Neurofilament heavy polypeptide protects against reduction in synaptopodin expression and prevents podocyte detachment
Bibliographic record
Abstract
Podocytes are highly specialized cells that line the glomerulus of the kidney and play a role in filtration. Podocyte injury plays a critical role in the development of many kidney diseases, but the underlying mechanisms remain unclear. In this study, we identified that neurofilament heavy polypeptide (NEFH), an intermediate filament component, protects podocyte from injury. We observed that NEFH was upregulated after ADRIAMYCIN(ADR)-induced podocyte injury in both mice and cultured murine podocytes. Immunofluorescence and co-immunoprecipitation analyses revealed that NEFH was colocalized with synaptopodin, a podocyte-specific marker. High NEFH expression in podocytes prevented the Adriamycin-induced reduction in synaptopodin expression. The siRNA-mediated knockdown of NEFH in podocytes reduced the number of vinculin-containing focal contacts, thereby reducing adhesion to the extracellular matrix and increasing podocyte detachment. In addition, NEFH expression was significantly increased in renal biopsy specimens from patients with focal segmental glomerulosclerosis and membranous nephropathy, but in those with minimal change disease. These findings indicate that NEFH is expressed in podocytes during the disease course and that it prevents the reduction in synaptopodin expression and detachment of podocytes.
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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 | 0.000 | 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) | 0.001 | 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".