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Record W7099537264

Editorial The neuroendocrine-immune interface gone awry in aldosteronism

2004· article· en· W7099537264 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicAnimal and Plant Science Education
Canadian institutionsnot available
Fundersnot available
KeywordsWastingProinflammatory cytokineHeart failurePathophysiologyInflammationWasting SyndromeRespiratory systemCardiomyopathy
DOInot available

Abstract

fetched live from OpenAlex

See article by Lal et al. (pages 437–447) in this issue. Congestive heart failure (CHF), a clinical syndrome with characteristic signs and symptoms, is a salt-avid state whose origins are rooted in neurohormonal activation, including the circulating renin-angiotensin-aldosterone system (RAAS). A systemic illness accompanies CHF and contributes to a progressive downhill clinical course and poor prognosis. Features include: (a) oxi/nitrosative stress in such diverse tissues as skin, skeletal muscle, heart, lymphocytes and monocytes; (b) a proinflammatory phenotype involving multiple tissues and blood and expressed as elevated levels of chemokines and such cytokines as IL-6 and TNF-a; and (c) a catabolic state with loss of lean tissue, fat and bone that eventuates in a wasting syndrome termed cardiac cachexia. The pathophysiology of CHF includes a neuroendocrineimmune interface gone awry. This brief commentary, prepared in response to the study from the Leenen laboratory in Ottawa [1], focuses on this interface in aldosteronism. Apologies are extended for the limited discussion and literature citations dictated by space constraints. 1. The neuroendocrine-immune interface The body’s trillions of cells are organized into specialized tissues (e.g., epi- and endothelium, lymphoid cells of respiratory and gastrointestinal mucosae) and organs. Together, they serve as functional units integral to preserving homeostasis, such as the regulation of respiratory gas exchange, the composition and osmotic balance of the

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.200
Threshold uncertainty score1.000

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)0.0010.001

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 head0.022
GPT teacher head0.315
Teacher spread0.294 · 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 designNot applicable
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
Published2004
Admission routes1
Has abstractyes

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