Oral contraceptives do not modify the risk of a second attack and disability accrual in a prospective cohort of women with a clinically isolated syndrome and early multiple sclerosis

Cohort study; Oral contraceptives; Second relapseEstudio de cohorte; Anticonceptivos orales; Segunda recaídaEstudi de cohorts; Anticonceptius orals; Segona recaigudaObjective: To evaluate whether oral contraceptive (OC) use is associated with the risk of a second attack and disability accrual in women with a clinically isolated syndrome (CIS) and early multiple sclerosis (MS). Methods: Reproductive information from women included in the Barcelona CIS prospective cohort was collected through a self-reported cross-sectional survey. We examined the relationship of OC exposure with the risk of a second attack and confirmed Expanded Disability Status Scale of 3.0 using multivariate Cox regression models, adjusted by age, topography of CIS, oligoclonal bands, baseline brain T2 lesions, body size at menarche, smoking, and disease-modifying treatment (DMT). OC and DMT exposures were considered as time-varying variables. Findings were confirmed with sensitivity analyses using propensity score models. Results: A total of 495 women were included, 389 (78.6%) referred to ever use OC and 341 (68.9%) started OC before the CIS. Exposure to OC was not associated with a second attack (adjusted hazard ratio (aHR) = 0.73, 95% confidence interval (CI) = 0.33–1.61) or disability accrual (aHR = 0.81, 95% CI = 0.17–3.76). Sensitivity analyses confirmed these results. Conclusion: OC use does not modify the risk of second attack or disability accrual in patients with CIS and early MS, once considered as a time-dependent exposure and adjusted by other potential confounders.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This project was supported by FIS PI15/0070 from Ministry of Economy and Competitiveness of Spain

Data Assimilation Enhancements to Air Force Weathers Land Information System

The United States Air Force (USAF) has a proud and storied tradition of enabling significant advancements in the area of characterizing and modeling land state information. 557th Weather Wing (557 WW; DoDs Executive Agent for Land Information) provides routine geospatial intelligence information to warfighters, planners, and decision makers at all echelons and services of the U.S. military, government and intelligence community. 557 WW and its predecessors have been home to the DoDs only operational regional and global land data analysis systems since January 1958. As a trusted partner since 2005, Air Force Weather (AFW) has relied on the Hydrological Sciences Laboratory at NASA/GSFC to lead the interagency scientific collaboration known as the Land Information System (LIS). LIS is an advanced software framework for high performance land surface modeling and data assimilation of geospatial intelligence (GEOINT) information

Deciphering multiple sclerosis disability with deep learning attention maps on clinical MRI

Deep learning; Disability; Structural MRIAprendizaje profundo; Discapacidad; Resonancia magnética estructuralAprenentatge profund; Discapacitat; Ressonància magnètica estructuralThe application of convolutional neural networks (CNNs) to MRI data has emerged as a promising approach to achieving unprecedented levels of accuracy when predicting the course of neurological conditions, including multiple sclerosis, by means of extracting image features not detectable through conventional methods. Additionally, the study of CNN-derived attention maps, which indicate the most relevant anatomical features for CNN-based decisions, has the potential to uncover key disease mechanisms leading to disability accumulation. From a cohort of patients prospectively followed up after a first demyelinating attack, we selected those with T1-weighted and T2-FLAIR brain MRI sequences available for image analysis and a clinical assessment performed within the following six months (N = 319). Patients were divided into two groups according to expanded disability status scale (EDSS) score: ≥3.0 and < 3.0. A 3D-CNN model predicted the class using whole-brain MRI scans as input. A comparison with a logistic regression (LR) model using volumetric measurements as explanatory variables and a validation of the CNN model on an independent dataset with similar characteristics (N = 440) were also performed. The layer-wise relevance propagation method was used to obtain individual attention maps. The CNN model achieved a mean accuracy of 79% and proved to be superior to the equivalent LR-model (77%). Additionally, the model was successfully validated in the independent external cohort without any re-training (accuracy = 71%). Attention-map analyses revealed the predominant role of frontotemporal cortex and cerebellum for CNN decisions, suggesting that the mechanisms leading to disability accrual exceed the mere presence of brain lesions or atrophy and probably involve how damage is distributed in the central nervous system.MS PATHS is funded by Biogen. This study has been possible thanks to a Junior Leader La Caixa Fellowship awarded to C. Tur (fellowship code is LCF/BQ/PI20/11760008) by “la Caixa” Foundation (ID 100010434). The salaries of C. Tur and Ll. Coll are covered by this award