Spatial Transcriptomics in Human Cardiac Tissue
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Spatial transcriptomics has transformed our understanding of gene expression by preserving the spatial context within tissues. This review focuses on the application of spatial transcriptomics in human cardiac tissues, exploring current technologies with a focus on commercially available platforms. We also highlight key studies utilizing spatial transcriptomics to investigate cardiac development, electro-anatomy, immunology, and ischemic heart disease. These studies demonstrate how spatial transcriptomics can be used in conjunction with other omics technologies to provide a more comprehensive picture of human health and disease. Despite its transformative potential, spatial transcriptomics comes with several challenges that limit its widespread adoption and broader application. By addressing these limitations and fostering interdisciplinary collaboration, spatial transcriptomics has the potential to become an essential tool in cardiovascular research. We hope this review serves as a practical guide for researchers interested in adopting spatial transcriptomics, particularly those with limited prior experience, by providing insights into current technologies, applications, and considerations for successful implementation.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it