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Record W4378714374 · doi:10.1038/s41467-023-38899-3

Natural killer cells and innate lymphoid cells 1 tune anxiety-like behavior and memory in mice via interferon-γ and acetylcholine

2023· article· en· W4378714374 on OpenAlexaff
Stefano Garofalo, Germana Cocozza, Alessandro Mormino, Giovanni Bernardini, Eleonora Russo, Donald Ielpo, Diego Andolina, Rossella Ventura, Katiuscia Martinello, Massimiliano Renzi, Sergio Fucile, Mattia Laffranchi, Eva Piano Mortari, Rita Carsetti, Giuseppe Sciumè, Silvano Sozzani, Angela Santoni, Marie‐Ève Tremblay, Richard M. Ransohoff, Cristina Limatola

Bibliographic record

VenueNature Communications · 2023
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune Cell Function and Interaction
Canadian institutionsUniversité Laval
FundersRegione LazioIstituto Italiano di TecnologiaAssociazione Italiana per la Ricerca sul CancroMinistero della SaluteSapienza Università di RomaMinistero dell’Istruzione, dell’Università e della RicercaEuropean Commission
KeywordsInnate lymphoid cellInterferonAcetylcholineInnate immune systemImmunologyAnxietyBiologyCell biologyMedicineImmune systemPsychiatry

Abstract

fetched live from OpenAlex

The mechanisms of communication between the brain and the immune cells are still largely unclear. Here, we characterize the populations of resident natural killer (NK) cells and innate lymphoid cells (ILC) 1 in the meningeal dura layer of adult mice. We describe that ILC1/NK cell-derived interferon-γ and acetylcholine can contribute to the modulation of brain homeostatic functions, shaping synaptic neuronal transmission and neurotransmitter levels with effects on mice behavior. In detail, the interferon-γ plays a role in the formation of non-spatial memory, tuning the frequency of GABAergic neurotransmission on cortical pyramidal neurons, while the acetylcholine is a mediator involved in the modulation of brain circuitries that regulate anxiety-like behavior. These findings disclose mechanisms of immune-to-brain communication that modulate brain functions under physiological conditions.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.777
Threshold uncertainty score0.762

Codex and Gemma teacher scores by category

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 science0.0000.001
Research integrity0.0000.001
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 head0.011
GPT teacher head0.262
Teacher spread0.251 · 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

Citations46
Published2023
Admission routes1
Has abstractyes

Explore more

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