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Changes in circulating biomarkers during a single hemodialysis session

2012· article· en· W2134432962 on OpenAlexvenueno aff
Tae Yamamoto, Marcelo Mazza do Nascimento, Shirley Yumi Hayashi, Abdul Rashid Qureshi, Jacek Waniewski, L BRODIN, Björn Anderstam, Britta Lind, Miguel C. Riella, Astrid Seeberger, Bengt Lindholm

Bibliographic record

VenueHemodialysis International · 2012
Typearticle
Languageen
FieldImmunology and Microbiology
TopicBiomarkers in Disease Mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHemodialysisFibroblast growth factor 23Internal medicineOsteoprotegerinInflammationHemoconcentrationEndocrinologyOxidative stressBiomarkerHyperphosphatemiaHematocritParathyroid hormoneCalciumBiochemistryChemistry

Abstract

fetched live from OpenAlex

The hemodialysis (HD) procedure induces an inflammatory response potentially contributing to cardiovascular disease. Here we investigated the acute impact of HD on circulating biomarkers. Circulating biomarkers (small solutes, middle molecular-sized peptides, and proteins) related to inflammation, oxidative stress, and vascular calcification (VC) were measured before and after a single session of HD in 45 clinically stable patients. Concentrations were corrected for ultrafiltration-induced hemoconcentration. Among vascular calcification-related biomarkers, osteoprotegerin and fetuin-A remained unchanged while fibroblast growth factor-23 (FGF23) decreased by -19%. Changes of FGF23 and changes of phosphate correlated (ρ = 0.61, P < 0.001). While C-reactive protein did not change, interleukin-6 (IL-6) increased by 14% and pentraxin 3 (PTX3) increased by 45%. IL-6 and PTX3 appear to be valid biomarkers of the intradialytic inflammatory response. VC-related markers were in general not affected by the single HD session; however, the observed correlation between acute changes of FGF-23 and phosphate during HD warrants further studies.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.018
GPT teacher head0.252
Teacher spread0.233 · 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 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

Citations33
Published2012
Admission routes1
Has abstractyes

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