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Hypothalamically induced emotional behavior and immunological changes in the cat

2001· article· en· W1999629135 on OpenAlexaff
Yoshinobu Mori, Jingyi Ma, Sansei Tanaka, Kyoji Kojima, Koji Mizobe, Chiharu Kubo, Nobutada Tashiro

Bibliographic record

VenuePsychiatry and Clinical Neurosciences · 2001
Typearticle
Languageen
FieldNeuroscience
TopicStress Responses and Cortisol
Canadian institutionsWestern University
Fundersnot available
KeywordsLymphocytePsychologyBitingEmotional stressStimulationHypothalamusInternal medicineEndocrinologyMedicineNeuroscienceBiology

Abstract

fetched live from OpenAlex

Numerous animal studies on the correlation between stress and immunity have been performed but few such studies have been made concerning the relationship between various kinds of stress-related emotional behavior and immunological changes. Electrical stimulation of the hypothalamus in cats elicits various emotional behaviors such as restlessness, defensive attack, defensive retreat and quiet biting attack. We examined changes in the lymphocyte proliferative responses and plasma cortisol level which accompanied such emotional behavior. A significant increase in plasma cortisol was observed in the restlessness, defensive attack and defensive retreat groups, but not in the quiet biting attack or non-response (control) groups. A significant increase in the lymphocyte proliferative responses to phytohemagglutinin (PHA) was observed in the restlessness and defensive attack groups but not in the defensive retreat, quiet biting attack or non-response groups. These results suggest that various kinds of emotional behavior appear to be differentially correlated with the lymphocyte proliferative responses, while also being differentially correlated with the plasma cortisol concentration. Because the changes in lymphocyte responses and plasma cortisol did not always completely correlate with one another, the changes in the lymphocyte responses are not considered to be influenced by plasma cortisol alone.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score0.316

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.103
GPT teacher head0.368
Teacher spread0.266 · 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 designObservational
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

Citations20
Published2001
Admission routes1
Has abstractyes

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