Histological assessment of an implantable optical sensor and spinal cord tissue interface
Bibliographic record
Abstract
Background: We developed an implantable optical sensor based on near-infrared spectroscopy (NIRS) to continuously monitor spinal cord oxygenation and hemodynamics in patients with acute spinal cord injury (SCI). As a safety assessment measure, we aimed to study the effect of near-infrared (NIR) light emission and contact compression of the NIRS sensor on spinal cord tissue structure. Our previous in-vitro heat tests indicated no heat generation by the NIRS sensor. This study evaluated whether the NIRS sensor resulted in any potential compression damage to the spinal cord using histological analysis. Methods: Six Yucatan mini-pigs received a T10 SCI. A custom implantable NIRS sensor (version 2) was placed extradurally on the spinal cord and fixed with magnets and cross-connectors. After seven days of continuous data collection at 100Hz, the sensor was removed to allow for histological examination of the spinal cord tissue. Cellular damage was observed in the spinal cord at the NIRS sensor placement site in two animals. The design, shape, and material of the NIRS sensor were significantly revised to reduce the sensor footprint, minimize the compression on the cord, increase the sensor flexibility, and improve its clinical application. An in-vivo pilot experiment was performed on a Yucatan miniature pig with a T10 SCI to evaluate potential compression damage of the spinal cord tissue from placement and direct contact of the refined NIRS sensor (version 5). A fibrin sealant, TISSEEL, was utilized to fix the version 5 NIRS sensor on the spinal cord. Result: There were no signs of cellular damage, indentation, and significant flattening on the dorsal surface of the spinal cord where the version 5 NIRS sensor was placed for up to 4.5 hours. Conclusion: The refined NIRS sensor did not cause any compression damage to the porcine spinal cord after implantation for 4.5 hours. Implanting this sensor on the spinal cord of SCI patients requires further in-vivo examinations to ensure the sensor is safe to use for up to 14 days.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".