Molecular imaging of cardiovascular inflammation
Bibliographic record
Abstract
Cardiovascular diseases (CVDs), including atherosclerosis, are chronic inflammatory diseases characterised by a complex and evolving tissue micro-environment. Molecular heterogeneity of inflammatory responses translates into clinical outcomes. However, current medical imaging modalities are unable to reveal the cellular and molecular events at a level of detail that would allow more accurate and timely diagnosis and treatment. This is an inherent limitation of the current imaging tools, which are restricted to anatomical or functional data. Molecular imaging-the visualisation and quantification of molecules in the body-is already established in the clinic in the form of PET, yet the use of PET in CVD is limited. In this visual review, we will guide you through the current state of molecular imaging research, assessing the respective strengths and weaknesses of molecular imaging modalities, including those already being used in the clinic such as PET and MRI and emerging technologies at preclinical stage, such as photoacoustic imaging. We discuss the basic principles of each technology and provide key examples of their application in imaging inflammation in CVD and the added value into the diagnostic decision-making process. Finally, we discuss the barriers to the rapid successful clinical translation of these novel diagnostic modalities. LINKED ARTICLES: This article is part of a themed issue on Molecular imaging - visual themed issue. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v178.21/issuetoc.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".