Multi-Walled Carbon Nanotube Filter for Activating Effector T-Cells
Bibliographic record
Abstract
Cancer is a major public health problem worldwide and the main cause of death in Canada. Effectively treating cancer remains a challenge given its ability to evade and suppress immune responses. Recently, adoptive T-cell transfer (ACT) has gained popularity as a promising cancer immunotherapy that enhances the effectiveness of white blood cells through ex vivo cellular and genetic engineering practices. T-cells are a type of white blood cell that proliferate and differentiate into effector T (T eff ) cells and memory T (T m ) cells which play a critical role in killing cancer cells. Therefore, the goal of this project is to enhance the efficacy of ACT technology by developing a functional filter. This filter aims to induce T eff cell activation by mechanical forces. Multi-walled carbon nanotubes (MWCNTs) will be used as the filtrate material and act as a functional coating given their attractive material properties such as high structural integrity, surface area and mechanical performances. To improve the biocompatibility of the MWCNT filters, the environmentally cautious surface modification treatment of ammonia plasma functionalization will be performed to add amine-functional groups on the surface. Target antibodies can potentially be immobilized on the amine functionalized MWCNT surfaces. Selective enhancement and proliferation of T-cells by mechanically passing them through immobilized target antibodies on plasma functionalized MWCNT filters may be a novel method of cancer treatment.
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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 | 0.001 | 0.002 |
| 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".