MétaCan
Menu
Back to cohort
Record W2000308853 · doi:10.1109/jsen.2012.2202105

Detection of Fluorophore-Tagged Recombinant Bovine Somatotropin (rbST) by Using a Silica-on-Silicon (SOS)-PDMS Lab-on-a-Chip

2012· article· en· W2000308853 on OpenAlexaff
Jayan Ozhikandathil, Simona Bǎdilescu, Muthukumaran Packirisamy

Bibliographic record

VenueIEEE Sensors Journal · 2012
Typearticle
Languageen
FieldEngineering
TopicBiosensors and Analytical Detection
Canadian institutionsConcordia University
Fundersnot available
KeywordsFluorophorePolydimethylsiloxaneBovine somatotropinMicrofluidicsLab-on-a-chipSiliconRecombinant DNAChipNanotechnologyMaterials scienceChemistryComputer scienceFluorescenceOptoelectronicsOpticsGeneBiochemistry

Abstract

fetched live from OpenAlex

The presence of potentially harmful substances in milk is a concern for consumers. The discovery of recombinant DNA technology allowed the production of large quantities of recombinant bovine somatotropin (rbST), which is allowed to be used to increase milk and meat production in many countries. The use of rbST is controversial because of its potential effects on animal and human health. Use of the existing large instruments for the detection of rbST suffers disadvantages such as the need of large quantities of reagents, increased time of assays and most importantly, the high cost of equipment, etc. In this paper, a novel optical lab-on-a-chip (LOC) is proposed for the detection of a fluorophore-tagged rbST. The advantages of a silica-on-silicon platform for the optical waveguide and polydimethylsiloxane for microfluidics are exploited for the fabrication of a low-cost LOC. The tagging of rbST with two different types of fluorophores, such as FITC and Alexa-647, is carried out and used for detection in the proposed LOC.

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 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 score0.058
Threshold uncertainty score1.000

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 integrity0.0000.001
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 head0.014
GPT teacher head0.227
Teacher spread0.213 · 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

Citations11
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Sensors JournalSame topicBiosensors and Analytical DetectionFrench-language works237,207