Investigation of Potential Therapeutic Application of Thioredoxin Therapy for Enhancement of NPCs Proliferation After Stroke
Bibliographic record
Abstract
Stroke is a leading cause of death and disability in Canada. The acute management of stroke \ninvolves the use of thrombolytics and/or mechanical recanalization. Despite successful implementation \nof these techniques, limited therapeutic benefits emphasize a current need for novel treatments. \nThe presence of endogenous neural precursor cells (NPCs) in the subventricular zone of the adult \nhuman central nervous system represents a potential therapeutic cell source in the setting of stroke. \nThese NPCs have a limited capacity to replace neurons, astrocytes and oligodendrocytes in response \nto stroke, but tissue demand ultimately exceeds this proliferative capacity. \nIn this project, we show that thioredoxin (Trx) treatment enhances NPC proliferation and modifies \ntheir differentiation. Application of Trx in an in-vitro model of NPC culture induced cell proliferation. The \neffect of Trx on NPC differentiation was investigated using cell specific immunocytochemistry, which \ndisplayed significantly higher numbers of oligodendrocyte (Oiig2+ cells) and lower numbers of GFAP+ \ncells in Trx pre-treated cultures. To investigate the effects of Trx in-vivo, a focal permanent \ndevascularisation lesion (stroke) was implemented in experimental rat models, and Trx was delivered \nafter intraventricular infusion. Immunohistochemistry revealed enhanced NPC proliferation in animals \nthat received Trx treatment versus animals that received the vehicle, as indicated by the presence of \nsignificantly higher levels of proliferation marker (BrdU) and stem cells (Sox2) expressing cells in both \nthe subventricular zone and cortex. \nUltimately, these results highlight Trx as a potential neuroprotective therapy in the setting of \nstroke that can enhance the endogenous NPC proliferation.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".