To report the incidence of the reoperation surgeries of nearly all the Rigicon Infla10 implants performed since device introduction in 1/2019. Inflatable penile prosthesis has some of the highest ...survival from revision surgery of any medical device implanted in humans 1. We expand on previous Rigicon Infla10 research, adding more patients and increasing follow-up duration 2.
535 patients had Rigicon Infla10 devices implanted from 1/2019 to 8/2022. 103 surgeons from 26 centers in 15 countries participated in the study. Patient information forms were analyzed from virtually all implantations. Explantation or revision surgery for mechanical failure, infection, other medical reasons, and patient dissatisfaction were cataloged. SPSS 25.0 (IBM) was used for the statistical analysis of Kaplan Meier survival statistics.
Mean follow-up was 24.2months (7-43months). Mean patient age was 56years. Reoperation was necessary for 3.5% of subjects. Revision for mechanical failure occurred in 2.24% (12/535). The rate of explant for patient dissatisfaction was 0.56% (3/535). Revision for component out of place was 0.37% (2/535) with an infection rate and unsuccessful Peyronie's correction being 0.19% (1/535). Survival from requiring another corrective surgery at 1, 2, and 3years was 96.4%, 95.0%, and 94.0%, respectively. These initial survival rates compare favorably to devices currently available, which have been repeatedly enhanced to improve reliability.
In its first 2-3years of availability, The Rigicon Infla10 inflatable penile prosthesis shows freedom from revision comparable to existing enhanced devices that have been on the market for decades.
Testosterone plays an important role in collagen metabolism, transforming growth factor-β1 expression, and wound healing, which are all critical factors in pathogenesis of Peyronie's disease. Some ...clinical studies have suggested an association between Peyronie's disease and hypogonadism.
We sought to investigate whether baseline total testosterone levels influence response to intralesional collagenase clostridium histolyticum in Peyronie's disease.
A retrospective review of patients receiving collagenase clostridium histolyticum injections with available total testosterone levels within 1 year of initial injection was conducted at a single institution. Baseline demographics, hypogonadal status, total testosterone, number of collagenase clostridium histolyticum cycles, and pre- and post-treatment degrees of curvature were collected. Hypogonadism was defined as total testosterone <300 ng/dL.
Thirty-six men were included with mean age of 58.2 years (SD 10.4) and mean body mass index 26.8 (SD 3.2). The mean total testosterone was 459.2 ng/dL (SD 144.0), and four (11.1%) were hypogonadal. Mean pre-treatment curvature was 47.6°, and mean post-treatment curvature was 27.8°, with mean improvement of 19.9° (40.1%). Hypogonadal status was not significantly associated with more severe curvature, 46.4° among hypogonadal men as to 57.5° among eugonadal men (p = 0.32). On linear regression analysis, total testosterone did not significantly predict improvement in degrees (β = -0.02; R
= 0.06; p = 0.14) or percent (β = 0.0; R
= 0.05; p = 0.18). Improvement in neither degrees nor percent differed significantly by hypogonadal status (p = 0.41 and 0.82, respectively). The cycle number did significantly predict greater improvement in curvature on both univariate and multivariate analyses (β = 5.7; R
= 0.34; p < 0.01).
Neither total testosterone nor hypogonadism is associated with a degree of improvement after collagenase clostridium histolyticum treatment.
The coronavirus disease 2019 (COVID-19) pandemic has been spreading quickly, threatening the public health system. Consequently, positive COVID-19 cases must be rapidly detected and treated. ...Automatic detection systems are essential for controlling the COVID-19 pandemic. Molecular techniques and medical imaging scans are among the most effective approaches for detecting COVID-19. Although these approaches are crucial for controlling the COVID-19 pandemic, they have certain limitations. This study proposes an effective hybrid approach based on genomic image processing (GIP) techniques to rapidly detect COVID-19 while avoiding the limitations of traditional detection techniques, using whole and partial genome sequences of human coronavirus (HCoV) diseases. In this work, the GIP techniques convert the genome sequences of HCoVs into genomic grayscale images using a genomic image mapping technique known as the frequency chaos game representation. Then, the pre-trained convolution neural network, AlexNet, is used to extract deep features from these images using the last convolution (conv5) and second fully-connected (fc7) layers. The most significant features were obtained by removing the redundant ones using the ReliefF and least absolute shrinkage and selection operator (LASSO) algorithms. These features are then passed to two classifiers: decision trees and k-nearest neighbors (KNN). Results showed that extracting deep features from the fc7 layer, selecting the most significant features using the LASSO algorithm, and executing the classification process using the KNN classifier is the best hybrid approach. The proposed hybrid deep learning approach detected COVID-19, among other HCoV diseases, with 99.71% accuracy, 99.78% specificity, and 99.62% sensitivity.
Abstract Hepatitis C virus (HCV) is a major global health concern, affecting millions of individuals worldwide. While existing literature predominantly focuses on disease classification using ...clinical data, there exists a critical research gap concerning HCV genotyping based on genomic sequences. Accurate HCV genotyping is essential for patient management and treatment decisions. While the neural models excel at capturing complex patterns, they still face challenges, such as data scarcity, that exist a lot in computational genomics. To overcome this challenges, this paper introduces an advanced deep learning approach for HCV genotyping based on the graphical representation of nucleotide sequences that outperforms classical approaches. Notably, it is effective for both partial and complete HCV genomes and addresses challenges associated with imbalanced datasets. In this work, ten HCV genotypes: 1a, 1b, 2a, 2b, 2c, 3a, 3b, 4, 5, and 6 were used in the analysis. This study utilizes Chaos Game Representation for 2D mapping of genomic sequences, employing self-supervised learning using convolutional autoencoder for deep feature extraction, resulting in an outstanding performance for HCV genotyping compared to various machine learning and deep learning models. This baseline provides a benchmark against which the performance of the proposed approach and other models can be evaluated. The experimental results showcase a remarkable classification accuracy of over 99%, outperforming traditional deep learning models. This performance demonstrates the capability of the proposed model to accurately identify HCV genotypes in both partial and complete sequences and in dealing with data scarcity for certain genotypes. The results of the proposed model are compared to NCBI genotyping tool.
