Hydrogel-Based Molecularly Imprinted Polymers for Biological Detection
Bibliographic record
Abstract
Molecularly imprinted polymers (MIPs) have become an important tool in the preparation of artificial and robust recognition materials that are capable of mimicking natural systems. MIPs have been regarded as 'antibody mimics' and have shown clear advantages over real antibodies for sensor technology. Currently, on-site diagnostic (OSD) and point-of-care (POC) biosensor development are heavily dominated by antibody-dependent immuno-sensors such as the lateral flow immuno-assay. Although antibodies exhibit a high degree of selectivity, any biological recognition element is inherently unstable with limited shelf-life, even when stored under optimum conditions. OSD and POC tests are essential for disease screening and treatment monitoring as part of emergency management. Introduced or naturally occurring pathogens can cause significant disruptions, raise panic in the population, and result in significant economic losses. Cheaper, smaller, and smarter devices for early detection of disease or environmental hazards ultimately lead to rapid containment and corrective action. To this end, there has been extensive research on detection platforms based on genetic or immune techniques. MIPs have proven to produce selective biological extractions that rival immunoaffinity-based separations, but without the tediously lengthy time-consuming process. MIPs could provide an alternative to antibodies, and ultimately lead to cheaper, smaller, and smarter biosensors.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.021 |
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".