Impact of fluorescent probes on albumin sorption profiles to ophthalmic biomaterials
Bibliographic record
Abstract
Bovine serum albumin (BSA) was conjugated to three organic fluorescent probes, 5-(4,6-dichloro-s-triazin-2-ylamino)fluorescein hydrochloride (DTAF), Rhodamine B isothiocyanate (RITC), and Lucifer yellow VS (LY). The protein sorption profile to one pHEMA-based (etafilcon A) and three silicone hydrogel (SH) contact lens types (lotrafilcon B, balafilcon A and senofilcon A) was determined using confocal laser scanning microscopy. In addition, all lenses were incubated in dye solutions containing the fluorescent probe alone; and in a separate experiment BSA accumulation was quantified using radiolabeling. The different fluorescent conjugates showed similar sorption profiles for the pHEMA-based lens, but marked differences for all SH lenses. Lotrafilcon B accumulated more protein on the surface as compared to the matrix, independent of the fluorescent probe used for conjugation. Protein sorption varied for senofilcon A, with DTAF-BSA sorbing primarily to the surface region, while the other conjugates penetrated in equal amounts into the matrix. Balafilcon A exhibited smaller differences between conjugates, with LY-BSA allowing the protein to fully penetrate the matrix, while the other conjugates showed minor surface adsorption. Sorption curves of unbound dyes were often similar compared to the conjugated results. BSA profiles to pHEMA-based and silicone hydrogel lenses were highly dependent on the fluorescent probe used and none of the probes accurately reflected quantitative protein levels for the lens materials investigated.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".