Mechanically Matched Silicone Brain Implants Reduce Brain Foreign Body Response
Bibliographic record
Abstract
Abstract Brain implants are increasingly used to treat neurological disorders and diseases. However, the brain foreign body response (FBR) elicited by implants affects neuro-electrical transduction and long-term reliability limiting their clinical adoption. The mismatch in Young’s modulus between silicon implants (∼180 GPa) and brain tissue (∼1-30 kPa) exacerbates the FBR resulting in the development of flexible implants from polymers such as polyimide (∼1.5-2.5 GPa). However, a stiffness mismatch of at least two orders of magnitude remains. Here, we introduce (i) the first mechanically matched brain implant (MMBI) made from silicone (∼20 kPa), (ii) new microfabrication methods, and (iii) a novel dissolvable sugar shuttle to reliably implant MMBIs. MMBIs were fabricated via vacuum-assisted molding using sacrificial sugar molds and were then encased in sugar shuttles that dissolved within 2 min after insertion into rat brains. Sections of rat neocortex implanted with MMBIs, PDMS implants, and silicon implants were analyzed by immunohistochemistry 3 and 9-weeks post-implantation. MMBIs resulted in significantly higher neuronal density and lower FBR within 50 µm of the tissue-implant interface compared to PDMS and silicon implants suggesting that materials mechanically matched to brain further minimize the FBR and could contribute to better implant functionality and long-term reliability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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 teacher head, 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".