Parallel microfluidic surface plasmon resonance imaging arrays
Bibliographic record
Abstract
Surface plasmon resonance imaging (SPRi) is a label-free technique used for the quantitation of binding affinities and concentrations for a wide variety of target molecules. Although SPRi is capable of determining binding constants for multiple ligands in parallel, current commercial instruments are limited to a single analyte stream on multiple ligand spots. Measurement of binding kinetics requires the serial introduction of different analyte concentrations; such repeated experiments are conducted manually and are therefore time-intensive. To address these challenges, we have developed an integrated microfluidic array using soft lithography techniques for high-throughput SPRi-based detection and determination of binding affinities of antibodies against protein targets. The device consists of 264 element-addressable chambers isolated by microvalves. The resulting 700 pL chamber volumes, combined with a serial dilution network for simultaneous interrogation of up to six different analyte concentrations, allow for further speeding detection times. To test for device performance, human alpha-thrombin was immobilized on the sensor surface and anti-human alpha-thrombin IgG was injected across the surface at different concentrations. The equilibrium dissociation constant was determined to be 5.0 +/- 1.9 nM, which agrees well with values reported in the literature. The interrogation of multiple ligands to multiple analytes in a single device was also investigated and samples were recovered with no cross-contamination. Since each chamber can be addressed independently, this array is capable of interrogating binding events from up to 264 different immobilized ligands against multiple analytes in a single experiment. The development of high-throughput protein analytic measurements is a critical technology for systems approaches to biology and medicine.
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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".