as oatp2 were involved in its transport. Morphine was not substrate for glucose or probenecid-sensitive transporters.