Improvement in quality of life and pain scores after laparoscopic management of deep endometriosis: a retrospective cohort study.

PURPOSE: This is a retrospective cohort study that evaluates the postoperative pain findings of a consecutive series of laparoscopic surgeries for deep endometriosis (DE). METHODS: This multi-center retrospective cohort study was carried out in university hospitals (Istanbul, Turkey). Sixty-five patients diagnosed through bimanual gynecologic examination, gynecologic ultrasound or magnetic resonance imaging-confirmed endometrioma and DE together; who underwent a laparoscopic surgery between 2013 and 2019 by a team of gynecologists, colorectal surgeons, and a urologist were retrospectively evaluated. The data were collected in a specific database and analyzed for postoperative pain outcomes through a comparison with preoperative symptoms scored using a visual analogue score (VAS), and the British Society of Gynecologic Endoscopy (BSGE) pelvic pain questionnaire. RESULTS: Sixty-five patients who met the criteria were included. The mean age of all patients was 35.0 ± 6.3 (range 22-50) years. The mean operative time was 121.3 ± 50.2 (range, 60-270) minutes. Preoperative and postoperative comparison of VAS scores for dysmenorrhea (8.57 vs. 2.91), dyspareunia (6.62 vs. 1.66), dyschezia (7.46 vs. 2.43), dysuria (5.67 vs. 1.34), chronic pelvic pain (4.11 vs. 1.22), and BSGE score (40.98 vs. 11.00) showed significantly reduced pain scores, respectively (p < 0.01). CONCLUSION: Laparoscopic management of DE is a valid treatment option in terms of reduced postoperative pain and increased quality of life according to pain score outcomes. To have more robust conclusions, a prospective cohort study with a larger sample size which evaluates patients who had segmental bowel resection and those who did not have segmental bowel resection is necessary.

Can follicular Emmprin and BMP 4 levels predict ICSI outcome?

PURPOSE: To evaluate the relationship of clinical pregnancy rates with bone morphogenetic proteins 2-4-7 (BMP 2, 4, 7), growth differentiation factor 9 (GDF 9), and Emmprin levels in follicular fluid of intracytoplasmic sperm injection patients. METHODS: Follicular fluid of 77 patients who underwent ICSI procedure was collected during the oocyte retrieval procedure. And follicular fluid levels of BMP 2, BMP 4, BMP 7, GDF 9, and Emmprin (Basigin) were measured and compared for clinical pregnancy rates. RESULTS: Follicular levels of BMP 4 was significantly higher whereas Emmprin levels were lower in patients who had achieved clinically diagnosed pregnancy compared with those who did not achieve clinical pregnancy after ICSI procedure (P = 0.007 and P = 0.035, respectively). BMP 2, BMP 7, and GDF 9 levels were comparable for both groups. CONCLUSION: Clinical pregnancy rates after ICSI may be associated with follicular fluid levels of Emmprin and BMP 4. Follicular levels of Emmprin and BMP 4 can be used as a marker (as markers for predicting ICSI outcomes) for a better ICSI outcome.

Hybrid imbalanced data classifier models for computational discovery of antibiotic drug targets.

Identification of drug candidates is an important but also difficult process. Given drug resistance bacteria that we face, this process has become more important to identify protein candidates that demonstrate antibacterial activity. The aim of this study is therefore to develop a bioinformatics approach that is more capable of identifying a small but effective set of proteins that are expected to show antibacterial activity, subsequently to be used as antibiotic drug targets. As this is regarded as an imbalanced data classification problem due to smaller number of antibiotic drugs available, a hybrid classification model was developed and applied to the identification of antibiotic drugs. The model was developed by taking into account of various statistical models leading to the development of six different hybrid models. The best model has reached the accuracy of as high as 50% compared to earlier study with the accuracy of less than 1% as far as the proportion of the candidates identified and actual antibiotics in the candidate list is concerned.

Classification of EEG recordings by using fast independent component analysis and artificial neural network.

Since there is no definite decisive factor evaluated by the experts, visual analysis of EEG signals in time domain may be inadequate. Routine clinical diagnosis requests to analysis of EEG signals. Therefore, a number of automation and computer techniques have been used for this aim. In this study we aim at designing a MLPNN classifier based on the Fast ICA that accurately identifies whether the associated subject is normal or epileptic. By analyzing a data set consisting of 100 normal and 100 epileptic EEG time series, we have found that the MLPNN classifier based on the Fast ICA achieved and sensitivity rate of 98%, and specificity rate of 90.5%. The results demonstrate that the testing performance of the neural network diagnostic system is found to be satisfactory and we think that this system can be used in clinical studies. Since the time series analysis of EEG signals is unsatisfactory and requires specialist clinicians to evaluate, this application brings objectivity to the evaluation of EEG signals.