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Record W4281569242 · doi:10.1126/science.abl6773

Microglia-mediated degradation of perineuronal nets promotes pain

2022· article· en· W4281569242 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueScience · 2022
Typearticle
Languageen
FieldMedicine
TopicPain Mechanisms and Treatments
Canadian institutionsMontreal Clinical Research InstituteUniversité LavalMcGill University
FundersCanadian Institutes of Health Research
KeywordsMicrogliaPerineuronal netNeuroscienceSpinal cordNociceptionNeuropathic painPeripheral nerve injuryMedicineNociceptorNerve injuryCentral nervous systemAnatomyBiologyInflammationPeripheral nerveReceptorImmunologyInternal medicine

Abstract

fetched live from OpenAlex

Activation of microglia in the spinal cord dorsal horn after peripheral nerve injury contributes to the development of pain hypersensitivity. How activated microglia selectively enhance the activity of spinal nociceptive circuits is not well understood. We discovered that after peripheral nerve injury, microglia degrade extracellular matrix structures, perineuronal nets (PNNs), in lamina I of the spinal cord dorsal horn. Lamina I PNNs selectively enwrap spinoparabrachial projection neurons, which integrate nociceptive information in the spinal cord and convey it to supraspinal brain regions to induce pain sensation. Degradation of PNNs by microglia enhances the activity of projection neurons and induces pain-related behaviors. Thus, nerve injury-induced degradation of PNNs is a mechanism by which microglia selectively augment the output of spinal nociceptive circuits and cause pain hypersensitivity.

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.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.388
Threshold uncertainty score0.242

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.016
GPT teacher head0.260
Teacher spread0.245 · 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