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Record W4311573317 · doi:10.1038/s41598-022-24698-1

Plasma complement component C2: a potential biomarker for predicting abdominal aortic aneurysm related complications

2022· article· en· W4311573317 on OpenAlexaff
Tiam Feridooni, Abdelrahman Zamzam, Mariya Popkov, Muzammil H. Syed, Niousha Djahanpour, Mark Wheatcroft, Rawand Abdin, Mohammad Qadura

Bibliographic record

VenueScientific Reports · 2022
Typearticle
Languageen
FieldMedicine
TopicAortic aneurysm repair treatments
Canadian institutionsMcMaster UniversityUniversity of TorontoSt. Michael's Hospital
FundersBlair Foundation
KeywordsAbdominal aortic aneurysmBiomarkerComplement (music)Aortic aneurysmMedicineComponent (thermodynamics)AneurysmCardiologyInternal medicineRadiologyBiologyBiochemistryGene

Abstract

fetched live from OpenAlex

Blood-based adjunctive measures that can reliably predict abdominal aortic aneurysm (AAA)-related complications hold promise for mitigating the AAA disease burden. In this pilot study, we sought to evaluate the prognostic performance of complement factors in predicting AAA-related clinical outcomes. We recruited consecutive AAA patients (n = 75) and non-AAA patients (n = 75) presenting to St. Michael's Hospital. Plasma levels of complement proteins were assessed at baseline, as well as prospectively measured regularly over a period of 2 years. The primary outcome was the incidence of rapidly progressing AAA (i.e. aortic expansion), defined as change in AAA diameter by either 0.5 cm in 6 months, or 1 cm in 12 months. Secondary outcomes included incidence of major adverse aortic events (MAAE) and major adverse cardiovascular events (MACE). All study outcomes (AAA diameter, MACE and MAAE) were obtained during follow-up. Multivariable adjusted Cox regression analyses were performed to assess the prognostic value of plasma C2 levels in patients with AAA regarding rapid aortic expansion and MAAE and MACE. Event-free survival rates of both groups were also compared. Compared to non-AAA patients, patients with AAA demonstrated significantly higher plasma concentrations of C1q, C4, Factor B, Factor H and Factor D, and significantly lower plasma concentrations of C2, C3, and C4b (p = 0.001). After a median of 24 months from initial baseline measurements, C2 was determined as the strongest predictor of rapid aortic expansion (HR 0.10, p = 0.040), MAAE (HR 0.09, p = 0.001) and MACE (HR 0.14, p = 0.011). Based on the data from the survival analysis, higher levels of C2 at admission in patients with AAA predicted greater risk for rapid aortic expansion and MAAE (not MACE). Plasma C2 has the potential to be a biomarker for predicting rapid aortic expansion, MAAE, and the eventual need for an aortic intervention in AAA patients.

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)0.001
metaresearch head score (Gemma)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0010.000

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 head0.027
GPT teacher head0.292
Teacher spread0.265 · 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

Citations9
Published2022
Admission routes1
Has abstractyes

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