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Record W2754342062 · doi:10.1139/cjpp-2017-0266

Transcerebral net exchange of vasoactive peptides and catecholamines during lipopolysaccharide-induced systemic inflammation in healthy humans

2017· article· en· W2754342062 on OpenAlexvenueno aff
Ronan M. G. Berg, Sarah Taudorf, Damian M. Bailey, Rasmus Holmboe Dahl, Carsten Lundby, Kirsten Møller

Bibliographic record

VenueCanadian Journal of Physiology and Pharmacology · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicS100 Proteins and Annexins
Canadian institutionsnot available
Fundersnot available
KeywordsVasoactiveInflammationVasoactive intestinal peptideSystemic inflammationLipopolysaccharideInternal medicineChemistryMedicineEndocrinologyPharmacologyNeuropeptideReceptor

Abstract

fetched live from OpenAlex

The systemic inflammatory response triggered by lipopolysaccharide (LPS) is associated with cerebral vasoconstriction, but the underlying mechanisms are unknown. We therefore examined whether a 4-hour intravenous LPS infusion (0.3 ng·kg−1) induces any changes in the transcerebral net exchange of the vasoactive peptides endothelin-1 (ET-1) and calcitonin-gene related peptide (CGRP) and catecholamines in human volunteers. Cerebral blood flow was measured by the Kety–Schmidt technique, and paired arterial-to-jugular venous blood samples were obtained for estimating the transcerebral exchange of ET-1, CGRP, and catecholamines by the Fick principle in 12 volunteers before and after LPS infusion. The cerebrovascular release of ET-1 was enhanced, whereas the transcerebral net exchange of CGRP and catecholamines was unaffected. Our findings thus point towards locally produced ET-1 within the cerebrovasculature as a contributor to cerebral vasoconstriction after LPS infusion.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.017
GPT teacher head0.279
Teacher spread0.263 · 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 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

Citations2
Published2017
Admission routes1
Has abstractyes

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