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Record W2165872678 · doi:10.25011/cim.v36i4.19957

Levels of interleukin-33 and interleukin-6 in patients with acute coronary syndrome or stable angina

2013· article· en· W2165872678 on OpenAlexvenueno aff
Cong-Lin Liu, Deliang Shen, Zhu Kui, Junnan Tang, Qimin Hai, Jinying Zhang

Bibliographic record

VenueClinical and investigative medicine · 2013
Typearticle
Languageen
FieldImmunology and Microbiology
TopicIL-33, ST2, and ILC Pathways
Canadian institutionsnot available
FundersZhengzhou University
KeywordsAcute coronary syndromeMedicineStable anginaInternal medicineUnstable anginaGastroenterologyInterleukinInterleukin 6EndocrinologyMyocardial infarctionCytokine

Abstract

fetched live from OpenAlex

PURPOSE: The purpose of this study was to investigate the levels of interleukin-33 (IL-33) and interleukin-6 (IL-6) in patients with acute coronary syndrome or stable angina. METHODS: Serum IL-33 and IL-6 were measured with Enzyme Linked Immuosorbent Assay (ELISA) in patients with acute coronary syndrome (ACS, n=40), and stable angina pectoris (SAP, n=43). IL-33 and IL-6 were also determined in 30 healthy subjects (control group). RESULTS: The serum level of IL-33 in the ACS group (78.60±44.84 ng/L) was lower than in the SAP (102.58±37.21 ng/L, P<0.01) or control groups (130.24±10.17 ng/L, P<0.01). The serum level of IL-6 in the ACS group (39.90±12.64 ng/L) was higher than in the SAP (18.68±11.89 ng/L, P<0.05) or control groups (6.28±17.72 ng/L, P<0.05). There were no differences in serum levels of IL-33 and IL-6 among the single-, double- and triple-vessel lesion groups. IL-33 and IL-6 levels were negatively correlated with each other in the ACS (r=-0.871, P<0.01) and SAP groups (r=-0.788, P<0.01). CONCLUSION: The serum level of IL-33 was lower in patients with ACS or SAP and was negatively correlated with the serum level of IL-6. Thus, IL-33 and IL-6 may be used as biomarkers for evaluating inflammatory response and severity of coronary heart disease in patients with ACS or SAP.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.008
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 head0.081
GPT teacher head0.289
Teacher spread0.208 · 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 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

Citations15
Published2013
Admission routes1
Has abstractyes

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