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Record W2794992252 · doi:10.1177/1744806918765799

No requirement of interlukine-1 for long-term potentiation in the anterior cingulate cortex of adult mice

2018· article· en· W2794992252 on OpenAlexaff
Jing-Shan Lu, Qian Song, Mingming Zhang, Min Zhuo

Bibliographic record

VenueMolecular Pain · 2018
Typearticle
Languageen
FieldMedicine
TopicPain Mechanisms and Treatments
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsLong-term potentiationAnterior cingulate cortexNeurosciencePsychologyTerm (time)Cingulate cortexMedicineInternal medicineCentral nervous systemCognitionReceptor

Abstract

fetched live from OpenAlex

Background The enhanced expression of cytokines in the pathological states suggests that they have important roles in the initiation or maintenance of disease states. FINDINGS: To determine the involvement of cytokines in chronic neuropathic pain, the expression of cytokines in the anterior cingulate cortex neurons in the ligation of the common peroneal nerve mice was investigated. We utilized a cytokine enzyme-linked immunosorbent assay plate array to detect 23 cytokines in total eight mice including a female, and no significant differences were found in those cytokines between the common peroneal nerve model and sham surgery mice. Quantification of TNF-α at protein level revealed the unvaried expression in the anterior cingulate cortex in both neuropathic pain and visceral pain, but enhanced expression in the insular cortex in the visceral pain. Furthermore, we found that the IL-Ira, a kind of IL-1 receptor antagonist, had no effect on the theta burst stimulation-induced long-term potentiation in the anterior cingulate cortex. Conclusions Cytokines are not involved in chronic neuropathic pain induced by nerve injury in the anterior cingulate cortex. Our findings suggested that cytokines may not be a viable drug target to treat chronic neuropathic pain in the anterior cingulate cortex.

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)0.000
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 score0.256
Threshold uncertainty score0.256

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.280
Teacher spread0.270 · 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

Citations3
Published2018
Admission routes1
Has abstractyes

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