Investigating and Optimizing Biomarker Microarrays to Enhance Biosensing Capabilities for Diagnostics
Bibliographic record
Abstract
Early-onset diagnostics, or the detection of disease before clinical symptoms arise, has gained traction for its potential to improve patient quality of life and health outcomes. Biosensors, found in point-of-care (POC) devices, facilitate early-onset diagnostics and disease monitoring by addressing the limitations of current diagnostics strategies, which include timeliness, cost-effectiveness, and accessibility. Biosensors often incorporate microarrays within their design to allow for the detection of disease-associated biomolecules, known as biomarkers. Microarrays are composed of capture biomolecules, such as monoclonal antibodies, that are immobilized through either contact or non-contact printing techniques. In the following thesis, we investigated microarray designs within novel biosensing platforms for diagnostic and disease monitoring applications. First, we highlighted the advantages and challenges of implementing different types of biosensors, detection methods, and biomolecule immobilization strategies. Additionally, we proposed a novel 3D microarray incorporating hydrogels composed purely of crosslinked bovine serum albumin (BSA) proteins decorated with capture antibodies (CAbs). Utilizing industry-standard inkjet printing, we developed and optimized a two-step fabrication protocol, by which BSA proteins and CAbs are printed first, followed by the crosslinking agent, 1-Ethyl-3-[3-dimethylaminopropyl]carbodiimide (EDC). Characterization of the unique three-dimensional (3D) microstructure and hydrogel parameters and conducting comparisons with standard two-dimensional (2D) microdots, showed that increasing biosensor surface area led to a 3X increase in signal amplification. The limits of detection (LODs) for cytokine biomarkers were 0.3pg/mL for interleukin-6 (IL-6) and 1pg/mL for tumor necrosis factor receptor I (TNF RI), which were highly sensitive compared to reported LODs from literature. Alongside the investigation of novel printing protocols, proof-of-concepts for multiplex detection and distinguishing clinical patient samples from healthy donors were also presented. Overall, this thesis demonstrated the fabrication and optimization of microarray development shows promise in improving current biosensor designs, allowing for enhanced early-onset disease detection and monitoring.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".