Childhood pet ownership and multiple sclerosis: A systematic review and meta-analysis

BackgroundMany studies have been conducted investigating a range of environmental factors which have been implicated in the pathogenesis of multiple sclerosis (MS). We collated available data about exposure to domestic animals before symptom onset in MS to perform a systematic review and meta-analysis.MethodsMedline, Embase and Cinahl were searched for relevant articles, based on pre-defined inclusion and exclusion criteria and reference lists were hand-searched. Data were extracted and critical analysis was conducted using the Newcastle-Ottawa criteria. Meta-analysis used random effects.ResultsStudy heterogeneity was high and study quality was variable. Random effects meta-analysis showed no associations with any pet ownership and development of MS.ConclusionIt is not possible to draw definitive conclusions from this work. The studies included had a high level of heterogeneity. There are many variables involved in pet ownership and exposure and the nature of the way these have been studied makes the analysis challenging

Pregnancy does not modify the risk of MS in genetically susceptible women

Objective To use the case-only gene-environment (G Embedded Image E) interaction study design to estimate interaction between pregnancy before onset of MS symptoms and established genetic risk factors for MS among White adult females. Methods We studied 2,497 female MS cases from 4 cohorts in the United States, Sweden, and Norway with clinical, reproductive, and genetic data. Pregnancy exposure was defined in 2 ways: (1) Embedded Image live birth pregnancy before onset of MS symptoms and (2) parity before onset of MS symptoms. We estimated interaction between pregnancy exposure and established genetic risk variants, including a weighted genetic risk score and both HLA and non-HLA variants, using logistic regression and proportional odds regression within each cohort. Within-cohort associations were combined using inverse variance meta-analyses with random effects. The case-only G × E independence assumption was tested in 7,067 individuals without MS. Results Evidence for interaction between pregnancy exposure and established genetic risk variants, including the strongly associated HLA-DRB1*15:01 allele and a weighted genetic risk score, was not observed. Results from sensitivity analyses were consistent with observed results. Conclusion Our findings indicate that pregnancy before symptom onset does not modify the risk of MS in genetically susceptible White females

Microcirculatory alterations in critically ill COVID-19 patients analyzed using artificial intelligence

Abstract Background The sublingual microcirculation presumably exhibits disease-specific changes in function and morphology. Algorithm-based quantification of functional microcirculatory hemodynamic variables in handheld vital microscopy (HVM) has recently allowed identification of hemodynamic alterations in the microcirculation associated with COVID-19. In the present study we hypothesized that supervised deep machine learning could be used to identify previously unknown microcirculatory alterations, and combination with algorithmically quantified functional variables increases the model’s performance to differentiate critically ill COVID-19 patients from healthy volunteers. Methods Four international, multi-central cohorts of critically ill COVID-19 patients and healthy volunteers (n = 59/n = 40) were used for neuronal network training and internal validation, alongside quantification of functional microcirculatory hemodynamic variables. Independent verification of the models was performed in a second cohort (n = 25/n = 33). Results Six thousand ninety-two image sequences in 157 individuals were included. Bootstrapped internal validation yielded AUROC(CI) for detection of COVID-19 status of 0.75 (0.69–0.79), 0.74 (0.69–0.79) and 0.84 (0.80–0.89) for the algorithm-based, deep learning-based and combined models. Individual model performance in external validation was 0.73 (0.71–0.76) and 0.61 (0.58–0.63). Combined neuronal network and algorithm-based identification yielded the highest externally validated AUROC of 0.75 (0.73–0.78) (P < 0.0001 versus internal validation and individual models). Conclusions We successfully trained a deep learning-based model to differentiate critically ill COVID-19 patients from heathy volunteers in sublingual HVM image sequences. Internally validated, deep learning was superior to the algorithmic approach. However, combining the deep learning method with an algorithm-based approach to quantify the functional state of the microcirculation markedly increased the sensitivity and specificity as compared to either approach alone, and enabled successful external validation of the identification of the presence of microcirculatory alterations associated with COVID-19 status