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Record W2461760070 · doi:10.1002/slct.201600334

Functionalized TiO <sub>2</sub> Nanoparticles as Labels for Immunoassay

2016· article· en· W2461760070 on OpenAlexaff
Marco Sarro, Claudio Baggiani, Cristina Giovannoli, Marta Cerruti, Paola Calza, Laura Anfossi

Bibliographic record

VenueChemistrySelect · 2016
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Nanomaterials in Catalysis
Canadian institutionsMcGill University
Fundersnot available
KeywordsImmunoassayDetection limitChromogenicHuman serum albuminChemistryChromatographySubstrate (aquarium)Covalent bondMatrix (chemical analysis)BiosensorNanoparticleMaterials scienceNanotechnologyAntibodyMedicineImmunology

Abstract

fetched live from OpenAlex

Abstract We designed a versatile inorganic probe based on functionalized TiO 2 nanoparticles for labeling immunoreagents and developing immunological tests. Similarly to enzymatic probes traditionally used in immunoassay, the TiO 2 probe could be covalently linked to antigens or antibodies and exploited as the signal reporter in immunoassays. The TiO 2 probe allowed revealing the rate of antigen‐antibody complex formation by promoting the oxidation of a suitable chromogenic substrate with absorption in the visible. We demonstrated the suitability of the TiO 2 probe as a label for immunoassay by coupling it to human serum albumin and developing a direct competitive assay to measure micro‐ and macro‐albuminuria for diabetes diagnosis. The developed TiO 2 ‐based assay showed high sensitivity (detection limit 1.4 mg l −1 ), wide dynamic range (6‐1270 mg l −1 ) and acceptable precision (within‐ and between‐assay coefficient of variation %&lt;20 %) and accuracy (75‐95 %) for measuring albumin in human urine. The method is fully compatible with materials and equipment of standard enzyme immunoassay (except for the need of UV irradiation), while the inorganic probe is more robust towards chemical and physical conditions and shows better long term stability compared to enzymes.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.006
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.001

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 head0.248
Teacher spread0.237 · 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.

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

Citations4
Published2016
Admission routes1
Has abstractyes

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