Predicting factors for excessive intraoperative blood loss during abdominal hysterectomy for benign gynecologic diseases

Objectives: To determine the incidence of excessive intraoperative blood loss (> 500 ml) and to examine risk factors associated with excessive blood loss during abdominal hysterectomy for benign gynecologic conditions. Methods: The clinical, operative, and histopathologic data of 597 women who underwent simple abdominal hysterectomy for benign gynecologic conditions at Maharaj Nakorn Chiang Mai hospital from January 2004 to December 2006 were retrospectively reviewed. Association of the clinico-pathologic factors and excessive blood loss was examined by using the chi-squared test or Fisher’s exact test as appropriate. Logistic regression analysis was performed to determine the independent factors that were associated with an increased risk of excessive blood loss. Results: The mean blood loss was 750 ml (350-1400). One hundred and seventy seven (29.6%) patients had excessive blood loss. From univariate analysis, age 250 gm, BMI > 25 kg/m2, preoperative diagnosis of endometriosis or tuboovarian abscess, and premenopause were associated with excessive blood loss. Multivariate analysis identified preoperative diagnosis, uterine weight, BMI, and menopausal status as independent factors determining the likelihood of excessive blood loss. Conclusion: Approximately one-third of the patients in this study had excessive blood loss. The chance of having excessive intraoperative blood loss can be predicted preoperatively by examining various clinical factors. This would lead to appropriate counseling, patient preparation, blood components request, and consultation planning

iBitter-Fuse: A Novel Sequence-Based Bitter Peptide Predictor by Fusing Multi-View Features.

Accurate identification of bitter peptides is of great importance for better understanding their biochemical and biophysical properties. To date, machine learning-based methods have become effective approaches for providing a good avenue for identifying potential bitter peptides from large-scale protein datasets. Although few machine learning-based predictors have been developed for identifying the bitterness of peptides, their prediction performances could be improved. In this study, we developed a new predictor (named iBitter-Fuse) for achieving more accurate identification of bitter peptides. In the proposed iBitter-Fuse, we have integrated a variety of feature encoding schemes for providing sufficient information from different aspects, namely consisting of compositional information and physicochemical properties. To enhance the predictive performance, the customized genetic algorithm utilizing self-assessment-report (GA-SAR) was employed for identifying informative features followed by inputting optimal ones into a support vector machine (SVM)-based classifier for developing the final model (iBitter-Fuse). Benchmarking experiments based on both 10-fold cross-validation and independent tests indicated that the iBitter-Fuse was able to achieve more accurate performance as compared to state-of-the-art methods. To facilitate the high-throughput identification of bitter peptides, the iBitter-Fuse web server was established and made freely available online. It is anticipated that the iBitter-Fuse will be a useful tool for aiding the discovery and de novo design of bitter peptides

SCMTHP: A New Approach for Identifying and Characterizing of Tumor-Homing Peptides Using Estimated Propensity Scores of Amino Acids.

Tumor-homing peptides (THPs) are small peptides that can recognize and bind cancer cells specifically. To gain a better understanding of THPs' functional mechanisms, the accurate identification and characterization of THPs is required. Although some computational methods for in silico THP identification have been proposed, a major drawback is their lack of model interpretability. In this study, we propose a new, simple and easily interpretable computational approach (called SCMTHP) for identifying and analyzing tumor-homing activities of peptides via the use of a scoring card method (SCM). To improve the predictability and interpretability of our predictor, we generated propensity scores of 20 amino acids as THPs. Finally, informative physicochemical properties were used for providing insights on characteristics giving rise to the bioactivity of THPs via the use of SCMTHP-derived propensity scores. Benchmarking experiments from independent test indicated that SCMTHP could achieve comparable performance to state-of-the-art method with accuracies of 0.827 and 0.798, respectively, when evaluated on two benchmark datasets consisting of Main and Small datasets. Furthermore, SCMTHP was found to outperform several well-known machine learning-based classifiers (e.g., decision tree, k-nearest neighbor, multi-layer perceptron, naive Bayes and partial least squares regression) as indicated by both 10-fold cross-validation and independent tests. Finally, the SCMTHP web server was established and made freely available online. SCMTHP is expected to be a useful tool for rapid and accurate identification of THPs and for providing better understanding on THP biophysical and biochemical properties

SCORPION is a stacking-based ensemble learning framework for accurate prediction of phage virion proteins.

Funder: Mahidol UniversityFunder: College of Arts, Media and Technology, Chiang Mai UniversityFunder: Chiang Mai UniversityFunder: Information Technology Service Center (ITSC) of Chiang Mai UniversityFast and accurate identification of phage virion proteins (PVPs) would greatly aid facilitation of antibacterial drug discovery and development. Although, several research efforts based on machine learning (ML) methods have been made for in silico identification of PVPs, these methods have certain limitations. Therefore, in this study, we propose a new computational approach, termed SCORPION, (StaCking-based Predictior fOR Phage VIrion PrOteiNs), to accurately identify PVPs using only protein primary sequences. Specifically, we explored comprehensive 13 different feature descriptors from different aspects (i.e., compositional information, composition-transition-distribution information, position-specific information and physicochemical properties) with 10 popular ML algorithms to construct a pool of optimal baseline models. These optimal baseline models were then used to generate probabilistic features (PFs) and considered as a new feature vector. Finally, we utilized a two-step feature selection strategy to determine the optimal PF feature vector and used this feature vector to develop a stacked model (SCORPION). Both tenfold cross-validation and independent test results indicate that SCORPION achieves superior predictive performance than its constitute baseline models and existing methods. We anticipate SCORPION will serve as a useful tool for the cost-effective and large-scale screening of new PVPs. The source codes and datasets for this work are available for downloading in the GitHub repository ( https://github.com/saeed344/SCORPION )

Guidelines for postoperative care in gynecologic/oncology surgery: Enhanced Recovery After Surgery (ERAS®) Society recommendations - Part II.

This article is freely available via Open Access. Click on the 'Additional Link' above to access the full-text via the publisher's site.Published (Open Access