Creation of glycoprotein imprinted self-assembled monolayers with dynamic boronate recognition sites and imprinted cavities for selective glycoprotein recognition
Bibliographic record
Abstract
Glycoproteins are involved in the pathogenesis and development of many diseases and are used as biomarkers for disease diagnosis. It is highly desirable to develop highly sensitive and selective methods for the detection of glycoproteins without the use of antibodies. Imprinting of proteins represents one of the most challenging tasks. Glycoprotein imprinted self-assembled monolayers (SAMs) were created, for the first time, from an oligo(ethylene glycol) (OEG) terminated 1,2-dithiolane derivative linked through an alkyl chain incorporated with two amide groups (DHAP) and combined functional thiols of p-mercaptophenylboronic acid (PMBA) and p-aminothiophenol (PATP) in aqueous media, without the use of polymerization initiators. Combined action of PMBA and PATP was essential for the development of boronate recognition sites for glycoproteins at the physiological pH, attributed to the water molecule-mediated Lewis acid-base interactions between the electron-deficient PMBA and the electron-rich PATP. DHAP played key roles not only in cementation of imprinted cavities by means of double hydrogen bond networks through the amide groups but also in resistance to nonspecific protein binding by terminal OEG moieties, as well as hydrogen bond binding sites from the amide groups exposed to imprinted cavities. The created glycoprotein imprinted SAMs showed excellent recognition selectivity of target glycoproteins. The strategy for tailor-made glycoprotein imprinted SAMs explores a new avenue to the creation of intelligent biomaterials and fabrication of chemosensors.
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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 | 0.001 | 0.000 |
| 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".