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Background: Psoriatic arthritis (PsA) and axial spondyloarthritis (axSpA) are inflammatory diseases with shared genetic backgrounds and clinical comorbidities. Headache, a common global health issue, affects over 50% of adults and encompasses various types, including migraine, tension-type, and cluster headaches. Migraine, the most prevalent, recurrent, and disabling type, is often associated with other medical conditions such as depression, epilepsy, and psoriasis, but little is known about the relationship between autoimmune disease and the risk of migraine., Methods: A cross-sectional study was conducted from July to November 2022, enrolling 286 participants, including 216 with PsA, 70 with axSpA, and 87 healthy controls., Results: Headache prevalence was significantly higher in the PsA (39.81%) and axSpA (45.71%) patients compared to the healthy controls. The prevalence of migraine without aura was also significantly higher in both the PsA (18.52%) and axSpA (28.57%) groups compared to the healthy controls., Conclusions: These findings underscore the high burden of headache and migraine in PsA and axSpA participants, highlighting the need for improved management and treatment strategies for these patients.

Post-bariatric hypoglycaemia (PBH) is a metabolic complication of bariatric surgery (BS), consisting of low post-prandial glucose levels in patients having undergone bariatric procedures. While BS is currently the most effective and relatively safe treatment for obesity and its complications, the development of PBH can significantly impact patients' quality of life and mental health. The diagnosis of PBH is still challenging, considering the lack of definitive and reliable diagnostic tools, and the fact that this condition is frequently asymptomatic. However, PBH's prevalence is alarming, involving up to 88% of the post-bariatric population, depending on the diagnostic tool, and this may be underestimated. Given the prevalence of obesity soaring, and an increasing number of bariatric procedures being performed, it is crucial that physicians are skilled to diagnose PBH and promptly treat patients suffering from it. While the milestone of managing this condition is nutritional therapy, growing evidence suggests that old and new pharmacological approaches may be adopted as adjunct therapies for managing this complex condition., (© 2023 The Authors. Diabetes/Metabolism Research and Reviews published by John Wiley & Sons Ltd.)

Antibodies are a class of proteins that recognize and neutralize pathogens by binding to their antigens. They are the most significant category of biopharmaceuticals for both diagnostic and therapeutic applications. Understanding how antibodies interact with their antigens plays a fundamental role in drug and vaccine design and helps to comprise the complex antigen binding mechanisms. Computational methods for predicting interaction sites of antibody-antigen are of great value due to the overall cost of experimental methods. Machine learning methods and deep learning techniques obtained promising results.In this work, we predict antibody interaction interface sites by applying HSS-PPI, a hybrid method defined to predict the interface sites of general proteins. The approach abstracts the proteins in terms of hierarchical representation and uses a graph convolutional network to classify the amino acids between interface and non-interface. Moreover, we also equipped the amino acids with different sets of physicochemical features together with structural ones to describe the residues. Analyzing the results, we observe that the structural features play a fundamental role in the amino acid descriptions. We compare the obtained performances, evaluated using standard metrics, with the ones obtained with SVM with 3D Zernike descriptors, Parapred, Paratome, and Antibody i-Patch., (© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Background: The ability to compare RNA secondary structures is important in understanding their biological function and for grouping similar organisms into families by looking at evolutionarily conserved sequences such as 16S rRNA. Most comparison methods and benchmarks in the literature focus on pseudoknot-free structures due to the difficulty of mapping pseudoknots in classical tree representations. Some approaches exist that permit to cluster pseudoknotted RNAs but there is not a general framework for evaluating their performance., Results: We introduce an evaluation framework based on a similarity/dissimilarity measure obtained by a comparison method and agglomerative clustering. Their combination automatically partition a set of molecules into groups. To illustrate the framework we define and make available a benchmark of pseudoknotted (16S and 23S) and pseudoknot-free (5S) rRNA secondary structures belonging to Archaea, Bacteria and Eukaryota. We also consider five different comparison methods from the literature that are able to manage pseudoknots. For each method we clusterize the molecules in the benchmark to obtain the taxa at the rank phylum according to the European Nucleotide Archive curated taxonomy. We compute appropriate metrics for each method and we compare their suitability to reconstruct the taxa., (© 2023. The Author(s).)

Background: Protein-protein interactions have pivotal roles in life processes, and aberrant interactions are associated with various disorders. Interaction site identification is key for understanding disease mechanisms and design new drugs. Effective and efficient computational methods for the PPI prediction are of great value due to the overall cost of experimental methods. Promising results have been obtained using machine learning methods and deep learning techniques, but their effectiveness depends on protein representation and feature selection., Results: We define a new abstraction of the protein structure, called hierarchical representations, considering and quantifying spatial and sequential neighboring among amino acids. We also investigate the effect of molecular abstractions using the Graph Convolutional Networks technique to classify amino acids as interface and no-interface ones. Our study takes into account three abstractions, hierarchical representations, contact map, and the residue sequence, and considers the eight functional classes of proteins extracted from the Protein-Protein Docking Benchmark 5.0. The performance of our method, evaluated using standard metrics, is compared to the ones obtained with some state-of-the-art protein interface predictors. The analysis of the performance values shows that our method outperforms the considered competitors when the considered molecules are structurally similar., Conclusions: The hierarchical representation can capture the structural properties that promote the interactions and can be used to represent proteins with unknown structures by codifying only their sequential neighboring. Analyzing the results, we conclude that classes should be arranged according to their architectures rather than functions., (© 2022. The Author(s).)

RNA molecules play crucial roles in various biological processes. Their three-dimensional configurations determine the functions and, in turn, influences the interaction with other molecules. RNAs and their interaction structures, the so-called RNA-RNA interactions, can be abstracted in terms of secondary structures, i.e., a list of the nucleotide bases paired by hydrogen bonding within its nucleotide sequence. Each secondary structure, in turn, can be abstracted into cores and shadows. Both are determined by collapsing nucleotides and arcs properly. We formalize all of these abstractions as arc diagrams, whose arcs determine loops. A secondary structure, represented by an arc diagram, is pseudoknot-free if its arc diagram does not present any crossing among arcs otherwise, it is said pseudoknotted. In this study, we face the problem of identifying a given structural pattern into secondary structures or the associated cores or shadow of both RNAs and RNA-RNA interactions, characterized by arbitrary pseudoknots. These abstractions are mapped into a matrix, whose elements represent the relations among loops. Therefore, we face the problem of taking advantage of matrices and submatrices. The algorithms, implemented in Python, work in polynomial time. We test our approach on a set of 16S ribosomal RNAs with inhibitors of Thermus thermophilus , and we quantify the structural effect of the inhibitors., (© 2021 Michela Quadrini, published by De Gruyter, Berlin/Boston.)

Summary: Current methods for comparing RNA secondary structures are based on tree representations and exploit edit distance or alignment algorithms. Most of them can only process structures without pseudoknots. To overcome this limitation, we introduce ASPRAlign, a Java tool that aligns particular algebraic tree representations of RNA. These trees neglect the primary sequence and can handle structures with arbitrary pseudoknots. A measure of comparison, called ASPRA distance, is computed with a worst-case time complexity of O(n2) where n is the number of nucleotides of the longer structure., Availability and Implementation: ASPRAlign is implemented in Java and source code is released under the GNU GPLv3 license. Code and documentation are freely available at https://github.com/bdslab/aspralign., Contact: luca.tesei@unicam.it., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: RNA secondary structure comparison is a fundamental task for several studies, among which are RNA structure prediction and evolution. The comparison can currently be done efficiently only for pseudoknot-free structures due to their inherent tree representation., Results: In this work, we introduce an algebraic language to represent RNA secondary structures with arbitrary pseudoknots. Each structure is associated with a unique algebraic RNA tree that is derived from a tree grammar having concatenation, nesting and crossing as operators. From an algebraic RNA tree, an abstraction is defined in which the primary structure is neglected. The resulting structural RNA tree allows us to define a new measure of similarity calculated exploiting classical tree alignment., Conclusions: The tree grammar with its operators permit to uniquely represent any RNA secondary structure as a tree. Structural RNA trees allow us to perform comparison of RNA secondary structures with arbitrary pseudoknots without taking into account the primary structure.

Background: Patients with end-stage renal disease are at increased risk for sudden cardiac death, although the utility of implantable cardioverter-defibrillators (ICDs) in these patients is unknown., Objectives: The purpose of this study was to evaluate whether end-stage renal disease is an independent risk factor for appropriate ICD therapy for ventricular tachycardia (VT) or ventricular fibrillation (VF) and to compare the long-term survival of ICD recipients with and without end-stage renal disease., Methods: A retrospective cohort study was performed on ICD recipients at a single center. The primary endpoint was first appropriate ICD therapy for VT/VF. The secondary endpoint was survival., Results: The study included 585 patients, 19 (3.2%) of whom had end-stage renal disease prior to device implantation. Average follow-up time was 2.2 +/- 2.4 years, during which time 156 patients (26.7%) received appropriate ICD therapy. End-stage renal disease was strongly associated with appropriate ICD therapy (hazard ratio 2.30, 95% confidence interval 1.17-4.54) and remained a significant predictor following adjustment for implant indication, ejection fraction, diabetes, hypertension, and beta-blocker use. Survival was significantly shorter in the end-stage renal disease patients, with a median survival time of 3.2 +/- 0.6 (SEM) years in the dialysis cohort and 7.4 +/- 0.5 (SEM) years in those without end-stage renal disease (log rank P = .009). The majority of deaths in the end-stage renal disease cohort were due to non-device-related infection., Conclusion: In this cohort, end-stage renal disease was the single greatest predictor of ICD therapies for VT/VF. The survival rate was significantly shorter than that of ICD recipients without end-stage renal disease, suggesting that comorbidities in end-stage renal disease patients meeting current implant indications may reduce the survival benefit of ICD placement in this population.