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Record W1587572559 · doi:10.1089/0897715041651060

Body Weight, Limb Size, and Muscular Properties of Early Paraplegic Mice

2004· article· en· W1587572559 on OpenAlexaff
Éric Landry, Jérôme Frenette, Pierre A. Guertin

Bibliographic record

VenueJournal of Neurotrauma · 2004
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsHindlimbForelimbSpinal cord injurySpinal cordMedicineAnatomyBody weightPhysical medicine and rehabilitationAnesthesiaInternal medicine

Abstract

fetched live from OpenAlex

Patients with spinal cord injury (SCI) typically experience body weight loss, motor function deficits, and a general decline of physical fitness. Animal models with these characteristics can serve to study the detailed adaptive changes following SCI. In the present study, we report the use of an adult paraplegic mouse model to study SCI-induced changes. We characterized the early effects of complete thoracic spinal cord transection on (1) whole body weight, (2) forelimb and hindlimb weight and volume, and (3) contractile properties of hindlimb extensor muscle. Drastic changes were found at 7 days post-spinal cord transection. These included a 24% loss in whole body weight accompanied by a large decrease of weight and volume in the forelimbs and the hindlimbs. We also observed in the soleus muscle, a 32% decrease in mass and maximal tetanic tension (Po) as well as a 21% and 48% increase in time-to-peak tension (TPT) and half-relaxation time (1/2 RT) respectively. After 28 days, all of the changes remained, except for 1/2 RT and TPT which nearly returned to control levels. Altogether, the results reveal that large changes in body weight, limb size and musculoskeletal properties occur within only one week after complete spinal cord transection. The use of paraplegic mouse models may provide new therapeutic approaches to restore motor and locomotor functions after SCI.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.728
Threshold uncertainty score0.377

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.091
GPT teacher head0.350
Teacher spread0.260 · 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 teacher head, 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

Citations41
Published2004
Admission routes1
Has abstractyes

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