Application of Cardiac Molecular Imaging Using Positron Emission Tomography in Evaluation of Drug and Therapeutics for Cardiovascular Disorders
Bibliographic record
Abstract
The incidence of cardiovascular disease is increasing with the aging population. This has stimulated a need for innovative means to evaluate and develop therapeutic strategies intended to improve patient care. Positron emission tomography (PET) imaging is an advanced nuclear imaging technology. The advantage of PET over other non-invasive imaging modalities is its ability to accurately measure tissue concentrations of specific radiolabeled compounds. These radioligands can be used as molecular probes to quantify physiological processes and the effects of therapy. Molecular imaging with PET has been applied to evaluate new and established drugs and therapies, as well as their effects on physiological parameters. New radiolabeled receptor ligands will also allow in vivo pharmacokinetic studies following drug treatment, yielding insights into drug delivery, optimal drug occupancy, and mechanism of action at the receptor level. These exciting tissue pharmacokinetic data could revolutionize evaluation of drug therapies in cardiovascular diseases. In addition, serial evaluations of these processes are now possible in both animals and humans permitting sensitive means to evaluate disease progression and therapies. New tools for imaging such as PET/CT and small animal PET broaden the potential of PET in drug evaluation. This review will describe the accuracy of PET as a non-invasive modality to quantify various parameters, and the application of PET in evaluating new and established therapies. This paper will also review the application of receptor ligand imaging and the principles of using surrogate physiological end-points in early drug development and evaluation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".