Factors Affecting De Novo Urinary Retention after Holmium Laser Enucleation of the Prostate

Objective

Patients can experience urinary retention (UR) after Holmium laser enucleation of the prostate (HoLEP) that requires bladder distension during the procedure. The aim of this retrospective study is to identify factors affecting the UR after HoLEP.

Materials and Methods

336 patients, which underwent HoLEP for a symptomatic benign prostatic hyperplasia between July 2008 and March 2012, were included in this study. Urethral catheters were routinely removed one or two days after surgery. UR was defined as the need for an indwelling catheter placement following a failure to void after catheter removal. Demographic and clinical parameters were compared between the UR (n = 37) and the non-urinary retention (non-UR; n = 299) groups.

Results

The mean age of patients was 68.3 (±6.5) years and the mean operative time was 75.3 (±37.4) min. Thirty seven patients (11.0%) experienced a postoperative UR. UR patients voided catheter free an average of 1.9 (±1.7) days after UR. With regard to the causes of UR, 24 (7.1%) and 13 (3.9%) patients experienced a blood clot-related UR and a non-clot related UR respectively. Using multivariate analysis (p<0.05), we found significant differences between the UR and the non-UR groups with regard to a morcellation efficiency (OR 0.701, 95% CI 0.498–0.988) and a bleeding-related complication, such as, a reoperation for bleeding (OR 0.039, 95% CI 0.004–0.383) or a transfusion (OR 0.144, 95% CI 0.027–0.877). Age, history of diabetes, prostate volume, pre-operative post-void residual, bladder contractility index, learning curve, and operative time were not significantly associated with the UR (p>0.05).

Conclusions

De novo UR after HoLEP was found to be self-limited and it was not related to learning curve, patient age, diabetes, or operative time. Efficient morcellation and careful control of bleeding, which reduces clot formation, decrease the risk of UR after HoLEP.     

Holmium Laser Enucleation versus Transurethral Resection in Patients with Benign Prostate Hyperplasia: An Updated Systematic Review with Meta-Analysis and Trial Sequential Analysis

Background

Holmium laser enucleation (HoLEP) in surgical treatment of benign prostate hyperplasia (BPH) potentially offers advantages over transurethral resection of the prostate (TURP).

Methods

Published randomized controlled trials (RCTs) were identified from PubMed, EMBASE, Science Citation Index, and the Cochrane Library up to October 10, 2013 (updated on February 5, 2014). After methodological quality assessment and data extraction, meta-analysis was performed using STATA 12.0 and Trial Sequential Analysis (TSA) 0.9 software.

Results

Fifteen studies including 8 RCTs involving 855 patients met the criteria. The results of meta-analysis showed that: a) efficacy indicators: there was no significant difference in quality of life between the two groups (P>0.05), but compared with the TURP group, Qmax was better at 3 months and 12 months, PVR was less at 6, 12 months, and IPSS was lower at 12 months in the HoLEP, b) safety indicators: compared with the TURP, HoLEP had less blood transfusion (RR 0.17, 95% CI 0.06 to 0.47), but there was no significant difference in early and late postoperative complications (P>0.05), and c) perioperative indicators: HoLEP was associated with longer operation time (WMD 14.19 min, 95% CI 6.30 to 22.08 min), shorter catheterization time (WMD −19.97 h, 95% CI −24.24 to −15.70 h) and hospital stay (WMD −25.25 h, 95% CI −29.81 to −20.68 h).

Conclusions

In conventional meta-analyses, there is no clinically relevant difference in early and late postoperative complications between the two techniques, but HoLEP is preferable due to advantage in the curative effect, less blood transfusion rate, shorter catheterization duration time and hospital stay. However, trial sequential analysis does not allow us to draw any solid conclusion in overall clinical benefit comparison between the two approaches. Further large, well-designed, multicentre/international RCTs with long-term data and the comparison between the two approaches remain open.     

Genome-wide identification of specific oligonucleotides using artificial neural network and computational genomic analysis

Background  

Genome-wide identification of specific oligonucleotides (oligos) is a computationally-intensive task and is a requirement for designing microarray probes, primers, and siRNAs. An artificial neural network (ANN) is a machine learning technique that can effectively process complex and high noise data. Here, ANNs are applied to process the unique subsequence distribution for prediction of specific oligos.     

A flexible R package for nonnegative matrix factorization

Background  

Nonnegative Matrix Factorization (NMF) is an unsupervised learning technique that has been applied successfully in several fields, including signal processing, face recognition and text mining. Recent applications of NMF in bioinformatics have demonstrated its ability to extract meaningful information from high-dimensional data such as gene expression microarrays. Developments in NMF theory and applications have resulted in a variety of algorithms and methods. However, most NMF implementations have been on commercial platforms, while those that are freely available typically require programming skills. This limits their use by the wider research community.     

Positioning Accuracy in Otosurgery Measured with Optical Tracking

Objectives

To assess positioning accuracy in otosurgery and to test the impact of the two-handed instrument holding technique and the instrument support technique on surgical precision. To test an otologic training model with optical tracking.

Study Design

In total, 14 ENT surgeons in the same department with different levels of surgical experience performed static and dynamic tasks with otologic microinstruments under simulated otosurgical conditions.

Methods

Tip motion of the microinstrument was registered in three dimensions by optical tracking during 10 different tasks simulating surgical steps such as prosthesis crimping and dissection of the middle ear using formalin-fixed temporal bone. Instrument marker trajectories were compared within groups of experienced and less experienced surgeons performing uncompensated or compensated exercises.

Results

Experienced surgeons have significantly better positioning accuracy than novice ear surgeons in terms of mean displacement values of marker trajectories. The instrument support and the two-handed instrument holding techniques significantly reduce surgeons’ tremor. The laboratory set-up presented in this study provides precise feedback for otosurgeons about their surgical skills and proved to be a useful device for otosurgical training.

Conclusions

Simple tremor compensation techniques may offer trainees the potential to improve their positioning accuracy to the level of more experienced surgeons. Training in an experimental otologic environment with optical tracking may aid acquisition of technical skills in middle ear surgery and potentially shorten the learning curve. Thus, simulated exercises of surgical steps should be integrated into the training of otosurgeons.     

Statistical learning techniques applied to epidemiology: a simulated case-control comparison study with logistic regression

Background  

When investigating covariate interactions and group associations with standard regression analyses, the relationship between the response variable and exposure may be difficult to characterize. When the relationship is nonlinear, linear modeling techniques do not capture the nonlinear information content. Statistical learning (SL) techniques with kernels are capable of addressing nonlinear problems without making parametric assumptions. However, these techniques do not produce findings relevant for epidemiologic interpretations. A simulated case-control study was used to contrast the information embedding characteristics and separation boundaries produced by a specific SL technique with logistic regression (LR) modeling representing a parametric approach. The SL technique was comprised of a kernel mapping in combination with a perceptron neural network. Because the LR model has an important epidemiologic interpretation, the SL method was modified to produce the analogous interpretation and generate odds ratios for comparison.     

PARPs database: A LIMS systems for protein-protein interaction data mining or laboratory information management system

Background  

In the "post-genome" era, mass spectrometry (MS) has become an important method for the analysis of proteins and the rapid advancement of this technique, in combination with other proteomics methods, results in an increasing amount of proteome data. This data must be archived and analysed using specialized bioinformatics tools.     

Effect of experimental treatment on GAPDH mRNA expression as a housekeeping gene in human diploid fibroblasts

Background  

Several genes have been used as housekeeping genes and choosing an appropriate reference gene is important for accurate quantitative RNA expression in real time RT-PCR technique. The expression levels of reference genes should remain constant between the cells of different tissues and under different experimental conditions. The purpose of this study was to determine the effect of different experimental treatments on the expression of glyceraldehyde 3-phosphate dehydrogenase (GAPDH) mRNA so that the reliability of GAPDH as reference gene for quantitative real time RT-PCR in human diploid fibroblasts (HDFs) can be validated. HDFs in 4 different treatment groups viz; young (passage 4), senescent (passage 30), H₂O₂-induced oxidative stress and γ-tocotrienol (GTT)-treated groups were harvested for total RNA extraction. Total RNA concentration and purity were determined prior to GAPDH mRNA quantification. Standard curve of GAPDH expression in serial diluted total RNA, melting curve analysis and agarose gel electrophoresis were used to determine the reliability of GAPDH as reference gene.     

Deep learning for DNase I hypersensitive sites identification

Background

The DNase I hypersensitive sites (DHSs) are associated with the cis-regulatory DNA elements. An efficient method of identifying DHSs can enhance the understanding on the accessibility of chromatin. Despite a multitude of resources available on line including experimental datasets and computational tools, the complex language of DHSs remains incompletely understood.

Methods

Here, we address this challenge using an approach based on a state-of-the-art machine learning method. We present a novel convolutional neural network (CNN) which combined Inception like networks with a gating mechanism for the response of multiple patterns and longterm association in DNA sequences to predict multi-scale DHSs in Arabidopsis, rice and Homo sapiens.

Results

Our method obtains 0.961 area under curve (AUC) on Arabidopsis, 0.969 AUC on rice and 0.918 AUC on Homo sapiens.

Conclusions

Our method provides an efficient and accurate way to identify multi-scale DHSs sequences by deep learning.

     

Formal kinetics of H1N1 epidemic

Background  

The formal kinetics of the H1N1 epidemic seems to take the form of an exponential curve. There is a good correlation between this theoretical model and epidemiological data on the number of H1N1-infected people. But this formal model leads to paradoxes about the dates when everyone becomes infected: in Mexico this will happen after one year, then in the rest of the world.     

FCI: an R-based algorithm for evaluating uncertainty of absolute real-time PCR quantification

Background  

FCI is an R code for analyzing data from real-time PCR experiments. This algorithm estimates standard curve features as well as nucleic acid concentrations and confidence intervals according to Fieller's theorem.     

Prediction of MHC class II binding peptides based on an iterative learning model

Background

Prediction of the binding ability of antigen peptides to major histocompatibility complex (MHC) class II molecules is important in vaccine development. The variable length of each binding peptide complicates this prediction. Motivated by a text mining model designed for building a classifier from labeled and unlabeled examples, we have developed an iterative supervised learning model for the prediction of MHC class II binding peptides.

Results

A linear programming (LP) model was employed for the learning task at each iteration, since it is fast and can re-optimize the previous classifier when the training sets are altered. The performance of the new model has been evaluated with benchmark datasets. The outcome demonstrates that the model achieves an accuracy of prediction that is competitive compared to the advanced predictors (the Gibbs sampler and TEPITOPE). The average areas under the ROC curve obtained from one variant of our model are 0.753 and 0.715 for the original and homology reduced benchmark sets, respectively. The corresponding values are respectively 0.744 and 0.673 for the Gibbs sampler and 0.702 and 0.667 for TEPITOPE.

Conclusion

The iterative learning procedure appears to be effective in prediction of MHC class II binders. It offers an alternative approach to this important predictionproblem.     

Arrival Time Correction for Dynamic Susceptibility Contrast MR Permeability Imaging in Stroke Patients

Purpose

To determine if applying an arrival time correction (ATC) to dynamic susceptibility contrast (DSC) based permeability imaging will improve its ability to identify contrast leakage in stroke patients for whom the shape of the measured curve may be very different due to hypoperfusion.

Materials and Methods

A technique described in brain tumor patients was adapted to incorporate a correction for delayed contrast delivery due to perfusion deficits. This technique was applied to the MRIs of 9 stroke patients known to have blood-brain barrier (BBB) disruption on T1 post contrast imaging. Regions of BBB damage were compared with normal tissue from the contralateral hemisphere. Receiver operating characteristic (ROC) analysis was performed to compare the detection of BBB damage before and after ATC.

Results

ATC improved the area under the curve (AUC) of the ROC from 0.53 to 0.70. The sensitivity improved from 0.51 to 0.67 and the specificity improved from 0.57 to 0.66. Visual inspection of the ROC curve revealed that the performance of the uncorrected analysis was worse than random guess at some thresholds.

Conclusions

The ability of DSC permeability imaging to identify contrast enhancing tissue in stroke patients improved considerably when an ATC was applied. Using DSC permeability imaging in stroke patients without an ATC may lead to false identification of BBB disruption.     

Unsupervised statistical clustering of environmental shotgun sequences

Background  

The development of effective environmental shotgun sequence binning methods remains an ongoing challenge in algorithmic analysis of metagenomic data. While previous methods have focused primarily on supervised learning involving extrinsic data, a first-principles statistical model combined with a self-training fitting method has not yet been developed.     

Characterization of EvaGreen and the implication of its physicochemical properties for qPCR applications

Background  

EvaGreen (EG) is a newly developed DNA-binding dye that has recently been used in quantitative real-time PCR (qPCR), post-PCR DNA melt curve analysis and several other applications. However, very little is known about the physicochemical properties of the dye and their relevance to the applications, particularly to qPCR and post PCR DNA melt curve analysis. In this paper, we characterized EG along with a widely used qPCR dye, SYBR Green I (SG), for their DNA-binding properties and stability, and compared their performance in qPCR under a variety of conditions.     

Erythropoietin improves operant conditioning and stability of cognitive performance in mice

Background  

Executive functions, learning and attention are imperative facets of cognitive performance, affected in many neuropsychiatric disorders. Recently, we have shown that recombinant human erythropoietin improves cognitive functions in patients with chronic schizophrenia, and that it leads in healthy mice to enhanced hippocampal long-term potentiation, an electrophysiological correlate of learning and memory. To create an experimental basis for further mechanistic insight into erythropoietin-modulated cognitive processes, we employed the Five Choice Serial Reaction Time Task. This procedure allows the study of the effects of erythropoietin on discrete processes of learning and attention in a sequential fashion.     

Baseline Correction for NMR Spectroscopic Metabolomics Data Analysis

Background  

We propose a statistically principled baseline correction method, derived from a parametric smoothing model. It uses a score function to describe the key features of baseline distortion and constructs an optimal baseline curve to maximize it. The parameters are determined automatically by using LOWESS (locally weighted scatterplot smoothing) regression to estimate the noise variance.     

Viscoelastic behaviour of human mesenchymal stem cells

Background  

In this study, we have investigated the viscoelastic behaviour of individual human adult bone marrow-derived mesenchymal stem cells (hMSCs) and the role of F-actin filaments in maintaining these properties, using micropipette aspiration technique together with a standard linear viscoelastic solid model.     

A unified approach to false discovery rate estimation

Background  

False discovery rate (FDR) methods play an important role in analyzing high-dimensional data. There are two types of FDR, tail area-based FDR and local FDR, as well as numerous statistical algorithms for estimating or controlling FDR. These differ in terms of underlying test statistics and procedures employed for statistical learning.