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Record W3146948187 · doi:10.1089/neu.2020.7255

Temporal Progression of Acute Spinal Cord Injury Mechanisms in a Rat Model: Contusion, Dislocation, and Distraction

2021· article· en· W3146948187 on OpenAlexaff
Stephen Mattucci, Jason Speidel, Jie Liu, Wolfram Tetzlaff, Thomas R. Oxland

Bibliographic record

VenueJournal of Neurotrauma · 2021
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsInternational Collaboration On Repair DiscoveriesUniversity of British Columbia
Fundersnot available
KeywordsWhite matterSpinal cord injuryMedicineAxonSpinal cordAnatomyCorticospinal tractMyelinMagnetic resonance imagingCentral nervous systemInternal medicineDiffusion MRIRadiology

Abstract

fetched live from OpenAlex

Traumatic spinal cord injuries (SCIs) occur due to different spinal column injury patterns, including burst fracture, dislocation, and flexion-distraction. Pre-clinical studies modeling different SCI mechanisms have shown distinct histological differences between these injuries both acutely (3 h and less) and chronically (8 weeks), but there remains a temporal gap. Different rates of injury progression at specific regions of the spinal cord may provide insight into the pathologies that are initiated by specific SCI mechanisms. Therefore, the objective of this study was to evaluate the temporal progression of injury at specific tracts within the white matter, for time-points of 3 h, 24 h, and 7 days, for three distinct SCI mechanisms. In this study, 96 male Sprague Dawley rats underwent one of three SCI mechanisms: contusion, dislocation, or distraction. Animals were sacrificed at one of three times post-injury: 3 h, 24 h, or 7 days. Histological analysis using eriochrome cyanide and immunostaining for MBP, SMI-312, neurofilament-H (NF-H), and β-III tubulin were used to characterize white matter sparing and axon and myelinated axon counts. The regions analyzed were the gracile fasciculus, cuneate fasciculus, dorsal corticospinal tract, and ventrolateral white matter. Contusion, dislocation, and distraction SCIs demonstrated distinct damage patterns that progressed differently over time. Myelinated axon counts were significantly reduced after dislocation and contusion injuries in most locations and time-points analyzed (compared with sham). This indicates early myelin damage often within 3 h. Myelinated axon counts after distraction dropped early and did not demonstrate any significant progression over the next 7 days. Important differences in white matter degeneration were identified between injury types, with distraction injuries showing the least variability across time-points These findings and the observation that white matter injury occurs early, and in many cases, without much dynamic change, highlight the importance of injury type in SCI research-both clinically and pre-clinically.

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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
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 head0.088
GPT teacher head0.437
Teacher spread0.349 · 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

Citations12
Published2021
Admission routes1
Has abstractyes

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