Surface plasmon resonance biosensing toward real biological sample analysis
Bibliographic record
Abstract
The development of monolayer chemistry based on amino acid and short peptides decreases significantly the nonspecific adsorption from biological samples such as serum. Nonspecific adsorption of proteins onto the surface of biosensors currently limits the applicability of many biosensing techniques in real biological samples. In order to minimize this problem, a methodology to immobilize short peptides on surface plasmon resonance (SPR) biosensors was developed using a short chain alkyl thiol monolayer derived with the selected peptides. The chain length of the alkane thiol linking the amino acid to the gold surface influences the physico-chemical properties of the layer and the amount of nonspecifically adsorbed proteins. Varying the composition of the monolayer with peptides formed from the natural amino acids investigates the physico-chemical properties required to minimize nonspecific adsorption of serum. It was observed from monolayers of single amino acids that the composition of the side chain of the amino acid greatly influences the resistance to nonspecific adsorption, with more polar, ionic and small chains resulting in an improved performance in biological samples. Building peptides of different lengths resulted in a further decrease of the amount of nonspecifically bound proteins from serum. Leaving the terminal carboxylic acid end of the peptide unreacted provides an anchoring point for a molecular receptor in the design of a biosensor. Biosensing will be demonstrated with a model system of β-lactamase.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".