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Record W4405056439 · doi:10.1126/science.adn2600

Active-reset protein sensors enable continuous in vivo monitoring of inflammation

2024· article· en· W4405056439 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueScience · 2024
Typearticle
Languageen
FieldEngineering
TopicBiosensors and Analytical Detection
Canadian institutionsUniversity of Toronto
FundersNational Heart, Lung, and Blood Institute
KeywordsIn vivoReset (finance)Continuous monitoringInflammationBiomarkerDissociation (chemistry)ChemistryBiomedical engineeringBiophysicsMedicineBiologyBiochemistryInternal medicineBiotechnology

Abstract

fetched live from OpenAlex

Continuous measurement of proteins in vivo is important for real-time disease management and prevention. Implantable sensors for monitoring small molecules such as glucose have been available for more than a decade. However, analysis of proteins remains an unmet need because the lower physiological levels require that sensors have high affinities, which are linked to long complexation half-lives ( t 1/2 ~20 hours) and slow equilibration when concentrations decrease. We report active-reset sensors by use of high-frequency oscillations to accelerate dissociation, which enables regeneration of the unbound form of the sensor within 1 minute. When implemented within implanted devices, these sensors allow for real-time, in vivo monitoring of proteins within interstitial fluid. Active-reset protein sensors track biomarker levels on a physiological timescale for inflammation monitoring in living animals.

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 score0.097
Threshold uncertainty score0.160

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.008
GPT teacher head0.231
Teacher spread0.223 · 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