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Background Clot characterization is, to the present days, a multimodal approach: scanning the clot by electron microscopy (SEM) is helpful for the visualization of fibrin structure along with laboratory parameters such as the clot waveform analysis (CWA) and thrombin generation in different settings of clot abnormalities. This study aimed to assess whether the coagulative parameters were consistent with the clot images texture acquired by SEM, and therefore to propose a more generalist and integrative approach to clots classification. Design and methods In this pilot study, the examined population consists of eight healthy subjects, seven patients affected by Acquired Hemophilia A (AHA) and seven patients treated with Vitamin K Antagonists (VKAs), similar for age and gender. We studied the velocity and acceleration (1st and 2nd derivative of the aPTT) of clot formation (CWA), the thrombin generation, and the clots’ scanning by SEM. Images acquired with SEM were then analyzed with the MATLAB software with the “Texture Analysis” methods to perform classification. Among the various texture parameters, we reported Contrast and Energy. Results Significant differences among healthy subjects, patients with AHA and those treated with VKAs were detected for the coagulative parameters. We found no differences between VKAs and AHA patients. Contrast and energy highlighted a significant difference among the three groups in agreement with the laboratory's parameters. We found no significant differences between VKAs and AHA patients. Conclusions The use of SEM, CWA and thrombin generation parameters may be a starting point for studies aimed to demonstrate the general characteristics of clot formation in different clinical conditions with a multiparametric approach.

Background: Metabolomic profiling has important diagnostic and prognostic value in heart failure (HF). We investigated whether left ventricular assist device (LVAD) support has an impact on the metabolomic profile of chronic HF patients and if specific metabolic patterns are associated with the development of adverse events.Methods: We applied untargeted metabolomics to detect and analyze molecules such as amino acids, sugars, fatty acids and other metabolites in plasma samples collected from thirty-three patients implanted with a continuous-flow LVAD. Data were analyzed at baseline, i.e., before implantation of the LVAD, and at long-term follow-up. Results: Our results reveal significant changes in the metabolomic profile after LVAD implant compared to baseline. In detail, we observed a pre-implant reduction in amino acid metabolism (aminoacyl-tRNA biosynthesis) and increased galactose metabolism, which reversed over the course of support [median follow-up 187 days (63–334 days)]. These changes were associated with improved patient functional capacity driven by LVAD therapy, according to NYHA functional classification of HF (NYHA class I-II: pre-implant=0% of the patients; post-implant =97% of the patients; P

Background Lactobacillus reuteri DSM 17938 is the only probiotic recommended for treatment of colicky infants, but its mechanism of action is not clear. The study aim was to examine urinary metabolomic fingerprint of colicky breastfed infants before and after 1 month of orally administered Lactobacillus reuteri DSM 17938 or placebo. Methods This randomized, blinded, placebo-controlled clinical trial was carried out with a well-documented probiotic. Thirty-two infants were enrolled, 16 in the probiotic group and 16 in the placebo group. Urine samples were collected from each subject before starting supplementation and at the end of the study period. Metabolomic profiles were obtained using a gas chromatography/mass spectrometry instrument. Subsequently, to compare groups before and after probiotic supplementation, univariate and multivariate statistical analysis were performed. Results In the L. reuteri treated group all metabolites for all class of nutrients (sugars, amino acids, carboxylic acids) resulted more abundant after the study period. The comparison with a control group (placebo treated), confirmed this effect on urines. Conclusions The metabolomic analysis of urine samples from infants treated with L. reuteri DSM 17938 allowed to detect some interesting features related to the effect of this treatment on urinary metabolome. To validate the results, a test on a larger cohort is required.

The electroencephalogram (EEG) has been proven to be a promising technique for personal identification and verification. Recently, the aperiodic component of the power spectrum was shown to outperform other commonly used EEG features. Beyond that, EEG characteristics may capture relevant features related to emotional states. In this work, we aim to understand if the aperiodic component of the power spectrum, as shown for resting-state experimental paradigms, is able to capture EEG-based subject-specific features in a naturalistic stimuli scenario. In order to answer this question, we performed an analysis using two freely available datasets containing EEG recordings from participants during viewing of film clips that aim to trigger different emotional states. Our study confirms that the aperiodic components of the power spectrum, as evaluated in terms of offset and exponent parameters, are able to detect subject-specific features extracted from the scalp EEG. In particular, our results show that the performance of the system was significantly higher for the film clip scenario if compared with resting-state, thus suggesting that under naturalistic stimuli it is even easier to identify a subject. As a consequence, we suggest a paradigm shift, from task-based or resting-state to naturalistic stimuli, when assessing the performance of EEG-based biometric systems.

Texture analysis has arisen as a tool to explore the amount of data contained in images that cannot be explored by humans visually. Radiomics is a method that extracts a large number of features from radiographic medical images using data-characterisation algorithms. These features, termed radiomic features, have the potential to uncover disease characteristics. The goal of both radiomics and texture analysis is to go beyond size or human-eye based semantic descriptors, to enable the non-invasive extraction of quantitative radiological data to correlate them with clinical outcomes or pathological characteristics. In the latest years there has been a flourishing sub-field of radiology where texture analysis and radiomics have been used in many settings. It is difficult for the clinical radiologist to cope with such amount of data in all the different radiological sub-fields and to identify the most significant papers. The aim of this review is to provide a tool to better understand the basic principles underlining texture analysis and radiological data mining and a summary of the most significant papers of the latest years.

The idea of estimating the statistical interdependence among (interacting) brain regions has motivated numerous researchers to investigate how the resulting connectivity patterns and networks may organize themselves under any conceivable scenario. Even though this idea has developed beyond its initial stages, its practical application is still far away from being widespread. One concurrent cause may be related to the proliferation of different approaches that aim to catch the underlying statistical interdependence among the (interacting) units. This issue has probably contributed to hindering comparisons among different studies. Not only do all these approaches go under the same name (functional connectivity), but they have often been tested and validated using different methods, therefore, making it difficult to understand to what extent they are similar or not. In this study, we aim to compare a set of different approaches commonly used to estimate the functional connectivity on a public EEG dataset representing a possible realistic scenario. As expected, our results show that source-level EEG connectivity estimates and the derived network measures, even though pointing to the same direction, may display substantial dependency on the (often arbitrary) choice of the selected connectivity metric and thresholding approach. In our opinion, the observed variability reflects the ambiguity and concern that should always be discussed when reporting findings based on any connectivity metric.

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Introduction Metabolomics identifies phenotypical groups with specific metabolic profiles, being increasingly applied to several pregnancy conditions. This is the first preliminary study analyzing placental metabolomics in normal weight (NW) and obese (OB) pregnancies. Methods Twenty NW (18.5 ≤ BMI Results Univariate analysis showed increased uracil levels while multivariate PLS-DA analysis revealed lower levels of LC-PUFA derivatives in the lipophilic phase and several metabolites with significantly different levels in the hydrophilic phase of OB vs NW. Discussion Placental metabolome analysis of obese pregnancies showed differences in metabolites involved in antioxidant defenses, nucleotide production, as well as lipid synthesis and energy production, supporting a shift towards higher placental metabolism. OB placentas also showed a specific fatty acids profile suggesting a disruption of LC-PUFA biomagnification. This study can lay the foundation to further metabolomic placental characterization in maternal obesity. Metabolic signatures in obese placentas may reflect changes occurring in the intrauterine metabolic environment, which may affect the development of adult diseases.

Background Recently, many academic research groups focused their attention on changes in human brain networks related to several kinds of pathologies and diseases. Generally speaking, Network Medicine promises to identify the principles to understand, at the molecular level, the human life complex system. We applied the theoretical approach of the Network Medicine mainly to brain diseases, testing the potentiality of the method to produce an early and more in-depth diagnostic process. However, the range of our topics and application of the Network Medicine spans from common neurological and neuropsychological spectrum disorders to other physiological systems or district, only apparently out of the action range of the neurological one. In the areas of Neurology and Neuropsychology we are studying brain networks in Tourette Syndrome [TS] and Multiple Sclerosis [MS]. Moreover, we are studying brain networking related to genotype alteration responsible for different gustatory stimuli processing in the brain. Further, we recently started to study infective diseases and immunological system. Main body of the abstract The analysis of brain networks is made feasible by the development and application of imaging acquisition methods based on the Magnetic Resonance Imaging and functional MRI, as well as the availability of innovative calculation tools from graph theory and complex dynamical systems. This paper highlights the Network Medicine concepts and the application of the functional connectivity for the brain networks description. We will describe all these methods with a reduced use of formulas, along with some technical tools available in the WEB to perform the calculations of the related parameters. We will briefly describe the freely available software CONN used for calculating different connectivity parameters. In all these studies, the networks characterisation is performed using indexes from metric and topology of the brain networks. The possibility of early recognition of the diseases by the identification of the alteration in the network’s parameters may significantly improve patient outcome, with also significant social community benefits. We intend to illustrate a possible pipeline for the Networks Brain application to neuroradiologists involved in the diagnosis of complex diseases. Short conclusion The multidisciplinary approach is one of the essential characteristics of journals in the imaging sciences field; the issues and the methodologies examined in this paper could be of great interest to the reader’s community. Furthermore, until today very few studies focused on the network medicine and connectivity explored in functional MRI. Hence this might have an additional value for the journal.

Pulmonary arterial hypertension (PAH) in systemic sclerosis (SS) identifies a poor prognosis subset of patients. Recent studies suggested a "metabolic theory" on the development of pulmonary arterial hypertension. On this basis we performed a metabolomic study in order to evaluate whether differences in pulmonary arterial blood metabolites were identifiable in SS patients with increased pulmonary vascular resistance (PVR).We studied 18 SS patients (age 58.7±15.6years) free of pulmonary fibrosis who underwent a right heart catheterization (RHC). A blood sample was collected during the RHC in the distal peripheral circulation of the pulmonary arteries to perform the metabolomic analysis.Based on PVR we divided the population into Group A (n=8; PVR=1.16±0.23WU) and Group B (n=10; PVR=2.67±0.67WU; p0.001 vs Group A). No significant differences were identified in terms of anthropometric, clinical, echo and therapeutic characteristics. At RHC the 2 groups showed a difference in mean pulmonary pressure values (Group A: 20±4mmHg; Group B: 27±3.4mmHg; p=0.03), with mild PAH in Group B. We applied an OSC-PLS-DA with a clear clusterization; SSc patients with PAH showed an increase in acetate, alanine, lactate, and lipoprotein levels and a decrease in γ-aminobutyrate, arginine, betaine, choline, creatine, creatinine, glucose, glutamate, glutamine, glycine, histidine, phenylalanine, and tyrosine levels CONCLUSIONS: Our results suggest that, despite similar clinical and disease-related parameters, SSc patients who develop PAH have an unfavorable metabolic profile able to cause an impaired production of metabolites with protective effects on endothelial cells.

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Lymphoma defines a group of different diseases. This study examined pre-treatment plasma samples from 66 adult patients (aged 20&ndash, 74) newly diagnosed with any lymphoma subtype, and 96 frequency matched population controls. We used gas chromatography-mass spectrometry (GC-MS) to compare the metabolic profile by case/control status and across the major lymphoma subtypes. We conducted univariate and multivariate analyses, and partial least square discriminant analysis (PLS-DA). When compared to the controls, statistically validated models were obtained for diffuse large B-cell lymphoma (DLBCL), chronic lymphocytic leukemia (CLL), multiple myeloma (MM), and Hodgkin lymphoma (HL), but not follicular lymphoma (FL). The metabolomic analysis highlighted interesting differences between lymphoma patients and population controls, allowing the discrimination between pathologic and healthy subjects: Important metabolites, such as hypoxanthine and elaidic acid, were more abundant in all lymphoma subtypes. The small sample size of the individual lymphoma subtypes prevented obtaining PLS-DA validated models, although specific peculiar features of each subtype were observed, for instance, fatty acids were most represented in MM and HL patients, while 2-aminoadipic acid, 2-aminoheptanedioic acid, erythritol, and threitol characterized DLBCL and CLL. Metabolomic analysis was able to highlight interesting differences between lymphoma patients and population controls, allowing the discrimination between pathologic and healthy subjects. Further studies are warranted to understand whether the peculiar metabolic patterns observed might serve as early biomarkers of lymphoma.

Background Human cytomegalovirus (HCMV) is one of the most frequent risk of viral infections during pregnancy. The aim of this study was to evaluate the metabolic profile in amniotic fluid (AF) samples obtained from HCMV-infected, and uninfected fetuses in order to elucidate changes in metabolic pathways during congenital HCMV infection and to recognize new potential diagnostic and/or prognostic biomarkers. Methods A retrospective cohort study was conducted on 63 pregnant women: 20 contracted primary HCMV infection during pregnancy and, subsequently, transmitted the virus to the fetus (transmitters); 20 contracted the infection without transmitting the virus to the fetus (non-transmitters); 23 who underwent amniocentesis for cytogenetic-based diagnosis were considered controls. Metabolomics analysis was performed by using the hyphenated technique Gas chromatography-mass spectrometry (GC–MS) followed by a multivariate statistical approach. Four PLS-DA models were generated: controls vs. transmitters; controls vs. non-transmitters; transmitters vs. non-transmitters; and asymptomatic infected vs. symptomatic infected newborns. Subsequently, these models were exploited for network mapping. Results Compared with controls, HCMV transmitters showed significantly increased levels in glutamine, glycine, serine, pyruvic acid, threonine, threonic acid, and cystine; conversely, unknown U1715 and U1804, glutamic acid, U1437, fructose, sugar-like A203003 and A203005, and tyrosine levels were found decreased. In non-transmitters, glutamine, serine, glycine, threonic acid, threonine, 1-monostearin, urea, and cystine were found increased, while sorbitol, unknown U1804, sugar-like A203003, U1751, xylitol, leucine and fructose were decreased. The comparison between transmitters and non-transmitters did not produce a statistically significant model. Unlike controls' profile, a common feature of HCMV infected subjects (transmitters and non-transmitters) was the activation of glutamine-glutamate and pyrimidine metabolic pathways. In addition, a clusterization for asymptomatic vs. symptomatic outcome was also observed due to alteration of fatty acids biosynthesis. Conclusions Metabolomics approach could highlight the significant modification of maternal and placental status during HCMV infection for both transmitter and non-transmitter subjects. A further separation was observed for asymptomatic vs. symptomatic HCMV congenital infections model. Therefore, metabolomics may be a promising tool to improve the accuracy of an early diagnosis, and the management of HCMV pregnancy-related infections.

Amniotic fluid (AF) is a biological fluid in which metabolite transport is regulated by the placenta, the permeable skin, fetal lung egress and gastric fluid. During pregnancy, the composition of AF changes from similar to the interstitial fluid of the mother, to a more complex system, influenced by the fetus's urine. Since AF reflects the mother's and the fetus's health status at the same time, it may be an important diagnostic tool for a wider spectrum of clinical conditions. Indeed, the metabolic characterization of AF in relation to pathological occurrences may lead to the discovery of new biomarkers for a better clinical practice. For this reason, metabolomics may be the most suitable strategy for this task. In this review, research works on metabolomic AF analysis are discussed according to the morbidity of interest, being preterm birth/labor, gestational age and diabetes and fetal malformations, along with a number of other important studies.

Objective: Premature rupture of membranes (PROM) and preterm premature rupture of membranes (pPROM) are frequent conditions with a not fully understood multifactorial etiology. It has been suggeste...

Objectives To assess whether there is mid-term reorganization in brain networks connectivity after Carotid Endarterectomy (CEA) using resting state functional connectivity Magnetic Resonance (fc-rsMR), with a special focus on the Default Mode Network (DMN). Materials and methods In this prospective exploratory study, 14 asymptomatic consecutive patients (10 males and 4 females, mean age 73.5) with unilateral, significant ICA stenosis eligible for CEA according to European Society for Vascular Surgery guidelines were prospectively recruited. The week before CEA procedure, each patient underwent both neurocognitive and rs-fcMR evaluations on the same day; the neurocognitive test consisted on a Mini Mental State Examination (MMSE). The same neurocognitive test and rs-fcMR examination were repeated on follow-up between 3–6 months after CEA. MMSE scores were compared using paired T-Student Test. Rs-fcMR Region Of Interest (ROI-to-ROI) and Seed-to-voxel group analysis were conducted using the CONN toolbox v18 and the SPM 12 software. Results Patients showed improvements in MMSE scores from before to after CEA (p-value = 0.0001). ROI-to-ROI analysis revealed several statistically significant connectivity changes following CEA, both in terms of positive and negative correlations; Seed-to-Voxel focusing on DMN revealed increased connectivity between medial prefrontal cortex (mPFC) and three different clusters of voxels. Conclusions CEA procedure is associated with an improvement in neurocognitive performance (according to MMSE testing) and reorganization of functional connectivity, including the DMN. These results represent a starting point in order to design further studies for a better understanding of the reorganization of brain networks following CEA, and to investigate the potential role of CEA as a therapeutic procedure for cognitive impairments in selected patients with critical ICA stenosis.

This research investigated local brain connectivity changes following Carotid Endarterectomy (CEA) by connectometry. Seventeen subjects (15 males and 2 females, mean age 74.1years), all eligible for CEA, were prospectively recruited in this exploratory study. On the same day within the week before the CEA, each patient underwent a cognitive evaluation with a Mini Mental State Examination (MMSE) and a Magnetic Resonance Imaging (MRI) exam that included a DTI sequence for the connectometry analysis. A second MMSE and the same MRI protocol were performed on follow-up, 3–6months after CEA. The MMSE scores were analyzed using T-Student tests. The connectometry analysis was performed using a multiple regression model to consider the effect of CEA, choosing three different T-score threshold (T-threshold) values (1, 2 and 3). Results were considered statistically valid for p value adjusted for False Discovery Rate (p-FDR) < 0.05. Comparison of pre-CEA and post-CEA MMSE scores showed improvement of MMSE scores after CEA. Connectometry analysis revealed no areas of statistically significant increased connectivity related to CEA for T-threshold value = 1 and 2, but showed statistically significant increase of connectivity after CEA in both cerebellar hemispheres and corpus callosum for T-threshold value = 3 (p-FDR = 0.0106667). The network property analysis showed improved small worldness (2.14%), clustering coefficient (1.64%), local (1.94%) and global efficiency (0.56%), and reduced characteristic path length (−0.52%) after CEA. These results suggest that CEA is associated both with cognitive performance improvement and changes in interhemispheric local connectivity in the corpus callosum and cerebellum.

Since pathologies and complications occurring during pregnancy and/or during labour may cause adverse outcomes for both newborns and mothers, there is a growing interest in metabolomic applications on pregnancy investigation. In fact, metabolomics has proved to be an efficient strategy for the description of several perinatal conditions. In particular, this study focuses on premature rupture of membranes (PROM) in pregnancy at term. For this project, urine samples were collected at three different clinical conditions: out of labour before PROM occurrence (Ph1), out of labour with PROM (Ph2), and during labour with PROM (Ph3). GC-MS analysis, followed by univariate and multivariate statistical analysis, was able to discriminate among the different classes, highlighting the metabolites most involved in the discrimination.

Background and purpose Hepatitis C virus (HCV) co-infection’s role on cognitive impairment of human immunodeficiency virus (HIV) positive patients is still debated and functional neuroimaging evaluation on this matter is lacking. To provide further insight about HCV’s neuro-effects on HIV associated neurocognitive disorder (HAND), we performed a pilot resting state (RS) functional connectivity magnetic resonance imaging (fcMRI) study to find eventual functional connectivity alteration that could reflect HCV related cognitive performance degradation. Methods Eighteen patients (8 HIV, 10 HIV + HCV), either impaired or not impaired, were assessed with RS fcMRI. A statistic model including cognitive testing results was elaborated during data processing to evaluate brain networks alteration related to actual cognitive status in patients. Results Statistically significant different patterns of connectivity were found: HCV co-infection modified 17 ROIs’ connectivity with 45 supra-threshold connections (p-FDR min 0.0022, max 0.0497). ROIs most involved were right pallidum, brainstem, vermian lobules 1–2 and right cerebellar lobule 10. Graph theory analysis did not demonstrate significant difference between networks, but HCV related modifications at ROI’s local level were found, with particular involvement of ROIs of frontal lobe, basal ganglia and cerebellum. Increased fronto-striatal dysfunctions have been already reported as consequences of HCV infection and could reflect an additive effect. Cerebellar alterations are associated with HIV and HAND, but not with HCV infection, suggesting a synergic effect of HCV. Conclusion Our study demonstrates RS fcMRI can help to understand the interactions between HIV and HCV co-infection, and our preliminary results suggest synergic effects of HCV in HIV-related brain functional modification.

Metabolomics is a new "omics" platform aimed at high-throughput identification, quantification and characterization of small-molecule metabolites. The metabolomics approach has been successfully applied to the classification different physiological states and identification of perturbed biochemical pathways. The purpose of the current investigation is the application of metabolomics to explore biological mechanisms which may lead to the onset of metabolic syndrome in adulthood.We evaluated differences in metabolites in the urine collected within 12 h from 23 infants with IUGR (IntraUterine Growth Restriction), or LGA (Large for Gestational Age), compared to control infants (10 patients defined AGA: Appropriate for Gestational Age). Urinary metabolites were quantified by GC-MS and used to highlight similarities between the two metabolic diseases and identify metabolic markers for their predisposition. Quantified metabolites were analyzed using a multivariate statistics coupled with receiver operator characteristic curve (ROC) analysis of identified biomarkers.Urinary myo-inositol was the most important discriminant between LGA + IUGR and control infants, and displayed an area under the ROC curve = 1.We postulate that the increase in plasma and consequently urinary inositol may constitute a marker of altered glucose metabolism during fetal development in both IUGR and LGA newborns.

To look for differences in the urinary metabolic profile and in the hematic asymmetric dimethylarginine (ADMA) levels between a group of young adults born preterm with an extremely low birthweight (1000 g; ex-ELBW; n=19) and a control group of subjects born at term with a weight appropriate for their gestational age (AGA; n=13); and to look for a possible correlation between the urinary metabolic profile in ex-ELBW and their hematic levels of ADMA.Urine samples were analyzed by (1)H nuclear magnetic resonance spectroscopy, and then submitted to unsupervised and supervised multivariate analysis. Samples of blood were collected and ADMA concentration was assessed by high-performance liquid chromatography.Using supervised PLS-DA (partial least squares discriminant analysis) model, the authors were able to discriminate between ex-ELBW and AGA. Statistically significant differences were detected in the ADMA levels between ex-ELBW and AGA (p0.02). Ex-ELBW metabolic profile correlated with ADMA concentrations (r=0.456, p0.05). Conversely, ADMA levels in AGA did not correlated with their metabolic profiles.This study demonstrates the relevance of the metabolomic technique as a predictive tool of the metabolic status in ex-ELBW. The relationship between ex-ELBW urinary metabolic profile and their blood ADMA levels suggests the presence of a subclinical cardio-renal involvement in these subjects.

Background: Colorectal cancer (CRC) has been confirmed to be the third most commonly diagnosed cancer in males and the second in females. We investigated the blood plasma metabolome in CRC patients and in healthy adults to elucidate the role of monosaccharides, amino acids, and their respective metabolic pathways as prognostic factors in patients with CRC. Methods: Fifteen patients with CRC and nine healthy adults were enrolled in the study and their blood plasma samples analyzed by gas chromatography-mass spectrometry (GC-MS). Univariate Student’s t-test, multivariate principal component analysis (PCA) and partial least square-discriminant analysis (PLS-DA) were conducted on MetaboAnalyst 4.0. The analysis of metabolic profiles was carried out by the web-based extension Metabolite Sets Enrichment Analysis (MSEA). Results: Overall, 125 metabolites were identified in plasma samples by GC-MS. In CRC patient samples, nine metabolites, including D-mannose and fructose, were significantly more abundant than in controls; conversely, eleven amino derivatives were less abundant, including methionine, valine, lysine, and proline. Methionine was significantly less abundant in died patients compared with survivors. The most significantly altered metabolic pathways in CRC patients are those involving monosaccharides (primarily the catabolic pathway of fructose and D-mannose), and amino acids (primarily methionine, valine, leucine, and isoleucine). Conclusions: The abundance of D-mannose in CRC patient samples contributes to inhibiting the growth of cancer cells, while the abundance of fructose may be consistent either with low consumption of fructose by aerobic glycolysis within cancer cells or with a high bioavailability of fructose from diet. The reduction in methionine concentration may be related to increased activity of the threonine and methionine catabolic pathways, confirmed by high levels of α-hydroxybutyrate.

In this feasibility study, we propose, for the first time, (1)H NMR spectroscopy coupled with mathematical strategies as a valid tool for body fluid (BF) trace identification in forensic science. In order to assess the ability of this approach to identify traces composed either by a single or by two different BFs, samples of blood, urine, saliva, and semen were collected from different donors, and binary mixtures were prepared. (1)H NMR analyses were carried out for all samples. Spectral data of the whole set were firstly submitted to unsupervised principal component analysis (PCA); it showed that samples of the same BF cluster well on the basis of their characterizing molecular components and that mixtures exhibit intermediate characteristics among BF typologies. Furthermore, samples were divided into a training set and a test set. An average NMR spectral profile for each typology of BF was obtained from the training set and validated as representative of each BF class. Finally, a fitting procedure, based on a system of linear equations with the four obtained average spectral profiles, was applied to the test set and the mixture samples; it showed that BFs can be unambiguously identified, even as components of a mixture. The successful use of this mathematical procedure has the advantage, in forensics, of overcoming bias due to the analyst's personal judgment. We therefore propose this combined approach as a valid, fast, and non-destructive tool for addressing the challenges in the identification of composite traces in forensics.

Metabolomics is a new way for the Systems Biology application to the Medicine, it is supported by the recent advancements in technology for the analytical description of the molecules mixtures in biological fluids, and it is becoming the revolutionary approach to the modern “personalized medicine” for therapies and treatments. Important “insights” come from the metabolomics application to the pain condition description and we will discuss about several classes of molecules and metabolites and several canonical pathways involved in the pain physiology revealed by the metabolomics approach: Sphingolipids , Glycerophospholipids , Steroid hormones . It is important to remark some pitfalls of metabolomics approach, not only for the pain description and treatments, but also for all the medical applications; especially the lack of a generalized application in all the laboratories of the Standard operative procedures (SOP) for the samples preparation and models realization. Nevertheless, Metabolomic can give us an exciting way to progress towards understanding the basic mechanisms of pain in humans and it also can represent a robust approach to some important aspects of this problem as the appropriateness of pharmacological treatments for all the pain condition, stable or progressive in acute or chronic conditions; this allows us to be confident about the paradigm of the metabolomics approach. A final remarkable point will regard the next-generation approaches of Big Data and metabolomics: integrating genomic, proteomic and metabolomic measurements, we will have the possibility to better understand at holistic level the biochemical process of the pain and to identify robust biological markers for pain-related diseases, diagnosis and treatments, efficacy monitoring: this will lead us to the “therapeutic omics approach” with the connection between the Genotype and the Phenotype, about “what could happen” and “what is happened”. These items should give the readers an overview of the situation about metabolomics and pain studies and to stimulate for a deeper approach by means of the bibliography reported.

Background: Perinatal asphyxia is a severe clinical condition affecting around four million newborns worldwide. It consists of an impaired gas exchange leading to three biochemical components: hypoxemia, hypercapnia and metabolic acidosis. Methods: The aim of this longitudinal experimental study was to identify the urine metabolome of newborns with perinatal asphyxia and to follow changes in urine metabolic profile over time. Twelve babies with perinatal asphyxia were included in this study; three babies died on the eighth day of life. Total-body cooling for 72 hours was carried out in all the newborns. Urine samples were collected in each baby at birth, after 48 hours during hypothermia, after the end of the therapeutic treatment (72 hours), after 1 week of life, and finally after 1 month of life. Urine metabolome at birth was considered the reference against which to compare metabolic profiles in subsequent samples. Quantitative metabolic profiling in urine samples was measured by gas chromatography mass spectrometry (GC-MS). The statistical approach was conducted by using the multivariate analysis by means of principal component analysis (PCA) and orthogonal partial least square discriminant analysis (OPLS-DA). Pathway analysis was also performed. Results: The most important metabolites depicting each time collection point were identified and compared each other. At birth before starting therapeutic hypothermia (TH), urine metabolic profiles of the three babies died after 7 days of life were closely comparable each other and significantly different from those in survivors. Conclusions: In conclusion, a plethora of data have been extracted by comparing the urine metabolome at birth with those observed at each time point collection. The modifications over time in metabolites composition and concentration, mainly originated from the depletion of cellular energy and homeostasis, seems to constitute a fingerprint of perinatal asphyxia.

We reported data concerning the Gas Chromatography–Mass Spectrometry (GC–MS) based metabolomic analysis of amniotic fluid (AF) samples obtained from pregnant women infected with Human Cytomegalovirus (HCMV). These data support the publication “Primary HCMV Infection in Pregnancy from Classic Data towards Metabolomics: an Exploratory analysis” (C. Fattuoni, F. Palmas, A. Noto, L. Barberini, M. Mussap, et al., 2016) [2]. GC–MS and Multivariate analysis allow to recognize the molecular phenotype of HCMV infected fetuses (transmitters) and that of HCMV non-infected fetuses (non-transmitters); moreover, GC–MS and multivariate analysis allow to distinguish and to compare the molecular phenotype of these two groups with a control group consisting of AF samples obtained in HCMV non-infected pregnant women. The obtained data discriminate controls from transmitters as well as from non-transmitters; no statistically significant difference was found between transmitters and non-transmitters.

The metabolome represents the collection of all metabolites in a biological cell, tissue, organ or organism, which are the end-products of cellular processes. Metabolomics is the systematic study of small-molecule metabolite profiles that specific cellular processes leave behind. RNA messenger gene expression data and proteomic analyses do not tell the whole story of what might be happening in a cell. Metabolic profiling, in turn, amplifies changes both in the proteome and the genome, and represents a more accurate approximation to the phenotype of an organism in health and disease. In this article, we have provided a description of metabolomics, in the presence of other, more familiar 'omics' disciplines, such as genomics and proteomics. In addition, we have reviewed the current rationale for metabolomics in cardiology, its basic methodology and the data actually available in human studies in this discipline. The discussed topics highlight the importance of being able to use the metabolomics information in order to understand disease mechanisms from a systems biology perspective as a noninvasive approach to diagnose, grade and treat cardiovascular diseases.

Objective: Metabolomics is a technique used to non-invasively determine a snapshot of the current metabolic status of an organism by analyzing intact tissue or bio-fluids. The aim of the present preliminary study was to analyze metabolic profiles in preterm infants complicated by respiratory distress syndrome (RDS) trough bronchoalveolar lavage fluid (BALF) measurement. Methods: Twelve BALF samples collected at birth prior surfactant, post-surfactant during mechanical ventilation and at extubation time-points, were analyzed by proton nuclear magnetic resonance spectroscopy and Gas chromatography-Mass Spectrometry (GC-MS). Results: GC-MS analysis identified 25 metabolites of whom 10 had a known molecular structure. They were: undecane, decanoic acid, dodecanoic acid, hexadecanoic acid, octadecanoic acid, hexadecanoic acid methyl ester, 9-octadecanoic acid, tetracosanoic acid, myristic acid, phosphate. These metabolites were over-expressed in BALF collected during mechanical ventilation after surfactant administration. Conclusions: The present preliminary data suggest that metabolic profile in BALF of RDS infants is becoming possible opening a new cue of metabolomics as promising tool in management of sick premature infants.