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Record W2095341139 · doi:10.1016/j.febslet.2004.05.024

Dihydrolipoamide dehydrogenase from porcine heart catalyzes NADH‐dependent scavenging of nitric oxide

2004· article· en· W2095341139 on OpenAlexaff
Abir U. Igamberdiev, Natalia V. Bykova, Werner Ens, Robert D. Hill

Bibliographic record

VenueFEBS Letters · 2004
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHemoglobin structure and function
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsDihydrolipoamide dehydrogenaseChemistryMethemoglobinDiaphoraseElectron acceptorMyoglobinNitriteNitric oxideDehydrogenasePhotochemistryElectron donorReductaseMethylene blueNADH dehydrogenaseEnzymeCyanideBiochemistryNitrateHemoglobinInorganic chemistryCatalysisOrganic chemistry

Abstract

fetched live from OpenAlex

Dihydrolipoamide dehydrogenase (DLDH; EC 1.8.1.4) from porcine heart is capable of using nitric oxide (NO) as an electron acceptor, with NADH as the electron donor, forming nitrate in the reaction. NADPH was not effective as an electron donor. The reaction had a pH optimum near 6 and was not inhibited by cyanide or diphenyleneiodonium ions. The Km for NADH was 10 microM, while that for NO was 0.5 microM. The rate of NO conversion was comparable to the rate of lipoamide conversion (200 micromol min(-1) mg(-1) protein at pH 6). Cytochrome c or myoglobin were poor electron acceptors by themselves but, in the presence of methylene blue, DLDH had an activity of 5-7 micromol min(-1) mg(-1) protein with these substrates, indicating that DLDH can act also as a methemoglobin reductase. While the Km of DLDH for NO is relatively low, it is in the physiological range of NO levels encountered in the tissue. The enzyme may, therefore, have a significant role in modifying NO levels under specific cell conditions.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.001

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 head0.004
GPT teacher head0.198
Teacher spread0.194 · 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 source (direct Gemma or distilled Codex), 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

Citations58
Published2004
Admission routes1
Has abstractyes

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