Bibliographic record
Abstract
Renal hypertrophy is an important contributor to end-stage renal disease, but little is known about its underlying physiological mechanisms, primarily because of complex etiologies, intricate gene-gene and gene-environment interactions. Kidney mass (Km) can be viewed as a proximal predictor of renal hypertrophy and, consequently, identifying the physiological mechanisms determining Km will probably facilitate our understanding of the factors causing renal hypertrophy. Genetic approaches are powerful in detecting etiological steps involved in pathways and cascades leading to Km control. Recent genetic analyses employing inbred rat models have defined 2 broad categories of genes known as quantitative trait loci (QTLs) responsible for Km. The first class controls Km independently of cardiovascular and hemodynamic phenotypes, suggesting that their underlying physiological mechanisms can be renal-specific and dissociated from those regulating cardiovascular traits. The second class of QTLs modulates Km as well as cardiovascular phenotypes, implying that these renal and cardiovascular traits may share physiological mechanisms. It is expected that some of the mechanisms discovered in animal models may be translated into humans. The strategies of gene discovery for KmQTLs consist of identifying gene candidates (e.g. gene profiling and targeted mutation screening) and in vivo functional validation (e.g. fine congenic resolution, transgenesis and gene targeting). Keywords: End stage renal disease, Renal Hypertrophy, QTL identification, renotropin, Aortic Masses, Small Interference RNAs
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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".