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Activating Resident Neural Precursor Cells in the Spinal Cord to Promote Neural Repair

2020· article· en· W3016949938 on OpenAlexaffabout
Emily Gilbert, Jessica Livingston, Monoleena Khan, Harini Kandavel, Tarlan Kehtari, Cindi M. Morshead

Bibliographic record

VenueThe FASEB Journal · 2020
Typearticle
Languageen
FieldNeuroscience
TopicNeurogenesis and neuroplasticity mechanisms
Canadian institutionsUniversity of WaterlooUniversity of Toronto
Fundersnot available
KeywordsNeural stem cellNeurogenesisSpinal cordSpinal cord injuryProgenitor cellNeuroscienceOligodendrocyteMultiple sclerosisPrecursor cellMedicineWhite matterExperimental autoimmune encephalomyelitisProgenitorStem cellCentral nervous systemBiologyImmunologyCellMyelinCell biologyMagnetic resonance imaging

Abstract

fetched live from OpenAlex

Within the mammalian spinal cord there are two distinct populations of neural precursor cells: neural stem and progenitor cells (NSPCs) and oligodendrocyte progenitor cells (OPCs). NSPCs are located within the periventricular zone of the spinal cord and are relatively quiescent and non‐neurogenic under homeostatic conditions. OPCs are located throughout the grey and white matter of the spinal cord and give rise to new oligodendrocytes throughout life. Both of these populations are activated following injury, however, their response is not sufficient for functional recovery. We propose that enhancing resident precursor activation is a promising approach to improve structural and functional outcomes following SCI. The FDA‐approved drug metformin (MET) has demonstrated efficacy in promoting improved outcomes in the injured brain through pleiotropic effects including: decreased inflammation, enhanced precursor cell activation and increased neurogenesis and oligogenesis. Here, we seek to establish the influence of MET treatment on NSPC and OPC populations and explore whether MET can improve functional recovery following injury in the spinal cord. Our cellular data reveal that in uninjured animals, MET treatment significantly expands the NSPC pool in females, but not males; increases neurogenesis in males only and enhances oligogenesis across both sexes. Conversely, we observed no effect of MET treatment on the number of OPCs in uninjured mice, irrespective of sex. To test the efficacy of MET treatment following injury, we used two distinct models: (1) a dorsal column injury in the thoracic spinal cord (SCI), and (2), a model of multiple sclerosis (experimental autoimmune encephalomyelitis, EAE). In both models, MET was delivered to mice immediately following injury and for 14 days. Following SCI, mice were tested on a skilled walking test on post‐injury days (PID) 7 and 14. While all mice showed a significant deficit on PID7, by PID14, MET treated animals were not significantly impaired relative to pre‐injury performance and naive controls. In the EAE model, we evaluated the efficacy of MET treatment through both clinical scoring (based on degree of paralysis) and gait analysis. EAE resulted in significant clinical deficits 14 days following induction, which were mitigated in mice that received MET. Additionally, our gait analysis revealed improved metrics in MET treated mice. Across both injury models we observed significantly reduced inflammation. Taken together, our results support MET as a viable therapeutic strategy to expand and enhance NSPCs, improve the response of OPCs and support functional recovery following spinal cord injury. Support or Funding Information Canadian Institute of Health Research Medicine by Design

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.077
GPT teacher head0.295
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes2
Has abstractyes

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