MétaCan
Menu
Back to cohort
Record W2767216529 · doi:10.1139/cjpp-2017-0423

Glutamine protection in an experimental model of acetaminophen nephrotoxicity

2017· article· en· W2767216529 on OpenAlexvenueno aff
Marco A. Brovedan, Sara M. Molinas, G Pisani, Liliana A. Monasterolo, Laura Trumper

Bibliographic record

VenueCanadian Journal of Physiology and Pharmacology · 2017
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicDrug-Induced Hepatotoxicity and Protection
Canadian institutionsnot available
Fundersnot available
KeywordsAcetaminophenPharmacologyNephrotoxicityChemistryKidneyGlutamineAntipyreticIn vivoMyeloperoxidaseAnalgesicRenal functionBiochemistryInflammationInternal medicineMedicineAmino acidBiology

Abstract

fetched live from OpenAlex

Acetaminophen (APAP) is a widely prescribed analgesic and antipyretic drug. In the present work, we studied the effects of glutamine (Gln) in an in vivo model of APAP-induced nephrotoxicity in male Wistar rats. Renal function, histological characteristics, and Na + ,K + -ATPase cortical abundance and distribution were analyzed. The appearance of HSP70 and actin in urine was also evaluated. Myeloperoxidase (MPO) activity in cortical tissue was measured as an index of the inflammatory response. Gln administration 30 min before APAP protected from the renal functional and histological damage promoted by APAP. Rats that received the dual treatment Gln and APAP (Gln/APAP) showed the same level of Na + ,K + -ATPase cortical induction as APAP-treated animals, but the enzyme maintained its normal basolateral localization. HSP70 abundance was increased up to the same level in the Gln, APAP, and Gln/APAP groups. Urinary HSP70 and actin were detected only in the APAP-treated animals, reinforcing the protection of renal tubular integrity afforded by the Gln pretreatment. Gln pretreatment also protected from the increment in MPO activity promoted by APAP. Our results support the idea that Gln pretreatment could be a therapeutic option to prevent APAP-induced renal injury.

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 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 score0.024
Threshold uncertainty score0.888

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
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 head0.162
GPT teacher head0.411
Teacher spread0.249 · 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

Citations8
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Physiology and PharmacologySame topicDrug-Induced Hepatotoxicity and ProtectionFrench-language works237,207