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Record W2167566912 · doi:10.1016/j.clpt.2004.12.198

Renal ontogeny of ifosfamide nephrotoxicity

2005· article· en· W2167566912 on OpenAlexaff
Katarina Aleksa, Naomi Halachmi, Shinya Ito, Gideon Koren

Bibliographic record

VenueClinical Pharmacology & Therapeutics · 2005
Typearticle
Languageen
FieldMedicine
TopicChemotherapy-induced organ toxicity mitigation
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsNephrotoxicityCYP3AOntogenyKidneyInternal medicineIfosfamideEndocrinologyBiologyMicrosomeMetabolismCytochrome P450MedicineEnzymeBiochemistryChemotherapyCisplatin

Abstract

fetched live from OpenAlex

Ifosfamide (IF) -induced nephrotoxicity adversely affects the health and well-being of children with cancer. We have recently shown age-dependent nephrotoxicity of IF, with younger children (less than 3 years of age) substantially more vulnerable. The mechanisms leading to this age-related IF-induced renal damage have not been identified. The hypothesis underlying this work was that there is renal ontogeny in the expression and activity of cytochrome P450 (CYP) enzymes responsible for IF metabolism to the nephrotoxic chloroacetaldehyde. The presence of renal CYP3A and 2B22 activity was evaluated in pigs between 1 day of age to adulthood, as was the metabolism of IF by renal microsomes to 2- and 3-dechloroethylifosfamide (2-DCEIF and 3-DCEIF). Kidney CYP3A mRNA expression peaked at 15 to 60 days (0.7–76±0.19 CYP3A/actin ratio) (P<0.001). Subsequently, this level decreased to adult values (0.54±0.03 CYP3A/actin ratio) (P=0.04). In a similar manner, there was an increase in IF metabolic rate between young (18±2 pM/mg protein/min) vs adults (12.2±0.17 pM/mg protein/min) (P=0.002). This is the first documentation of ontogeny of renal CYP3A and of renal IF metabolism. These data suggest that age-dependent IF nephrotoxicity is, at least in part, due to ontogeny in the production chloroacetaldehyde. Clinical Pharmacology & Therapeutics (2005) 77, P80–P80; doi: 10.1016/j.clpt.2004.12.198

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.095
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.092
GPT teacher head0.433
Teacher spread0.340 · 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.

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

Citations0
Published2005
Admission routes1
Has abstractyes

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