In demyelinating diseases including multiple sclerosis (MS), neural stem cells (NSCs) can replace damaged oligodendrocytes if the local microenvironment supports the required differentiation process. Although chitinase-like proteins (CLPs) form part of this microenvironment, their function in this differentiation process is unknown. Here, we demonstrate that murine Chitinase 3-like-3 (Chi3l3/Ym1), human Chi3L1 and Chit1 induce oligodendrogenesis. In mice, Chi3l3 is highly expressed in the subventricular zone, a stem cell niche of the adult brain, and in inflammatory brain lesions during experimental autoimmune encephalomyelitis (EAE). We find that silencing Chi3l3 increases severity of EAE. We present evidence that in NSCs Chi3l3 activates the epidermal growth factor receptor (EGFR), thereby inducing Pyk2-and Erk1/2- dependent expression of a pro-oligodendrogenic transcription factor signature. Our results implicate CLP-EGFR-Pyk2-MEK-ERK as a key intrinsic pathway controlling oligodendrogenesis.

Background Smartphones and their built-in sensors allow for measuring functions in disease-related domains through mobile tests. This could improve disease characterization and monitoring, and could potentially support treatment decisions for multiple sclerosis (MS), a multifaceted chronic neurological disease with highly variable clinical manifestations. Practice effects can complicate the interpretation of both improvement over time by potentially exaggerating treatment effects and stability by masking deterioration. Objective The aim of this study is to identify short-term learning and long-term practice effects in 6 active tests for cognition, dexterity, and mobility in user-scheduled, high-frequency smartphone-based testing. Methods We analyzed data from 264 people with self-declared MS with a minimum of 5 weeks of follow-up and at least 5 repetitions per test in the Floodlight Open study, a self-enrollment study accessible by smartphone owners from 16 countries. The collected data are openly available to scientists. Using regression and bounded growth mixed models, we characterized practice effects for the following tests: electronic Symbol Digit Modalities Test (e-SDMT) for cognition; Finger Pinching and Draw a Shape for dexterity; and Two Minute Walk, U-Turn, and Static Balance for mobility. Results Strong practice effects were found for e-SDMT (n=4824 trials), Finger Pinching (n=19,650), and Draw a Shape (n=19,019) with modeled boundary improvements of 40.8% (39.9%-41.6%), 86.2% (83.6%-88.7%), and 23.1% (20.9%-25.2%) over baseline, respectively. Half of the practice effect was reached after 11 repetitions for e-SDMT, 28 repetitions for Finger Pinching, and 17 repetitions for Draw a Shape; 90% was reached after 35, 94, and 56 repetitions, respectively. Although baseline performance levels were highly variable across participants, no significant differences between the short-term learning effects in low performers (5th and 25th percentile), median performers, and high performers (75th and 95th percentile) were found for e-SDMT up to the fifth trial (β=1.50-2.00). Only small differences were observed for Finger Pinching (β=1.25-2.5). For U-Turn (n=15,051) and Static Balance (n=16,797), only short-term learning effects could be observed, which ceased after a maximum of 5 trials. For Two Minute Walk (n=14,393), neither short-term learning nor long-term practice effects were observed. Conclusions Smartphone-based tests are promising for monitoring the disease trajectories of MS and other chronic neurological diseases. Our findings suggest that strong long-term practice effects in cognitive and dexterity functions have to be accounted for to identify disease-related changes in these domains, especially in the context of personalized health and in studies without a comparator arm. In contrast, changes in mobility may be more easily interpreted because of the absence of long-term practice effects, even though short-term learning effects might have to be considered.

Background There is an unmet need for reliable and sensitive measures for better monitoring people with multiple sclerosis (PwMS) to detect disease progression early and adapt therapeutic measures accordingly. Objective To assess reliability of extracted features and meaningfulness of 11 tests applied through a smartphone application (“dreaMS”). Methods PwMS (age 18–70 and EDSS ≤ 6.5) and matched healthy volunteers (HV) were asked to perform tests installed on their smartphone once or twice weekly for 5 weeks. Primary outcomes were test–retest reliability of test features (target: intraclass correlation [ICC] ≥ 0.6 or median coefficient of variation [mCV] < 0.2) and reported meaningfulness of the tests by PwMS. Meaningfulness was self-assessed for each test on a 5-point Likert scale (target: mean score of > 3) and by a structured interview. ClinicalTrials.gov Identifier: NCT04413032. Results We included 31 PwMS (21 [68%] female, mean age 43.4 ± 12.0 years, median EDSS 3.0 [range 1.0–6.0]) and 31 age- and sex-matched healthy volunteers. Out of 133 features extracted from 11 tests, 89 met the preset reliability criteria. All 11 tests were perceived as highly meaningful to PwMS. Conclusion The dreaMS app reliably assessed features reflecting key functional domains meaningful to PwMS. More studies with longer follow-up are needed to prove validity of these measures as digital biomarkers in PwMS.

Background Wearable sensor technologies have the potential to improve monitoring in people with multiple sclerosis (MS) and inform timely disease management decisions. Evidence of the utility of wearable sensor technologies in people with MS is accumulating but is generally limited to specific subgroups of patients, clinical or laboratory settings, and functional domains. Objective This review aims to provide a comprehensive overview of all studies that have used wearable sensors to assess, monitor, and quantify motor function in people with MS during daily activities or in a controlled laboratory setting and to shed light on the technological advances over the past decades. Methods We systematically reviewed studies on wearable sensors to assess the motor performance of people with MS. We scanned PubMed, Scopus, Embase, and Web of Science databases until December 31, 2022, considering search terms “multiple sclerosis” and those associated with wearable technologies and included all studies assessing motor functions. The types of results from relevant studies were systematically mapped into 9 predefined categories (association with clinical scores or other measures; test-retest reliability; group differences, 3 types; responsiveness to change or intervention; and acceptability to study participants), and the reporting quality was determined through 9 questions. We followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) reporting guidelines. Results Of the 1251 identified publications, 308 were included: 176 (57.1%) in a real-world context, 107 (34.7%) in a laboratory context, and 25 (8.1%) in a mixed context. Most publications studied physical activity (196/308, 63.6%), followed by gait (81/308, 26.3%), dexterity or tremor (38/308, 12.3%), and balance (34/308, 11%). In the laboratory setting, outcome measures included (in addition to clinical severity scores) 2- and 6-minute walking tests, timed 25-foot walking test, timed up and go, stair climbing, balance tests, and finger-to-nose test, among others. The most popular anatomical landmarks for wearable placement were the waist, wrist, and lower back. Triaxial accelerometers were most commonly used (229/308, 74.4%). A surge in the number of sensors embedded in smartphones and smartwatches has been observed. Overall, the reporting quality was good. Conclusions Continuous monitoring with wearable sensors could optimize the management of people with MS, but some hurdles still exist to full clinical adoption of digital monitoring. Despite a possible publication bias and vast heterogeneity in the outcomes reported, our review provides an overview of the current literature on wearable sensor technologies used for people with MS and highlights shortcomings, such as the lack of harmonization, transparency in reporting methods and results, and limited data availability for the research community. These limitations need to be addressed for the growing implementation of wearable sensor technologies in clinical routine and clinical trials, which is of utmost importance for further progress in clinical research and daily management of people with MS. Trial Registration PROSPERO CRD42021243249; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=243249