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Record W3132884103 · doi:10.1021/cen-09906-scicon2

Glucose meter–based device detects pathogens

2021· article· en· W3132884103 on OpenAlex
Celia Henry Arnaud

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
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

VenueC&EN Global Enterprise · 2021
Typearticle
Languageen
FieldEngineering
TopicBiosensors and Analytical Detection
Canadian institutionsnot available
Fundersnot available
KeywordsGlucose meterMetreComputer scienceBiologyDiabetes mellitusPhysics

Abstract

fetched live from OpenAlex

Researchers have co-opted glucose meters to detect infectious agents, including SARS-CoV-2, by pairing the devices with synthetic gene circuits that produce glucose in response to target analytes. The work is “an important advance in synthetic biology toward more practical applications,” Yi Lu, a chemist at the University of Illinois at Urbana-Champaign, who has previously used glucose meters to detect other analytes, writes in an email. Evan Amalfitano, a graduate student in Keith Pardee’s group at the University of Toronto, and coworkers have designed gene circuits that generate glucose in response to a target analyte—usually an RNA sequence specific to a pathogen of interest. The researchers add to their system RNA that they have extracted and amplified from a biological sample. The RNA binds to a “toehold switch,” an RNA loop with segments that are complementary to the target RNA. When the RNA binds, the toehold switch opens and triggers the

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.387
Threshold uncertainty score0.705

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 studies0.0000.000
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 head0.006
GPT teacher head0.216
Teacher spread0.210 · 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