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Development of a Polymersome Blood Ammonia Assay Coupled with a Portable Near-Infrared Fluorometer

2024· article· en· W4401109030 on OpenAlexafffund
Marie‐Lynn Al‐Hawat, Justine Caron, Sarah Djebbar, Simon Matoori

Bibliographic record

VenueACS Bio & Med Chem Au · 2024
Typearticle
Languageen
FieldMedicine
TopicRenal function and acid-base balance
Canadian institutionsUniversité de Montréal
FundersFaculté de pharmacie, Université de MontréalFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsFluorometerInfraredAmmoniaChemistryPolymersomeNanotechnologyMaterials scienceOpticsBiochemistryFluorescencePhysicsOrganic chemistryCopolymerAmphiphile

Abstract

fetched live from OpenAlex

High Resolution Image Download MS PowerPoint Slide Ammonia is a key biomarker in inborn and acquired liver disease. As clinical point-of-care blood ammonia assays are lacking, we developed a polymersome formulation for point-of-care blood ammonia sensing combined with a portable fluorometer. A pH-sensitive near-infrared (NIR) fluorescent dye was identified, which showed a strong fluorescence increase at acidic pH values. Building on reports on ammonia-selective PS- b -PEG polymersomes, these polymersomes were loaded with the NIR dye. These NIR fluorescent polymersomes sensed ammonia in a clinically relevant range in ammonia-spiked fresh whole blood with high linearity ( R 2 = 0.9948) after 5 min using a conventional tabletop plate reader. Subsequently, the assay was tested with a portable fluorometer. An ammonia-dependent fluorescence increase was detected in ammonia-spiked fresh mouse blood after 5 min using the portable fluorometer. The NIR dye-loaded PS- b -PEG polymersomes rapidly sensed ammonia with high linearity in whole blood. This assay was successfully combined with a portable fluorometer and only required 3 μL of blood. These findings motivate a further development and clinical translation of this point-of-care blood ammonia assay.

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.

How this classification was reachedexpand

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 score0.023
Threshold uncertainty score0.586

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 head0.011
GPT teacher head0.237
Teacher spread0.226 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2024
Admission routes2
Has abstractyes

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