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Record W4401689074 · doi:10.1016/j.carpath.2024.107686

Acute pericardial postischemic inflammatory responses: Characterization using a preclinical porcine model

2024· article· en· W4401689074 on OpenAlexafffund
Ali Fatehi Hassanabad, Jeannine Turnbull, Cheryl Hall, Friederike I. Schoettler, Mortaza Fatehi Hassanabad, Eleanor Love, Emilie de Chantal, Jameson A. Dundas, Carmina Albertine Isidoro, Sun Min Kim, Rosalie Morrish, Barb McLellan, Anna N. Zarzycki, Guoqi Teng, Darrell D. Belke, Bryan Har, Paul W.M. Fedak, Justin Deniset

Bibliographic record

VenueCardiovascular Pathology · 2024
Typearticle
Languageen
FieldMedicine
TopicPericarditis and Cardiac Tamponade
Canadian institutionsUniversity of AlbertaUniversity of CalgaryLibin Cardiovascular Institute of Alberta
FundersCanadian Institutes of Health Research
KeywordsMatrix metalloproteinaseChemokineInflammatory responseMedicineInflammationMatrix (chemical analysis)ImmunologyInternal medicineChemistry

Abstract

fetched live from OpenAlex

• The pericardial space is a rich reservoir of biologically active markers. • Given their proximity to the heart, the contents of the pericardial space may harbor important information that could reflect cardiac physiology and pathology. • The acute inflammatory response that takes place in the pericardial space after myocardial infarction (MI) is not known. • Herein, we use a porcine model to characterize the acute, post-MI, pericardial inflammatory response. • Future work should determine whether acute local changes contribute to long-term outcomes such as fibrosis or biventricular dysfunction, and whether such outcomes can be prevented by precisely targeting and inhibiting markers that populate the pericardial space acutely post-MI. Pericardial fluid (PF) contains cells, proteins, and inflammatory mediators, such as cytokines, chemokines, growth factors, and matrix metalloproteinases. To date, we lack an adequate understanding of the inflammatory response that acute injury elicits in the pericardial space. To characterize the inflammatory profile in the pericardial space acutely after ischemia/reperfusion. Pigs were used to establish a percutaneous ischemia/reperfusion injury model. PF was removed from pigs at different time points postanesthesia or postischemia/reperfusion. Flow cytometry was used to characterize the immune cell composition of PF, while multiplex analysis was performed on the acellular portion of PF to determine the concentration of inflammatory mediators. There was a minimum of 3 pigs per group. While native PF mainly comprises macrophages, we show that neutrophils are the predominant inflammatory cell type in the pericardial space after injury. The combination of acute ischemia/reperfusion (IR) and repeatedly accessing the pericardial space significantly increases the concentration of interleukin-1 beta (IL-1β) and interleukin-1 receptor antagonist (IL-1ra). IR significantly increases the pericardial concentration of TGFβ1 but not TGFβ2. We observed that repeated manipulation of the pericardial space can also drive a robust pro-inflammatory response, resulting in a significant increase in immune cells and the accumulation of potent inflammatory mediators in the pericardial space. In the present study, we show that both IR and surgical manipulation can drive robust inflammatory processes in the pericardial space, consisting of an increase in inflammatory cytokines and alteration in the number and composition of immune cells. A schematic depiction of the study design, methodology, and summarization of the major findings.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.924
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.030
GPT teacher head0.312
Teacher spread0.282 · 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

Citations3
Published2024
Admission routes2
Has abstractyes

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