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Record W4281569242 · doi:10.1126/science.abl6773

Microglia-mediated degradation of perineuronal nets promotes pain

2022· article· en· W4281569242 on OpenAlexafffund
Shannon Tansley, Ning Gu, Alba Ureña Guzmán, Weihua Cai, Calvin Wong, Kevin C. Lister, Einer Muñoz-Pino, Noosha Yousefpour, R. Brian Roome, Jordyn Heal, Neil Wu, Annie Castonguay, Graham Lean, Elizabeth M. Muir, Artur Kania, Masha Prager‐Khoutorsky, Ji Zhang, Christos G. Gkogkas, James W. Fawcett, Luda Diatchenko, Alfredo Ribeiro‐da‐Silva, Yves De Koninck, Jeffrey S. Mogil, Arkady Khoutorsky

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.

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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.016
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

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

Citations192
Published2022
Admission routes2
Has abstractyes

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