Redistribution of Integrins in Tubular Epithelial Cells during Diabetic Glycogen Nephrosis
Bibliographic record
Abstract
<i>Background/Aims:</i> Even though many aspects of glycogen nephrosis in diabetes have already been studied, adhesion interactions between the glycogen-accumulating clear cells and the tubular basement membranes have not been addressed. As integrins play key roles in cell-to-matrix interactions, we investigated the expression and distribution of α<sub>3</sub>-, α<sub>V</sub>-, β<sub>1</sub>- and β<sub>3</sub>-integrin subunits in renal tissues from streptozotocin-induced hyperglycemic rats (3 months old) and their age-matched controls as well as from streptozotocin-injected normoglycemic animals. <i>Methods:</i> The levels and distribution of integrins were studied by immunocytochemistry and Western blot analysis. <i>Results:</i> Immunoblotting analysis of fractions enriched in glycogen-accumulating clear cells demonstrated enhanced expression of α<sub>3</sub>, α<sub>V</sub> and β<sub>1</sub> subunits while expression of β<sub>3</sub> did not differ from controls. The most striking cytochemical result was the redistribution of the α<sub>3</sub>-, α<sub>V</sub>-, and the β<sub>1</sub>-integrin subunits to the apical plasma membrane of these cells. This was found by light and electron microscopy. <i>Conclusion:</i> Our results suggest that the altered expression and distribution of integrins in clear cells of diabetic animals must have defined roles in the development of the renal tubulopathy.
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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".