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Stress, immunity, cytokines and depression

2002· article· en· W2011349945 on OpenAlexaff
Hymie Anisman

Bibliographic record

VenueActa Neuropsychiatrica · 2002
Typearticle
Languageen
FieldNeuroscience
TopicStress Responses and Cortisol
Canadian institutionsCarleton University
Fundersnot available
KeywordsNeurochemicalStressorPsychoneuroimmunologyCytokinePsychologyNeurosciencePsychogenic diseaseImmune systemMoodDepression (economics)Chronic stressMedicineImmunologyPsychiatry

Abstract

fetched live from OpenAlex

The current issue of Acta Neuropsychiatrica presents a series of papers which together provide a broad overview relating stress, immunity, cytokine activity and depressive illness, as well as the influence of cytokines on other neurological disorders. This introduction to the issue presents a broad perspective of the impact of stressors on immune functioning in animal studies and in humans, considering the potential effects of acute, subchronic and chronic stressors, as well as the contribution of previous stressor experience in promoting neurochemical and immunological alterations. Given the supposition that cytokines may act as immunotransmitters, and immune challenge may be viewed as a stressor, a brief review is provided concerning the impact of stressors and cytokine challenges on central neurochemical functioning, with particular emphasis on the commonalties between the effects of these treatments. It is suggested that by virtue of the neurochemical changes imparted by cytokines, a depressive affect may be instigated, just as it is in response to psychogenic stressors. To this end, an overview is presented concerning the relationship between cytokines and depression, as well as the influence of cytokine treatments on behavioral changes in animal studies and among patients receiving immunotherapy. Provisionally, the data support the view that activation of the inflammatory response system may contribute to affective illness, and that cytokines may act as signaling molecules to activate central nervous system processes regulating mood states.

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 score0.516
Threshold uncertainty score0.604

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 science0.0000.000
Research integrity0.0000.000
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 head0.025
GPT teacher head0.252
Teacher spread0.226 · 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

Citations27
Published2002
Admission routes1
Has abstractyes

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