Podocytes in health and glomerular disease
Bibliographic record
Abstract
Podocytes are highly specialized, terminally differentiated cells in the glomerulus of the kidney and these cells play a central role in blood filtration. In this review, we comprehensively describe the cell biology of podocytes under healthy conditions and in glomerular disorders wherein podocyte injury is a major pathological mechanism. First, the molecular mechanisms that maintain podocyte actin cytoskeleton structure, permanent cell cycle exit, and metabolism under healthy conditions are described. Secondly, the mechanisms of podocyte injury, including genetic alterations and external insults that ultimately disrupt podocyte actin cytoskeleton dynamics or interrupt podocyte quiescence and mitochondrial metabolism are discussed. This understanding forms the basis of described potential therapeutic agents that act by modulating dysregulated podocyte cytoskeleton organization, prevent or reverse cell cycle re-entry, and re-establish normal mitochondrial energy production. Lastly, the application of modern techniques such as single cell RNA sequencing, super resolution microscopy, atomic force microscopy, and glomerular organoids is improving the resolution of mechanistic podocytopathy knowledge. Taken together, our review provides critical insights into the cellular and molecular mechanisms leading to podocyte loss, necessary for the advancement of therapeutic development in glomerular diseases.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".