Bibliographic record
Abstract
The kidney plays a vital role in the elimination of xenobiotics including drugs, toxins, and endogenous metabolites. Renal drug elimination involves 3 major processes: glomerular filtration, tubular secretion, and tubular reabsorption. Although glomerular filtration is a simple unidirectional diffusion process, renal tubular secretion and/or reabsorption can involve saturable processes mediated by multiple highly specialized membrane transport systems. Current research has identified that these transport proteins play a significant role in the efficient removal and/or reabsorption of pharmacological agents. Since the majority of membrane transporters have broad substrate specificity, there is a significant risk for drug-drug interactions through competition for similar transport pathways. This article will focus on the cellular expression, localization, and transport properties of various renal drug transport systems (ie, organic anion, organic cation, nucleoside, and adenosine triphosphate [ATP]-dependent efflux transporters). Specific examples of drugs that are transported by each of these mechanisms will be provided. Clinically relevant drug-drug interactions involving renal drug transporters will be discussed to guide the clinician in understanding and preventing these interactions.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".