Electrospinning Coating of Nitinol Guidewire with Manganese Ion Chelated Melanin Nanoparticles for Interventional Magnetic Resonance Imaging
Bibliographic record
Abstract
Unlike X ray-based imaging modalities such as fluoroscopy, C-arm, angiography, and computed tomography which have adverse effects of ionizing radiation on both patient's body and medical staff during the interventional operations, magnetic resonance imaging (MRI) provides X ray free platform, high contrast in soft tissues and physiological data along with anatomical images which improves the accuracy of the diagnosis.Although many promising prototypes were fabricated in the past, the realization of interventional operations with the MRI guidance is still limited due to the lack of both MRI safe and MRI visible invasive medical devices.This study investigated the feasibility of electrospinning coating to enhance the visibility of MRI safe interventional instruments.For this purpose, a nitinol guidewire sample was coated with manganese (Mn 2+ ) chelated melanin nanoparticles (MNPs) using the electrospinning technique.A positive contrast (bright signal) provided by MNPs+Mn 2+ deposition over nitinol guidewire sample was confirmed in images acquired using 7.0 T animal MRI scanner.Results including contrast to noise ratio values measured by DICOM Viewer and image processing showed that electrospinning presenting a promising coating technique for uniform deposition of metal ion-chelated MNPs over MRI safe invasive medical devices improving their traceability during interventional operations performed with MRI guidance.
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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) | 0.000 | 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 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".