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Background: The consumption of seeds as food has become increasingly common due to their numerous health benefits. Among these, the seeds of the Zibibbo grape from Pantelleria, a native species of southern Italy, remain largely unexplored and are usually considered waste material from viticulture. Nevertheless, Zibibbo grape seeds may offer health benefits, particularly for the elderly and people with metabolic disorders, due to their potential content of beneficial compounds such as polyphenols., Methods: The Zibibbo grape seeds extract (ZSE) was characterized using UV-visible spectrophotometry and HPLC chromatography. The antioxidant activity of ZSE was measured by different colorimetric assays and Electronic Paramagnetic Resonance (EPR). Additionally, specific in vitro tests were conducted on human osteoblast cell lines (Saos-2 and MG63) aiming to evaluate the ZSE's effects on bone turnover and metabolism. Western blotting was used to assess the impact on specific proteins and pathways related to bone health., Results: The ZSE contained almost ~3 mg/mL of carbohydrates and phenolic compounds, including rutin (~6.4 ppm) and hesperidin (~44.6 ppm). The extracts exhibited an antioxidant activity greater than 90% across all tests performed. Moreover, the Zibibbo seed extracts exerted a significant proliferative effect on the Saos-2 cell human osteoblast-like cell line, also modulating the phosphorylation of specific kinases involved in cell health and metabolism., Conclusions: Zibibbo grape seeds are rich in phenolic compounds, especially flavonoids with strong antioxidant and free radical scavenging properties. ZSE demonstrated beneficial effects on bone metabolism and osteoblast proliferation, suggesting potential for counteracting osteodegenerative conditions like osteoporosis. If confirmed through further studies, Zibibbo grape seed phenolic compounds could serve as an adjunctive therapy for osteoporosis, helping to slow aging and bone degeneration.

Foods contaminants pose a challenge for food producers and consumers. Due to its spontaneous formation during heating and storage, hydroxymethylfurfural (HMF) is a prevalent contaminant in foods rich in carbohydrates and proteins. Colorimetric assays, such as the Seliwanoff test, offer a rapid and cost-effective method for HMF quantification but require careful optimization to ensure accuracy. We addressed potential interference in the Seliwanoff assay by systematically evaluating parameters like incubation time, temperature, and resorcinol or hydrochloric acid concentration, as well as the presence of interfering carbohydrates. Samples were analyzed using a UV-Vis spectrophotometer in scan mode, and data obtained were validated using HPLC, which also enabled quantification of unreacted HMF for assessing the protocol's accuracy. Incubation time and hydrochloric acid percentage positively influenced the colorimetric assay, while the opposite effect was observed with the increase in resorcinol concentration. Interference from carbohydrates was eliminated by reducing the acid content in the working reagent. HPLC analyses corroborated the spectrophotometer data and confirmed the efficacy of the proposed method. The average HMF content in balsamic vinegar samples was 1.97 ± 0.94 mg/mL. Spectrophotometric approaches demonstrated to efficiently determine HMF in complex food matrices. The HMF levels detected in balsamic vinegars significantly exceeded the maximum limits established for honey. This finding underscores the urgent need for regulations that restrict contaminant levels in various food products.

Background: Scoliosis is curvature of the spine, often found in adolescents, which can impact on quality of life. Generally, scoliosis is diagnosed by measuring the Cobb angle, which represents the gold standard for scoliosis grade quantification. Commonly, scoliosis evaluation is conducted in person by medical professionals using traditional methods (i.e., involving a scoliometer and/or X-ray radiographs). In recent years, as has happened in various medicine disciplines, it is possible also in orthopedics to observe the spread of Information and Communications Technology (ICT) solutions (i.e., software-based approaches). As an example, smartphone applications (apps) and web-based applications may help the doctors in screening and monitoring scoliosis, thereby reducing the number of in-person visits. Objectives: This paper aims to provide an overview of the main features of the most popular scoliosis ICT tools, i.e., apps and web-based applications for scoliosis diagnosis, screening, and monitoring. Several apps are assessed and compared with the aim of providing a valid starting point for doctors and patients in their choice of software-based tools. Benefits for the patients may be: reducing the number of visits to the doctor, self-monitoring of scoliosis. Benefits for the doctors may be: monitoring the scoliosis progression over time, managing several patients in a remote way, mining the data of several patients for evaluating different therapeutic or exercise prescriptions. Materials and Methods: We first propose a methodology for the evaluation of scoliosis apps in which five macro-categories are considered: (i) technological aspects (e.g., available sensors, how angles are measured); (ii) the type of measurements (e.g., Cobb angle, angle of trunk rotation, axial vertebral rotation); (iii) availability (e.g., app store and eventual fee to pay); (iv) the functions offered to the user (e.g., posture monitoring, exercise prescription); (v) overall evaluation (e.g., pros and cons, usability). Then, six apps and one web-based application are described and evaluated using this methodology. Results: The results for assessment of scoliosis apps are shown in a tabular format for ease of understanding and intuitive comparison, which can help the doctors, specialists, and families in their choice of scoliosis apps. Conclusions: The use of ICT solutions for spinal curvature assessment and monitoring brings several advantages to both patients and orthopedics specialists. Six scoliosis apps and one web-based application are evaluated, and a guideline for their selection is provided.

Network alignment is a fundamental task in network analysis. In the biological field, where the protein-protein interaction (PPI) is represented as a graph, network alignment allowed the discovery of underlying biological knowledge such as conserved evolutionary pathways and functionally conserved proteins throughout different species. A recent trend in network science concerns network embedding, i.e., the modelling of nodes in a network as a low-dimensional feature vector. In this survey, we present an overview of current PPI network embedding alignment methods, a comparison among them, and a comparison to classical PPI network alignment algorithms. The results of this comparison highlight that: (i) only five network embeddings for network alignment algorithms have been applied in the biological context, whereas the literature presents several classical network alignment algorithms; (ii) there is a need for developing an evaluation framework that may enable a unified comparison between different algorithms; (iii) the majority of the proposed algorithms perform network embedding through matrix factorization-based techniques; (iv) three out of five algorithms leverage external biological resources, while the remaining two are designed for domain agnostic network alignment and tested on PPI networks; (v) two algorithms out of three are stated to perform multi-network alignment, while the remaining perform pairwise network alignment.

Multiple myeloma (MM) is the second most frequent hematological malignancy in the world although the related pathogenesis remains unclear. Gene profiling studies, commonly carried out through next-generation sequencing (NGS) and Microarrays technologies, represent powerful tools for discovering prognostic markers in MM. NGS technologies have made great leaps forward both economically and technically gaining in popularity. As NGS techniques becomes simpler and cheaper, researchers choose NGS over microarrays for more of their genomic applications. However, Microarrays still provide significant benefits with respect to NGS. For instance, RNA-Seq requires more complex bioinformatic analysis with respect to Microarray as well as it lacks of standardized protocols for analysis. Therefore, a synergy between the two technologies may be well expected in the future. In order to take up this challenge, a valid tool for integrative analysis of MM data retrieved through NGS techniques is MMRFBiolinks, a new R package for integrating and analyzing datasets from the Multiple Myeloma Research Foundation (MMRF) CoMMpass (Clinical Outcomes in MM to Personal Assessment of Genetic Profile) study, available at MMRF Researcher Gateway (MMRF-RG), and at the National Cancer Institute Genomic Data Commons (NCI-GDC) Data Portal. Instead of developing a completely new package from scratch, we decided to leverage TC-GABiolinks, an R/Bioconductor package, because it provides some useful methods to access and analyze MMRF-CoMMpass data. An integrative analysis workflow based on the usage of MMRFBiolinks is illustrated.In particular, it leads towards a comparative analysis of RNA-Seq data stored at GDC Data Portal that allows to carry out a Kaplan Meier (KM ) Survival Analysis and an enrichment analysis for a Differential Gene Expression (DGE) gene set.Furthermore, it deals with MMRF-RG data for analyzing the correlation between canonical variants and treatment outcome as well as treatment class. In order to show the potential of the workflow, we present two case studies. The former deals with data of MM Bone Marrow sample types available at GDC Data Portal. The latter deals with MMRF-RG data for analyzing the correlation between canonical variants in a gene set obtained from the case study 1 and the treatment outcome as well as the treatment class., (© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

In order to understand the mechanisms underlying the onset and the drug responses in multiple myeloma (MM), the second most frequent hematological cancer, the use of appropriate bioinformatic tools for integrative analysis of publicly available genomic data is required. We present MMRFBiolinks, a new R package for integrating and analyzing datasets from the Multiple Myeloma Research Foundation (MMRF) CoMMpass (Clinical Outcomes in MM to Personal Assessment of Genetic Profile) study, available at MMRF Researcher Gateway (MMRF-RG), and from the National Cancer Institute Genomic Data Commons (NCI-GDC) Data Portal. The package provides several methods for integrative analysis (array-array intensity correlation, Kaplan-Meier survival analysis) and visualization (response to treatments plot) of MMRF data, for performing an easily comprehensible analysis workflow. MMRFBiolinks extends the TCGABiolinks package by providing 13 new functions to analyze MMRF-CoMMpass data: six dealing with MMRF-RG data and seven with NCI-GDC data. As validation of the tool, we present two cases studies for searching, downloading and analyzing MMRF data. The former presents a workflow for identifying genes involved in survival depending on treatment. The latter presents an analysis workflow for analyzing the Best Overall (BO) response through correlation plots between the BO Response with respect to treatments, time, duration of treatment and annotated variants, as well as through Kaplan-Meier survival curves. The case studies demonstrate how MMRFBiolinks is able of overcoming the limitations of the analysis tools available at NCI-GDC and MMRF-RG, facilitating and making more comprehensive the retrieval, downloading and analysis of MMRF data., (© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

The knowledge of genetic variants in genes involved in drug metabolism may be translated into reduction of adverse drug reactions, increase of efficacy, healthcare outcomes improvement and economic benefits. Many high-throughput tools are available for the genotyping of Single Nucleotide Polymorphisms (SNPs) known to be related to drugs and xenobiotics metabolism. DMET TM platform represents an example of SNPs panel to discover biomarkers correlated to efficacy or toxicity in common and rare diseases. The difficulty in analyzing the mole of information generated by DMET TM platform led to the development and implementation of algorithms and tools for statistical and data mining analysis. These softwares allow efficient handling of the omics data to validate the explorative SNPs identified by DMET assay and to correlate them with drug efficacy, toxicity and/or cancer susceptibility. In this review we present a suite of bioinformatic frameworks for the preprocessing and analysis of DMET-SNPs data. In particular, we introduce a workflow that uses the GenoMetric Query Language, a high-level query language specifically designed for genomics, able to query public datasets (such as ENCODE, TCGA, GENCODE annotation dataset, etc.) as well as to combine them with private datasets (e.g., output from Affymetrix® DMET TM Platform).

Background: Competing health concerns present real obstacles to people living with diabetes and other chronic diseases as well as to their primary care providers. Guideline implementation interventions rarely acknowledge this, leaving both patients and providers feeling overwhelmed by the volume of recommended actions. Interprofessional (IP) shared decision-making (SDM) with the use of decision aids may help to set treatment priorities. We developed an evidence-based SDM intervention for patients with diabetes and other conditions that was framed by the IP-SDM model and followed a user-centered approach. Our objective in the present study is to pilot an IP-SDM and goal-setting toolkit following the Knowledge-to-Action Framework to assess (1) intervention fidelity and the feasibility of conducting a larger trial and (2) impact on decisional conflict, diabetes distress, health-related quality of life and patient assessment of chronic illness care., Methods/design: A two-step, parallel-group, clustered randomized controlled trial (RCT) will be conducted, with the primary goal being to assess intervention fidelity and the feasibility of conducting a larger RCT. The first step is a provider-directed implementation only; the second (after a 6-month delay) involves both provider- and patient-directed implementation. Half of the clusters will be assigned to receive the IP-SDM toolkit, and the other will be assigned to be mailed a diabetes guidelines summary. Individual interviews with patients, their family members and health care providers will be conducted upon trial completion to explore toolkit use. A secondary purpose of this trial is to gather estimates of the toolkit's impact on decisional conflict. Secondary outcomes include diabetes distress, quality of life and chronic illness care, which will be assessed on the basis of patient-completed questionnaires of validated scales at baseline and at 6 and 12 months. Multilevel hierarchical regression models will be used to account for the clustered nature of the data., Discussion: An individualized approach to patients with multiple chronic conditions using SDM and goal setting is a desirable strategy for achieving guideline-concordant treatment in a patient-centered fashion. Our pilot trial will provide insights regarding strategies for the routine implementation of such interventions in clinical practice, and it will offer an assessment of the impact of this approach., Trial Registration: Clinicaltrials.gov Identifier: NCT02379078. Date of Registration: 11 February 2015.