Objective. Vitamin C may be a beneficial adjunct for decreasing sore throat following tracheal intubation. Therefore, the aim of this study was to evaluate the effect of vitamin C infusion on post-intubation sore throat reduction. Methods. This double-blind randomized study was conducted on 70 patients undergoing elective laparoscopic surgery in Shahid Sadoughi Hospital. The patients were allocated into two groups (experimental and control). All patients received 2 mg midazolam and 100 microgram fentanyl citrate premedication. Thirty minutes after induction of anesthesia, 5 mg morphine was given to all patients. In the next step, the experimental group received vitamin C (2 g) mixed with normal saline for the total injection volume of 500 mL during 30 min and those in control group received normal saline without vitamin C.
Quantitative structure-activity relationship (QSAR) is an effective computational technique for drug design that relates the chemical structures of compounds to their biological activities. Feature selection is an important step in QSAR based drug design to select the most relevant descriptors. One of the most popular feature selection methods for classification problems is Fisher score which aim is to minimize the within-class distance and maximize the between-class distance. In this study, the properties of Fisher criterion were extended for QSAR models to define the new distance metrics based on the continuous activity values of compounds with known activities. Then, a semi-supervised feature selection method was proposed based on the combination of Fisher and Laplacian criteria which exploits both compounds with known and unknown activities to select the relevant descriptors. To demonstrate the efficiency of the proposed semi-supervised feature selection method in selecting the relevant descriptors, we applied the method and other feature selection methods on three QSAR data sets such as serine/threonine-protein kinase PLK3 inhibitors, ROCK inhibitors and phenol compounds. The results demonstrated that the QSAR models built on the selected descriptors by the proposed semi-supervised method have better performance than other models. This indicates the efficiency of the proposed method in selecting the relevant descriptors using the compounds with known and unknown activities. The results of this study showed that the compounds with known and unknown activities can be helpful to improve the performance of the combined Fisher and Laplacian based feature selection methods.
Background: Breast cancer is the most common cancer in women. Histological grade and type of tumor are morphological findings that play a main role in breast cancer classification. Markers including Estrogen receptor (ER), progesterone receptor (PR), and Her 2 can be used in routine clinical labs to predict response or resistance to treatment for using new drugs.
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