Evaluating aptamers for their application in T cell magnetic nanoparticle-based isolation
Bibliographic record
Abstract
Chimeric Antigen Receptor (CAR) T cell therapy is a relatively new anti-cancer therapeutic strategy, with several treatments already approved by the Food and Drug Administration (FDA).In this technology, a patient's T cells are removed and genetically engineered to express cell receptors commonly found on cancer cells and act as a "living drug" when readministered into the patient.Magnetic-based cell sorting strategies have become increasingly useful for isolating the necessary T cells from patients, relying on cell specific antibodies.The increased use of antibody-based isolation of cells is surprising given the current limitations including reduction of cell viability post-isolation thereby hindering the efficacy of downstream CAR T cell applications.Alternative affinity reagents such as stable nucleic acid aptamers instead of antibodies could address these limitations, improving the efficacy of CAR T cell treatment.In this thesis, I compared aptamers for different T cell receptors and conjugated them to magnetic nanoparticles for the application of developing a T-cell capture system.First, I functionalized both uncoated and silica-coated magnetic nanoparticles (MNPs) with streptavidin.Following surface property and binding characterizations, I selected silica coated beads as optimal for magnetic cell capture.Next, I identified reported aptamers that bind to important T cell receptors including CD3, CD4, and CD8.Using flow cytometric analysis, I performed a head-to-head comparison of their interaction properties including binding affinity and specificity.The study demonstrated that the aptamers were sensitive to buffer and temperature conditions: small changes impacted both tight binding and specificity.The aptamers
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".