3PC-038 Autologous serum eye drops preparation: approach to the filtration step impact on the concentration of active molecules
Bibliographic record
Abstract
Background and Importance Autologous serum eye drops (ASEDs) are pharmaceutical preparations used in severe dry eye disease. Sterility is a specification for eye drops, which can be obtained by filtration. Any molecule with a mean diameter greater than the filter porosity is then retained. EGF (Epidermal Growth Factor) is one of the active molecules (AMs) in ASEDs. With an intermediate molecular mass (MM) (180 kDa), its investigation makes possible to predict the impact of filtration on the concentration of other molecules. Aim and Objectives To evaluate the impact of this sterilisation method on AM by measuring EGF concentrations before/after filtration of collected sera. Material and Methods Four 4 mL tubes of human serum (P1-P4) were used, all from a hospital biological collection. Each serum underwent the following operations: zero filtration, clarifying filtration (CF, at 0.45 µm porosity) and sterilising filtration (SF, at 0.20 µm). The assay was performed in duplicate using the ELISA technique (Quantikine® Human EGF Immunoassay kit, R&D System, USA). The impact of filtration is considered significant if the relative difference in concentrations after the process exceeds 7.5%. Results The EGF concentration (pg/mL) in each unfiltered serum represents the maximum concentration (100%), allowing the impact of filtrations to be expressed as relative percentages of this maximum. Under CF, these percentages were respectively, for P1 to P4: 96.2%, 97.2%, 92.8% and 97.1%, representing a reduction in concentrations between 2.8% and 7.2%. Under SF, the percentages were: 94.8%, 93.4%, 91.1% and 95.9% respectively, representing a reduction of 4.1% to 8.9%. Conclusion and Relevance As expected, EGF concentrations decrease after filtration, especially when the porosity of the filter used is low. Moreover, the significance threshold is reached for P3 under SF. We may suppose that smaller AMs (ie IGF-1, MM 7.6 kDa; TGF-β1, MM 25 kDa) will be less retained. For larger AMs such as fibronectin (MM around 450kDa), the decrease in concentration is likely to have an impact on the ASEDs efficacy, justifying a more specific study. Other methods of ensuring the microbiological safety of ASEDs should probably also be considered. References and/or Acknowledgements Conflict of Interest No conflict of interest.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".