Sodium Phytate‐Incorporated Gelatin‐Silicate Nanoplatelet Composites for Enhanced Cohesion and Hemostatic Function of Shear‐Thinning Biomaterials
Bibliographic record
Abstract
Shear-thinning biomaterials (STBs) based on gelatin-silicate nanoplatelets (SNs) are emerging as an alternative to conventional coiling and clipping techniques in the treatment of vascular anomalies. Improvements in the cohesion of STB hydrogels pave the way toward their translational application in minimally invasive therapies such as endovascular embolization repair. In the present study, sodium phytate (Phyt) additives are used to tune the electrostatic network of SNs-gelatin STBs, thereby promoting their mechanical integrity and facilitating injectability through standard catheters. We show that an optimized amount of Phyt enhances storage modulus by approximately one order of magnitude and reduces injection force by ≈58% without compromising biocompatibility and hydrogel wet stability. The Phyt additives are found to decrease the immune responses induced by SNs. In vitro embolization experiments suggest a significantly lower rate of failure in Phyt-incorporated STBs than in control groups. Furthermore, the addition of Phyt leads to accelerated blood coagulation (reduces clotting time by ≈45% compared to controls) due to the contributions of negatively charged phosphate groups, which aid in the prolonged durability of STB in coagulopathic patients. Therefore, the proposed approach is an effective method for the design of robust and injectable STBs for minimally invasive treatment of vascular malformations.
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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".