Can rodent models of diabetic kidney disease clarify the significance of early hyperfiltration?: recognizing clinical and experimental uncertainties
Bibliographic record
Abstract
In the past, hyperfiltration and increased glomerular capillary pressure have been identified as important determinants of the development of DN (diabetic nephropathy). Recently, some basic research and clinical reviews on DN have omitted identifying hyperfiltration as an important risk factor. At the same time, different rodent models of DN have been described without and with documented hyperfiltration. In the present review, the importance of hyperfiltration is reassessed, reviewing key clinical and research studies, including the first single nephron studies in a mouse model of DN. From clinical studies of Type 1 and Type 2 diabetes mellitus, it is clear that many patients do not have early hyperfiltration and, even when present, its contribution to subsequent DN remains uncertain. Key mechanisms underlying hyperfiltration in rodent models are reviewed. Findings on intrarenal NO metabolism and the control of single-nephron GFR (glomerular filtration rate) in rodent models of DN are also presented. Characterization of valid experimental models of DN should include a careful delineation of the absence or presence of early hyperfiltration, with special efforts made to establish the specific role hyperfiltration may play in the emergence of DN.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".