Noncontact 3D Bioprinting of Proteinaceous Microarrays for Highly Sensitive Immunofluorescence Detection within Clinical Samples
Bibliographic record
Abstract
Immunofluorescence assays are extensively used for the detection of disease-associated biomarkers within patient samples for direct diagnosis. Unfortunately, these 2D microarrays suffer from low repeatability and fail to attain the low limits of detection (LODs) required to accurately discern disease progression for clinical monitoring. While three-dimensional microarrays with increased biorecognition molecule density stand to circumvent these limitations, their viscous component materials are not compatible with current microarray fabrication protocols. Herein, we introduce a platform for 3D microarray bioprinting, wherein a two-step printing approach enables the high-throughput fabrication of immunosorbent hydrogels. The hydrogels are composed entirely of cross-linked proteins decorated with clinically relevant capture antibodies. Compared to two-dimensional microarrays, these proteinaceous microarrays offer 3-fold increases in signal intensity. When tested with clinically relevant biomarkers, ultrasensitive single-plex and multiplex detection of interleukin-6 (LOD 0.3 pg/mL) and tumor necrosis factor receptor 1 (LOD 1 pg/mL) is observed. When challenged with clinical samples, these hydrogel microarrays consistently discern elevated levels of interleukin-6 in blood plasma derived from patients with systemic blood infections. Given their easy-to-implement, high-throughput fabrication, and ultrasensitive detection, these three-dimensional microarrays will enable better clinical monitoring of disease progression, yielding improved patient outcomes.
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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 | 0.001 |
| 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) | 0.001 | 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".