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Major depressive disorder (MDD) is a recurrent episodic mood disorder that represents the third leading cause of disability worldwide. In MDD, several factors can simultaneously contribute to its development, which complicates its diagnosis. According to practical guidelines, antidepressants are the first-line treatment for moderate to severemajor depressive episodes. Traditional treatment strategies often follow a one-size-fits-all approach, resulting in suboptimal outcomes for many patients who fail to experience a response or recovery and develop the so-called "therapy-resistant depression". The high biological and clinical inter-variability within patients and the lack of robust biomarkers hinder the finding of specific therapeutic targets, contributing to the high treatment failure rates. In this frame, precision medicine, a paradigm that tailors medical interventions to individual characteristics, would help allocate themost adequate and effective treatment for each patientwhile minimizing its side effects. In particular, multi-omic studies may unveil the intricate interplays between genetic predispositions and exposure to environmental factors through the study of epigenomics, transcriptomics, proteomics, metabolomics, gut microbiomics, and immunomics. The integration of the flow of multi-omic information into molecular pathways may produce better outcomes than the current psychopharmacological approach, which targets singular molecular factors mainly related to the monoamine systems, disregarding the complex network of our organism. The concept of systembiomedicine involves the integration and analysis of enormous datasets generated with different technologies, creating a "patient fingerprint", which defines the underlying biological mechanisms of every patient. This review, centered on precision medicine, explores the integration of multi-omic approaches as clinical tools for prediction in MDD at a single-patient level. It investigates how combining the existing technologies used for diagnostic, stratification, prognostic, and treatment-response biomarkers discovery with artificial intelligence can improve the assessment and treatment of MDD. [ABSTRACT FROM AUTHOR]

The Mediterranean diet (MD) and Western diet (WD) are poles apart as dietary patterns. Despite the availability of epidemiological tools to estimate the adherence to MD, to date, there is a lack of combined scores. We developed MEDOC, a food frequency questionnaire (FFQ) designed to calculate a combined adherence score for both diets and validated it on 213 subjects. The test–retest reliability revealed all frequency questions falling within the acceptable range of 0.5 to 0.7 (Pearson correlation coefficient) in younger (<30 years old) subjects, while 1 question out of 39 fell below the range in older (>30 years old) participants. The reproducibility for portion size was less satisfying, with, respectively, 38.2% and 70.5% of questions falling below 0.5 (Cohen's Kappa index) for younger and older subjects. The good correlation (R = 0.63, p < 0.0001 for subjects younger than 30 years and R = 0.54, p < 0.0001 for subjects older than 30 years, Pearson's correlation coefficient) between the MEDOC score and the MediDietScore (MDS) confirmed the validity of the MEDOC score in identifying patients who adhere to the MD. Harnessing the capabilities of this innovative tool, we aim to broaden the existing perspective to study complex dietary patterns in nutritional epidemiology studies. [ABSTRACT FROM AUTHOR]

Multiple sclerosis (MS) is a debilitating autoimmune condition primarily affecting young adults, and its rise is evident globally. Despite this, its precise etiology remains elusive. Both genetic and environmental factors contribute to MS susceptibility; however, the link between diet and MS lacks substantial evidence due to limited large-scale studies. We exploited the UK Biobank resources to explore the nexus between diet, lifestyle, and MS risk. The dietary and lifestyle habits of MS incident cases, derived from a general food frequency questionnaire (FFQ) completed by all participants at study enrollment, were compared to those of subjects who did not develop MS during the follow-up. Our findings suggest the protective role of moderate oily fish consumption and weekly alcohol intake. Furthermore, by analyzing food intake data obtained through 24 h recall, completed by a subset of participants, we found a protective, though non-significant, trend of an increased adherence to the Mediterranean diet (MD). These findings, derived from the analysis of the UK Biobank and representing an unprecedented approach for this inquiry, warrant further exploration and integration in future research. [ABSTRACT FROM AUTHOR]

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder lacking reliable biomarkers for early diagnosis and disease progression monitoring. This study aimed to identify the novel biomarkers in plasmatic extracellular vesicles (EVs) isolated from ALS patients and healthy controls (HCs). A total of 61 ALS patients and 30 age-matched HCs were enrolled in the study and the protein content of circulating EVs was analyzed by shotgun proteomics. The study was divided into a discovery phase (involving 12 ALS and 12 HC patients) and a validation one (involving 49 ALS and 20 HC patients). In the discovery phase, more than 300 proteins were identified, with 32 proteins showing differential regulation in ALS patients compared to HCs. In the validation phase, over 400 proteins were identified, with 20 demonstrating differential regulation in ALS patients compared to HCs. Notably, seven proteins were found to be common to both phases, all of which were significantly upregulated in EVs from ALS patients. Most of them have previously been linked to ALS since they have been detected in the serum or cerebrospinal fluid of ALS patients. Among them, proteoglycan (PRG)-4, also known as lubricin, was of particular interest since it was significantly increased in ALS patients with normal cognitive and motor functions. This study highlights the significance of EVs as a promising avenue for biomarker discovery in ALS. Moreover, it sheds light on the unexpected role of PRG-4 in relation to cognitive status in ALS patients. [ABSTRACT FROM AUTHOR]

Osteopontin (OPN) is a multifunctional protein that plays diverse roles in physiological and pathological processes. It acts as a molecular bridge between cells and their extracellular environment, interacting with various cell surface receptors and participating in cellular processes such as adhesion, migration, inflammation, and signaling pathways. OPN exists in different isoforms and undergoes post-translational modifications that can alter its properties and functions. In physiological contexts, OPN contributes to tissue maintenance, wound healing, immune system regulation, and stress response mechanisms. However, it is also involved in the pathogenesis of conditions such as atherosclerosis, cancer, autoimmune disorders, chronic inflammation, and sepsis. OPN can serve as a diagnostic and prognostic biomarker for these disorders, and certain OPN gene polymorphisms are associated with disease susceptibility or progression. This Special Issue of Biomedicines explores the multifaceted roles of OPN in health and disease through eight papers, including studies on its role as a biomarker for glomerulopathies, coronary artery disease, melanoma metastasis, and dental biofilm formation. The issue also includes reviews on OPN's involvement in the central nervous system, thyroid cancer, and infectious diseases. Overall, OPN plays a central role in various biological activities and continues to be a subject of ongoing research to further understand its significance in different contexts. [Extracted from the article]

Biomaterials are extensively used as replacements for damaged tissue with bioactive glasses standing out as bone substitutes for their intrinsic osteogenic properties. However, biomaterial implantation has the following risks: the development of implant-associated infections and adverse immune responses. Thus, incorporating metallic ions with known antimicrobial properties can prevent infection, but should also modulate the immune response. Therefore, we selected silver, copper and tellurium as doping for bioactive glasses and evaluated the immunophenotype and cytokine profile of human T-cells cultured on top of these discs. Results showed that silver significantly decreased cell viability, copper increased the T helper (Th)-1 cell percentage while decreasing that of Th17, while tellurium did not affect either cell viability or immune response, as evaluated via multiparametric flow cytometry. Multiplex cytokines assay showed that IL-5 levels were decreased in the copper-doped discs, compared with its undoped control, while IL-10 tended to be lower in the doped glass, compared with the control (plastic) while undoped condition showed lower expression of IL-13 and increased MCP-1 and MIP-1β secretion. Overall, we hypothesized that the Th1/Th17 shift, and specific cytokine expression indicated that T-cells might cross-activate other cell types, potentially macrophages and eosinophils, in response to the scaffolds. [ABSTRACT FROM AUTHOR]

This editorial from the journal Biomedicines titled "Osteopontin: A Versatile Biomarker—Insights and Innovations from Three Decades of Research" discusses the significance of osteopontin (OPN) as a biomarker in different diseases and its potential as a diagnostic and prognostic tool. The editorial includes twelve papers that explore OPN's mechanisms of action in various pathological conditions, such as Hodgkin's lymphoma, multiple sclerosis, acute pancreatitis, and melanoma. It also highlights the role of OPN in chronic subdural hematoma and its potential as a therapeutic target. The article provides a comprehensive overview of the current understanding of OPN and its implications in different diseases. The authors emphasize the need for further research on OPN as a biomarker in cancer, autoimmune diseases, and inflammatory diseases. [Extracted from the article]

Proteomic analysis of extracellular vesicles presents several challenges due to the unique nature of these small membrane-bound structures. Alternative analyses could reveal outcomes hidden from standard statistics to explore and develop potential new biological hypotheses that may have been overlooked during the initial evaluation of the data. An analysis sequence focusing on deviating protein expressions from donors' primary cells was performed, leveraging machine-learning techniques to analyze small datasets, and it has been applied to evaluate extracellular vesicles' protein content gathered from mesenchymal stem cells cultured on bioactive glass discs doped or not with metal ions. The goal was to provide additional opportunities for detecting details between experimental conditions that are not entirely revealed with classic statistical inference, offering further insights regarding the experimental design and assisting the researchers in interpreting the outcomes. The methodology extracted a set of EV-related proteins whose differences between conditions could be partially explainable with statistics, suggesting the presence of other factors involved in the bioactive glasses' interactions with tissues. Outlier identification of extracellular vesicles' protein expression levels related to biomaterial preparation was instrumental in improving the interpretation of the experimental outcomes. [ABSTRACT FROM AUTHOR]