Lysosphingolipid Quantitation in Plasma and Dried-Blood Spots Using Targeted High-Resolution Mass Spectrometry.

Sphingolipidoses are rare inherited metabolic diseases belonging to lysosomal diseases. Early and accurate diagnosis is crucial for effective management and treatment. In this study, we aimed to develop a robust method to accelerate the diagnosis of these sphingolipidoses using dried blood spots and plasma. We employed high-resolution mass spectrometry coupled with liquid chromatography (LC-HRMS) to analyze 6 lysosphingolipids (GlcSph/Psychosine, LysoGb3, LysoSM, LysoSM509, LysoGM1, and LysoGM2) on dried blood spots and plasma samples. The method was used to measure the lysosphingolipid levels in a group of 30 control subjects and 204 samples from patients with sphingolipidoses (61 dB and 143 plasma) including Fabry, Gaucher, GM2 Gangliodosis, Niemann-Pick type A/B, and Niemann-Pick type C. The developed multiplex LC-HRMS method demonstrated linearity, precision, and quantification performances particularly for GlcSph/Psychosine and LysoGb3 on samples including controls and patients with sphingolipidoses. LysoSM showed recovery variability, wherease LysoGM1 and LysoGM2 showed higher matrix effect. Our study presents a high-resolution mass spectrometry method along with the established cutoff values, providing a valuable tool for targeted screening, accurate diagnosis, and monitoring sphingolipidoses. Furthermore, DBS showed reliable results that lay the path to a broader adoption for screening these diseases

Hypertrichosis and Diabetes:

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Simerror‎ : un Serious Game sur la chambre des erreurs

La chambre des erreurs est un outil de simulation en santé s’inscrivant dans une démarche globale de gestion des risques associés aux soins. Comprendre les erreurs liées aux soins pour mieux les dépasser, tel est l’enjeu de cet outil de simulation en santé. Cet outil mis en place, depuis 2011, dans les établissements de santé principalement lors de la semaine de la sécurité des patients (SSP), se heurte à plusieurs difficultés (contraintes logistiques, moyens humains, accès limité aux structures hospitalières, difficulté d’adaptation des erreurs aux publics visés etc.). Suite à ces observations, l’OMéDIT Normandie, en partenariat avec le CHU de Rouen et le Centre de lutte contre le cancer Henri Becquerel, a développé un Serious Game sur la chambre des erreurs. L’objectif de ce projet est de concevoir un outil numérique, ludique et facilement accessible. Ce projet est réalisé en étroite collaboration avec une équipe technique et pédagogique de l’université de Rouen spécialisée dans la création de Serious Games. Ce projet a reçu le soutien de l’université de Rouen et de l’agence régionale de santé de Normandie. La version test de Simerror a été déployée, pendant la SSP de 2016, dans 3 établissements de santé pilotes. Le jeu propose de retrouver 7 erreurs choisies de façon aléatoire parmi une banque d’erreurs en comportant 14. Cela représente 3 432 combinaisons d’erreurs différentes. Selon le questionnaire de satisfaction remis à l’ensemble des participants, le jeu a été très apprécié. Le retour des participants via le questionnaire a permis d’améliorer ce Serious Game pour une diffusion sur le site internet de l’OMéDIT Normandie. Simerror poursuit son développement, de nouvelles erreurs seront ajoutées dans la chambre du patient ainsi que deux nouvelles scènes (bloc opératoire et hospitalisation au domicile du patient). L’objectif est de diffuser la nouvelle version pour la SSP de 2017. Ce jeu sérieux sera prochainement intégré dans le cadre de la formation initiale des étudiants et de la formation continue des professionnels de santé

Plasma Metabolic and Inflammatory Protein Signatures in Psychiatric Disorders

Psychiatric disorders, particularly schizophrenia (SCZ), bipolar disorder (BD), and schizoaffective disorder (SAD), present significant diagnostic challenges. Current diagnostic methods rely on clinical observation and self-reported symptoms, leading to under-diagnosis and delayed treatment. To address this gap, we applied mass spectrometry-based metabolomic profiling and targeted analysis of inflammatory proteins to plasma samples from patients versus controls, aiming to uncover disease-related molecular patterns and enhance our understanding of the underlying pathophysiology of these complex disorders. This study included 26 patients with BD, 34 with SCZ, 16 with SAD, and age- and sex-matched controls. All diagnoses were established according to DSM-5 criteria. Unsupervised analysis shows a clear separation between controls and patients, indicating distinct metabolic and inflammatory profiles. However, the lack of clear differentiation among the three disease subgroups suggests shared biological profiles across these psychiatric disorders. Biomolecules driving this separation between controls and patients includes decreased levels of proinflammatory cytokines, amino acids, and glycerophospholipids, and increased levels of acylcarnitines. This study represents a step towards addressing the limitations of current diagnostic approaches to severe psychiatric disorders, which rely heavily on clinical symptoms, by using omics approaches to refine their diagnosis and treatment

Metabolic remodeling in glioblastoma: a longitudinal multi-omics study

Abstract Monitoring tumor evolution and predicting survival using non-invasive liquid biopsy is an unmet need for glioblastoma patients. The era of proteomics and metabolomics blood analyzes, may help in this context. A case–control study was conducted. Patients were included in the GLIOPLAK trial (ClinicalTrials.gov Identifier: NCT02617745), a prospective bicentric study conducted between November 2015 and December 2022. Patients underwent biopsy alone and received radiotherapy and temozolomide. Blood samples were collected at three different time points: before and after concomitant radiochemotherapy, and at the time of tumor progression. Plasma samples from patients and controls were analyzed using metabolomics and proteomics, generating 371 omics features. Descriptive, differential, and predictive analyses were performed to assess the relationship between plasma omics feature levels and patient outcome. Diagnostic performance and longitudinal variations were also analyzed. The study included 67 subjects (34 patients and 33 controls). A significant differential expression of metabolites and proteins between patients and controls was observed. Predictive models using omics features showed high accuracy in distinguishing patients from controls. Longitudinal analysis revealed temporal variations in a few omics features including CD22, CXCL13, EGF, IL6, GZMH, KLK4, and TNFRSP6B. Survival analysis identified 77 omics features significantly associated with OS, with ERBB2 and ITGAV consistently linked to OS at all timepoints. Pathway analysis revealed dynamic oncogenic pathways involved in glioblastoma progression. This study provides insights into the potential of plasma omics features as biomarkers for glioblastoma diagnosis, progression and overall survival. Clinical implication should now be explored in dedicated prospective trials