Dissecting and Visualizing the Functional Diversity of Cardiac Macrophages
Bibliographic record
Abstract
Cardiac macrophages represent a functionally diverse population of cells involved in cardiac homeostasis, repair, and remodeling. With recent advancements in single-cell technologies, it is possible to elucidate specific macrophage subsets based on transcriptional signatures and cell surface protein expression to gain a deep understanding of macrophage diversity in the heart. The use of fate-mapping technologies and parabiosis studies have provided insight into the ontogeny and dynamics of macrophages identifying subsets derived from embryonic and adult definitive hematopoietic progenitors that include tissue-resident and bone marrow monocyte-derived macrophages, respectively. Within the heart, these subsets have distinct tissue niches and functional roles in the setting of homeostasis and disease, with cardiac resident macrophages representing a protective cell population while bone marrow monocyte-derived cardiac macrophages have a context-dependent effect, triggering both proinflammatory tissue injury, but also promoting reparative functions. With the increased understanding of the clinical relevance of cardiac macrophage subsets, there has been an increasing need to detect and measure cardiac macrophage compositions in living animals and patients. New molecular tracers compatible with positron emission tomography/computerized tomography and positron emission tomography/ magnetic resonance imaging have enabled investigators to noninvasively and serially visualize cardiac macrophage subsets within the heart to define associations with disease and measure treatment responses. Today, advancements within this thriving field are poised to fuel an era of clinical translation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".