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Record W2333893582 · doi:10.4103/1673-5374.175042

Macrophage polarization in nerve injury: do Schwann cells play a role?

2016· review· en· W2333893582 on OpenAlexaff
Jo Anne Stratton, PrajayT Shah

Bibliographic record

VenueNeural Regeneration Research · 2016
Typereview
Languageen
FieldNeuroscience
TopicNerve injury and regeneration
Canadian institutionsHotchkiss Brain InstituteOntario Brain InstituteUniversity of Calgary
Fundersnot available
KeywordsSchwann cellCell biologyMacrophageMacrophage polarizationPeripheral nervous systemNerve injuryNeurosciencePeripheral nerve injuryImmune systemRegeneration (biology)BiologyImmunologyIn vitroCentral nervous system

Abstract

fetched live from OpenAlex

In response to peripheral nerve injury, the inflammatory response is almost entirely comprised of infiltrating macrophages. Macrophages are a highly plastic, heterogenic immune cell, playing an indispensable role in peripheral nerve injury, clearing debris and regulating the microenvironment to allow for efficient regeneration. There are several cells within the microenvironment that likely interact with macrophages to support their function - most notably the Schwann cell, the glial cell of the peripheral nervous system. Schwann cells express several ligands that are known to interact with receptors expressed by macrophages, yet the effects of Schwann cells in regulating macrophage phenotype remains largely unexplored. This review discusses macrophages in peripheral nerve injury and how Schwann cells may regulate their behavior.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.123
GPT teacher head0.428
Teacher spread0.304 · 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
GenreReview

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

Citations86
Published2016
Admission routes1
Has abstractyes

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