A Rare Case of Cervical Squamous Cell Carcinoma Concurrent with Sinonasal Inverted Papilloma: A Case Report.

Introduction: Cervical cancer is the fourth most common malignancy in women worldwide, and sinonasal inverted papilloma (SIP) is a rare benign sinus tumor with characteristics including a destructive growth pattern, high recurrence rate, and common malignant transformation. Cervical squamous cell carcinoma (SCC) combined with SIP has not been reported thus far. Case Presentation: A 55-year-old woman was diagnosed with cervical SCC in our center and treated with concurrent radiochemotherapy. During the follow-up period after the completion of cervical cancer treatment, the progression of cervical squamous cell carcinoma was first considered because the squamous cell carcinoma antigen (SCCA) levels remained high and slowly increased. However, SIP was found after a detailed investigation. The SCCA levels returned to normal after surgery. Two months after the surgery, because SCCA slowly increased again, it was found that the SIP recurred. After additional surgical treatment, the SCCA level returned to normal. Discussion and Conclusion: First, SCCA is an important indicator for monitoring changes in cervical SCC. When the changes in SCCA levels are inconsistent with the prognosis of cervical SCC, we should be vigilant about considering the possibility of other diseases existing at other sites in the body, which might lead to the earlier detection and treatment of SIP. Second, We recommended that SCCA be used as a routine monitoring index for SIP. If available, SCCA1 and SCCA2 should be evaluated to provide a more detailed assessment. Finally, for a high recurrence rate of SIP, anti-HPV treatment might be considered to reduce the risk of recurrence.

HIV-1 protease cleavage site prediction based on two-stage feature selection method.

Knowledge of the mechanism of HIV protease cleavage specificity is critical to the design of specific and effective HIV inhibitors. Searching for an accurate, robust, and rapid method to correctly predict the cleavage sites in proteins is crucial when searching for possible HIV inhibitors. In this article, HIV-1 protease specificity was studied using the correlation-based feature subset (CfsSubset) selection method combined with Genetic Algorithms method. Thirty important biochemical features were found based on a jackknife test from the original data set containing 4,248 features. By using the AdaBoost method with the thirty selected features the prediction model yields an accuracy of 96.7% for the jackknife test and 92.1% for an independent set test, with increased accuracy over the original dataset by 6.7% and 77.4%, respectively. Our feature selection scheme could be a useful technique for finding effective competitive inhibitors of HIV protease.

Predicting the metabolic pathways of small molecules based on their physicochemical properties.

How to correctly and efficiently map small molecule to its possible metabolic pathway is a meaningful topic to metabonomics research. In this work, a novel approach to address this problem was introduced to encode physicochemical properties of small molecules. Based on this encoding method, a two stage feature selection method called mRMR-FFSAdaBoost was adopted to map small molecules to their corresponding metabolic pathways possible. As a result, the accuracies of 10-folds cross-validation test and independent set test for predicting the metabolic pathways of small molecules reached 83.88% and 85.23%, respectively. An online server for predicting metabolic pathways of unknown small molecules as described in this paper is accessible at http://chemdata.shu.edu.cn:8080/PathwayPrediction/.

Prediction of the functional roles of small molecules in lipid metabolism based on ensemble learning.

As many diseases like high cholesterol are referred to lipid metabolism, studying the lipid metabolic pathway has a positive effect on finding the knowledge about interactions between different elements within high complex living systems. Here, we employed a typical ensemble learning method, Bagging learner, to study and predict the possible sub lipid metabolic pathway of small molecules based on physical and chemical features of the compounds. As a result, jackknife cross validation test and independent set test on the model reached 89.85% and 91.46%, respectively. Therefore, our predictor may be used for finding the new compounds which participate in lipid metabolic procedures.