Intelligent two-phase dual authentication framework for Internet of Medical Things.

The Internet of Medical Things (IoMT) has revolutionized healthcare by bringing real-time monitoring and data-driven treatments. Nevertheless, the security of communication between IoMT devices and servers remains a huge problem because of the inherent sensitivity of the health data and susceptibility to cyber threats. Current security solutions, including simple password-based authentication and standard Public Key Infrastructure (PKI) approaches, typically do not achieve an appropriate balance between security and low computational overhead, resulting in the possibility of performance bottlenecks and increased vulnerability to attacks.

Advanced neural network-based model for predicting court decisions on child custody.

Predicting court rulings has gained attention over the past years. The court rulings are among the most important documents in all legal systems, profoundly impacting the lives of the children in case of divorce or separation. It is evident from literature that Natural language processing (NLP) and machine learning (ML) are widely used in the prediction of court rulings.

Suggested guidelines in reporting results from mediation analysis, standardized or unstandardized?

Mediation analysis is commonly implemented in psychological, epidemiological, and social behavior studies to identify potential factors that mediate associations between exposures and physical or psychological outcomes. Various analytical tools are available to perform mediation analyses, among which Mplus is widely used due to its user-friendly interface. In practice, sumptuous results provided by Mplus, such as the estimated standardized and unstandardized effect sizes, can be difficult for researchers to choose to match their studies.

Extended dipeptide composition framework for accurate identification of anticancer peptides.

The identification of anticancer peptides (ACPs) is crucial, especially in the development of peptide-based cancer therapy. The classical models such as Split Amino Acid Composition (SAAC) and Pseudo Amino Acid Composition (PseAAC) lack the incorporation of feature representation. These advancements improve the predictive accuracy and efficiency of ACP identification.

An effective deep learning-based approach for splice site identification in gene expression.

A crucial stage in eukaryote gene expression involves mRNA splicing by a protein assembly known as the spliceosome. This step significantly contributes to generating and properly operating the ultimate gene product. Since non-coding introns disrupt eukaryotic genes, splicing entails the elimination of introns and joining exons to create a functional mRNA molecule.

DeepSplice: a deep learning approach for accurate prediction of alternative splicing events in the human genome.

Alternative splicing (AS) is a crucial process in genetic information processing that generates multiple mRNA molecules from a single gene, producing diverse proteins. Accurate prediction of AS events is essential for understanding various physiological aspects, including disease progression and prognosis. Machine learning (ML) techniques have been widely employed in bioinformatics to address this challenge.

Epigenetic Features in Newborns Associated with Preadolescence Lung Function and Asthma Acquisition during Adolescence.

Unlabelled: The association between newborn DNA methylation (DNAm) and asthma acquisition (AA) during adolescence has been suggested. Lung function (LF) has been shown to be associated with asthma risk and its severity. However, the role of LF in the associations between DNAm and AA is unclear, and it is also unknown whether the association between DNAm and AA is consistent with that between DNAm and LF.

Brain Tumor Segmentation from MRI Images Using Handcrafted Convolutional Neural Network.

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.

Deep GRU-CNN Model for COVID-19 Detection From Chest X-Rays Data.

In the current era, data is growing exponentially due to advancements in smart devices. Data scientists apply a variety of learning-based techniques to identify underlying patterns in the medical data to address various health-related issues. In this context, automated disease detection has now become a central concern in medical science.

Total Pelvic Exenteration for Locally Advanced and Recurrent Rectal Cancer: Urological Outcomes and Adverse Events.

Background: Little has been reported on urological complications of total pelvic exenteration (TPE) for locally advanced or recurrent rectal cancer.

Objective: To assess urological reconstructive outcomes and adverse events in this setting.

Design, Setting, And Participants: A total of 104 patients underwent TPE from 2004 to 2016 in this single-centre, retrospective study.

Predictors of Poor Response and Adverse Events Following Botulinum Toxin A for Refractory Idiopathic Overactive Bladder: A Systematic Review.

Context: Botulinum toxin A (BTX-A) injections are effective in managing refractory overactive bladder (OAB). However, some patients exhibit a poor response and/or experience adverse events (AEs) such as voiding dysfunction necessitating clean intermittent self-catheterisation (CISC) and urinary tract infections (UTIs).

Objective: To systematically evaluate whether poor response/AEs to BTX-A for idiopathic OAB are predictable.