Pathogenesis of common glomerular diseases – role of the podocyte cytoskeleton
Bibliographic record
Abstract
Abstract: Glomerulus is the filtration unit of the kidney where the first step of urine formation takes place. In the glomerulus, water and small molecules including waste products of the body are filtered into the urine, while large molecules essential for body function such as albumin are retained. When this barrier function of the kidney is impaired, protein leakage into the urine (proteinuria) occurs. Proteinuria is not only a hallmark of many glomerular diseases but also a prognostic marker of kidney disease progression. Visceral glomerular epithelial cells (commonly called podocytes) are known to have an important role in the maintenance of glomerular barrier function. In the last decade, remarkable progress has been made in podocyte biology, mainly led by the discoveries of important proteins that work together to maintain the intricate morphology and function of podocytes. Most of these so-called podocyte proteins modulate the actin cytoskeleton either directly or indirectly. The aim of the current review is to discuss the pathogenesis of common glomerular diseases with a particular focus on the role of the actin cytoskeleton in podocytes. The diseases covered include minimal change disease, focal segmental glomerulosclerosis (idiopathic and hereditary), membranous nephropathy, hypertensive glomerulosclerosis, and diabetic nephropathy. Keywords: glomerular disease, podocyte, cytoskeleton, proteinuria
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".