Detailed mechanistic investigation into the S-nitrosation of cysteamine
Bibliographic record
Abstract
The nitrosation of cysteamine (H2NCH2CH2SH) to produce cysteamine-S-nitrosothiol (CANO) was studied in slightly acidic medium by using nitrous acid prepared in situ. The stoichiometry of the reaction was H2NCH2CH2SH + HNO2 → H2NCH2CH2SNO + H2O. On prolonged standing, the nitrosothiol decomposed quantitatively to yield the disulfide, cystamine: 2H2NCH2CH2SNO → H2NCH2CH2S–SCH2CH2NH2 + 2NO. NO2 and N2O3 are not the primary nitrosating agents, since their precursor (NO) was not detected during the nitrosation process. The reaction is first order in nitrous acid, thus implicating it as the major nitrosating agent in mildly acidic pH conditions. Acid catalyzes nitrosation after nitrous acid has saturated, implicating the protonated nitrous acid species, the nitrosonium cation (NO+) as a contributing nitrosating species in highly acidic environments. The acid catalysis at constant nitrous acid concentrations suggests that the nitrosonium cation nitrosates at a much higher rate than nitrous acid. Bimolecular rate constants for the nitrosation of cysteamine by nitrous acid and by the nitrosonium cation were deduced to be 17.9 ± 1.5 (mol/L)–1 s–1 and 6.7 × 104 (mol/L)–1 s–1, respectively. Both Cu(I) and Cu(II) ions were effective catalysts for the formation and decomposition of the cysteamine nitrosothiol. Cu(II) ions could catalyze the nitrosation of cysteamine in neutral conditions, whereas Cu(I) could only catalyze in acidic conditions. Transnitrosation kinetics of CANO with glutathione showed the formation of cystamine and the mixed disulfide with no formation of oxidized glutathione (GSSG). The nitrosation reaction was satisfactorily simulated by a simple reaction scheme involving eight reactions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".