Clinical challenges and opportunities related to the biological responses experienced by indwelling and implantable bioelectronic medical devices
Bibliographic record
Abstract
Implantable electrodes have been utilized for decades to stimulate, sense, or monitor a broad range of biological processes, with examples ranging from glucose monitoring devices to cochlear implants. While the underlying science related to the application of electrodes is a mature field, preclinical and clinical studies have demonstrated that there are still significant challenges in vivo associated with a lack of control over tissue-material interfacial interactions, especially over longer time frames. Herein we discuss the current challenges and opportunities for implantable electrodes and the associated bioelectronic interfaces across the clinical landscape with a focus on emerging technologies and the obstacles of biofouling, microbial colonization, and the foreign body response. Overcoming these challenges is predicted to open the door for a new generation of implantable medical devices and significant associated clinical impact. STATEMENT OF SIGNIFICANCE: Implantable electrodes have been utilised for decades to stimulate, sense, or monitor a broad range of biological processes, with examples ranging from glucose monitoring devices to cochlear implants. Next-generation bioelectronic implantable medical devices promise an explosion of new applications that have until this point in time been impossible to achieve. However, there are several persistent biological challenges hindering the realisation of these new applications. We present a clinical perspective on how these biological challenges have shaped the device market and clinical trial landscape. Specifically, we present statistical breakdowns of current device applications and discuss biofouling, the foreign body response, and microbial colonisation as the main factors that need to be addressed before a new generation of devices can be explored.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".