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Record W4410221748 · doi:10.1016/j.cellimm.2025.104963

Investigating the effect of neuro-immune communication on immune responses in health and disease: Exploring immunological disorders

2025· review· en· W4410221748 on OpenAlexafffund
Fatemeh Hesampour, Charles N. Bernstein, Jean‐Eric Ghia

Bibliographic record

VenueCellular Immunology · 2025
Typereview
Languageen
FieldNeuroscience
TopicVagus Nerve Stimulation Research
Canadian institutionsChildren's Hospital Research Institute of ManitobaUniversity of Manitoba
FundersCanadian Institutes of Health Research
KeywordsImmune systemBiologyDiseaseImmunologyNeuroscienceMedicinePathology

Abstract

fetched live from OpenAlex

Recent recognition of the intricate nervous-immune system interplay has prompted research into the specific cellular components involved in these interactions. Emerging evidence suggests that immune and neural cells collaborate within distinct units and act in concert to regulate tissue function and provide protection. These specialized neuro-immune cell units have been identified in diverse body tissues, ranging from lymphoid organs to the bone marrow and mucosal barriers. Their significance has become increasingly apparent as they are recognized as pivotal regulators influencing a broad spectrum of physiological and pathological processes. This recognition extends to critical roles in hematopoiesis, organ function, inflammatory responses, and intricate tissue repair processes. This review explores the bidirectional communication between the nervous and immune systems. The focus is on understanding the profound impact of this communication on immune cells within key anatomical sites, such as the bone marrow, gastrointestinal tract, and lymphoid organs. By examining these interactions, this review aims to shed light on how this intricate network operates under normal and pathological conditions, offering insights into the mechanisms underlying health and disease.

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)0.002
metaresearch head score (Gemma)0.021
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.968
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.021
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.002
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 head0.119
GPT teacher head0.378
Teacher spread0.259 · 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.

Study designOther design
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

Citations4
Published2025
Admission routes2
Has abstractyes

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