Nitrocellulose Compartmentalized Linker Array for Highly Sensitive Three-Dimensional Antibody Microarray-Based Multiplex Bioassays
Bibliographic record
Abstract
Microarrays are a powerful tool for creating multiplex bioassays but remain unavailable to most biomedical and biological research laboratories due to the high cost of the equipment needed to fabricate them. Moreover, conventional methods of microarray fabrication can cause the loss of bioactivity of the delicate bioreagents, thus compromising the assay performance. We have developed a nitrocellulose compartmentalized linker array (nCLA) technology that creates 3D antibody microarrays by simply pipetting microliter antibody solutions into compartments prepatterned with nitrocellulose microarrays through which the antibodies are self-assembled into microarrays via binding onto nitrocellulose. To form the nitrocellulose microarray with the background region deactivated, a selective background deactivation technique has been developed. As a proof of concept, three cancer-related proteins, EGFR, TNF-α, and GM-CSF, were measured using nCLA in a multiplexed sandwich immunoassay. Low picograms per milliliter limits of detection were achieved in both antigen-spiked PBS and human serum, demonstrating its potential in biomedical research. The nCLA provides a new method to fabricate membrane-based, microarray-printer-free, highly sensitive, and scalable bioassays. It maintains reagent bioactivity and forms antibody microarrays via simple pipetting, making the powerful 3D microarray technology widely available to the biomedical research community.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".