Surface Plasmon Resonance Imaging Measurements of the Inhibition of Shiga-like Toxin by Synthetic Multivalent Inhibitors
Bibliographic record
Abstract
A variety of new methodologies to pattern biomolecules on surfaces and to detect binding events are currently being developed for high-throughput assay applications. Carbohydrates serve as attachment sites for toxins, bacteria, and viruses. Immobilized carbohydrate units can thus be used to directly detect these agents or as a platform for inhibitor assessment. In this work, modified glycosides were patterned on gold surfaces to monitor the binding of the homopentameric B5 cell-recognition subunit of the Shiga-like toxin (SLT). Binding was detected with the label-free method of surface plasmon resonance (SPR) imaging. Two synthetic multivalent inhibitors were used in order to effect inhibitory binding, and SPR imaging is presented as a simple alternative to ELISA for the study of toxin inhibition. In contrast to existing methods for the study of carbohydrate-protein interactions, in particular ELISA, the use of micropatterned sensor surfaces is shown to be advantageous due to a decrease in complications and manual labor from numerous blocking, washing, and labeling steps. Carbohydrate receptor density on the sensor surface was optimized in order to effect the maximum binding of the SLT. The IC50 values determined were in the low-nanomolar range for each of the two inhibitors studied.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".