Bibliographic record
Abstract
In the past few years there has been a rapid expansion of interest in the study of single cells, especially through the new techniques that involve single-cell RNA sequencing (scRNA-seq). Recently, these techniques have provided new insights into kidney health and disease, including insights into diabetic kidney disease (DKD). However, despite the interest and the technological advances, the study of individual cells in DKD is not a new concept. Many clinicians and researchers who work within the DKD space may be familiar with experimental techniques that actually involve the study of individual cells, but may be unfamiliar with newer scRNA-seq technology. Here, with the goal of improving accessibility to the single-cell field, we provide a primer on single-cell studies with a focus on DKD. We situate the technology in its historical context and provide a brief explanation of the common aspects of the different technologies available. Then we review some of the most important recent studies of kidney (patho)biology that have taken advantage of scRNA-seq techniques, before emphasizing the new insights into the molecular pathogenesis of DKD gleaned with these techniques. Finally, we highlight common pitfalls and limitations of scRNA-seq methods and we look toward the future to how single-cell experiments may be incorporated into the study of DKD and how to interpret the findings of these experiments.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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 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".