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Partial sciatic nerve ligation induced more dramatic increase of neuropeptide Y immunoreactive axonal fibers in the gracile nucleus of middle-aged rats than in young adult rats

2000· article· en· W1972259116 on OpenAlexaff
Weiya Ma, Mark A. Bisby

Bibliographic record

VenueJournal of Neuroscience Research · 2000
Typearticle
Languageen
FieldMedicine
TopicPain Mechanisms and Treatments
Canadian institutionsQueen's University
Fundersnot available
KeywordsNeuropeptide Y receptorSciatic nerveDorsal root ganglionNeuropathic painMedicineNeuropeptideAxotomyAnatomyImmunocytochemistryNerve injuryEndocrinologyInternal medicineCentral nervous systemAnesthesiaDorsumReceptor

Abstract

fetched live from OpenAlex

Neuropeptide changes in primary sensory neurons caused by partial nerve injury are likely involved in the development of neuropathic pain. In this study, using immunocytochemistry, we examined neuropeptide Y (NPY) expression in lumbar dorsal root ganglion (DRG) cells of young adult (2-3 months old) and middle-aged (8-10 months old) rats 4 weeks after partial sciatic nerve ligation (PSNL). Significantly higher NPY immunoreactivity was induced in the injured side DRG neurons, the dorsal horn and the gracile nuclei in middle-aged rats than in young rats. Using combined fluorescent dye tracing and NPY immunostaining, we found in middle-aged rats that 46% injured DRG neurons projected to the gracile nucleus and 45% of injured neurons were also NPY-IR, whereas 42% spared DRG neurons projected to the gracile nucleus and 18% of spared neurons were also NPY-IR. Thus PSNL induces NPY up-regulation in spared as well as injured DRG neurons, both contribute to the increased NPY immunoreactivity in the gracile nucleus in the middle-aged rats. The more dramatic increase of NPY in DRG neurons of middle-aged rats after PSNL shows that the responses to partial nerve injury are age-dependent, that suggests a possible relevance to the higher incidence of neuropathic pain in human middle age.

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)0.003
metaresearch head score (Gemma)0.002
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 score0.867
Threshold uncertainty score0.357

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.084
GPT teacher head0.365
Teacher spread0.282 · 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

Citations26
Published2000
Admission routes1
Has abstractyes

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