A nanofluidic bioarray chip for fast and high-throughput detection of antibodies in biological fluids
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Immunoassays have become a standard in secretome analysis in clinical and research analysis. In this field there is a need for a high throughput method that uses low sample volumes. Microfluidics and nanofluidics have been developed for this purpose. Our lab has developed a nanofluidic bioarray (NBA) chip with the goal being a high throughput system that assays low sample volumes against multiple probes. A combination of horizontal and vertical channels are produced to create an array antigens on the surface of the NBA chip in one dimension that is probed by flowing in the other dimension antibodies from biological fluids. We have tested the NBA chip by immobilizing streptavidin and then biotinylated peptide to detect the presence of a mouse monoclonal antibody (MAb) that is specific for the peptide. Bound antibody is detected by an AlexaFluor 647 labeled goat (anti-mouse IgG) polyclonal antibody. Using the NBA chip, we have successfully detected peptide binding by small-volume (0.5 μl) samples containing 50 attomoles (100 pM) MAb.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it