Bibliographic record
Abstract
Iron oxide nanoparticles (IONPs) have played a pivotal role in the development of nanomedicine owing to their versatile functions at the nanoscale, that facilitates targeted delivery, high contrast imaging, and on-demand therapy. Some biomedical inadequacies of IONPs on its' own, such as poor resolution of IONP-based Magnetic Resonance Imaging (MRI), can be overcome by co-incorporating optical probes onto them, which can be either molecule-based or nanoparticulate. Optical probe-incorporated IONPs, together with two prominent non-ionizing radiation sources (i.e. magnetic field and light), enables a myriad of biomedical applications from early detection to targeted treatment of various diseases. However, selecting right optically active photosensitizer is the key for nanomaterials' function. For example, UV sensitive dye methylene blue has been studied extensively, however, due to the limitation of UV light penetration depth in tissues limit it's use as a photosensitizer. Therefore, we found an alternate photosensitizer, Indocyanine Green (ICG) in connection with Royal Blackburn Teaching Hospital who has been using routinely for Liver cancer surgery due to it's affinity towards Liver tumour and visualisation using Near Infrared (NIR) light. In our hypothesis, ICG incorporated magnetic nanoparticles can be administered and monitored via NIR imaging (diagnosis) before applying the combination of AMF+NIR light for localized heating (therapy).
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".