Objectives To compare the effectiveness of bupivacaine with adrenaline with that of carbonated bupivacaine with adrenaline on pain, onset of anesthesia and duration of anesthesia following surgical removal of impacted mandibular third molar. Study design All the patients who underwent surgical removal of impacted mandibular third molar and who fulfilled our inclusion and exclusion criteria from 1st June 2013 to 30th June 2014 were included in our study. Patients who were diagnosed as having impacted mandibular third molar were randomly allocated to two groups namely group A (bupivacaine with adrenaline), group B (carbonated bupivacaine with adrenaline). Pain during deposition of local anesthetic, onset of anesthesia and duration of anesthesia were compared between the two groups. The collected data were subjected to statistical analysis by Chi Square test, Mann-Whitney U test. Results and conclusion The efficacy of carbonated bupivacaine with adrenaline is more compared with bupivacaine with adrenaline in decreasing pain on deposition of local anesthetic solution and in rapid onset of anesthesia. The duration of anesthesia for carbonated bupivacaine with adrenaline and bupivacaine with adrenaline had no significant difference. The use of carbonated bupivacaine with adrenaline will reduce the patient discomfort both intraoperatively and post-operatively.
One of the most dangerous diseases is Human Cancer, it is caused by instability of the multiple molecular alterations in the genetic. The most common one among the different forms of human cancer is the skin cancer. With help of different techniques like segmentation and feature extraction the skin cancer can be identified and analyzed at the early stage. The malignant melanoma skin cancer which occurs due to the high concentration of the melanocytic cells present beneath the skin is demonstrated in this paper. In this paper, for detecting of the malignant melanoma skin cancer the ABCD rule dermoscopy technology is used. First, the Image Acquisition technique then the pre-processing, segmentation, skin Feature Selection determines lesion characterization and classification methods are the several techniques used to find melanoma skin lesion. By digital image processing methods like border detection, color, symmetry detection and diameter detection the feature extraction is done and for the extraction of the texture-based feature LBP is used. As a classifier ANN is used in this proposed method. Finally, to classify the benign or malignant stage the Back Propagation Neural Network is used.
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