Social Cognition in Individuals with Multiple Sclerosis and Co-morbid Diabetes: The Role of Metformin and the Biomarker Monoacylglycerol Lipase
Bibliographic record
Abstract
Multiple sclerosis (MS) can be associated with social cognition deficits.Metformin, a drug used to treat type II diabetes mellitus (DMII) improves social cognition in mice by repressing monoacylglycerol lipase (MgII) in the brain.Social cognition was compared in people with MS (PwMS) and comorbid DMII who are and who are not treated with metformin.We recruited 22 individuals with MS and DMII, and 17 matched healthy controls to establish baseline levels of the outcome measures.Differences were assessed in social cognitive outcomes and MgII levels between metformin and nonmetformin, and healthy control groups.Mgll levels were lowest in the metformin group, although differences were not statistically significant.There were no social cognition differences between the metformin and nonmetformin groups, but the MS group overall performed worse than controls on certain social cognition domains.Implications of these results are reported, with suggestions for future social cognition and MS investigations.constructive feedback, and advice for my thesis.This endeavour would also not have been possible without Dr.Jason Berard.Thank you for answering all my questions, no matter how big, small, or silly.I truly appreciate your ability to make me feel like I'm never alone in this scary world of academia.Thank you also to Jordan for your invaluable help with the data collection process and for your enjoyable company during long administrative days.Dr. Jing Wang and Matthew Seegobin, your pioneering work with Mgll and metformin made this thesis possible, and I am grateful for your patience as I navigated myself around novel techniques.I would also like to acknowledge everyone at the Ottawa Hospital Multiple Sclerosis Clinic for their invaluable support, particularly Dr. Mark Freedman, Alicia Storey, Dawn Carle, and Jessica Mitchel.Importantly, I thank the participants themselves, without whom this thesis would not be possible.I would also like to extend my gratitude to my committee
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".