Hemostatic Ability of Thermosensitive Biologically Active Gelatin-Alginate Hydrogels Modified with Humic Acids and Impregnated with Aminocaproic Acid
Bibliographic record
Abstract
The article studies the hemostatic ability of thermosensitive biopolymer hydrogels containing 14% by weight of gelatin and 6.4% by weight of sodium alginate, impregnated with a hemostatic agent aminocaproic acid ~0.2 g/ml, with the addition of humic acids with antioxidant, antibacterial, fungicidal and anti-inflammatory properties. Modification of hydrogels with humic acids slightly increases viscosity, but maintains the gel-sol transition temperature close to the physiological temperature of about 37 °C, which allows them to melt on human skin or inside a wound, ensuring the delivery of aminocaproic acid. SEM images showed that the developed hydrogels have a layered internal morphology, which is improved due to better swelling of the hydrogels contained humic acids, which promotes the dissolution of aminocaproic acid inside the hydrogels and its subsequent rapid delivery to the bleeding site when applying a hydrogel dressing. It has been experimentally established that the concentration of humic acids in hydrogels of no more than 5 wt.% promotes blood clotting due to the entry of aminocaproic acid into it from the hydrogels. The aminocaproic acid delivered at physiological temperature from these hydrogels can shorten the blood clotting time to the lower limit of the normal clotting time range. The clotting time of the hydrogel with 5 wt.% humic acid is only 95 s, which confirms its particularly effective hemostatic ability.
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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".