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 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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".