Data on the removal of peroxides from functionalized polyethylene glycol (PEG) and effects on the stability and sensitivity of resulting PEGylated conjugates
Bibliographic record
Abstract
Cyanine-5 (Cy5) is a fluorescent dye active in the far-red region of the visible spectrum (λex = 646 nm, λem = 662 nm) [1]. While Cy5 displays fluorescence in its oxidized form, it can be readily converted to its non-fluorescent hydrocyanine equivalent (H-Cy5) when exposed to reducing agents. H-Cy5 can then be converted back to its fluorescent oxidized form when exposed to reactive oxygen species (ROS), allowing it to act as a highly sensitive, high wavelength fluorescent ROS sensor [2]. However, H-Cy5 is a small, poorly water-soluble molecule, and is rapidly taken up into cells in vivo, preventing its use for sensing extracellular ROS, which are implicated in inflammation, wound healing, and other processes [3], [4], [5], [6]. A solution to this lies in the conjugation of Cy5 to a polyethylene glycol (PEG) polymer, increasing its solubility [7]. This conjugate (Cy5-PEG) can be reduced to H-Cy5-PEG to allow the highly sensitive detection of ROS in an aqueous extracellular environment. However, after PEG conjugation, a significant decrease in stability and sensitivity is observed, likely owing to the presence of ROS contaminants in commercial samples of PEG. It has been reported that these ROS impurities can be removed from PEG through a simple freeze-drying procedure [8]. Here, we demonstrate that a simple, straightforward method for the purification of PEG can allow the synthesis of a highly functional, water-soluble ROS sensor that could be used for extracellular ROS sensing.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".