Hemostatic Gelatin-Alginate Hydrogels Modified with Humic Acids and Impregnated with Aminocaproic Acid
Bibliographic record
Abstract
The work is devoted to the development of safe and biocompatible multicomponent gelatin-alginate hydrogels modified with humic acids (HA) and impregnated with the antifibrinolytic agent aminocaproic acid (АА).These hydrogels are designed to be effective hemostatic materials with anti-inflammatory properties and the ability to deliver in less than 30 seconds to deep and hidden areas of hemorrhages. Studies of the crystal structure by X-ray diffraction analysis and non-covalent interactions of molecules by Fourier transform infrared spectroscopy of the developed hemostatic gelatin-alginate hydrogels modified with bactericidal and anti-inflammatory humic acids made it possible to identify the optimal concentrations of HA from 2.5 wt.%. up to 5 wt.%. At such concentrations of HA, gelatin-alginate hydrogels have a semicrystalline structure. Due to non-covalent bonds between polymer chains, they are thermo-responsive with a gel-sol transition temperature of about 37 °C. Impregnation of these hydrogels with aminocaproic acid led to an almost threefold increase in their swelling, which facilitated the dissolution of AA in the hydrogels and its subsequent delivery to the wound. Experiments simulating the transmembrane transport of aminocaproic acid from the developed gelatin-alginate hydrogels confirmed their ability to rapidly deliver up to 494± 3 mg of AA from 5 ml of hydrogel to the wound.
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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) | 0.001 | 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".