SPR Biosensing in Crude Serum Using Ultralow Fouling Binary Patterned Peptide SAM
Bibliographic record
Abstract
Near-zero fouling monolayers based on binary patterned peptides allow low nanomolar detection of the matrix metalloproteinase-3 (MMP-3) directly in crude bovine serum, without sample pretreatment, secondary antibody detection or signal amplification. The peptide 3-MPA-HHHDD-OH (3-MPA, 3-mercaptopropionic acid) was found optimal compared to other binary patterned peptides based on 3-MPA-A x -B y -OH, where 0 ≤ x, y ≤ 5, and x + y = 5, and compared to PEG. In this study, amino acid A was His, Asp, Ser, or Leu, and amino acid B was His, Asp, or Ser. Zwitterionic peptides and other peptides exhibited excellent resistance to nonspecific adsorption. Binary patterned peptides were capped with 3-MPA on the N -terminus providing a monolayer with the C-terminus carboxylic acid available to subsequently immobilize antibodies. Thereby, an IgG biosensor demonstrated the efficiency of binary patterned peptides in SPR biosensing with a detection limit of 1−10 pM in PBS, similar to other optical or electrochemical techniques. This protocol was applied to establish a calibration curve for MMP-3, an analyte of clinical interest for many pathologies and a potential indicator of cancer. The LOD for MMP-3 was 0.14 nM in PBS, with a linearity of up to 50 nM. With the use of PBS calibration, MMP-3 was quantified at low nanomolar in undiluted bovine serum. The SPR response in serum was statistically the same as in PBS. A sensor exposed to blank serum exhibited negligible nonspecific adsorption. Hence, binary patterned peptides are suitable for biosensing directly in complex biological matrixes.
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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) | 0.001 | 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 | 0.001 | 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 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".