A HPLC-based methylene blue/methylene green method for accurate measurement of hydrogen sulfide in plasma
Bibliographic record
Abstract
Hydrogen sulfide (H 2 S) is an endogenously produced gasotransmitter with important physiological roles in cardiovascular, neurological, and metabolic health. Accurate quantification of circulating H 2 S remains technically challenging due to its volatility, reactivity, and rapid oxidation. Here, we present a high-performance liquid chromatography (HPLC)-based methylene blue method using methylene green as an internal reference (MBMG) for plasma H 2 S quantification with improved sensitivity, reproducibility, and compatibility. Using this approach, a linear standard curve was generated for NaHS concentrations between 0.05–10 μM. The method successfully detected significantly higher plasma H 2 S levels in wild-type (WT) mice (∼0.5 μM) compared to CSE-knockout (KO) mice (∼0.15 μM), with both groups showing expected increases following NaHS intraperitoneal injection. Specificity testing revealed minimal interference from biologically relevant sulfur species; only cysteine at supraphysiological concentrations generated a negligible signal. Plasma cysteine levels remained unchanged following NaHS injection. Post-reaction samples remained stable for at least six months at −80 °C, facilitating remote analysis and long-term storage. Collectively, the MBMG method provides a practical and sensitive platform for reliable plasma H 2 S measurement, with broad applicability in basic, translational, and clinical research settings.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".