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The biomedical and bioengineering potential of protein nanocompartments

2020· review· en· W3015742158 on OpenAlex
Aubrey M. Demchuk, Trushar R. Patel

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueBiotechnology Advances · 2020
Typereview
Languageen
FieldEnvironmental Science
TopicBacteriophages and microbial interactions
Canadian institutionsUniversity of CalgaryUniversity of AlbertaUniversity of Lethbridge
FundersAlberta Innovates
KeywordsNanobiotechnologyBiocompatible materialNanocagesNanotechnologySynthetic biologyChemistryComputational biologyBiologyBiochemistryMaterials scienceNanoparticle

Abstract

fetched live from OpenAlex

Protein nanocompartments (PNCs) are self-assembling biological nanocages that can be harnessed as platforms for a wide range of nanobiotechnology applications. The most widely studied examples of PNCs include virus-like particles, bacterial microcompartments, encapsulin nanocompartments, enzyme-derived nanocages (such as lumazine synthase and the E2 component of the pyruvate dehydrogenase complex), ferritins and ferritin homologues, small heat shock proteins, and vault ribonucleoproteins. Structural PNC shell proteins are stable, biocompatible, and tolerant of both interior and exterior chemical or genetic functionalization for use as vaccines, therapeutic delivery vehicles, medical imaging aids, bioreactors, biological control agents, emulsion stabilizers, or scaffolds for biomimetic materials synthesis. This review provides an overview of the recent biomedical and bioengineering advances achieved with PNCs with a particular focus on recombinant PNC derivatives.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.998
Threshold uncertainty score0.430

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.008
GPT teacher head0.265
Teacher spread0.257 · 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