In this study (trial registration: NCT02166021), we aimed to evaluate the optimal way of administration, the safety and the clinical efficacy of mesenchymal stem cell (MSC) transplantation in patients with active and progressive multiple sclerosis. Forty-eight patients (28 males and 20 females) with progressive multiple sclerosis (Expanded Disability Status Scale: 3.0–6.5, mean : 5.6 ± 0.8, mean age: 47.5 ± 12.3) and evidence of either clinical worsening or activity during the previous year, were enrolled (between 2015 and 2018). Patients were randomized into three groups and treated intrathecally (IT) or intravenously (IV) with autologous MSCs (1 × 106/kg) or sham injections. After 6 months, half of the patients from the MSC-IT and MSC-IV groups were retreated with MSCs, and the other half with sham injections. Patients initially assigned to sham treatment were divided into two subgroups and treated with either MSC-IT or MSC-IV. The study duration was 14 months. No serious treatment-related safety issues were detected. Significantly fewer patients experienced treatment failure in the MSC-IT and MSC-IV groups compared with those in the sham-treated group (6.7%, 9.7%, and 41.9%, respectively, P = 0.0003 and P = 0.0008). During the 1-year follow-up, 58.6% and 40.6% of patients treated with MSC-IT and MSC-IV, respectively, exhibited no evidence of disease activity compared with 9.7% in the sham-treated group (P < 0.0001 and P < 0.0048, respectively). MSC-IT transplantation induced additional benefits on the relapse rate, on the monthly changes of the T2 lesion load on MRI, and on the timed 25-foot walking test, 9-hole peg test, optical coherence tomography, functional MRI and cognitive tests. Treatment with MSCs was well-tolerated in progressive multiple sclerosis and induced short-term beneficial effects regarding the primary end points, especially in the patients with active disease. The intrathecal administration was more efficacious than the intravenous in several parameters of the disease. A phase III trial is warranted to confirm these findings.

Introduction: Healthcare professionals (HCPs) appear to be at increased risk for negative psychological outcomes [e.g. depression, anxiety, post-traumatic stress disorder (PTSD), moral distress] and associated impacts on functioning throughout the COVID-19 pandemic. HCPs working on designated COVID-19 units may be further impacted than their colleagues not on these units given added demands of patient care and risk of contracting COVID-19. Little is known, however, about the mental health and functioning of specific professional groups beyond nurses and physicians, including respiratory therapists (RTs), over the course of the pandemic. Accordingly, the purpose of the present study was to characterize the mental health and functioning of Canadian RTs and compare profiles between RTs working on and off designated COVID-19 units. Methods: Canadian RTs completed an online survey between February and June 2021, including demographic information (e.g. age, sex, gender,) and measures of depression, anxiety, stress, PTSD, moral distress and functional impairment. Descriptive statistics, correlation analyses and between-groups comparisons were conducted to characterize RTs and compare profiles between those on and off COVID-19 units. Results: Two hundred and eighteen ( N = 218) RTs participated in this study. The estimated response rate was relatively low (6.2%) Approximately half of the sample endorsed clinically relevant symptoms of depression (52%), anxiety (51%) and stress (54%) and one in three (33%) screened positively for potential PTSD. All symptoms correlated positively with functional impairment ( p 's < .05). RTs working on COVID-19 units reported significantly greater patient-related moral distress compared to those not on these units ( p < .05). Conclusion: Moral distress and symptoms of depression, anxiety, stress and PTSD were prevalent among Canadian RTs and were associated with functional impacts. These results must be interpreted with caution given a low response rate, yet raise concern regarding the long-term impacts of pandemic service among RTs.

We present PuPl (Pupillometry Pipeliner), an Octave-compatible library of Matlab functions for processing pupillometry data with an easy-to-use graphical user interface (GUI). PuPl's preprocessing tools include blink correction, data smoothing, and gaze correction. PuPl can also define and sort trials, and segment data to isolate event-related pupil dilation responses. PuPl's flexible tabular export tools enable a wide variety of statistical analyses. Furthermore, PuPl can translate GUI interactions into a Matlab script, enabling easy creation and reconfiguration of reusable data processing pipelines. Finally, PuPl is designed to be extensible, and users can easily contribute functionality as best practices for pupillometry evolve. Here we demonstrate PuPl by replicating published results using publicly available data. PuPl can be downloaded from github. com/kinleyid/pupl.

Accurate prediction of progression in subjects at risk of Alzheimer's disease is crucial for enrolling the right subjects in clinical trials. However, a prospective comparison of state-of-the-art algorithms for predicting disease onset and progression is currently lacking. We present the findings of The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge, which compared the performance of 92 algorithms from 33 international teams at predicting the future trajectory of 219 individuals at risk of Alzheimer's disease. Challenge participants were required to make a prediction, for each month of a 5-year future time period, of three key outcomes: clinical diagnosis, Alzheimer's Disease Assessment Scale Cognitive Subdomain (ADAS-Cog13), and total volume of the ventricles. No single submission was best at predicting all three outcomes. For clinical diagnosis and ventricle volume prediction, the best algorithms strongly outperform simple baselines in predictive ability. However, for ADAS-Cog13 no single submitted prediction method was significantly better than random guessing. On a limited, cross-sectional subset of the data emulating clinical trials, performance of best algorithms at predicting clinical diagnosis decreased only slightly (3% error increase) compared to the full longitudinal dataset. Two ensemble methods based on taking the mean and median over all predictions, obtained top scores on almost all tasks. Better than average performance at diagnosis prediction was generally associated with the additional inclusion of features from cerebrospinal fluid (CSF) samples and diffusion tensor imaging (DTI). On the other hand, better performance at ventricle volume prediction was associated with inclusion of summary statistics, such as patient-specific biomarker trends. The submission system remains open via the website https://tadpole.grand-challenge.org, while code for submissions is being collated by TADPOLE SHARE: https://tadpole-share.github.io/. Our work suggests that current prediction algorithms are accurate for biomarkers related to clinical diagnosis and ventricle volume, opening up the possibility of cohort refinement in clinical trials for Alzheimer's disease.

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