Nanosurgical Resection of Malignant Brain Tumors: Beyond the Cutting Edge
Bibliographic record
Abstract
Advances in surgical procedures and improvements in patient outcomes have resulted from applications of new technologies in the operating room over the past three decades. All surgeons would be excited about the possibilities of improving their resections of tumors for patients with cancer if a new technology were introduced to facilitate this. In this issue of ACS Nano, Karabeber et al. use a hand-held Raman scanner to probe the completeness of resection of glioblastoma multiforme (GBM), the most malignant brain cancer, in a genetically engineered mouse model. They show that the hand-held scanner could accurately detect gold-silica surface-enhanced Raman scattering nanoparticles embedded within the GBM, resulting in a complete tumor resection. In this Perspective, we review potential applications of nanotechnologies to neurosurgery and describe how new systems, such as the one described in this issue, may be brought closer to the operating room through modifications in nanoparticle size, overcoming the obstacles presented by the blood-brain barrier, and functionalizing nanoparticle conjugates so that they reach their target at highest concentrations possible. Finally, with adaptations of the actual hand-held Raman scanner device itself, one can envision the day when "nanosurgical" procedures will be a part of the surgeon's armamentarium.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".