Outcomes of Cystectomy with MAINZ Pouch II and Epispadias Repair in Exstrophy Epispadias Complex in Adults: A Single-centre Experience from Pakistan.

OBJECTIVE: To evaluate surgical outcomes and renal functions after cystectomy + MAINZ Pouch II and epispadias repair as a staged procedure in adult patients with exstrophy epispadias complex (EEC). STUDY DESIGN: Descriptive study. PLACE AND DURATION OF STUDY: Department of Urology, Sindh Institute of Urology and Transplantation (SIUT), Karachi, from January 2004 to December 2020. METHODOLOGY: A total of 33 patients with EEC were treated. Out of which, 20 underwent cystectomy + MAINZ Pouch II with epispadias repair as a staged procedure. Out of these, 17 had a follow-up period of more than a year and were included in the study. The patients were followed up after 6 weeks of surgery, at 6 months, one year, and at the end of follow-up. The assessed variables included the patients' renal function tests, malignancy potential, morning erections, ejaculations, night emissions, day-and-night-time urinary frequency, and overall happiness of patients at the end of follow-up. RESULTS: The mean age was 25.1 ± 7.5 years and mean follow-up duration was 7.8 ± 5.2 years. Postoperatively, there was a rise in blood urea from 27.8 to 35.08 mg/dl with a concurrent fall in serum bicarbonate from a mean of 23.5 to 20.2 mEq/dl. All patients were continent during the day-time postoperatively; whereas, two patients experienced nocturnal enuresis. All male patients exhibited good erections and ejaculations, but there was persistence of dorsal chordae in 4 (23.3%) patients. All were delighted, happy and pleased with the surgical outcomes and had returned to normal life. No rectal or sigmoid malignancy was observed. CONCLUSION: In adult EEC patients, cystectomy + MAINZ Pouch II and epispadias repair is safe and effective. Key Words: Adults, Exstrophy epispadias complex, MAINZ pouch II, Pakistan.

Outcomes of Transurethral Resection of Prostate in Patients with and without Renal Failure Secondary to an Enlarged Prostate.

The natural history of benign enlargement of the prostate is variable and ranges from mild symptoms to chronic retention and renal failure. In this study, the outcomes of patients with urinary retention alone were compared with those of chronic retention and renal failure caused by an enlarged prostate. The first group had 79, while the second group had 20 patients included. The mean maximum flow rate after transurethral resection of the prostate (TURP) in the two groups was 16.9 ± 7.9 vs. 14.6 ± 4.1 ml/sec (p value > 0.05), and the mean post-void volume was 15.1 ± 27.6 vs. 21.7 ± 35.7 ml (p value > 0.05), respectively. However, the residual symptoms after surgery were higher in the chronic retention group. It was concluded that patients, with chronic retention experience and higher postoperative residual storage symptoms, after transurethral resection of the prostate, are able to void without a catheter and their renal functions were stabilised. Key Words: Transurethral resection of the prostate, Prostatic hyperplasia, Renal insufficiency, Urinary bladder neck obstruction.

Phonocardiogram signal analysis for classification of Coronary Artery Diseases using MFCC and 1D adaptive local ternary patterns.

Coronary Artery Diseases (CADs) are a dominant cause of worldwide fatalities. The development of accurate and timely diagnosis routines is imperative to reduce these risks and mortalities. Coronary angiography, an invasive and expensive technique, is currently used as a diagnostic tool for the detection of CAD but it has some procedural hazards, i.e., it requires arterial puncture, and the subject gets exposed to iodinated radiation. Phonocardiography (PCG), a non-invasive and inexpensive technique, is a modality employing heart sounds to diagnose heart diseases but it requires only trained medical personnel to apprehend cardiac murmurs in clinical environments. Furthermore, there is a strong compulsion to characterize CAD into its types, such as Single vessel coronary artery disease (SVCAD), Double vessel coronary artery disease (DVCAD), and Triple vessel coronary artery disease (TVCAD) to assist the cardiologist in decision making about the treatment procedure followed. This paper presents a computer-aided diagnosis system for the categorization of CAD and its types based on Phonocardiogram (PCG) signal analysis. The raw PCG signals were denoised via empirical mode decomposition (EMD) to remove redundant information and noise. Next, we extract MFCC and proposed 1D-Adaptive Local Ternary Patterns (1D-ALTP) and fused them serially to get a strong feature representation of multiple PCG signal classes. Features were further reduced through Multidimensional Scaling (MDS) and subjected to several classification methods such as support vector machines (SVM), Decision Tree (DT), and K-nearest neighbors (KNN) in a comparative fashion. The best classification performances of 98.3% and 97.2% mean accuracies were obtained through SVM with the cubic kernel for binary and multiclass experiments, respectively. The performance of the proposed system is comprehensively tested through 10-fold cross-validation and hold-out train-test techniques to avoid model overfitting. Comparative analysis with existing approaches advocates the superiority of the proposed approach.

HCF-CRS: A Hybrid Content based Fuzzy Conformal Recommender System for providing recommendations with confidence.

A Recommender System (RS) is an intelligent system that assists users in finding the items of their interest (e.g. books, movies, music) by preventing them to go through huge piles of data available online. In an effort to overcome the data sparsity issue in recommender systems, this research incorporates a content based filtering technique with fuzzy inference system and a conformal prediction approach introducing a new framework called Hybrid Content based Fuzzy Conformal Recommender System (HCF-CRS). The proposed framework is implemented to be used in the domain of movies and it provides quality recommendations to users with a confidence level and an improved accuracy. In our proposed framework, first, a Content Based Filtering (CBF) technique is applied to create a user profile by considering the history of each user. CBF is useful in the situations like: lack of demographic information and the data sparsity problems. Second, a Fuzzy based technique is incorporated to find the similarities and differences between the user profile and the movies in the dataset using a set of fuzzy rules to get a predicted rating for each movie. Third, a Conformal prediction algorithm is implemented to calculate the non-conformity measure between the predicted ratings produced by fuzzy system and the actual ratings from the dataset. A p-value (confidence measure) is computed to give a level of confidence to each recommended item and a bound is set on the confidence level called a significance level ε, according to which the movies only above the specified significance level are recommended to user. By building a confidence centric hybrid conformal recommender system using the content based filtering approach with fuzzy logic and conformal prediction algorithm, the reliability and the accuracy of the system is considerably enhanced. The experiments are evaluated on MovieLens and Movie Tweetings datasets for recommending movies to the users and they are compared with other state-of-the-art recommender systems. Finally, the results confirm that the proposed algorithms perform better than the traditional ones.

IntelliHealth: A medical decision support application using a novel weighted multi-layer classifier ensemble framework.

Accuracy plays a vital role in the medical field as it concerns with the life of an individual. Extensive research has been conducted on disease classification and prediction using machine learning techniques. However, there is no agreement on which classifier produces the best results. A specific classifier may be better than others for a specific dataset, but another classifier could perform better for some other dataset. Ensemble of classifiers has been proved to be an effective way to improve classification accuracy. In this research we present an ensemble framework with multi-layer classification using enhanced bagging and optimized weighting. The proposed model called "HM-BagMoov" overcomes the limitations of conventional performance bottlenecks by utilizing an ensemble of seven heterogeneous classifiers. The framework is evaluated on five different heart disease datasets, four breast cancer datasets, two diabetes datasets, two liver disease datasets and one hepatitis dataset obtained from public repositories. The analysis of the results show that ensemble framework achieved the highest accuracy, sensitivity and F-Measure when compared with individual classifiers for all the diseases. In addition to this, the ensemble framework also achieved the highest accuracy when compared with the state of the art techniques. An application named "IntelliHealth" is also developed based on proposed model that may be used by hospitals/doctors for diagnostic advice.

Barcoding of fresh water fishes from Pakistan.

DNA bar-coding is a taxonomic method that uses small genetic markers in organisms' mitochondrial DNA (mt DNA) for identification of particular species. It uses sequence diversity in a 658-base pair fragment near the 5' end of the mitochondrial cytochrome c oxidase subunit 1 (CO1) gene as a tool for species identification. DNA barcoding is more accurate and reliable method as compared with the morphological identification. It is equally useful in juveniles as well as adult stages of fishes. The present study was conducted to identify three farm fish species of Pakistan (Cyprinus carpio, Cirrhinus mrigala, and Ctenopharyngodon idella) genetically. All of them belonged to family cyprinidae. CO1 gene was amplified. PCR products were sequenced and analyzed by bioinformatic software. Conspecific, congenric, and confamilial k2P nucleotide divergence was estimated. From these findings, it was concluded that the gene sequence, CO1, may serve as milestone for the identification of related species at molecular level.

A Relation Extraction Framework for Biomedical Text Using Hybrid Feature Set.

The information extraction from unstructured text segments is a complex task. Although manual information extraction often produces the best results, it is harder to manage biomedical data extraction manually because of the exponential increase in data size. Thus, there is a need for automatic tools and techniques for information extraction in biomedical text mining. Relation extraction is a significant area under biomedical information extraction that has gained much importance in the last two decades. A lot of work has been done on biomedical relation extraction focusing on rule-based and machine learning techniques. In the last decade, the focus has changed to hybrid approaches showing better results. This research presents a hybrid feature set for classification of relations between biomedical entities. The main contribution of this research is done in the semantic feature set where verb phrases are ranked using Unified Medical Language System (UMLS) and a ranking algorithm. Support Vector Machine and Naïve Bayes, the two effective machine learning techniques, are used to classify these relations. Our approach has been validated on the standard biomedical text corpus obtained from MEDLINE 2001. Conclusively, it can be articulated that our framework outperforms all state-of-the-art approaches used for relation extraction on the same corpus.

BagMOOV: A novel ensemble for heart disease prediction bootstrap aggregation with multi-objective optimized voting.

Conventional clinical decision support systems are based on individual classifiers or simple combination of these classifiers which tend to show moderate performance. This research paper presents a novel classifier ensemble framework based on enhanced bagging approach with multi-objective weighted voting scheme for prediction and analysis of heart disease. The proposed model overcomes the limitations of conventional performance by utilizing an ensemble of five heterogeneous classifiers: Naïve Bayes, linear regression, quadratic discriminant analysis, instance based learner and support vector machines. Five different datasets are used for experimentation, evaluation and validation. The datasets are obtained from publicly available data repositories. Effectiveness of the proposed ensemble is investigated by comparison of results with several classifiers. Prediction results of the proposed ensemble model are assessed by ten fold cross validation and ANOVA statistics. The experimental evaluation shows that the proposed framework deals with all type of attributes and achieved high diagnosis accuracy of 84.16 %, 93.29 % sensitivity, 96.70 % specificity, and 82.15 % f-measure. The f-ratio higher than f-critical and p value less than 0.05 for 95 % confidence interval indicate that the results are extremely statistically significant for most of the datasets.

Global optimization ensemble model for classification methods.

Supervised learning is the process of data mining for deducing rules from training datasets. A broad array of supervised learning algorithms exists, every one of them with its own advantages and drawbacks. There are some basic issues that affect the accuracy of classifier while solving a supervised learning problem, like bias-variance tradeoff, dimensionality of input space, and noise in the input data space. All these problems affect the accuracy of classifier and are the reason that there is no global optimal method for classification. There is not any generalized improvement method that can increase the accuracy of any classifier while addressing all the problems stated above. This paper proposes a global optimization ensemble model for classification methods (GMC) that can improve the overall accuracy for supervised learning problems. The experimental results on various public datasets showed that the proposed model improved the accuracy of the classification models from 1% to 30% depending upon the algorithm complexity.