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Record W4313812357 · doi:10.1101/2023.01.08.523173

Cortical astrocyte N-Methyl-D-Aspartate receptors influence whisker barrel activity and sensory discrimination

2023· preprint· en· W4313812357 on OpenAlexafffund
Noushin Ahmadpour, Meher Kantroo, Michael Stobart, Tania Salamovska, Finnegan O’Hara, Dustin Erickson, Sofia Carrion‐Falgarona, Jillian L. Stobart

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2023
Typepreprint
Languageen
FieldNeuroscience
TopicNeuroscience and Neuropharmacology Research
Canadian institutionsUniversity of Manitoba
FundersUniversity of ManitobaMitacsNatural Sciences and Engineering Research Council of CanadaFondation Brain CanadaManitoba Medical Service FoundationResearch Manitoba
KeywordsAstrocyteNeuroscienceSensory systemSomatosensory systemCalcium imagingIonotropic effectReceptorNMDA receptorGlutamatergicGlutamate receptorBiologyChemistryCalciumCentral nervous systemBiochemistry

Abstract

fetched live from OpenAlex

Abstract Cortical astrocytes encode sensory information through their calcium dynamics, but it remains unclear if modulation of astrocyte calcium transients can change somatosensory circuits and behaviour in vivo . Here, we used a novel knockdown approach to selectively reduce astrocyte N-methyl-D-aspartate receptors (NMDAR). We found that these ionotropic receptors contribute to astrocyte Ca 2+ transients encoding sensory information. This was essential for the optimal processing of sensory information in nearby neurons, since a reduction in astrocyte NMDARs caused circuit dysfunction and impaired neuronal responses to stimulation. This led to sensory discrimination deficits in the animal. Overall, our findings show that astrocytes can rapidly respond to glutamatergic transmission via their NMDAR and these receptors are an important component for astrocyte-neuron interactions that regulate cortical sensory discrimination in vivo .

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0020.001

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 head0.055
GPT teacher head0.309
Teacher spread0.255 · 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 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
Published2023
Admission routes2
Has abstractyes

Explore more

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