Bibliographic record
Abstract
This thesis advances the state of the art in biomolecule detection by allowing for quantitative detection using radioactive labels whose decay is detected by a pixelated complementary metal-oxide-semiconductor (CMOS), or CMOS-compatible, sensor.Additionally, the usefulness of this technology as a testbed for radioimmunotherapy (RIT) pharmaceuticals is considered.For the first time a CMOS image sensor has been used to detect the presence of radiolabelled target biomolecules captured on a functionalized surface.Using aptamer functionalization the system successfully detected phosphorus-32 labelled adenosine triphosphate (ATP) at a surface concentration of 2.3 × 10 7 molecules/cm 2 , well below those typically associated with fluorescence-based sensor architectures.The system has also demonstrated its amenability to multiplexed biomolecule detection.Geant4, a Monte Carlo toolkit for simulating the passage of radiation through matter, was used to model the detection system.This system has applications in quantitative biomolecule detection and in the development of RIT pharmaceuticals employing beta particle emitting isotopes.Also for the first time, a MOS sensor has been designed, fabricated, and tested for use in the characterization of targeted alpha therapy (TAT) pharmaceuticals.The sensor consists of a 16 × 16 array of 100 µm square alpha particle sensitive cells ii fabricated in-house using a simple nMOS process.A subset of the cells are functionalized for the attachment of chelators under investigation for new pharmaceuticals.To demonstrate the utility of this sensor as a characterization platform, cells functionalized with 1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid (DOTA)-DNA conjugates were used to chelate americium-241 from solution, and the alpha particle emissions over the surface of the IC measured.The IC was able to quantitatively determine the amount of alpha emitter present over each cell, allowing the chelator and chelating chemistry to be assessed.Without any optimization of the chelation chemistry, a 21% increase of emissions was detected on cells functionalized with DOTA relative to unfunctionalized cells.iii I am grateful to many people who have encouraged and supported me as I took this opportunity to explore my curiosity.My supervisor, mentor, and friend, Dr. Garry Tarr has been supporting me, patiently, from the very beginnings of my academic career.I cannot thank him enough for all that he has done.It was by taking the opportunity to continue my education that I met my wife, Svetlana Demtchenko.She has had a tremendous impact on my life, and has been a constant source of encouragement.She's a wonderfully bright, honest and caring woman who has truly made this experience life changing
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".