Silica-Based Nanomaterials For Cancer Theranostics
Bibliographic record
Abstract
Nanomedicine is the medical application of nanomaterials.It is expected to have a revolutionary impact on health care.One of the most exciting concepts in nanomedicine is the development of multifunctional nanoparticles that enable the simultaneous detection and treatment of diseases with unprecedented precision (theranostics).This thesis focuses on the development of an ultrasmall silica-based nanomaterials platform for theranostics applications.The synthesis and detailed characterization of a variety of silica-based nanomaterials are discussed with novel structures and tunable dimensions in the sub-10nm regime.Fluorescence correlation spectroscopy is introduced as an appropriate tool for fluorescent nanoparticle characterization.Particular focus is on surface modifications of nanoparticles synthesized in water with biocompatible polyethylene glycol and cancer targeting ligands.Such clinically applicable silica-based nanomaterials (C'dots) have received FDA approval as an investigational new drug (IND).Multiple human clinical trials with C' dots on cancer patients are currently ongoing at Memorial Sloan Kettering Cancer Center (MSKCC).The study of silica nanomaterials further provides insights to fundamental questions regarding the early formation mechanisms of self-assembled nanostructures.To that end investigations of the formation process of quasi-crystalline mesoporous silica nanoparticles is discussed that highlight structural details of building blocks and pathways responsible for the switch between crystalline (cubic symmetry) and quasicrystalline states.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.057 | 0.025 |
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".