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Spinal cord injury leads to atrophy in pelvic ganglia neurons

2020· article· en· W3007883844 on OpenAlexafffund
Arshdeep Marwaha, Rahul Sachdeva, Diana V. Hunter, Matt S. Ramer, Andrei V. Krassioukov

Bibliographic record

VenueExperimental Neurology · 2020
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsUniversity of British Columbia HospitalInternational Collaboration On Repair DiscoveriesUniversity of British Columbia
FundersSpinal ResearchCanadian Institutes of Health ResearchWings for Life
KeywordsSpinal cord injuryMedicineAtrophySpinal cordNeuroprotectionGenitourinary systemNeurosciencePathologyBiologyAnatomyInternal medicine

Abstract

fetched live from OpenAlex

Among the most devastating sequelae of spinal cord injury (SCI) are genitourinary and gastrointestinal dysfunctions. Post-ganglionic neurons in pelvic ganglia (PG) directly innervate and regulate the function of the lower urinary tract (LUT), bowel, and sexual organs. A better understanding of how SCI affects PG neurons is essential to develop therapeutic strategies for devastating gastrointestinal and genitourinary complications ensuing after injury. To evaluate the impact of SCI on the morphology of PG neurons, we used a well- characterized rat model of upper thoracic SCI (T3 transection) that causes severe autonomic dysfunction. Using immunohistochemistry for neuronal markers, the neuronal profile size frequency distribution was quantified at one-, four-, and eight-weeks post SCI using recursive translation. Our investigation revealed an SCI-dependent leftward shift in neuronal size (i.e. atrophy), observable as early as one-week post injury. However, this effect was more pronounced at four and eight-weeks post-SCI. These findings demonstrate the first characterization of SCI-associated temporal changes in morphology of PG neurons and warrant further investigation to facilitate development of therapeutic strategies for recovery of autonomic functions following 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 categoriesInsufficient payload (model declined to judge)
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 score0.454
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.057
GPT teacher head0.400
Teacher spread0.342 · 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.

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

Citations2
Published2020
Admission routes2
Has abstractyes

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