Hydrophobic Polystyrene‐Modified Gelatin Enhances Fast Hemostasis and Tissue Regeneration in Traumatic Brain Injury
Bibliographic record
Abstract
Hemostatic sealant is required to deal with blood loss, especially in the scenario of traumatic brain injury (TBI), which presents high rates of morbidity and disability. Hemostasis in surgery with traditional gelatin-based sealants often leads to blood loss and other issues in brain because of the hydrophilic gelatin swelling. Herein, hydrophobic effects on the hemostasis in TBI surgery are studied by tuning the chain length of polystyrene (PS) onto methylacrylated gelatin (Gel-MA). The hydrophobicity and hemostatic efficiency can be tuned by controlling the length of PS groups. The platelet activation of modified sealants Gel-MA-2P, Gel-MA-P, and Gel-MA-0.5P is as much as 17.5, 9.1, and 2.1 times higher than Gel-MA in vitro. The hemostatic time of Gel-MA-2P, Gel-MA-P, and Gel-MA-0.5P groups is 2.0-, 1.6-, and 1.1-folds faster than that in Gel-MA group in TBI mice. Increased formation of fibrins and platelet aggregation can also be observed in vitro by scanning electron microscopy. Animal's mortality is lowered by 46%, neurologic deficiency is reduced by 1.5 times, and brain edema is attenuated by 10%. Protein expression is further investigated to exhibit toxic iron-related processes caused by delayed hemostasis and activation of platelets via PI3K/PKC-α signaling. The hydrophobic Gel-MA has the potential in hemostatic TBI and promotes nervous system recovery in brain with the potentials in clinics.
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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".