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Record W2902832840 · doi:10.18192/osurj.v1i1.3696

Comparison of regenerative neurogenesis in response to CNS injury between adult zebrafish and mice

2018· article· en· W2902832840 on OpenAlexaffvenue
Kuan-En Chung

Bibliographic record

VenueUniversity of Ottawa Science Undergraduate Research Journal · 2018
Typearticle
Languageen
FieldNeuroscience
TopicNeurogenesis and neuroplasticity mechanisms
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsNeurogenesisZebrafishNeuroscienceGliosisSpinal cord injuryCentral nervous systemBiologyRegeneration (biology)AstrocyteNeural stem cellGlial scarPopulationSpinal cordCell biologyStem cellMedicine

Abstract

fetched live from OpenAlex

The difference between adult zebrafish and mice in their regenerative capacity following central nervous system (CNS) injury is influenced by the permissiveness of the brain microenvironment aside from the intrinsic neurogenic potential of the cell population. In adult zebrafish, glia cells largely retain their radial characteristics and neurogenic capacity, and the zebrafish brain shows full recovery after traumatic brain injury (TBI) as well as spinal cord injury (SCI). Conversely, in mice, radial glia (RG) have largely differentiated into astrocytes. Excluding certain brain regions, following TBI, reactive astrocytes that show the potential to become neural stem cells (NSCs) in vitro remain strictly non-neurogenic in vivo due to the presence of inhibitory factors in the microenvironment. Combined with prolonged inflammation and gliosis, injury to the CNS eventually results in formation of a glial scar further impeding regeneration. However in rodents, suppression of neurogenesis may be a protection mechanism against possible detrimental side-effects of neurogenesis in the long term.

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 score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.082
GPT teacher head0.370
Teacher spread0.288 · 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

Citations1
Published2018
Admission routes2
Has abstractyes

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