Development of hydrogel platforms for increased QCM-D biointerface sensitivity in real-time immunoassay of sepsis-related biomarkers
Bibliographic record
Abstract
This doctoral thesis describes the development of novel rapid deposition hydrogel platforms that serve as biointerfaces for real-time immunoassay using quartz crystal microgravimetry (QCM). Biointerface development was undertaken with the goal of developing a simple system relying on affordable technology to achieve real-time immunoassay performance equivalent to more complex and involved protocols. The primary advantage of the hydrogel biointerfaces developed herein lies in their rapid preparation using affordable, non-toxic reagents. Compositions developed over three sequential development cycles rely on chemically cross-linking carboxymethylcellulose, which serves to covalently immobilise recognition elements through amine coupling, to polyethyleneimine. The various compositions require 10 minutes or less to deposit, a substantial improvement over competing self-assembled monolayer protocols requiring incubations ranging from hours to days using highly toxic reagents. Additional benefit lies in the immunoassay functionality of the biointerface, as these compositions excel in the traditional performance criteria of surface regeneration, minimisation of non-specific protein binding, and assay detection limit. The peak detection limit achieved using a sandwich assay for a 17 kDa cytokine was 25 ng/mL in buffer and 500 ng/mL in a 1:3 serum dilution, with generic immunoassay capability for other cytokines demonstrated. Reusability of the developed biointerfaces is equally strong, with up to twenty regeneration cycles demonstrated without diminished sensitivity. Finally, mass-based estimates of non-specific serum adsorption indicate that the composition developed during the final design iteration equals the performance of the best protein-resistant biointerfaces currently available in the literature.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".