Development of Bioanalytical Assays for Biomolecular Interactions Using Surface Plasmon Resonance
Bibliographic record
Abstract
This thesis explores the applicability of surface plasmon resonance (SPR) with the aim to improve and suggest novel improvements in the subject matter with respect to biomolecular interactions. Such analytical techniques could be applied in the field of environmental, biological, and pharmaceutical chemistry, as well as surface science. In the first chapter, the backgrounds of Legionella pneumophila and Alzheimer’s disease (AD) were introduced. The chapter also described the key physical and chemical aspects of SPR and electrochemical impedance spectroscopy (EIS). Based on these analytical techniques, we explored the interactions of Legionella collagen-like (Lcl) proteins with glycosaminoglycans in Chapter 2. Moreover, the effect of tandem repeat units and self-aggregation analysis of Lcl proteins was also performed using SPR. Chapter 3 looked at the interaction of two AD-related proteins, amyloid-beta (Aβ) and apolipoprotein E4 (ApoE4). The interaction between these two proteins has been hypothesized to play a significant role in the early stages of AD. Lastly, from the practice of surface-based analytical techniques, an improvement in surface chemistry was suggested to achieve better sensitivity and anti-non-specific adsorption (anti-NSA) properties. A novel surface modification linker for gold surfaces was synthesized by our group to have improved stability and anti-NSA characteristics as described in Chapter 4. The linker was synthesized and characterized to support structure and function. The anti-NSA properties of the novel linker were demonstrated by both SPR and cyclic voltammetry (CV). To optimize the performance on studying protein-protein and protein-small molecule interactions, it would be most ideal to use our linker in conjunction to a surface spacer. In the end, we concluded the findings from the above-mentioned research studies and the future directions on those topics were expressed in detail (Chapter 5).
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".