A Cohesive Shear-Thinning Biomaterial for Catheter-Based Minimally Invasive Therapeutics
Bibliographic record
Abstract
Shear-thinning hydrogels are suitable biomaterials for catheter-based minimally invasive therapies; however, the tradeoff between injectability and mechanical integrity has limited their applications, particularly at high external shear stress such as that during endovascular procedures. Extensive molecular crosslinking often results in stiff, hard-to-inject hydrogels that may block catheters, whereas weak crosslinking renders hydrogels mechanically weak and susceptible to shear-induced fragmentation. Thus, controlling molecular interactions is necessary to improve the cohesion of catheter-deployable hydrogels. To address this material design challenge, we have developed an easily injectable, nonhemolytic, and noncytotoxic shear-thinning hydrogel with significantly enhanced cohesion via controlling noncovalent interactions. We show that enhancing the electrostatic interactions between weakly bound biopolymers (gelatin) and nanoparticles (silicate nanoplatelets) using a highly charged polycation at an optimum concentration increases cohesion without compromising injectability, whereas introducing excessive charge to the system leads to phase separation and loss of function. The cohesive biomaterial is successfully injected with a neuroendovascular catheter and retained without fragmentation in patient-derived three-dimensionally printed cerebral aneurysm models under a physiologically relevant pulsatile fluid flow, which would otherwise be impossible using the noncohesive hydrogel counterpart. This work sheds light on how charge-driven molecular and colloidal interactions in shear-thinning physical hydrogels improve cohesion, enabling complex minimally invasive procedures under flow, which may open new opportunities for developing the next generation of injectable biomaterials.
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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) | 0.000 | 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".