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Record W4317376685 · doi:10.1016/j.celrep.2023.112010

Time-dependent and selective microglia-mediated removal of spinal synapses in neuropathic pain

2023· article· en· W4317376685 on OpenAlexafffund
Noosha Yousefpour, Samantha Locke, Haley Deamond, Chengyang Wang, Lucas Marques, Manon St-Louis, Johanne Ouellette, Arkady Khoutorsky, Yves De Koninck, Alfredo Ribeiro‐da‐Silva

Bibliographic record

VenueCell Reports · 2023
Typearticle
Languageen
FieldMedicine
TopicPain Mechanisms and Treatments
Canadian institutionsUniversité LavalMcGill UniversityMcGill University Health Centre
FundersCanadian Institutes of Health ResearchLouise and Alan Edwards FoundationCanada Research Chairs
KeywordsNeuroscienceNeuropathic painMicrogliaSynapseInhibitory postsynaptic potentialExcitatory postsynaptic potentialSynaptic pruningSpinal cordMedicineBiologyInflammationImmunology

Abstract

fetched live from OpenAlex

Neuropathic pain is a debilitating condition resulting from damage to the nervous system. Imbalance of spinal excitation and inhibition has been proposed to contribute to neuropathic pain. However, the structural basis of this imbalance remains unknown. Using a preclinical model of neuropathic pain, we show that microglia selectively engulf spinal synapses that are formed by central neurons and spare those of peripheral sensory neurons. Furthermore, we reveal that removal of inhibitory and excitatory synapses exhibits distinct temporal patterns, in which microglia-mediated inhibitory synapse removal precedes excitatory synapse removal. We also find selective and gradual increase in complement depositions on dorsal horn synapses that corresponds to the temporal pattern of microglial synapse pruning activity and type-specific synapse loss. Together, these results define a specific role for microglia in the progression of neuropathic pain pathogenesis and implicate these immune cells in structural remodeling of dorsal horn circuitry.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.238
Threshold uncertainty score0.406

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.010
GPT teacher head0.243
Teacher spread0.232 · 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

Citations50
Published2023
Admission routes2
Has abstractyes

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