Integrated microfluidic device for the separation, decomposition and detection of low molecular weight S-nitrosothiols
Bibliographic record
Abstract
S-nitrosothiols (RSNOs) are very important biomolecules that play crucial roles in many physiological and physiopathological processes. They act as NO-donors and are candidates for future medicines. Their identification and quantitation are therefore important for biomedical applications. One, two or more RSNOs can then be combined to design a drug and therefore, the quantification of each is important to establish an acceptable quality control process. Till date, miniaturized devices have been used to detect RSNOs based on their total quantitation without a preceding separation step. This study reports on an original and integrated microdevice allowing for the successive electrokinetic separation of low molecular weight RSNOs, their decomposition under metal catalysis, and their quantitation by amperometric detection of the produced nitrite in the end-channel arrangement, leading to their quantitation in a single run. For this purpose, a commercial SU-8/Pyrex microfluidic system was coupled to a portable and wireless potentiostat. Different operating and running parameters were optimized to achieve the best analytical data, allowing for an LOD equal to 20 μM. The simultaneous separation of S-nitrosoglutathione and S-nitrosocysteine was successfully obtained within 75 s. The proposed methodology using SU-8/Pyrex microfluidic devices opens new possibilities to investigate future drug candidates for NO-donors.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".