Go With the Microfluidic Flow – and Analyze the Nanoparticle's Journey to the Tumour
Bibliographic record
Abstract
Traditional chemotherapy for cancer treatment often elicits dangerous side-effects and limits the dose that can be administered to patients. In order to alleviate these side-effects, researchers have turned to using nanoparticles to deliver drugs specifically to tumour sites. However, nanoparticles encounter many biological obstacles that prevent them from accumulating efficiently at the tumour sites, and these obstacles are difficult to characterize using animal models. In my thesis, I develop microfluidic tools to examine these obstacles. First, it is difficult to examine how far nanoparticles infiltrate into solid tumours because light cannot enter the tumour. My first project was to design a microfluidic device that renders microscale tissues transparent, so that nanoparticle infiltration into the tumours can be quantified by microscopy. Second, nanoparticles are usually injected into the bloodstream to treat tumours, and will be subject to the fluid dynamic effects of blood flow. These effects impact nanoparticle accumulation in the tumour. In my second project, I engineered an artificial blood vessel inside a microfluidic chip, allowing researchers to control the flow rate of the nanoparticles while they interact with the cells of a blood vessel. From this project, we learned that more nanoparticles travel through the blood vessel when the flow rate is decreased. Lastly, the flow rate of blood is heavily influenced by the shape of the blood vessel network. In tumours, the blood vessel network is chaotic and unpredictable, making the blood flow rate uncontrollable. In my third project, I produced microfluidic devices that contain micro-channels in the shape of tumour blood vessel networks. These devices are fabricated from 3D images of tumours from animal models, and are real-life replicas that can be reproducibly and predictably produced. It is possible to use these devices to examine how the shape of the tumour vasculature affects the blood flow rate, and therefore the accumulation of nanoparticles at the tumour. In summary, my thesis produced three microfluidic systems that enable researchers to examine three biological obstacles that prevent nanoparticles from being used effectively for anti-cancer treatment.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".