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Record W4378172599 · doi:10.1177/17448069231179011

Silent synapses in pain-related anterior cingulate cortex

2023· review· en· W4378172599 on OpenAlexafffund
Min Zhuo

Bibliographic record

VenueMolecular Pain · 2023
Typereview
Languageen
FieldNeuroscience
TopicNeuroscience and Neuropharmacology Research
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsLong-term potentiationNeurosciencePostsynaptic potentialAMPA receptorSilent synapseSynaptic plasticityAnterior cingulate cortexMetaplasticityPostsynaptic densityNeuronal memory allocationHippocampusNeuroplasticityNMDA receptorPsychologyMedicineExcitatory postsynaptic potentialReceptorInhibitory postsynaptic potentialInternal medicineCognition

Abstract

fetched live from OpenAlex

Synaptic plasticity such as Long-term potentiation (LTP) is a key mechanism for learning in central synapses including the cortex. There are two least two major forms of LTPs: presynaptic LTP and postsynaptic LTP. For postsynaptic LTP, the potentiation of AMPA receptor-mediated responses through protein phosphorylation is thought to be a key mechanism. Silent synapses have been reported in the hippocampus, but it is thought to be mainly present in the cortex during early development, and may contribute to maturation of the cortical circuit. However, recent several lines of evidence demonstrate that silent synapses may exist in mature synapses of adult cortex, and they can be recruited by LTP-inducing protocols, as well as chemical-induced LTP. In pain-related cortical regions, silent synapses may not only contribute to cortical excitation after peripheral injury, but also the recruitment of new cortical circuits as well. Thus, it is proposed that silent synapses and modification of functional AMPA receptors and NMDA receptors may play important roles in chronic pain, including phantom 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 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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.003

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 head0.082
GPT teacher head0.388
Teacher spread0.306 · 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

Citations9
Published2023
Admission routes2
Has abstractyes

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