The impact of PM2.5, PM10 and NO2 on Covid-19 severity in a sample of patients with multiple sclerosis: A case-control study.

BACKGROUND: Many studies investigated the association between air pollution and Covid-19 severity but the only study focusing on patients with Multiple Sclerosis (MS) exclusively evaluated exposure to PM2.5. We aim to study, in a sample of MS patients, the impact of long-term exposure to PM2.5, PM10 and NO2 on Covid-19 severity, described as occurrence of pneumonia. METHODS: A 1:2 ratio case-control study was designed, differentiating cases and controls based on Covid-19 pneumonia. Associations between pollutants and outcome were studied using logistic regression. Weighted quantile sum (WQS) logistic regression was used to identify the individual contribution of each pollutant within the mixture; Least Absolute Shrinkage and Selection Operator (LASSO) penalized regression was performed to confirm the variable selection from WQS. All the analyses were adjusted for confounders selected a priori. RESULTS: Of the 615 eligible patients, 491 patients provided detailed place of exposure and were included in the principal analysis. Higher concentrations of air pollutants were associated with increased odds of developing Covid-19 pneumonia (PM2.5: 3rd vs 1st tercile OR(95% CI)=2.26(1.29;3.96); PM10: 3rd vs 1st tercile OR(95% CI)=2.12(1.22;3.68); NO2: 3rd vs 1st tercile OR(95% CI)=2.12(1.21;3.69)). Pollutants were highly correlated with each other; WQS index was associated to an increased risk of pneumonia (ß=0.44; p-value=0.004) and the main contributors to this association were NO2 (41%) and PM2.5 (34%). Consistently, Lasso method selected PM2.5 and NO2. CONCLUSIONS: Higher long-term exposure to PM2.5, PM10 and NO2 increased the odds of Covid-19 pneumonia among MS patients and the most dangerous pollutants were NO2 and PM2.5.

Correction: Evaluation of molecular inversion probe versus TruSeq® custom methods for targeted next-generation sequencing.

[This corrects the article DOI: 10.1371/journal.pone.0238467.].

Peripheral nerve morphogenesis induced by scaffold micropatterning.

Several bioengineering approaches have been proposed for peripheral nervous system repair, with limited results and still open questions about the underlying molecular mechanisms. We assessed the biological processes that occur after the implantation of collagen scaffold with a peculiar porous micro-structure of the wall in a rat sciatic nerve transection model compared to commercial collagen conduits and nerve crush injury using functional, histological and genome wide analyses. We demonstrated that within 60 days, our conduit had been completely substituted by a normal nerve. Gene expression analysis documented a precise sequential regulation of known genes involved in angiogenesis, Schwann cells/axons interactions and myelination, together with a selective modulation of key biological pathways for nerve morphogenesis induced by porous matrices. These data suggest that the scaffold's micro-structure profoundly influences cell behaviors and creates an instructive micro-environment to enhance nerve morphogenesis that can be exploited to improve recovery and understand the molecular differences between repair and regeneration.

The use of magnetic resonance imaging in multiple sclerosis: lessons learned from clinical trials.

Magnetic resonance imaging (MRI) is an important paraclinical tool for the diagnosis of multiple sclerosis (MS) and for monitoring its disease course. The efficacy of most of the available MS disease-modifying treatments has been tested in clinical trials where MRI-derived quantities served as primary or secondary outcome measures. However, conventional MRI measures (i.e., the number and volume of contrast-enhancing, the volumes of T2-hyperintense and T1-hypointense lesions and the assessment of brain volume changes) are limited in terms of pathological specificity and, as a consequence, are modestly correlated with clinical measures of disease activity and have a modest prognostic value as predictors of MS evolution. In the present review, we discuss the main factors potentially responsible for the so-called 'clinical MRI paradox' and how modern quantitative MR-based techniques might contribute to, at least partially, overcome it. The lessons learned from MS trials suggest that future applications of MRI to assess MS evolution should rely upon the use of composite measures thought to reflect the various components of the disease, as well as on study protocols specifically designed on the individual trial characteristics.