Cardiovascular utility of single cell RNA-Seq
Bibliographic record
Abstract
PURPOSE OF REVIEW: Cardiovascular diseases remain the leading causes of morbidity and mortality globally. Single-cell RNA sequencing has the potential to improve diagnostics, risk stratification, and provide novel therapeutic targets that have the potential to improve patient outcomes. RECENT FINDINGS: Here, we provide an overview of the basic processes underlying single-cell RNA sequencing, including library preparation, data processing, and downstream analyses. We briefly discuss how the technique has been adapted to related medical disciplines, including hematology and oncology, with short term translational impact. We discuss potential applications of this technology within cardiology as well as recent innovative research within the field. We also discuss future directions to translate this technology to other high impact clinical areas. SUMMARY: The use of single-cell RNA sequencing technology has made significant advancements in the field of cardiology, with ongoing growth in terms of applications and uptake. Most of the current research has focused on structural or atherosclerotic heart disease. Future areas that stand to benefit from this technology include cardiac electrophysiology and cardio-oncology.
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 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) | 0.002 | 0.002 |
| 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 | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".