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Record W2162395010 · doi:10.1155/2012/962347

Brain Natriuretic Peptide Production and Secretion in Inflammation

2012· article· en· W2162395010 on OpenAlexaff
Tsuneo Ogawa

Bibliographic record

VenueJournal of Transplantation · 2012
Typearticle
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMedicineInflammationBrain natriuretic peptideInternal medicineHeart failurePathogenesisMyocarditisDiseaseSepsisSecretionHormoneTumor necrosis factor alphaCardiologyDownregulation and upregulationHeart diseaseEndocrinologyGene

Abstract

fetched live from OpenAlex

Gene expression and secretion of the cardiac polypeptide hormones atrial natriuretic factor (ANF) and brain natriuretic peptide (BNP) are simultaneously upregulated in various cardiac disorders such as congestive heart failure, ischemic heart disease, and hypertensive heart disease, in which hemodynamic or neuroendocrine changes are key components in the progression of disease. However, during acute cardiac allograft rejection, plasma BNP levels are increased but not those of ANF. Successful treatment of the rejection episode decreases the elevated plasma BNP to prerejection values suggesting that substances related to inflammation may selectively influence BNP gene expression. Indeed, cytokines such as TNFα and IL-1β selectively stimulate cardiac BNP at the transcriptional and translational levels in cardiomyocyte cultures without affecting ANF. This selective BNP increase is seen in vivo, in addition to acute cardiac allograft rejection, in several circumstances where inflammation significantly contributes to the pathogenesis of disease such as in sepsis and in acute myocarditis.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.266
Teacher spread0.253 · 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 source (direct Gemma or distilled Codex), 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

Citations44
Published2012
Admission routes1
Has abstractyes

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