Immunologic and Tissue Biocompatibility of Flexible/Stretchable Electronics and Optoelectronics
Bibliographic record
Abstract
Recent development of flexible/stretchable integrated electronic sensors and stimulation systems has the potential to establish an important paradigm for implantable electronic devices, where shapes and mechanical properties are matched to those of biological tissues and organs. Demonstrations of tissue and immune biocompatibility are fundamental requirements for application of such kinds of electronics for long-term use in the body. Here, a comprehensive set of experiments studies biocompatibility on four representative flexible/stretchable device platforms, selected on the basis of their versatility and relevance in clinical usage. The devices include flexible silicon field effect transistors (FETs) on polyimide and stretchable silicon FETs, InGaN light-emitting diodes (LEDs), and AlInGaPAs LEDs, each on low modulus silicone substrates. Direct cytotoxicity measured by exposure of a surrogate fibroblast line and leachable toxicity by minimum essential medium extraction testing reveal that all of these devices are non-cytotoxic. In vivo immunologic and tissue biocompatibility testing in mice indicate no local inflammation or systemic immunologic responses after four weeks of subcutaneous implantation. The results show that these new classes of flexible implantable devices are suitable for introduction into clinical studies as long-term implantable electronics.
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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".