Poly(2‐hydroxyethyl methacrylate) surface chemistry and modulus differentially modulate neutrophils and lens epithelial cells—possible implications in cellular responses to intraocular lenses
Bibliographic record
Abstract
During cataract surgery, diseased lenses in the eye are surgically removed and replaced with polymeric artificial intraocular lenses (IOLs). Patients can experience a complication called posterior capsular opacification (PCO) that is corrected through the removal of part of the posterior capsule using a neodymium: yttrium-aluminum-garnet (Nd-YAG) laser to restore the optical path. These interventions have increased costs and can damage the retina and the IOL. PCO develops when lens epithelial cells (LECs) proliferate, migrate, and undergo epithelial-to-mesenchymal transition. Neutrophils involved in the immune response triggered during implantation impact LEC behavior and produce damaging neutrophil extracellular traps (NETs). In this research, poly(2-hydroxyethyl methacrylate) (PHEMA) -based disks were synthesized with varying amounts of comonomer (HEMA with 0, 2, and 12 mol% MMA) and functionalized with carboxyl and amine groups, yielding nine different hydrogels. Material and chemical properties of the disks were characterized, and neutrophil-like HL60 cells and B3 LECs were incubated with the disks. HL60 cell behavior was more strongly influenced by chemical functionalization than by mechanical properties with increases in adherence and NET accumulation. Conversely, the behavior and viability of B3 LECs were more strongly influenced by mechanical properties with increases in cell adhesion and α-SMA expression with increasing compressive moduli. Interestingly, B3 LECs had decreased viability and increased α-SMA expression when cultured on PHEMA2 disks pretreated with isolated NETs. Critical to the understanding of PCO and its prevention are both surface chemistry and mechanics as well as the inflammatory response.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".