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Record W1980678353 · doi:10.1021/tx0001934

In Vitro Generation of Peroxynitrite by 2- and 4-Hydroxyestrogens in the Presence of Nitric Oxide

2001· article· en· W1980678353 on OpenAlexaff
Benoît Paquette, André M. Cantin, Suzanne Kocsis-Bédard, Sophie Barry, Rosalie Lemay, Jean‐Paul Jay‐Gerin

Bibliographic record

VenueChemical Research in Toxicology · 2001
Typearticle
Languageen
FieldMedicine
TopicNitric Oxide and Endothelin Effects
Canadian institutionsCentre Hospitalier Universitaire de SherbrookeUniversité de Sherbrooke
Fundersnot available
KeywordsPeroxynitriteChemistryNitric oxideSuperoxideHydroxyl radicalBiochemistryRadicalEnzymeOrganic chemistry

Abstract

fetched live from OpenAlex

Estrogen metabolism is altered in most, if not all, breast cancer tumors. These alterations primarily lead to the formation of the catechol estrogen metabolites, 2- and 4-hydroxyestrogens, which can generate superoxide anion radicals (O(2)(*)(-)) through the redox cycling of semiquinone/quinone derivatives. In breast cancer cells, the activity of nitric oxide synthase is also frequently elevated, resulting in an increased level of exposure to nitric oxide ((*)NO). Since (*)NO rapidly reacts with O(2)(*)(-) to produce the peroxynitrite anion (ONOO(-)), this study was undertaken to determine whether ONOO(-) can be generated when 2- and 4-hydroxyestrogens are incubated in vitro with (*)NO donor compounds. Using dihydrorhodamine 123 as a specific probe for ONOO(-) formation, a ratio of 100 microM dipropylenetriamine NONOate (DPTA/NO) to 10 microM 4-hydroxyestradiol (4-OHE(2)) gave an optimal ONOO(-) production of 11.9 +/- 1.9 microM (mean +/- SD). Quantification of ONOO(-) was not modified by mannitol, supporting the idea that the hydroxyl radical was not involved. This production of ONOO(-) required the presence of the catechol structure of estrogen metabolites since all methoxyestrogens that were tested were inactive. Hydroxyestrogen metabolites derived from estradiol showed the same efficiency in producing ONOO(-) as those originating from estrone. With DPTA/NO, the 4-hydroxyestrogens generated 30-40% more ONOO(-) than the 2-hydroxyestrogens. Optimal production of ONOO(-) was assessed with DPTA/NO and diethylenetriamine NONOate (initial (*)NO generation rates of 0.76 and 0.08 microM min(-1), respectively). With faster (*)NO-releasing compounds, such as diethylamine NONOate and spermine NONOate, lower levels of ONOO(-) were detected. These data suggest that once the optimal concentration of (*)NO was obtained, the reaction between (*)NO and 4-OHE(2) was saturated. The excess of (*)NO would probably react with aqueous oxygen to form nitrite (NO(2)(-)). Since the third-order reaction rate for the reaction between 2(*)NO and O(2) is 2 x 10(6) M(-2) s(-1), it can therefore be suggested that the reaction between (*)NO and 4-OHE(2) occurs at a faster rate.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.015
Threshold uncertainty score0.270

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.088
GPT teacher head0.389
Teacher spread0.301 · 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

Citations11
Published2001
Admission routes1
Has abstractyes

Explore more

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