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Acute traumatic spinal cord injury induces glial activation in the cynomolgus macaque (<i>Macaca fascicularis</i>)

2012· article· en· W1508436468 on OpenAlexaff
A. Dusty Miller, Susan V. Westmoreland, N. R. Evangelous, A. Graham, John B. Sledge, Shanker Nesathurai

Bibliographic record

VenueJournal of Medical Primatology · 2012
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsMcMaster University
FundersNational Center for Research Resources
KeywordsMicrogliaMacaqueSpinal cordMedicineTraumatic injurySpinal cord injuryPathologyNitric oxide synthaseTraumatic brain injuryMyelinNeuroscienceMacrophageCentral nervous systemInflammationNitric oxideBiologyImmunologyInternal medicineSurgery

Abstract

fetched live from OpenAlex

BACKGROUND: Traumatic spinal cord injury leads to direct myelin and axonal damage and leads to the recruitment of inflammatory cells to site of injury. Although rodent models have provided the greatest insight into the genesis of traumatic spinal cord injury (TSCI), recent studies have attempted to develop an appropriate non-human primate model. METHODS: We explored TSCI in a cynomolgus macaque model using a balloon catheter to mimic external trauma to further evaluate the underlying mechanisms of acute TSCI. RESULTS: Following 1hour of spinal cord trauma, there were focal areas of hemorrhage and necrosis at the site of trauma. Additionally, there was a marked increased expression of macrophage-related protein 8, MMP9, IBA-1, and inducible nitric oxide synthase in macrophages and microglia at the site of injury. CONCLUSIONS: This data indicate that acute TSCI in the cynomolgus macaque is an appropriate model and that the earliest immunohistochemical changes noted are within macrophage and microglia populations.

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 score0.005
Threshold uncertainty score0.011

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 integrity0.0000.000
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 head0.061
GPT teacher head0.435
Teacher spread0.374 · 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

Citations20
Published2012
Admission routes1
Has abstractyes

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