Future Perspectives on the Automation and Biocompatibility of Molecularly Imprinted Polymers for Healthcare Applications
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Molecular recognition is of crucial importance in several healthcare applications, such as sensing, drug delivery, and therapeutics. Molecularly imprinted polymers (MIPs) present an interesting alternative to biological receptors (e.g., antibodies, enzymes) for this purpose since synthetic receptors overcome the limited robustness, flexibility, high-cost, and potential for inhibition that comes with natural recognition elements. However, off the shelf MIP products remain limited, which is likely due to the lack of a scalable production approach that can manufacture these materials in high yields and narrow and defined size distributions to have full control over their properties. In this Perspective, we will confer how breakthroughs in the automation of MIP design, manufacturing, and evaluation of performance will accelerate the (commercial) implementation of MIPs in healthcare technology. In addition, we will discuss how prediction of the in vivo behavior of MIPs with animal-free technologies (e.g., 3D tissue models) will be critical to assess their clinical potential.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".