Bibliographic record
Abstract
Nanoparticles are used to carry medical agents to diseased sites for treatment and diagnosis. When nanoparticles are administered in biological environments, hundreds of proteins rapidly adsorb onto their surface and form a protein layer known as the protein corona. These adsorbed proteins cover up the original surface of the nanoparticle and form a new interface that interacts with cell and tissues. Strategies to use targeting or blocking molecules on the nanoparticle to circumvent or abolish protein corona formation have been unsuccessful. The protein corona forms regardless of the material composition, surface chemistry, size and shape. Consequently, it is necessary to understand how the protein corona directs interactions with cells and tissues to design effective nanoparticles. In this thesis, I developed methods to understand and use the protein corona as a nanomaterial for delivery. In aim 1, I developed a workflow combining mass spectrometry, genome-wide screens and bioinformatic analysis to identify interactions between specific proteins in the protein corona and receptors on the cell surface. This approach allows us to systematically discover the molecular interactions formed between the protein corona and cells. Given that the protein corona dictates biological fate rather than the underlyingnanoparticle, I turned the protein corona into a standalone nanomaterial in aim 2. This eliminated the need for the underlying nanoparticle which is often foreign to the body. Altogether, this thesis provides researchers with the foundational tools needed to understand the biological behavior of the protein corona and to use it as a nanomaterial for effective and predictable delivery. This thesis has broad implications because it presents a paradigm shift away from preventing the protein corona and towards exploiting it for delivery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".