Sardinian Infants of Diabetic Mothers: A Metabolomics Observational Study.

Gestational diabetes mellitus (GDM) is a condition characterized by glucose intolerance, with hyperglycemia of varying severity with onset during pregnancy. An uncontrolled GDM can lead to an increased risk of morbidity in the fetus and newborn, and an increased risk of obesity or developing type 2 diabetes, hypertension or neurocognitive developmental impairment in adulthood. In this study, we used nuclear magnetic resonance (NMR) spectroscopy and gas chromatography-mass spectrometry (GS-MS) to analyze the urinary metabolomic profile of newborns of diabetic mothers (NDMs) with the aim of identifying biomarkers useful for the monitoring of NDMs and for early diagnosis of predisposition to develop related chronic diseases. A total of 26 newborns were recruited: 21 children of diabetic mothers, comprising 13 in diet therapy (NDM-diet) and 8 in insulin therapy (NDM-insulin), and 5 control children of non-diabetic mothers (CTR). Urine samples were collected at five time points: at birth (T1), on the third day of life (T2), one week (T3), one month (T4) and six months postpartum (T5). At T1, variations were observed in the levels of seven potential biomarkers (acetate, lactate, glycylproline/proline, isocitrate, N,N-dimethylglycine, N-acetylglucosamine and N-carbamoyl-aspartate) in NMD-insulin infants compared to NDM-diet and CTR infants. In particular, the altered metabolites were found to be involved in several metabolic pathways such as citrate metabolism, glycine, serine and threonine metabolism, arginine and proline metabolism, amino sugar and nucleotide sugar metabolism, and pyruvate metabolism. In contrast, these changes were not visible at subsequent sampling times. The impact of early nutrition (maternal and formula milk) on the metabolomic profile was considered as a potential contributing factor to this finding.

Beep4Me: Automatic Ticket Validation to Support Fare Clearing and Service Planning.

An integrated transport service fare system, supported by an agreement for ticket revenue sharing among service providers, is an essential component to improve the experience of the users who can find single tickets for the integrated transport services they look for. A challenge is to find a model to share the revenue which all providers agree on. A solution is to adopt data-driven approaches where user-generated data are collected to extract information on the extent each transport service was used. This is consistently used. However, it suffers from incomplete data, as not all users always validate their ticket when checking out or when switching lines. We studied all technologies available to support automatic ticket validation in order to record when the users access and exit each service line. The contributions of this work are the following: we give an in-depth description of the inner workings of this novel approach describing how we take advantage of each technology; we present the developed solution (Beep4Me), which adds new functionalities to an existing mobile ticketing platform; and we describe our testing framework, which includes most cases users might encounter during a trip. Our results demonstrate how it is possible to collect key data related to validations which can be used first for clearing purposes and then for network planning/fleet optimization.

Gut microbiota and metabolome distinctive features in Parkinson disease: Focus on levodopa and levodopa-carbidopa intrajejunal gel.

BACKGROUND AND PURPOSE: Recent data suggest that imbalances in the composition of the gut microbiota (GM) could exacerbate the progression of Parkinson disease (PD). The effects of levodopa (LD) have been poorly assessed, and those of LD-carbidopa intestinal gel (LCIG) have not been evaluated so far. The aim of this study was to identify the effect of LD and LCIG, in particular, on the GM and metabolome. METHODS: Fecal DNA samples from 107 patients with a clinical diagnosis of PD were analyzed by next-generation sequencing of the V3 and V4 regions of the 16S rRNA gene. PD patients were classified in different groups: patients on LCIG (LCIG group, n = 38) and on LD (LD group, n = 46). We also included a group of patients (n = 23) without antiparkinsonian medicaments (Naïve group). Fecal metabolic extracts were evaluated by gas chromatography mass spectrometry. RESULTS: The multivariate analysis showed a significantly higher abundance in the LCIG group of Enterobacteriaceae, Escherichia, and Serratia compared to the LD group. Compared to the Naïve group, the univariate analysis showed a reduction of Blautia and Lachnospirae in the LD group. Moreover, an increase of Proteobacteria, Enterobacteriaceae, and a reduction of Firmicutes, Lachnospiraceae, and Blautia was found in the LCIG group. No significant difference was found in the multivariate analysis of these comparisons. The LD group and LCIG group were associated with a metabolic profile linked to gut inflammation. CONCLUSIONS: Our results suggest that LD, and mostly LCIG, might significantly influence the microbiota composition and host/bacteria metabolism, acting as stressors in precipitating a specific inflammatory intestinal microenvironment, potentially related to the PD state and progression.

The Potential of Metabolomics in the Diagnosis of Thyroid Cancer.

Thyroid cancer is the most common endocrine system malignancy. However, there is still a lack of reliable and specific markers for the detection and staging of this disease. Fine needle aspiration biopsy is the current gold standard for diagnosis of thyroid cancer, but drawbacks to this technique include indeterminate results or an inability to discriminate different carcinomas, thereby requiring additional surgical procedures to obtain a final diagnosis. It is, therefore, necessary to seek more reliable markers to complement and improve current methods. "Omics" approaches have gained much attention in the last decade in the field of biomarker discovery for diagnostic and prognostic characterisation of various pathophysiological conditions. Metabolomics, in particular, has the potential to identify molecular markers of thyroid cancer and identify novel metabolic profiles of the disease, which can, in turn, help in the classification of pathological conditions and lead to a more personalised therapy, assisting in the diagnosis and in the prediction of cancer behaviour. This review considers the current results in thyroid cancer biomarker research with a focus on metabolomics.

Metabolomics Fingerprint Induced by the Intranigral Inoculation of Exogenous Human Alpha-Synuclein Oligomers in a Rat Model of Parkinson's Disease.

Parkinson's disease (PD) is considered a synucleinopathy because of the intraneuronal accumulation of aggregated α-synuclein (αSyn). Recent evidence points to soluble αSyn-oligomers (αSynO) as the main cytotoxic species responsible for cell death. Given the pivotal role of αSyn in PD, αSyn-based models are crucial for the investigation of toxic mechanisms and the identification of new therapeutic targets in PD. By using a metabolomics approach, we evaluated the metabolic profile of brain and serum samples of rats infused unilaterally with preformed human αSynOs (HαSynOs), or vehicle, into the substantia nigra pars compacta (SNpc). Three months postinfusion, the striatum was dissected for striatal dopamine (DA) measurements via High Pressure Liquid Chromatography (HPLC) analysis and mesencephalon and serum samples were collected for the evaluation of metabolite content via gas chromatography mass spectrometry analysis. Multivariate, univariate and correlation statistics were applied. A 40% decrease of DA content was measured in the HαSynO-infused striatum as compared to the contralateral and the vehicle-infused striata. Decreased levels of dehydroascorbic acid, myo-inositol, and glycine, and increased levels of threonine, were found in the mesencephalon, while increased contents of fructose and mannose, and a decrease in glycine and urea, were found in the serum of HαSynO-infused rats. The significant correlation between DA and metabolite content indicated that metabolic variations reflected the nigrostriatal degeneration. Collectively, the metabolomic fingerprint of HαSynO-infused rats points to an increase of oxidative stress markers, in line with PD neuropathology, and provides hints for potential biomarkers of PD.

Metabolic fingerprinting of chorionic villous samples in normal pregnancy and chromosomal disorders.

OBJECTIVE: Placenta-related biological samples are used in biomedical research to investigate placental development. Metabolomics represents a promising approach for studying placental metabolism in an effort to explain physiological and pathological mechanisms. The aim of this study was to investigate metabolic changes in chorionic villi during the first trimester of pregnancy in euploid and aneuploid cases. METHODS: Samples from 21 women (13 euploid and eight aneuploid) were analyzed with 1 H-nuclear magnetic resonance (NMR), gas chromatography-mass spectrometry (GC-MS), and high-performance liquid chromatography (HPLC). Multivariate statistical analysis was performed, and differences in metabolites were used to identify the altered metabolic pathways. RESULTS: A regression model to test the correlation between fetal crown-rump length (CRL) and metabolic profile of chorionic villi was performed in euploid pregnancies (R2 was 0.69 for the NMR analysis and 0.94 for the GC-MS analysis). Supervised analysis was used to compare chorionic villi of euploid and aneuploid fetuses (NMR: R2 X = 0.70, R2 Y = 0.65, Q2  = 0.30, R2 X = 0.62; GC-MS: R2 Y = 0.704, Q2  = 0.444). Polyol pathways, myo-inositol, and oxidative stress seem to have a fundamental role in euploid and aneuploid pregnancies. CONCLUSION: Polyol pathways may have a crucial role in energy production in early pregnancy. Excessive activation in aneuploid pregnancies may lead to increased oxidative stress. Metabolomics represents a promising approach to investigate placental metabolic changes.

Metabolic characterization of amniotic fluids of fetuses with enlarged nuchal translucency.

Background In prenatal diagnosis, a thickened nuchal translucency (NT) is one of the most sensitive and specific markers for several defects but it may also be found in 5% of healthy fetuses. The pathophysiological causes that lead to an increase in NT are not yet fully understood. Metabolomics represents a new promising approach, useful for studying different metabolites in biological organisms in response to environmental stressors. The aim of our study was to investigate the metabolomic profile of the amniotic fluid samples (AFS) of euploid fetuses with enlarged nuchal translucency (ENT) compared to a control group (C group). Methods This study was carried out on a group of women who underwent second-trimester amniocentesis for advanced maternal age (C group) or for NT ≥95th percentile (ENT group) found during first-trimester aneuploidy screening. AFS were analyzed with proton nuclear magnetic resonance (1H-NMR) and high-performance liquid chromatography (HPLC), and subsequent multivariate and univariate statistical analyses were conducted, followed by pathway analysis. Results In total, 67 AFS from the C group and 23 from the ENT group were analyzed. Partial least square discriminate analysis was carried out (R2X=0.784, R2Y=0.658, Q2=0.622, P<0.0001). A different metabolic profile was observed in the ENT group compared with the C group, suggesting an energetic shift to a glycolytic phenotype in an oxidative environment in the ENT group compared to the C group. Conclusion Metabolomic studies enable the identification of metabolic alterations occurring in fetuses with ENT. These findings may provide a new basis for better understanding the pathophysiological mechanisms in this prenatal phenomenon.

Virtual User in the IoT: Definition, Technologies and Experiments.

Virtualization technologies are characterizing major advancements in the Internet of Things (IoT) arena, as they allow for achieving a cyber-physical world where everything can be found, activated, probed, interconnected, and updated at both the virtual and the physical levels. We believe these technologies should apply to human users other than things, bringing us the concept of the Virtual User (VU). This should represent the virtual counterpart of the IoT users with the ultimate goal of: (i) avoiding the user from having the burden of following the tedious processes of setting, configuring and updating IoT services the user is involved in; (ii) acting on behalf of the user when basic operations are required; (iii) exploiting to the best of its ability the IoT potentialities, always taking always account the user profile and interests. Accordingly, the VU is a complex representation of the user and acts as a proxy in between the virtual objects and IoT services and application; to this, it includes the following major functionalities: user profiling, authorization management, quality of experience modeling and management, social networking and context management. In this respect, the major contributions of this paper are to: provide the definition of VU, present the major functionalities, discuss the legal issues related to its introduction, provide some implementation details, and analyze key performance aspects in terms of the capability of the VU to correctly identify the user profile and context.

Italian cohort of patients affected by inflammatory bowel disease is characterised by variation in glycerophospholipid, free fatty acids and amino acid levels.

BACKGROUND: Inflammatory bowel disease is a group of pathologies characterised by chronic inflammation of the intestine and an unclear aetiology. Its main manifestations are Crohn's disease and ulcerative colitis. Currently, biopsies are the most used diagnostic tests for these diseases and metabolomics could represent a less invasive approach to identify biomarkers of disease presence and progression. OBJECTIVES: The lipid and the polar metabolite profile of plasma samples of patients affected by inflammatory bowel disease have been compared with healthy individuals with the aim to find their metabolomic differences. Also, a selected sub-set of samples was analysed following solid phase extraction to further characterise differences between pathological samples. METHODS: A total of 200 plasma samples were analysed using drift tube ion mobility coupled with time of flight mass spectrometry and liquid chromatography for the lipid metabolite profile analysis, while liquid chromatography coupled with triple quadrupole mass spectrometry was used for the polar metabolite profile analysis. RESULTS: Variations in the lipid profile between inflammatory bowel disease and healthy individuals were highlighted. Phosphatidylcholines, lyso-phosphatidylcholines and fatty acids were significantly changed among pathological samples suggesting changes in phospholipase A2 and arachidonic acid metabolic pathways. Variations in the levels of cholesteryl esters and glycerophospholipids were also found. Furthermore, a decrease in amino acids levels suggests mucosal damage in inflammatory bowel disease. CONCLUSIONS: Given good statistical results and predictive power of the model produced in our study, metabolomics can be considered as a valid tool to investigate inflammatory bowel disease.

Metabolomic Alterations in Thyrospheres and Adherent Parental Cells in Papillary Thyroid Carcinoma Cell Lines: A Pilot Study.

Papillary thyroid carcinoma (PTC), is characterized by a heterogeneous group of cells, including cancer stem cells (CSCs), crucially involved in tumor initiation, progression and recurrence. CSCs appear to have a distinct metabolic phenotype, compared to non-stem cancer cells. How they adapt their metabolism to the cancer process is still unclear, and no data are yet available for PTC. We recently isolated thyrospheres, containing cancer stem-like cells, from B-CPAP and TPC-1 cell lines derived from PTC of the BRAF-like expression profile class, and stem-like cells from Nthy-ori3-1 normal thyreocyte-derived cell line. In the present study, gas chromatography/mass spectrometry metabolomic profiles of cancer thyrospheres were compared to cancer parental adherent cells and to non cancer thyrospheres profiles. A statistically significant decrease of glycolytic pathway metabolites and variations in Krebs cycle metabolites was found in thyrospheres versus parental cells. Moreover, cancer stem-like cells showed statistically significant differences in Krebs cycle intermediates, amino acids, cholesterol, and fatty acids content, compared to non-cancer stem-like cells. For the first time, data are reported on the metabolic profile of PTC cancer stem-like cells and confirm that changes in metabolic pathways can be explored as new biomarkers and targets for therapy in this tumor.

Blood metabolomic fingerprint is distinct in healthy coronary and in stenosing or microvascular ischemic heart disease.

BACKGROUND: The endothelium is a key variable in the pathogenesis of atherosclerosis and its complications, particularly coronary artery disease (CAD). Current evidence suggests that the endothelial status can be regarded as an integrated index of individual atherogenic and anti-atherogenic properties, and that the interaction between circulating factors and the arterial wall might be critical for atherogenesis. In organism-level investigations, a functional view is provided by metabolomics, the study of the metabolic profile of small molecules. We sought to verify whether metabolomic analysis can reveal the presence of coronary microenvironment peculiarities associated with distinct manifestations of CAD. METHODS: Thirty-two coronary blood samples were analyzed using 1H-NMR-based metabolomics. Samples collected from patients with evidence of myocardial ischemia formed the case group, and were further divided into the stenotic-disease (SD) group (N = 13) and absence of stenosis (microvascular disease; "Micro") group (N = 8); specimens of patients presenting no evidence of ischemic heart disease (dilated cardiomyopathy, valvular diseases) constituted the control group (N = 11). RESULTS: Application of an orthogonal partial least squares discriminant analysis (OPLS-DA) model to the entire dataset clearly separated the samples into 3 groups, indicating 3 distinct metabolic fingerprints. Relative to control-group members, Micro patients showed a higher content of 2-hydroxybutirate, alanine, leucine, isoleucine, and N-acetyl groups and lower levels of creatine/phosphocreatine, creatinine, and glucose, whereas SD patients showed higher levels of 3-hydroxybutirate and acetate and a lower content of 2-hydroxybutirate. Moreover, relative to SD patients, Micro patients showed higher levels of 2-hydroxybutirate, alanine, leucine, and N-acetyl groups and lower levels of 3-hydroxybutirate and acetate. CONCLUSIONS: Specific coronary microenvironments are likely associated with distinct development and pathological expression of CAD.

1H NMR spectroscopy-based metabolomics analysis for the diagnosis of symptomatic E. coli-associated urinary tract infection (UTI).

BACKGROUND: Urinary tract infection (UTI) is one of the most common diagnoses in girls and women, and to a lesser extent in boys and men younger than 50 years. Escherichia coli, followed by Klebsiella spp. and Proteus spp., cause 75-90% of all infections. Infection of the urinary tract is identified by growth of a significant number of a single species in the urine, in the presence of symptoms. Urinary culture is an accurate diagnostic method but takes several hours or days to be carried out. Metabolomics analysis aims to identify biomarkers that are capable of speeding up diagnosis. METHODS: Urine samples from 51 patients with a prior diagnosis of Escherichia coli-associated UTI, from 21 patients with UTI caused by other pathogens (bacteria and fungi), and from 61 healthy controls were analyzed. The 1H-NMR spectra were acquired and processed. Multivariate statistical models were applied and their performance was validated using permutation test and ROC curve. RESULTS: Orthogonal Partial Least Squares-discriminant Analysis (OPLS-DA) showed good separation (R2Y = 0.76, Q2=0.45, p < 0.001) between UTI caused by Escherichia coli and healthy controls. Acetate and trimethylamine were identified as discriminant metabolites. The concentrations of both metabolites were calculated and used to build the ROC curves. The discriminant metabolites identified were also evaluated in urine samples from patients with other pathogens infections to test their specificity. CONCLUSIONS: Acetate and trimethylamine were identified as optimal candidates for biomarkers for UTI diagnosis. The conclusions support the possibility of a fast diagnostic test for Escherichia coli-associated UTI using acetate and trimethylamine concentrations.

Dynamic Involvement of Real World Objects in the IoT: A Consensus-Based Cooperation Approach.

A significant role in the Internet of Things (IoT) will be taken by mobile and low-cost unstable devices, which autonomously self-organize and introduce highly dynamic and heterogeneous scenarios for the deployment of distributed applications. This entails the devices to cooperate to dynamically find the suitable combination of their involvement so as to improve the system reliability while following the changes in their status. Focusing on the above scenario, we propose a distributed algorithm for resources allocation that is run by devices that can perform the same task required by the applications, allowing for a flexible and dynamic binding of the requested services with the physical IoT devices. It is based on a consensus approach, which maximizes the lifetime of groups of nodes involved and ensures the fulfillment of the requested Quality of Information (QoI) requirements. Experiments have been conducted with real devices, showing an improvement of device lifetime of more than 20 % , with respect to a uniform distribution of tasks.

Statistical Health Monitoring Applied to a Metabolomic Study of Experimental Hepatocarcinogenesis: An Alternative Approach to Supervised Methods for the Identification of False Positives.

In a typical metabolomics experiment, two or more conditions (e.g., treated versus untreated) are compared, in order to investigate the potential differences in the metabolic profiles. When dealing with complex biological systems, a two-class classification is often unsuitable, since it does not consider the unpredictable differences between samples (e.g., nonresponder to treatment). An approach based on statistical process control (SPC), which is able to monitor the response to a treatment or the development of a pathological condition, is proposed here. Such an approach has been applied to an experimental hepatocarcinogenesis model to discover early individual metabolic variations associated with a different response to the treatment. Liver study was performed by nuclear magnetic resonance (NMR) spectroscopy, followed by multivariate statistical analysis. By this approach, we were able to (1) identify which treated samples have a significantly different metabolic profile, compared to the control (in fact, as confirmed by immunohistochemistry, the method correctly classified 7 responders and 3 nonresponders among the 10 treated animals); (2) recognize, for each individual sample, the metabolites that are out of control (e.g., glutathione, acetate, betaine, and phosphocholine). The first point could be used for classification purposes, and the second point could be used for a better understanding of the mechanisms underlying the early phase of carcinogenesis. The statistical control approach can be used for diagnosis (e.g., healthy versus pathological, responder versus nonresponder) and for generation of an individual metabolic profile, leading to a better understanding of the individual pathological processes and to a personalized diagnosis and therapy.

Managing the Quality of Experience in the Multimedia Internet of Things: A Layered-Based Approach.

This paper addresses the issue of evaluating the Quality of Experience (QoE) for Internet of Things (IoT) applications, with particular attention to the case where multimedia content is involved. A layered IoT architecture is firstly analyzed to understand which QoE influence factors have to be considered in relevant application scenarios. We then introduce the concept of Multimedia IoT (MIoT) and define a layered QoE model aimed at evaluating and combining the contributions of each influence factor to estimate the overall QoE in MIoT applications. Finally, we present a use case related to the remote monitoring of vehicles during driving practices, which is used to validate the proposed layered model, and we discuss a second use case for smart surveillance, to emphasize the generality of the proposed framework. The effectiveness in evaluating classes of influence factors separately is demonstrated.

Metabolomic approach to profile functional and metabolic changes in heart failure.

BACKGROUND: Heart failure (HF) is characterized by a series of adaptive changes in energy metabolism. The use of metabolomics enables the parallel assessment of a wide range of metabolites. In this study, we appraised whether metabolic changes correlate with HF severity, assessed as an impairment of functional contractility, and attempted to interpret the role of metabolic changes in determining systolic dysfunction. METHODS: A 500 MHz proton nuclear magnetic resonance ((1)H-NMR)-based analysis was performed on blood samples from three groups of individuals: 9 control subjects (Group A), 9 HF patients with mild to moderate impairment of left ventricle ejection fraction (LVEF: 41.9 ± 4.0 %; Group B), and 15 HF patients with severe LVEF impairment (25.3 ± 10.3 %; Group C). In order to create a descriptive model of HF, a supervised orthogonal projection on latent structures discriminant analysis (OPLS-DA) was applied using speckle tracking-derived longitudinal strain rate as the Y-variable in the multivariate analysis. RESULTS: OPLS-DA identified three metabolic clusters related to the studied groups achieving good values for R(2) [R(2)(X) = 0.64; R(2)(Y) = 0.59] and Q(2) (0.39). The most important metabolites implicated in the clustering were 2-hydroxybutyrate, glycine, methylmalonate, and myo-inositol. CONCLUSIONS: The results demonstrate the suitability of metabolomics in combination with functional evaluation techniques in HF staging. This innovative tool should facilitate investigation of perturbed metabolic pathways in HF and their correlation with the impairment of myocardial function.

CDK4/6 inhibition in hormone receptor-positive/HER2-negative breast cancer: Biological and clinical aspects.

A dysregulated cell division, one of the key hallmarks of cancer, results in uncontrolled cellular proliferation. This aberrant process, mediated by a dysregulated cell-cycle machinery and overactivation of cyclin-dependent kinase (CDK) 4 and 6, can potentially promote tumorigenesis. The clinical application of CDK 4/6 inhibitors, developed to inhibit cell-cycle progression, in the treatment regimens of breast cancer (BC) patients is expanding. Currently, three agents, ribociclib, palbociclib, and abemaciclib, are approved for treating patients with hormone receptor-positive and human epidermal growth factor receptor 2 (HER2)-negative metastatic BC. In addition, abemaciclib is FDA and EMA-approved for patients with hormone receptor-positive HER2-negative, node-positive, early BC at high risk of recurrence. Emerging data suggest potential anti-tumor effects beyond cell cycle arrest, providing novel insights into the agent's mechanisms of action. As a result, a broader application of the CDK4/6 inhibitors in patients with cancer is achieved, contributing to enhanced optimized treatment in the adjuvant and neoadjuvant settings. Herein, the immunomodulatory activities of CDK4/6 inhibitors, their impact on the cell's metabolic state, and the effect on the decision of the cell to undergo quiescence or senescence are discussed. Moreover, this review provides an update on clinical trial outcomes and the differences in the underlying mechanisms between the distinct CDK4/6 inhibitors.

Metabolomic profiling of Fiore Sardo cheese: Investigation of the influence of thermal treatment and ripening time using univariate and multivariate classification techniques.

The effect of different sub-pasteurization heat treatments and different ripening times was investigated in this work. The metabolite profiles of 95 cheese samples were analyzed using GC-MS in order to determine the effects of thermal treatment (raw milk, 57 °C and 68 °C milk thermization) and ripening time (105 and 180 days). ANOVA test on GC-MS peaks complemented with false discovery rate correction was employed to identify the compounds whose levels significantly varied over different ripening times and thermal treatments. The univariate t-test classifier and Partial Least Square Discriminant Analysis (PLS-DA) provided acceptable classification results, with an overall accuracy in cross-validation of 76% for the univariate model and 72% from the PLS-DA. The metabolites that mostly changed with ripening time were amino acids and one endocannabinoid (i.e., arachidonoyl amide), while compounds belonging to the classes of biogenic amines and saccharides resulted in being strongly affected by the thermization process.

Clinical Efficacy and Metabolomics Modifications Induced by Polyphenol Compound Supplementation in the Treatment of Residual Dizziness following Semont Maneuver in Benign Paroxysmal Positional Vertigo (BPPV) of the Posterior Semicircular Canal (PSC): Preliminary Results.

Benign paroxysmal positional vertigo (BPPV) represents the most frequent cause of peripheral vertigo. In most cases, it is successfully treated using the canalith repositioning procedure, but it is often followed by continuous lightheadedness in the absence of vertigo or nystagmus (residual dizziness, RD). Our aim is to describe the clinical effectiveness and the urine metabolomics profile of treating these patients with polyphenol compound supplementation. We enrolled 30 patients reporting RD after BPPV of the posterior semicircular canal (PSC) successfully treated using the Semont maneuver. Supplementation with a polyphenol compound was administered for 60 days, and patients were evaluated after 30 and 60 days of treatment using self-administered questionnaires (Visual Analog Scales for Dizziness and Nausea, Dizziness Handicap Inventory, DHI) and urine metabolomics analysis performed using 1H-NMR spectroscopy and multivariate followed by univariate analysis. Most patients reported excellent or good efficacy in the treatment of RD with a significant decrease in VAS and DHI values. The metabolomics analysis identified six significant metabolites related to the treatment, namely 1-methylnicotinamide, anserine, hippurate, lysine, methyl succinate and urea, indicating the inflammatory activities and antioxidant properties of the polyphenol compound. These preliminary data suggest that supplementation with a polyphenol compound could induce some metabolic changes that can help in recovery from RD. However, future steps will require confirmation with a more significant cohort of patients and an extension of the metabolomics evaluation to other problems concerning the different clinical aspects of BPPV, such as the high rate of relapse.

Humoral responses to wild type and ancient BA.1 SARS-CoV-2 variant after heterologous priming vaccination with ChAdOx1 nCoV-19 and BNT162b2 booster dose.

Several countries have recommended a booster dose of Pfizer BNT162b2 vaccine for subjects under the age of 60, who have already received the first dose of ChAdOx1. This is due to several ChAdOx1 vaccine-associated adverse vascular events and thrombocytopenia. Neutralization assay and quantitative IgG anti-SARS-CoV-2 Spike antibody (anti-S-IgG) were conducted to investigate the long-term responses to vaccine treatment in a cohort of Sardinian participants, who have received heterologous Prime-Boost Vaccination via ChAdOx1 vector vaccine and a booster dose via BNT162b2. The obtained results were compared with those of a cohort of healthcare workers (HCW) who received homologous BNT162b2 (BNT/BNT/BNT) vaccination. One month (T2) and five months after the second and before the third dose (T3), anti-spike antibody or neutralizing titers in the subjects vaccinated with ChAdOx1-S/BNT162b2 were significantly higher than those who experienced the ChAdOx1-S/ChAdOx1-S or BNT162b2/BNT162b2 schedule. These results suggest that a ChAdOx1-S/BNT162b2 regimen provides a more robust antibody response than either of the homologous regimens. However, the anti-spike antibodies or neutralizing titers after the third injection (mRNA vaccine) of ChAdOx1-S as a second dose and BNT162b2 were not statistically different. Homologous and heterologous vaccination provided a strong antibody response. Neutralizing activities were also described against the Omicron BA.1 variant in a sub-group (40) representative of the three vaccination regimens among our cohort.