The effect of uterine fibroid embolization on lower urinary tract symptoms.

INTRODUCTION AND HYPOTHESIS: The objective was to determine the effect of uterine fibroid embolization (UFE) on lower urinary tract symptoms (LUTS) and quality of life (QoL). METHODS: This prospective study included women with symptomatic fibroids and LUTS who underwent UFE between March 2008 and May 2010. Subjects underwent pre-procedural pelvic magnetic resonance imaging (MRI) and completed the Urogenital Distress Inventory (UDI-6), Incontinence Impact Questionnaire (IIQ-7), Prolapse and Incontinence Sexual Questionnaire (PISQ-12), Uterine Fibroid Symptom Quality of Life questionnaire (UFS-QoL), and a standardized 48-h bladder diary at baseline and 3 months after the procedure. Patient Global Impression of Improvement (PGI-I) assessed post-procedural patient satisfaction. The primary outcome was subjective improvement in LUTS at 3 months, as measured by a decrease in UDI-6 score. Univariate analysis, paired t test and a stepwise regression analysis were appropriately conducted. RESULTS: Fifty-seven patients underwent UFE and completed bladder diaries and questionnaires. At 3 months after UFE, patients reported a significant decrease in UDI-6, IIQ-7, and UFS-QoL, indicating an improvement in urinary symptoms and QoL. Bladder diaries showed a significant reduction in daytime and night-time voids. No difference was found in incontinence episodes. Uterine volume, dominant fibroid size, fibroid location, and MRI-confirmed bladder compression did not affect the difference in UDI-6 scores. In a stepwise regression model, BMI had a significant impact on the change in UDI-6 score, with a decrease of 1.18 points for each 1 unit increase in BMI. CONCLUSION: Uterine fibroid embolization significantly improves LUTS and urinary-related QoL. Obesity seems to attenuate this effect.

ProteDNA: a sequence-based predictor of sequence-specific DNA-binding residues in transcription factors.

This article presents the design of a sequence-based predictor named ProteDNA for identifying the sequence-specific binding residues in a transcription factor (TF). Concerning protein-DNA interactions, there are two types of binding mechanisms involved, namely sequence-specific binding and nonspecific binding. Sequence-specific bindings occur between protein sidechains and nucleotide bases and correspond to sequence-specific recognition of genes. Therefore, sequence-specific bindings are essential for correct gene regulation. In this respect, ProteDNA is distinctive since it has been designed to identify sequence-specific binding residues. In order to accommodate users with different application needs, ProteDNA has been designed to operate under two modes, namely, the high-precision mode and the balanced mode. According to the experiments reported in this article, under the high-precision mode, ProteDNA has been able to deliver precision of 82.3%, specificity of 99.3%, sensitivity of 49.8% and accuracy of 96.5%. Meanwhile, under the balanced mode, ProteDNA has been able to deliver precision of 60.8%, specificity of 97.6%, sensitivity of 60.7% and accuracy of 95.4%. ProteDNA is available at the following websites: http://protedna.csbb.ntu.edu.tw/, http://protedna.csie.ntu.edu.tw/, http://bio222.esoe.ntu.edu.tw/ProteDNA/.

Predicting RNA-binding residues from evolutionary information and sequence conservation.

BACKGROUND: RNA-binding proteins (RBPs) play crucial roles in post-transcriptional control of RNA. RBPs are designed to efficiently recognize specific RNA sequences after it is derived from the DNA sequence. To satisfy diverse functional requirements, RNA binding proteins are composed of multiple blocks of RNA-binding domains (RBDs) presented in various structural arrangements to provide versatile functions. The ability to computationally predict RNA-binding residues in a RNA-binding protein can help biologists reveal important site-directed mutagenesis in wet-lab experiments. RESULTS: The proposed prediction framework named "ProteRNA" combines a SVM-based classifier with conserved residue discovery by WildSpan to identify the residues that interact with RNA in a RNA-binding protein. Although these conserved residues can be either functionally conserved residues or structurally conserved residues, they provide clues on the important residues in a protein sequence. In the independent testing dataset, ProteRNA has been able to deliver overall accuracy of 89.78%, MCC of 0.2628, F-score of 0.3075, and F0.5-score of 0.3546. CONCLUSIONS: This article presents the design of a sequence-based predictor aiming to identify the RNA-binding residues in a RNA-binding protein by combining machine learning and pattern mining approaches. RNA-binding proteins have diverse functions while interacting with different categories of RNAs because these proteins are composed of multiple copies of RNA-binding domains presented in various structural arrangements to expand the functional repertoire of RNA-binding proteins. Furthermore, predicting RNA-binding residues in a RNA-binding protein can help biologists reveal important site-directed mutagenesis in wet-lab experiments.

DNA-binding residues and binding mode prediction with binding-mechanism concerned models.

BACKGROUND: Protein-DNA interactions are essential for fundamental biological activities including DNA transcription, replication, packaging, repair and rearrangement. Proteins interacting with DNA can be classified into two categories of binding mechanisms - sequence-specific and non-specific binding. Protein-DNA specific binding provides a mechanism to recognize correct nucleotide base pairs for sequence-specific identification. Protein-DNA non-specific binding shows sequence independent interaction for accelerated targeting by interacting with DNA backbone. Both sequence-specific and non-specific binding residues contribute to their roles for interaction. RESULTS: The proposed framework has two stage predictors: DNA-binding residues prediction and binding mode prediction. In the first stage - DNA-binding residues prediction, the predictor for DNA specific binding residues achieves 96.45% accuracy with 50.14% sensitivity, 99.31% specificity, 81.70% precision, and 62.15% F-measure. The predictor for DNA non-specific binding residues achieves 89.14% accuracy with 53.06% sensitivity, 95.25% specificity, 65.47% precision, and 58.62% F-measure. While combining prediction results of sequence-specific and non-specific binding residues with OR operation, the predictor achieves 89.26% accuracy with 56.86% sensitivity, 95.63% specificity, 71.92% precision, and 63.51% F-measure. In the second stage, protein-DNA binding mode prediction achieves 75.83% accuracy while using support vector machine with multi-class prediction. CONCLUSION: This article presents the design of a sequence based predictor aiming to identify sequence-specific and non-specific binding residues in a transcription factor with DNA binding-mechanism concerned. The protein-DNA binding mode prediction was introduced to help improve DNA-binding residues prediction. In addition, the results of this study will help with the design of binding-mechanism concerned predictors for other families of proteins interacting with DNA.