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.

Surface Proteome of Plasma Extracellular Vesicles as Biomarkers for Pneumonia and Acute Exacerbation of Chronic Obstructive Pulmonary Disease.

BACKGROUND: Community-acquired pneumonia (CAP) and acute exacerbation of chronic obstructive pulmonary disease (AECOPD) represent a major burden of disease and death and their differential diagnosis is critical. A potential source of relevant accessible biomarkers are blood-borne small extracellular vesicles (sEVs). METHODS: We performed an extracellular vesicle array to find proteins on plasma sEVs that are differentially expressed and possibly allow the differential diagnosis between CAP and AECOPD. Plasma samples were analyzed from 21 healthy controls, 24 patients with CAP, and 10 with AECOPD . The array contained 40 antibodies to capture sEVs, which were then visualized with a cocktail of biotin-conjugated CD9, CD63, and CD81 antibodies. RESULTS: We detected significant differences in the protein decoration of sEVs between healthy controls and patients with CAP or AECOPD. We found CD45 and CD28 to be the best discrimination markers between CAP and AECOPD in receiver operating characteristic analyses, with an area under the curve >0.92. Additional ensemble feature selection revealed the possibility to distinguish between CAP and AECOPD even if the patient with CAP had COPD, with a panel of CD45, CD28, CTLA4 (cytotoxic T-lymphocyte-associated protein 4), tumor necrosis factor-R-II, and CD16. CONCLUSION: The discrimination of sEV-associated proteins is a minimally invasive method with potential to discriminate between CAP and AECOPD.

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

Background and aims: Postinterventional 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. Methods: We 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. Results: Of 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). Conclusion: Approximately 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.