Kidney Growth in Normal and Diabetic Mice is not Affected by Human Insulin-Like Growth Factor Binding Protein-1 Administration
Bibliographic record
Abstract
Insulin-like growth factor I (IGF-I) accumulates in the kidney following the onset of diabetes, initiating diabetic renal hypertrophy. Increased renal IGF-I protein content, which is not reflected in messenger RNA (mRNA) levels, suggests that renal IGF-I accumulation is due to sequestration of circulating IGF-I rather than to local synthesis. It has been suggested that IGF-I is trapped in the kidney by IGF binding protein 1 (IGFBP-1). We administered purified human IGFBP-1 (hIGFBP-1) to nondiabetic and diabetic mice as three daily sc injections for 14 days, starting 6 days after induction of streptozotocin diabetes when the animals were overtly diabetic. Markers of early diabetic renal changes (i.e., increased kidney weight, glomerular volume, and albuminuria) coincided with accumulation of renal cortical IGF-I despite decreased mRNA levels in 20-day diabetic mice. Human IGFBP-1 administration had no effect on increased kidney weight or albuminuria in early diabetes, although it abolished renal cortical IGF-I accumulation and glomerular hypertrophy in diabetic mice. Increased IGF-I levels in kidneys of normal mice receiving hIGFBP-1 were not reflected on kidney parameters. IGFBP-1 administration in diabetic mice had only minor effects on diabetic renal changes. Accordingly, these results did not support the hypothesis that IGFBP-1 plays a major role in early renal changes in diabetes.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".