Effect of Methylcellulose Molecular Weight on the Properties of Self-Assembling MC-g-PNtBAm Nanogels
Bibliographic record
Abstract
The efficiency of drug delivery to the eye using topical drop therapy is limited by the ocular clearance mechanisms. Nanocarriers, able to encapsulate bioactive compounds and slow down their release, may allow for prolonged on-eye residence times when combined with topical application for treatment of ocular conditions. Previously, self-assemblies of methylcellulose (MC) hydrophobized with N-tert-butylacrylamide side chains (MC-g-PNtBAm) were developed. The purpose of the current study was to investigate the impact of the methylcellulose backbone length on the properties of the nanogels. We synthesized MC-g-PNtBAm nanogels using four different molecular weights of MC with two degrees of hydrophobic modification and investigated the physical and chemical properties of the resulting polymeric nanogels. While no significant change could be observed at a high degree of hydrophobization, properties were affected at a lower one. Increasing the molecular weight of MC improved the swelling capacity of the nanogels, increasing their size in water. An effect on the drug release was also noted. Nanogels prepared using MC with a molecular weight of 30 kDa did not retain as much dexamethasone and released it faster compared to those prepared using 230 kDa MC. Thus, besides the degree of hydrophobization, the length of MC chains provides another means of tuning the properties of MC-g-PNtBAm nanogels.
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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 | 0.001 |
| 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) | 0.001 | 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".