MétaCan
Menu
Back to cohort
Record W3011474800 · doi:10.1254/jpssuppl.93.0_2-s24-1

Regulation of pain signaling by the innate immune system

2020· article· en· W3011474800 on OpenAlexaff
Junting Huang, María A. Gandini, Saïd M’Dahoma, Daniel A. Muruve, Gerald W. Zamponi

Bibliographic record

VenueProceedings for Annual Meeting of The Japanese Pharmacological Society · 2020
Typearticle
Languageen
FieldNeuroscience
TopicNeuroinflammation and Neurodegeneration Mechanisms
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsInnate immune systemDownregulation and upregulationTLR2NeuroscienceReceptorPattern recognition receptorTRPV1Immune systemBiologyCell biologyImmunologyMedicineTransient receptor potential channelInternal medicineGene

Abstract

fetched live from OpenAlex

The innate immune system is the body's first response to infections and its activation gives rise to pain. How the innate immune system interacts with the sensory nervous system and contributes to pain is poorly understood. We have shown previously that intraplantar CFA injection leads to an upregulation of the deubiquitinase USP5 in dorsal root ganglia and spinal cord, and this in turn leads to an increase in the numbers of Cav3.2 T-type calcium channels in an activity dependent manner. Blocking USP5 interactions with cell permeant disruptor peptides mediates analgesia. Here we demonstrate that specific Toll-like receptors (TLRs) are up-regulated in response to CFA injection. This leads to macrophage infiltration into the dorsal root ganglia, and the production of interleukin 33 (IL33) which acts on sensory neuron to increase their activity. Block of spinal IL33/ST2 receptor signals attenuates CFA-induced inflammatory pain. The CFA induced upregulation of USP5 is abolished in TLR2 null mice, altogether indicating that the CFA mediated dysregulation of T-type calcium channel activity involves the activation of a TLR2-IL33-ST2 pathway, leading to the development of inflammatory pain.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.057
Threshold uncertainty score0.319

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
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 head0.036
GPT teacher head0.267
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

Citations0
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueProceedings for Annual Meeting of The Japanese Pharmacological SocietySame topicNeuroinflammation and Neurodegeneration MechanismsFrench-language works237,207