Boron rich nanotube drug carrier system is suited for boron neutron capture therapy
Bibliographic record
Abstract
Boron neutron capture therapy (BNCT) is a two-step therapeutic process that utilizes Boron-10 in combination with low energy neutrons to effectively eliminate targeted cells. This therapy is primarily used for difficult to treat head and neck carcinomas; recent advances have expanded this method to cover a broader range of carcinomas. However, it still remains an unconventional therapy where one of the barriers for widespread adoption is the adequate delivery of Boron-10 to target cells. In an effort to address this issue, we examined a unique nanoparticle drug delivery system based on a highly stable and modular proteinaceous nanotube. Initially, we confirmed and structurally analyzed ortho-carborane binding into the cavities of the nanotube. The high ratio of Boron to proteinaceous mass and excellent thermal stability suggest the nanotube system as a suitable candidate for drug delivery into cancer cells. The full physicochemical characterization of the nanotube then allowed for further mechanistic molecular dynamic studies of the ortho-carborane uptake and calculations of corresponding energy profiles. Visualization of the binding event highlighted the protein dynamics and the importance of the interhelical channel formation to allow movement of the boron cluster into the nanotube. Additionally, cell assays showed that the nanotube can penetrate outer membranes of cancer cells followed by localization around the cells' nuclei. This work uses an integrative approach combining experimental data from structural, molecular dynamics simulations and biological experiments to thoroughly present an alternative drug delivery device for BNCT which offers additional benefits over current delivery methods.
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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 | 0.001 | 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".