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Record W4214658103 · doi:10.3410/f.739753771.793584085

Faculty Opinions recommendation of Microglial trogocytosis and the complement system regulate axonal pruning in vivo.

2021· dataset· en· W4214658103 on OpenAlexfundno aff
Judith S Eisen, Joseph Bruckner

Bibliographic record

VenueFaculty Opinions – Post-Publication Peer Review of the Biomedical Literature · 2021
Typedataset
Languageen
FieldNeuroscience
TopicNeuroinflammation and Neurodegeneration Mechanisms
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchFonds de Recherche du Québec - SantéPlexxikonFaculty of Medicine, McGill UniversityMcGill University
KeywordsMicrogliaBiologyNeuroscienceCell biologyCD46Complement systemEx vivoAxonXenopusIn vivoImmunologyInflammationImmune systemGeneticsGene

Abstract

fetched live from OpenAlex

Partial phagocytosis-called trogocytosis-of axons by microglia has been documented in ex vivo preparations but has not been directly observed in vivo.The mechanisms that modulate microglial trogocytosis of axons and its function in neural circuit development remain poorly understood.Here, we directly observe axon trogocytosis by microglia in vivo in the developing Xenopus laevis retinotectal circuit.We show that microglia regulate pruning of retinal ganglion cell axons and are important for proper behavioral response to dark and bright looming stimuli.Using bioinformatics, we identify amphibian regulator of complement activation 3, a homolog of human CD46, as a neuronally expressed synapse-associated complement inhibitory molecule that inhibits trogocytosis and axonal pruning.Using a membrane-bound complement C3 fusion protein, we demonstrate that enhancing complement activity enhances axonal pruning.Our results support the model that microglia remodel axons via trogocytosis and that neurons can control this process through expression of complement inhibitory proteins.

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)0.001
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.051
Threshold uncertainty score0.171

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0060.007
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0510.054

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 head0.044
GPT teacher head0.331
Teacher spread0.286 · 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 designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueFaculty Opinions – Post-Publication Peer Review of the Biomedical Literature→Same topicNeuroinflammation and Neurodegeneration Mechanisms→French-language works237,207→