Experimental optimization of an <i>in situ</i> forming hydrogel for hemorrhage control
Bibliographic record
Abstract
The fabrication of a novel in situ forming hydrogel composed of a multifunctional poly(ethylene glycol) (PEG) N-hydroxysuccinimide ester (NHS) and poly(allylamine hydrochloride) (PAA) was investigated. FTIR confirmed that PAA formed the hydrogel matrix (i.e., the formation of a PAA-like hydrogel). A factorial experiment was conducted to identify the key parameters that controlled gelation time, gel content, and swelling properties. The type of PEG (e.g., 4- and 6-arm) appeared to play a major role in determining all three performance parameters, with the greatest effect on gelation time. Other influencing factors include (a) the PEG concentration, which contributes to the gelation time and gel content; (b) pH of the buffer used for dissolving each polymer, which can affect the gelation time; and (c) PAA molecular weights, which contribute to the gel content and swelling. The concentration of PAA solution had no significant effects on hydrogel formation and properties within the investigated range, presumably due to negligible changes in the crosslinking density of the hydrogels. The PAA buffer pH influenced the gel content as well. Finally, thromboelastography was used to examine the effects of each polymer and their in situ gelation on blood coagulation in vitro. All individual polymers tested reduced clot strength, while the gelation of the polymers enhanced overall procoagulant effects. These results suggest that the biomaterial can be optimized to provide a combination of rapid gelation and swelling properties suitable for hemorrhage control and thus warrant further studies in animal bleeding models.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".