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Record W2331415774 · doi:10.1139/cjpp-2013-0250

Effects of serum lipoproteins on cyclosporine A cellular uptake and renal toxicity in vitro

2013· article· en· W2331415774 on OpenAlexaffvenue
Dion R. Brocks, Hetal R Chaudhary, Mohamed Ben‐Eltriki, Marwa E. Elsherbiny, Ayman O.S. El‐Kadi

Bibliographic record

VenueCanadian Journal of Physiology and Pharmacology · 2013
Typearticle
Languageen
FieldMedicine
TopicPharmacological Effects and Toxicity Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsToxicityHyperlipidemiaKidneyLipoproteinEndocrinologyIn vitroInternal medicineRhodamine 123PharmacologyChemistryCholesterolBiologyMedicineBiochemistry

Abstract

fetched live from OpenAlex

In-vitro studies were performed to shed light on previous findings that showed increased uptake of cyclosporine A in the kidneys and liver of hyperlipidemic rats, and increased signs of kidney toxicity. Hepatocytes were obtained from rats, cultured, and exposed to a diluted serum from hyperlipidemic rats. Some cells were also exposed to lipid-lowering drugs. After washing out the rat serum or lipid-lowering drugs, cells were exposed to cyclosporine A embedded in serum lipoproteins. Pretreatment with hyperlipidemic serum and lipid-lowering drugs was associated with an increased uptake of cyclosporine A. As expected, atorvastatin caused an increase in low density lipoprotein receptor and a decrease in MDR1A mRNA in the hepatocytes. A decrease in NRK-52E rat renal tubular cellular viability caused by cyclosporine A was noted when cells were preincubated with diluted hyperlipidemic serum. This was matched with evidence of hyperlipidemic-serum-associated increases in the NRK-52E cellular uptake of cyclosporine A and rhodamine-123. The findings of these experiments suggested that in hyperlipidemia the expression and (or) the functional activity of P-glycoprotein was diminished, leading to greater hepatic and renal uptake of cyclosporine A, and renal cellular toxicity.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.227
Threshold uncertainty score0.467

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.010
GPT teacher head0.253
Teacher spread0.243 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2013
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal of Physiology and PharmacologySame topicPharmacological Effects and Toxicity StudiesFrench-language works237,207