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Record W3090297287 · doi:10.2144/btn-2020-0091

Molecularly Imprinted Polymers in Biological Applications

2020· review· en· W3090297287 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueBioTechniques · 2020
Typereview
Languageen
FieldChemistry
TopicAnalytical chemistry methods development
Canadian institutionsnot available
FundersDivision of Materials ResearchQueen's UniversityAllmänna Sjukhusets i Malmö Stiftelse för Bekämpande av CancerHorizon 2020 Framework ProgrammeMalmö HögskolaBundesanstalt für Materialforschung und -Prüfung
KeywordsMolecularly imprinted polymerGlycanMolecular imprintingNanotechnologyTemplateComputational biologyChemistryBiologyMaterials scienceBiochemistry

Abstract

fetched live from OpenAlex

Molecularly imprinted polymers (MIPs) are currently widely used and further developed for biological applications. The MIP synthesis procedure is a key process, and a wide variety of protocols exist. The templates that are used for imprinting vary from the smallest glycosylated glycan structures or even amino acids to whole proteins or bacteria. The low cost, quick preparation, stability and reproducibility have been highlighted as advantages of MIPs. The biological applications utilizing MIPs discussed here include enzyme-linked assays, sensors, in vivo applications, drug delivery, cancer diagnostics and more. Indeed, there are numerous examples of how MIPs can be used as recognition elements similar to natural antibodies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.993
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.077
GPT teacher head0.378
Teacher spread0.301 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it