Characterization of the mechanisms involved in the gender differences in<i>p</i>-aminohippurate renal elimination in rats
Bibliographic record
Abstract
Gender differences in the renal handling on drugs and toxins have received too little attention. In the present study, a variety of preparations were used to examine the basis for the greater effectiveness of the male kidneys in the elimination of p-aminohippurate (PAH) in rats. Renal clearance of PAH was significantly lower in female rats as consequence of its smaller filtered and secreted load. The gender difference in the filtered load may be accounted for the lower value of glomerular filtration rate (GFR) displayed by female rats as compared with males. The lower value of the renal blood flow observed in females might explain, at least in part, the decrease in the GFR and in the secreted load of PAH. In females, maximal uptake for PAH transport into renal basolateral membrane vesicles decreased to 52+/-9% (P < 0.05) and Michaelis-Menten constant for PAH uptake into renal brush border membrane vesicles was increased to 163+/-8% (P < 0.05). These changes might also explain the lower secreted load of PAH. The sex difference in the renal clearance of PAH was also evidenced by the reduced systemic clearance observed in female rats.
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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".