Search

Objective: To ascertain whether a poor response and adverse events (voiding dysfunction and urinary tract infection) were predictable for first time botulinum toxin-A (BTX-A) injections in a patient cohort of refractory idiopathic overactive bladder with detrusor overactivity.Methods: Patients who received BTX-A injections for the first time between the dates of March 2004-August 2017 were analyzed in this single center study. Urogenital Distress Inventory short form (UDI-6) questionnaires were collected both preinjection and postinjection prospectively. A poor response was defined as a decrease of less than 16.7 on the UDI-6 questionnaire. Additional information was gathered from patient records in a retrospective fashion. Predictors of poor response, voiding dysfunction, and UTI were analyzed with multivariate logistic regression analysis.Results: Seventy-four patients were analyzed. The only predictor of poor response was male gender (OR, 5.45; 95% CI 1.83-16.47; P = .002). Lower maximum urinary flow rates (OR, 0.91; 95% CI, 0.83-0.99; P = .023), male gender (OR, 5.14; 95% CI 1.41-18.72; P = .013), and hysterectomy in females (OR, 4.55; 95% CI, 1.09-18.87; P = .038) were predictors of clean intermittent self catheterisation (CISC). There was an increased risk of UTIs in patients who performed CISC (OR, 5.26; 95% CI 1.38-20.0; P = .015).Conclusion: Male gender was associated with a poor response to BTX-A injections and increased risk of CISC. Lower maximum urinary flow rates and women with hysterectomies were at increased risk of requiring CISC postinjection. Performing CISC was associated with increased risk of UTI. These factors could be helpful when counselling or selecting patients. [ABSTRACT FROM AUTHOR]

Brain tumor segmentation in medical imaging is a critical task for diagnosis and treatment while preserving patient data privacy and security. Traditional centralized approaches often encounter obstacles in data sharing due to privacy regulations and security concerns, hindering the development of advanced AI-based medical imaging applications. To overcome these challenges, this study proposes the utilization of federated learning. The proposed framework enables collaborative learning by training the segmentation model on distributed data from multiple medical institutions without sharing raw data. Leveraging the U-Net-based model architecture, renowned for its exceptional performance in semantic segmentation tasks, this study emphasizes the scalability of the proposed approach for large-scale deployment in medical imaging applications. The experimental results showcase the remarkable effectiveness of federated learning, significantly improving specificity to 0.96 and the dice coefficient to 0.89 with the increase in clients from 50 to 100. Furthermore, the proposed approach outperforms existing convolutional neural network (CNN)- and recurrent neural network (RNN)-based methods, achieving higher accuracy, enhanced performance, and increased efficiency. The findings of this research contribute to advancing the field of medical image segmentation while upholding data privacy and security. [ABSTRACT FROM AUTHOR]

Brain tumor segmentation from magnetic resonance imaging (MRI) scans is critical for the diagnosis, treatment planning, and monitoring of therapeutic outcomes. Thus, this research introduces a novel hybrid approach that combines handcrafted features with convolutional neural networks (CNNs) to enhance the performance of brain tumor segmentation. In this study, handcrafted features were extracted from MRI scans that included intensity-based, texture-based, and shape-based features. In parallel, a unique CNN architecture was developed and trained to detect the features from the data automatically. The proposed hybrid method was combined with the handcrafted features and the features identified by CNN in different pathways to a new CNN. In this study, the Brain Tumor Segmentation (BraTS) challenge dataset was used to measure the performance using a variety of assessment measures, for instance, segmentation accuracy, dice score, sensitivity, and specificity. The achieved results showed that our proposed approach outperformed the traditional handcrafted feature-based and individual CNN-based methods used for brain tumor segmentation. In addition, the incorporation of handcrafted features enhanced the performance of CNN, yielding a more robust and generalizable solution. This research has significant potential for real-world clinical applications where precise and efficient brain tumor segmentation is essential. Future research directions include investigating alternative feature fusion techniques and incorporating additional imaging modalities to further improve the proposed method's performance. [ABSTRACT FROM AUTHOR]

Coronavirus infection 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has affected tens of millions of people worldwide, since it was declared a pandemic by the World Health Organization (WHO) on March 11, 2020. There is an urgent need for safe and effective inhibitory vaccines to cure this pandemic. A increased amount of related research has been published. This study aimed to provide the current status of COVID-19 vaccine using bibliometric analysis. Various strategies have been designed to contain the COVID-19 pandemic. Among them, vaccine development is main agenda in spite of the unknown duration of the protection time. Various vaccines have been under clinical trials have been developing with promising results in different countries. The protective effectiveness and the short-term and long-term side effects of the vaccines are one of the major concerns. Therefore, comparing the protective efficacy and risks of vaccination is needed for the worldwide control of COVID-19 through herd immunity. Overall, all COVID-19 vaccines had a high effectiveness against the original strain and the variants of concern, and were well tolerated. BNT162b2, mRNA-1273 and Sputnik V after two doses had the highest efficacy (>90%) in preventing symptomatic cases in phase III trials. mRNA vaccines, AZD1222, and CoronaVac were effective in preventing symptomatic COVID-19 and severe infections against Alpha, Beta, Gamma or Delta variants. Regarding observational real-life data, full immunization with mRNA vaccines and AZD1222 seems to effectively prevent SARS-CoV-2 infection against the original strain and Alpha and Beta variants but with less effectiveness against the Delta strain. A decline in infection protection was observed at 6 months for BNT162b2 and AZD1222. Serious adverse event rates were rare for mRNA vaccines--anaphylaxis 2.5-4.7 cases per million doses, myocarditis 3.5 cases per million doses--and were similarly rare for all other vaccines. There are subtle differences in the mechanism by which the different vaccine products introduced within host cells to persuade immunity. Many successful vaccines of the 20 century utilized the target proteins directly such as the tetanus and pertussis vaccine. [ABSTRACT FROM AUTHOR]

Early detection of brain tumors is critical to ensure successful treatment, and medical imaging is essential in this process. However, analyzing the large amount of medical data generated from various sources such as magnetic resonance imaging (MRI) has been a challenging task. In this research, we propose a method for early brain tumor segmentation using big data analysis and patch-based convolutional neural networks (PBCNNs). We utilize BraTS 2012–2018 datasets. The data is preprocessed through various steps such as profiling, cleansing, transformation, and enrichment to enhance the quality of the data. The proposed CNN model utilizes a patch-based architecture with global and local layers that allows the model to analyze different parts of the image with varying resolutions. The architecture takes multiple input modalities, such as T1, T2, T2-c, and FLAIR, to improve the accuracy of the segmentation. The performance of the proposed model is evaluated using various metrics, such as accuracy, sensitivity, specificity, Dice similarity coefficient, precision, false positive rate, and true positive rate. Our results indicate that the proposed method outperforms the existing methods and is effective in early brain tumor segmentation. The proposed method can also assist medical professionals in making accurate and timely diagnoses, and thus improve patient outcomes, which is especially critical in the case of brain tumors. This research also emphasizes the importance of big data analysis in medical imaging research and highlights the potential of PBCNN models in this field. [ABSTRACT FROM AUTHOR]

These codes may improve population health through future interventions to alleviate health disparities and foster comprehensive care for sepsis survivors. ICD-10-CMS codes were used to identify sepsis cases using the "Angus definition" and social determinants of health (SDOH) using Z codes (Z55-Z65). [Extracted from the article]

Bone marrow transplant (BMT) is an effective surgical treatment for bone marrow-related disorders. However, several associated risk factors can impair long-term survival after BMT. Machine learning (ML) technologies have been proven useful in survival prediction of BMT receivers along with the influences that limit their resilience. In this study, an efficient classification model predicting the survival of children undergoing BMT is presented using a public dataset. Several supervised ML methods were investigated in this regard with an 80-20 train-test split ratio. To ensure prediction with minimal time and resources, only the top 11 out of the 59 dataset features were considered using Chi-square feature selection method. Furthermore, hyperparameter optimization (HPO) using the grid search cross-validation (GSCV) technique was adopted to increase the accuracy of prediction. Four experiments were conducted utilizing a combination of default and optimized hyperparameters on the original and reduced datasets. Our investigation revealed that the top 11 features of HPO had the same prediction accuracy (94.73%) as the entire dataset with default parameters, however, requiring minimal time and resources. Hence, the proposed approach may aid in the development of a computer-aided diagnostic system with satisfactory accuracy and minimal computation time by utilizing medical data records. [ABSTRACT FROM AUTHOR]

Heart failure is a chronic cardiac condition characterized by reduced supply of blood to the body due to impaired contractile properties of the muscles of the heart. Like any other cardiac disorder, heart failure is a serious ailment limiting the activities and curtailing the lifespan of the patient, most often resulting in death sooner or later. Detection of survival of patients with heart failure is the path to effective intervention and good prognosis in terms of both treatment and quality of life of the patient. Machine learning techniques can be critical in this regard since they can be used to predict the survival of patients with heart failure in advance, allowing patients to receive appropriate treatment. Hence, six supervised machine learning algorithms have been studied and applied to analyze a dataset of 299 individuals from the UCI Machine Learning Repository and predict their survivability from heart failure. Three distinct approaches have been followed using Decision Tree Classifier, Logistic Regression, Gaussian Naïve Bayes, Random Forest Classifier, K-Nearest Neighbors, and Support Vector Machine algorithms. Data scaling has been performed as a preprocessing step utilizing the standard and min–max scaling method. However, grid search cross-validation and random search cross-validation techniques have been employed to optimize the hyperparameters. Additionally, the synthetic minority oversampling technique and edited nearest neighbor (SMOTE-ENN) data resampling technique are utilized, and the performances of all the approaches have been compared extensively. The experimental results clearly indicate that Random Forest Classifier (RFC) surpasses all other approaches with a test accuracy of 90% when used in combination with SMOTE-ENN and standard scaling technique. Therefore, this comprehensive investigation portrays a vivid visualization of the applicability and compatibility of different machine learning algorithms in such an imbalanced dataset and presents the role of the SMOTE-ENN algorithm and hyperparameter optimization for enhancing the performances of the machine learning algorithms. [ABSTRACT FROM AUTHOR]

Association rule mining (ARM) is used for discovering frequent itemsets for interesting relationships of associative and correlative behaviors within the data. This gives new insights of great value, both commercial and academic. The traditional ARM techniques discover interesting association rules based on a predefined minimum support threshold. However, there is no known standard of an exact definition of minimum support and providing an inappropriate minimum support value may result in missing important rules. In addition, most of the rules discovered by these traditional ARM techniques refer to already known knowledge. To address these limitations of the minimum support threshold in ARM techniques, this study proposes an algorithm to mine interesting association rules without minimum support using predicate logic and a property of a proposed interestingness measure (g measure). The algorithm scans the database and uses g measure's property to search for interesting combinations. The selected combinations are mapped to pseudo-implications and inference rules of logic are used on the pseudo-implications to produce and validate the predicate rules. Experimental results of the proposed technique show better performance against state-of-the-art classification techniques, and reliable predicate rules are discovered based on the reliability differences of the presence and absence of the rule's consequence. [ABSTRACT FROM AUTHOR]

Background: The aim of study was to investigate differences in clinicopathological (CPC) features of colorectal cancer (CRC) patients between both genders. Setting and Design: We did a retrospective chart review of CRC patients confirmed by histopathology. Materials and Methods: 414 patients were included in study from King Abdul- Aziz medical city Jeddah. Charts of patients were reviewed; data was analyzed on SPSS version 20. Results: Majority of patients were males (58.7%) who were slightly older than females (mean age 57.7 versus 54.6 years) (p value 0.033). The most common age group was fifty to eighty years in both genders (64% patients). Abdominal pain was reported more in females (57% vs. 41.5% p value 0.002) while there was no difference in reporting bleeding per rectum between both genders (50.2% vs. 49.3% p value 0.953). History of diabetes mellitus (DM), smoking and colonic polyps) was more common in males (67 versus 34 patients), (21 versus 3 patients) and (10 versus 1 patient respectively. Patients presenting with acute surgical abdominal complications were less than 20%. Only 33 (19.2%) of females received preoperative chemotherapy vs. 80 (33%) of males (p value 0.001). There was no significant gender differences regarding site, histopathology grade and surgical treatment received. Conclusions: There are important gender differences in CPC features of CRC patients in our population. Female patients tend to be slightly younger than males and there is less association with smoking, DM and previous colonic polyps. Abdominal pain was the most common presenting symptom among females. Both genders presented at similar stage of disease. These observations should be taken in consideration in making colorectal screening guidelines and public awareness programs. [ABSTRACT FROM AUTHOR]

Helicobacter Pylori is a known causal agent of gastric malignancies and peptic ulcers. The extremophile nature of this bacterium is protecting it from designing a potent drug against it. Therefore, the use of computational approaches to design antigenic, stable and safe vaccine against this pathogen could help to control the infections associated with it. Therefore, in this study, we used multiple immunoinformatics approaches along with other computational approaches to design a multi-epitopes subunit vaccine against H. Pylori. A total of 7 CTL and 12 HTL antigenic epitopes based on c-terminal cleavage and MHC binding scores were predicted from the four selected proteins (CagA, OipA, GroEL and cagA). The predicted epitopes were joined by AYY and GPGPG linkers. Β-defensins adjuvant was added to the N-terminus of the vaccine. For validation, immunogenicity, allergenicity and physiochemical analysis were conducted. The designed vaccine is likely antigenic in nature and produced robust and substantial interactions with Toll-like receptors (TLR-2, 4, 5, and 9). The vaccine developed was also subjected to an in silico cloning and immune response prediction model, which verified its efficiency of expression and the immune system provoking response. These analyses indicate that the suggested vaccine may produce particular immune responses against H. pylori, but laboratory validation is needed to verify the safety and immunogenicity status of the suggested vaccine design. [ABSTRACT FROM AUTHOR]