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Record W3197312520

Quantifying Inflammation in Infarcted Myocardial Tissue with Severely Reduced Flow: A Hybrid PET/MRI Approach Using a Prolonged Constant Infusion of 18F-FDG and Gd-DTPA

2018· article· en· W3197312520 on OpenAlexaff
Benjamin Wilk, Gerald Wisenberg, Jane Sykes, John Butler, Michael S. Kovacs, Robert T. Thompson, Jonathan D. Thiessen, Frank S. Prato

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsLawson Health Research InstituteLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsMedicineHeart failureInflammationMagnetic resonance imagingMyocardial infarctionBlood flowCardiologyBolus (digestion)PathologyNuclear medicineInternal medicineRadiology
DOInot available

Abstract

fetched live from OpenAlex

108 Introduction: Cardiovascular disease is the leading cause of death worldwide. Heart failure, specifically, is predominantly caused by disregulation of inflammation often seen after a heart attack [1]. MRI has shown promise in detecting characteristics that can increase the risk of heart failure, including: infarct size, presence of hemorrhage and presence and size of an area of extreme low blood flow within the infarct called the region of microvascular obstruction (MO) [2]. What is needed is an imaging method that can distinguish between pro-inflammatory (neutrophils and M1 macrophages) and anti-inflammatory (M2 macrophages) cells as disregulation occurs when the pro-inflammatory phase is prolonged. It has been shown that 18FDG/PET can, in principle, distinguish between the pro- and anti-inflammatory cell types [3] however post heart attack there are three problems: a) 18FDG cannot penetrate the MO after intravenous bolus injection, b) resting healthy myocardium also has uptake of 18FDG and c) partial volume effects interfere with identification of MO tissue. A constant infusion may be able to penetrate the MO while simplifying kinetic modelling [4,5]. Here we present initial results addressing these three issues using a canine model of heart failure post heart attack and PET/MRI imaging during a simultaneous prolonged constant infusion of 18FDG and an extracellular MRI contrast agent (a gadolinium chelate e.g. Magnevist). Methods: Two animals were imaged at 5 days after a heart attack. During the 150-minute constant infusion of Magnevist and 18FDG, MRI T1-maps and 3D T1 weighted were acquired every 10 minutes; PET images were binned in 3-minute frames. Suppression of 18FDG uptake by cardiomyocytes was initiated at 40 minutes using a heparin injection and the start of a 50-minute lipid infusion. Results: a) The MRI T1-maps allow the identification of the infarcted tissue and the zone of MO with relatively little partial volume (figure 1B). b) The MRI T1-maps can be used to infer the degree of penetration into the MO zone. In one animal, the MO was penetrated after 150 minutes (figure 1C) while the other did not have a visible infarct at any time (not shown). c) The 18FDG uptake in normal heart cells was eliminated after suppression. d) The suppression of myocardial glucose uptake lasted at least 60 minutes after the lipid infusion was stopped (figure 1A and 1G). e) The MRI contrast agent deposition in the infarcted tissue including the MO zone was not affected by suppression. f) The 18FDG uptake in infarcted tissue and the zone of MO was not affected by suppression. Discussion: This PET/MRI protocol can be used in the canine model of heart failure to investigate therapies to regulate the inflammatory response. This PET/MRI protocol can be used in patients to detect disregulation of the inflammatory responses and to evaluate therapy. Future Work: Additional animals need to be studied. [1] Frangogiannis, NG. (2014). Nat Rev Cardiol. 11(5): 255-265. [2] Kali A. (2016). Circulation Cardiovasc Imaging. 9(11): e004996. [3] Thackeray, J. (2017). SNMMI 2017. J Nucl Med. 58:302. [4] Prato, FS. (2015). J Nucl Med. 56(2): 299-304. [5] Wilk, B. (2016). SNMMI 2016. J Nucl Med. 57:120.

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)0.000
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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.034
GPT teacher head0.319
Teacher spread0.285 · 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".

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Citations1
Published2018
Admission routes1
Has abstractyes

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