Extrazelluläre Vesikel als neue Biomarker bei der Diagnostik entzündlicher Lungenerkrankungen

Die ambulant erworbene Pneumonie (CAP) und die akut exazerbierte COPD (AECOPD) sind zwei häufige Erkrankungen, die weltweit mit einer hohen Morbidität und Mortalität einhergehen. Da sie sich klinisch sehr ähnlich präsentieren können, ist die Differentialdiagnostik schwierig. Kleine Extrazelluläre Vesikel (sEV) gewinnen als neue Biomarker zunehmend an Aufmerksamkeit. Ihre Oberflächenproteine und andere Bestandteile sind abhängig von der Ursprungszelle und könnten einen Schritt in Richtung „Liquid Biopsie“ darstellen. In der vorliegenden Pilotstudie wurden die Oberflächenproteine von Plasma- Exosomen auf ihre Aussagekraft zur Diagnostik einer CAP oder AECOPD untersucht. Dazu wurden 40 Oberflächenproteine mittels EV-Array bestimmt. Im Fokus der Studie stand die Fähigkeit der Biomarker, die Studiengruppen voneinander zu unterscheiden. Außerdem wurde betrachtet, inwiefern sich die Parameter zur Einschätzung des Schweregrades einer Pneumonie eignen. Als weitere Parameter wurden Differentialblutbild und CRP, klinische Parameter und klinische Scores (CRB-56, CURB, PSI, GOLD 1-4, GOLD A, B, C, D und mMRC) erhoben. Insgesamt wurden die Plasma-Proben von 55 Probanden untersucht, darunter waren 24 CAP-Patienten, 10 AECOPD-Patienten und 21 gesunde Probanden. Der EV-Array ergab signifikante Unterschiede zwischen Gesunden, CAP-Patienten und AECOPD-Patienten. Zur Unterscheidung zwischen CAP und AECOPD waren CD45 und CD28 am besten geeignet, die ROC-Analyse ergab eine AUC > 0,92. Zur Unterscheidung zwischen CAP-Patienten und Gesunden ergaben sich für CD45 und CD16 die besten Werte. Eine Unterteilung in unkomplizierte und schwere CAP war mit ICAM-1 am besten möglich. Da die Abgrenzung einer CAP zur Exazerbation bei COPD-Patienten besonders schwierig ist, wurde untersucht, ob eine Unterscheidung zwischen CAP und AECOPD auch möglich war, wenn die CAP-Patienten als Vorerkrankung eine COPD aufwiesen. Die Methode der Ensemble Feature Selection ergab hierfür ein Panel bestehend aus CD45, CD28, CTLA4, TNF-R-II und CD16. Insgesamt ist der EV-Array in dieser Pilotstudie eine einfache und minimalinvasive diagnostische Möglichkeit, um zwischen Gesunden, CAP-Patienten und AECOPDPatienten zu unterscheiden

The effect of inadvertent systemic hypothermia after mechanical thrombectomy in patients with large-vessel occlusion stroke

Background and aimsPostinterventional hypothermia is a frequent complication in patients with large-vessel occlusion strokes (LVOS) after mechanical thrombectomy (MT). This inadvertent hypothermia might potentially have neuroprotective but also adverse effects on patients’ outcomes. The aim of the study was to determine the rate of hypothermia in patients with LVOS receiving MT and its influence on functional outcome.MethodsWe performed a monocentric, retrospective study using a prospectively derived databank, including all LVOS patients receiving MT between 2015 and 2021. Predictive values of postinterventional body temperature and body temperature categories (hyperthermia (≥38°C), normothermia (35°C–37.9°C), and hypothermia (<35°C)) on functional outcome were analyzed using multivariable Bayesian logistic regression models. Favorable outcome was defined as modified Rankin Scale (mRS) ≤3.ResultsOf the 480 included LVOS patients with MT (46.0% men; mean ± SD age 73 ± 12.9 years), 5 (1.0%) were hyperthermic, 382 (79.6%) normothermic, and 93 (19.4%) hypothermic. Postinterventional hypothermia was significantly associated with unfavorable functional outcome (mRS > 3) after 90 days (OR 2.06, 95% CI 1.01–4.18, p = 0.045). For short-term functional outcome, patients with hypothermia had a higher discharge NIHSS (OR 1.38, 95% CI 1.06 to 1.79, p = 0.015) and a higher change of NIHSS from admission to discharge (OR 1.35, 95% CI 1.03 to 1.76, p = 0.029).ConclusionApproximately a fifth of LVOS patients in this cohort were hypothermic after MT. Hypothermia was an independent predictor of unfavorable functional outcomes. Our findings warrant a prospective trial investigating active warming during MT

A mRNA panel for differentiation between acute exacerbation or pneumonia in COPD patients

IntroductionPatients suffering from chronic obstructive pulmonary disease (COPD) are prone to acute exacerbations (AECOPD) or community acquired pneumonia (CAP), both posing severe risk of morbidity and mortality. There is no available biomarker that correctly separates AECOPD from COPD. However, because CAP and AECOPD differ in aetiology, treatment and prognosis, their discrimination would be important.MethodsThis study analysed the ability of selected candidate transcripts from peripheral blood mononuclear cells (PBMCs) to differentiate between patients with AECOPD, COPD & CAP, and CAP without pre-existing COPD.ResultsIn a previous study, we identified differentially regulated genes between CAP and AECOPD in PBMCs. In the present new cohort, we tested the potential of selected candidate PBMC transcripts to differentiate at early time points AECOPD, CAP+COPD, and CAP without pre-existing COPD. Expression of YWHAG, E2F1 and TDRD9 held predictive power: This gene set predicted diseases markedly better (model accuracy up to 100%) than classical clinical markers like CRP, lymphocyte count and neutrophil count (model accuracy up to 82%).DiscussionIn summary, in our cohort expression levels of YWHAG, E2F1 and TDRD9 differentiated with high accuracy between COPD patients suffering from acute exacerbation or CAP

Plasma Lipidomic Profiling Using Mass Spectrometry for Multiple Sclerosis Diagnosis and Disease Activity Stratification (LipidMS)

This investigation explores the potential of plasma lipidomic signatures for aiding in the diagnosis of Multiple Sclerosis (MS) and evaluating the clinical course and disease activity of diseased patients. Plasma samples from 60 patients with MS (PwMS) were clinically stratified to either a relapsing-remitting (RRMS) or a chronic progressive MS course and 60 age-matched controls were analyzed using state-of-the-art direct infusion quantitative shotgun lipidomics. To account for potential confounders, data were filtered for age and BMI correlations. The statistical analysis employed supervised and unsupervised multivariate data analysis techniques, including a principal component analysis (PCA), a partial least squares discriminant analysis (oPLS-DA) and a random forest (RF). To determine whether the significant absolute differences in the lipid subspecies have a relevant effect on the overall composition of the respective lipid classes, we introduce a class composition visualization (CCV). We identified 670 lipids across 16 classes. PwMS showed a significant increase in diacylglycerols (DAG), with DAG 16:0;0_18:1;0 being proven to be the lipid with the highest predictive ability for MS as determined by RF. The alterations in the phosphatidylethanolamines (PE) were mainly linked to RRMS while the alterations in the ether-bound PEs (PE O-) were found in chronic progressive MS. The amount of CE species was reduced in the CPMS cohort whereas TAG species were reduced in the RRMS patients, both lipid classes being relevant in lipid storage. Combining the above mentioned data analyses, distinct lipidomic signatures were isolated and shown to be correlated with clinical phenotypes. Our study suggests that specific plasma lipid profiles are not merely associated with the diagnosis of MS but instead point toward distinct clinical features in the individual patient paving the way for personalized therapy and an enhanced understanding of MS pathology