Bibliographic record
Abstract
PURPOSE OF REVIEW: To maintain electrolyte homeostasis, the kidneys reabsorb more than 99% of the filtered Na under physiological conditions, resulting in less than 1% of the filtered Na excreted in urine. In contrast, due to distal tubular secretion, urinary K output may exceed filtered load. This review focuses on a relatively new methodology for investigating renal epithelial transport, computational modelling and highlights recent insights regarding renal Na and K transport and O2 consumption under pathophysiological conditions, with a focus on nephrectomy. RECENT FINDINGS: Recent modelling studies investigated the extent to which the adaptive response to nephrectomy, which includes elevation in single-nephron glomerular filtration rate and tubular transport capacity, may achieve balance but increases O2 consumption per nephron. Simulation results pointed to potential mechanisms in a hemi-nephrectomized rat that may attenuate the natriuresis response under K load, or that may augment the natriuretic, diuretic and kaliuretic effects of sodium glucose cotransporter 2 inhibition. SUMMARY: Computational models provide a systemic approach for investigating system perturbations, such as those induced by drug administration or genetic alterations. Thus, computational models can be a great asset in data interpretation concerning (but not limited to) renal tubular transport and metabolism.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".