Effects of composition and pH on the degradation of hyaluronate and carboxymethyl cellulose gels and release of nanocrystalline silver
Bibliographic record
Abstract
Adhesions are fibrous tissue connections which are a common complication of surgical procedures and may be prevented by protecting tissue surfaces and reducing inflammation. The combination of biodegradable polymers and nanocrystalline silver can be used to create an anti-inflammatory gel to be applied during surgery. In this study, sodium hyaluronate and sodium carboxymethyl cellulose were added in concentrations from 0.25% to 1% w/v to aqueous nanocrystalline silver solutions to create viscous gels. Gels were loaded into dialysis cassettes and placed in PBS for 3 days. pH was adjusted using potassium phosphate monobasic and sodium hydroxide. Release of silver into the PBS was measured at several time points. Polymer degradation was compared by measuring the viscosity of the gels before and after the experiment. Gels lost up to 84% of initial viscosity over 3 days and released between 24% and 41% of the added silver. Gels with higher initial viscosity did not have a greater degree of degradation, as measured by percent viscosity reduction, but still resulted in a higher final viscosity. Silver release was not significantly impacted by pH or composition, but still varied between experimental groups.
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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) | 0.000 | 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".