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Record W2089947577 · doi:10.1080/00498250310001638460

Effects of renal diseases on the regulation and expression of renal and hepatic drug-metabolizing enzymes: a review

2004· review· en· W2089947577 on OpenAlex
Hesham M. Korashy, Reem H. Elbekai, Ayman O.S. El‐Kadi

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueXenobiotica · 2004
Typereview
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmacogenetics and Drug Metabolism
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsKidneyIntracellularBiologyEnzymeChemistryPharmacologyEndocrinologyInternal medicineMedicineCell biologyBiochemistry

Abstract

fetched live from OpenAlex

1. The activity of drug-metabolizing enzymes (DMEs) in extrahepatic organs is highest in the kidneys. Generally, the kidneys contain most, if not all, of the DMEs found in the liver. Surprisingly, some of these DMEs show higher activity in the kidneys than in the liver. 2. Most of the renal DMEs are localized in the cortex of the kidneys, especially in the proximal tubules. DMEs are also found in the distal tubules and collecting ducts. 3. Renal diseases such as acute and chronic renal failure and renal cell carcinoma alter the regulation of both hepatic and extrahepatic phase I and II DMEs. Changes in the expression of these DMEs seem to be tissue and species specific. 4. Generally, there is significant down-regulation of most of the phase I and a few of phase II DMEs at the protein, mRNA and activity levels. Unfortunately, the mechanisms leading to the alteration in DMEs in renal diseases remain unclear, although many theories have been made. 5. The presence of some circulating factors such as cytokines, nitric oxide, parathyroid hormones and increased intracellular calcium play a role in the regulation of DMEs in renal diseases.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.860
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.064
GPT teacher head0.410
Teacher spread0.345 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it