From Virus Protection to Cell Isolation and Biomarker Discovery with Aptamers
Bibliographic record
Abstract
New affinity molecules such as nucleic acid aptamers are in demand in the science and medical fields. Current aptamer selection technologies can generate unique aptamers with desired properties to targets of interest. My thesis describes a series of investigations on the protection of an oncolytic virus, the isolation of target cells from biological fluids, and aptamer-facilitated biomarker discovery. We tested individual aptamers and constructed a tetramer aptamer structure (quadramer) to increase virus infectivity. The quadramer protects vesicular stomatitis virus (VSV) during freeze–thaw cycles, shields the virus from neutralizing antibodies and increases viral active units. In addition to aptamers, we screened carbohydrate-based ice recrystallization inhibitors for the possible elimination of the cold chain of Vaccinia virus, VSV, and Herpes virus-1. N-octyl-gluconamide provides the longest shelf life for Vaccinia virus and Herpes virus-1 as tested according to the World Health Organization’s requirements for viral vaccines efficiency during transportation and distribution. We generated switchable aptamers capable of isolating cells expressing LIFR, NRP1, DLL4, uPAR, or PTCH1. These aptamers bind to the receptor positive cells in the presence of Mg2+ and Ca2+, and release the pure cells upon addition of EDTA. The aptamers were applied for a sequential positive immunomagnetic isolation of cells from mice bone marrow. We also utilized fluorescence-activated cell sorting (FACS) in our aptamer selections to develop switchable aptamers to positive isolation of monocytes from human blood. Moreover, we have selected non-switchable aptamers as an affinity probe to the cells expressing Axl receptor for immunofluorescent analysis and cell sorting. We determined aptamers to CD107a and applied them for biomarker discovery with mass spectrometry and found that CD107a was co-expressing with PD-1. Furthermore, we identified CD91 as binding partners to our aptamers in human monocytes using FACS and orbitrap mass spectrometry.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".