Microelectrochemical Smart Needle for Real Time Minimally Invasive Oximetry
Bibliographic record
Abstract
A variety of brain disorders such as neural injury, brain dysfunction, vascular malformation, and neurodegenerative diseases are associated with abnormal levels of oxygen. Current methods to directly monitor tissue oxygenation in the brain are expensive and invasive, suffering from a lack of accuracy. Electrochemical detection has been used as an invasiveness and cost-effectiveness method, minimizing pain, discomfort, and injury to the patient. In this work, we developed a minimally invasive needle-sensor with a high surface area to monitor O2 levels in the brain using acupuncture needles. The approach was to directly etch the iron from stainless steel acupuncture needles via a controlled pitting corrosion process, obtaining a high microporous surface area. In order to increase the conductivity and selectivity, we designed and applied for the first time a low-cost coating process using non-toxic chemicals to deposit high surface area carbon nanoparticle, catalytically active laccase, and biocompatible polypyrrole. The physicochemical properties of the materials were characterized as well as their efficacy and viability as probes for the electrochemical detection of PO2. Our modified needles exhibited efficient electrocatalysis and high selectivity toward O2, with excellent repeatability. We well engineered a small diagnostic tool to monitor PO2, minimally invasive, able to monitor real-time O2 in vivo complex environments.
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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".