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Record W2999772965 · doi:10.3389/fncel.2019.00575

The Role of Neuronal Pentraxin 2 (NP2) in Regulating Glutamatergic Signaling and Neuropathology

2020· review· en· W2999772965 on OpenAlexafffund
Georgina Chapman, Ushananthini Shanmugalingam, Patrice D. Smith

Bibliographic record

VenueFrontiers in Cellular Neuroscience · 2020
Typereview
Languageen
FieldImmunology and Microbiology
TopicBiomarkers in Disease Mechanisms
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGlutamatergicNeuroscienceNeuropathologySynaptic plasticityBiologyExcitatory postsynaptic potentialNeurotransmissionReceptorGlutamate receptorDiseaseBiochemistryMedicineInternal medicineInhibitory postsynaptic potential

Abstract

fetched live from OpenAlex

Pentraxins are a superfamily of evolutionarily conserved proteins that are characterized by their multimeric architecture and their calcium-dependent binding. They can be broadly grouped into two subfamilies: short pentraxins and long pentraxins. Pentraxins regulate many processes in the brain as well as the periphery. Neuronal pentraxin 2 (NP2/NPTX2), also known as neuronal activity-regulated pentraxin (Narp), is an immediate-early gene that has been shown to play a critical role in guiding synaptic plasticity. NP2 has been previously linked to excitatory neurotransmission, based on its ability to aggregate excitatory receptors in the central nervous system. The mechanisms mediating the effects of NP2 on excitatory neurotransmission remain unclear and warrants further investigation. This review article focuses on the biological features of NP2 and discusses the literature supporting a role for NP2 and other pentraxins in glutamatergic signaling. An analysis of evidence around the role of pentraxins in neuropathology is also reviewed.

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 score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
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 head0.013
GPT teacher head0.233
Teacher spread0.220 · 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

Citations50
Published2020
Admission routes2
Has abstractyes

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