MétaCan
Menu
← Back to cohort

Responses of Central Microglial Cells and Ganglionic Macrophages to Peripheral Injury and Disease

2016· article· en· W2980369949 on OpenAlexaff

Bibliographic record

VenueThe FASEB Journal · 2016
Typearticle
Languageen
FieldNeuroscience
TopicNeuroinflammation and Neurodegeneration Mechanisms
Canadian institutionsQueen's University
Fundersnot available
KeywordsMicrogliaPeripheralDiseaseSpinal cord injuryNeuroscienceSpinal cordMedicinePathologyFunction (biology)BiologyImmunologyCell biologyInflammationInternal medicine

Abstract

fetched live from OpenAlex

Microglial cells in the brain and spinal cord are exquisitely sensitive to injury and disease, responding by activation that results in changes of morphology and function. These cells are also responsive to trauma/inflammation in peripheral targets, such that injury or disease of motor, sensory and/or autonomic fibers can result in robust activation of microglia centrally. This activation of microglia triggers the presentation of phenotypes that resembles cells with macrophage‐like properties (i.e., increased cytokine production). Curiously, resident macrophages in peripheral neural tissues (e.g., nerves and ganglia) display many similar morphological and functional properties of central microglia. We have used a number of injury paradigms to assess the activation of central microglial cells and peripheral macrophages, as a consequence of direct or indirect neural damage. These experimental models include i) unilateral partial sciatic nerve ligation (a model of peripheral nerve damage), ii) unilateral injection of monosodium‐iodoacetate (MIA) into the hind limb footpad (a model of cutaneous/joint inflammation), and dextran sodium sulfate (DSS) in the drinking water (a model of colonic inflammation). We have also assessed the activation of central microglial cells and peripheral macrophages in aging models (i.e., NGF transgenic mice and TgCRND8 mice). The transmembrane p75 neurotrophin receptor (p75NTR) binds with comparable affinity to neurotrophins (e.g., nerve growth factor, NGF) and cytokines (e.g., tumor necrosis factor alpha, TNFα). Both neurotrophins and cytokines have been implicated in the pathophysiological features of sensory neuron dysfunction as a consequence of injury, disease, and aging. For instance, following peripheral nerve damage, macrophages (i.e., resident and/or infiltrating blood‐borne) in the associated dorsal root ganglia (DRG) display increased process extension, which is indicative of activation. Central microglial cells act in kind. Our studies have sought to determine whether the expression of p75NTR in adult mice affects the activation of microglial cells in the spinal cord and macrophages in sensory ganglia after peripheral nerve/tissue injury. Effects we have measured in p75NTR‐deficient mice are curious because central microglial cells and peripheral macrophages (damaged or otherwise) do not express p75NTR. Since target tissue damage and/or inflammation can stimulate increased p75NTR levels by ganglionic glial cells (i.e., satellite and Schwann cells), we have speculated that such glial expression of p75NTR plays a role in the activation of central microglial cells and peripheral macrophages. One possible mechanism may be through p75NTR‐mediated sequestration of increased levels of neurotrophins and/or cytokines by glial cells, thereby minimizing microglia/macrophage activation in mice following injury and inflammation. Support or Funding Information Queen's University

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.017
GPT teacher head0.249
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueThe FASEB Journal→Same topicNeuroinflammation and Neurodegeneration Mechanisms→French-language works237,207→