SPR Biosensing in Crude Serum Using Ultralow Fouling Binary Patterned Peptide SAM
Why this work is in the frame
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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 <or= x, y <or= 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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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