Challenges for cysteamine stabilization, quantification, and biological effects improvement
Bibliographic record
Abstract
The aminothiol cysteamine, derived from coenzyme A degradation in mammalian cells, presents several biological applications. However, the bitter taste and sickening odor, chemical instability, hygroscopicity, and poor pharmacokinetic profile of cysteamine limit its efficacy. The use of encapsulation systems is a good methodology to overcome these undesirable properties and improve the pharmacokinetic behavior of cysteamine. Besides, the conjugation of cysteamine to the surface of nanoparticles is generally proposed to improve the intra-oral delivery of cyclodextrin-drug inclusion complexes, as well as to enhance the colorimetric detection of compounds by a gold nanoparticle aggregation method. On the other hand, the detection and quantification of cysteamine is a challenging mission due to the lack of a chromophore in its structure and its susceptibility to oxidation before or during the analysis. Derivatization agents are therefore applied for the quantification of this molecule. To our knowledge, the derivatization techniques and the encapsulation systems used for cysteamine delivery were not reviewed previously. Thus, this review aims to compile all the data on these methods as well as to provide an overview of the various biological applications of cysteamine focusing on its skin application.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".