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Record W3091201856 · doi:10.26434/chemrxiv.12118914.v1

A Rapid and Quantitative Serum Test for SARS-CoV-2 Antibodies with Portable Surface Plasmon Resonance Sensing

2020· preprint· en· W3091201856 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueChemRxiv · 2020
Typepreprint
Languageen
FieldMedicine
TopicSARS-CoV-2 detection and testing
Canadian institutionsRegroupement Québécois sur les Matériaux de PointeUniversité LavalUniversité de MontréalPROTEO
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchUniversité de Montréal
KeywordsSurface plasmon resonanceAntibodySevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Coronavirus disease 2019 (COVID-19)VirologyChemistryMedicineNanotechnologyImmunologyMaterials scienceNanoparticleInfectious disease (medical specialty)DiseasePathology

Abstract

fetched live from OpenAlex

We report a surface plasmon resonance (SPR) sensor detecting nucleocapsid antibodies specific against the novel coronavirus 2019 (SARS-CoV-2) in undiluted human serum. When exposed to SARS-CoV-2, the immune system responds by expressing antibodies at levels that can be detected and monitored to identify the patient population immunized against SARD-CoV-2 and support efforts to deploy a vaccine strategically. A SPR sensor coated with a peptide monolayer and functionalized with SARS-CoV-2 nucleocapsid recombinant protein detected anti-SARS-CoV-2 antibodies in the nanomolar range. This bioassay was performed on a portable SPR instrument in undiluted human serum and results were collected within 15 minutes of sample/sensor contact. This strategy paves the way to point-of-care and label-free rapid testing for 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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.049
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0000.000
Research integrity0.0000.001
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 head0.092
GPT teacher head0.328
Teacher spread0.236 · 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