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Record W2011617830 · doi:10.1039/c0an00061b

Characterization of bacteria using its O-antigen with surface-enhanced Raman scattering

2010· article· en· W2011617830 on OpenAlexaff
Igor Osorio‐Román, Ricardo F. Aroca, J. Astudillo, Betty Matsuhiro, C. Vásquez, J.M. Pérez

Bibliographic record

VenueThe Analyst · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSpectroscopy Techniques in Biomedical and Chemical Research
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsRaman scatteringAntigenBacteriaEscherichia coliChemistryRaman spectroscopySalmonellaCharacterization (materials science)MicrobiologyMaterials scienceBiologyBiochemistryNanotechnologyGeneGeneticsPhysicsOptics

Abstract

fetched live from OpenAlex

The O-antigen determines the specificity of bacterial serotype, a sort of bacterial fingerprinting. In this work we report the extraction, purification and characterization of the O-antigen of two pathogenic bacteria, Escherichia coli O16 and Salmonella typhimurium. Molecular fingerprints found in the vibrational spectra represent a powerful analytical technique for identification (or differentiation) of molecular moieties in complex systems such as pathogens. In addition, advantages of vibrational Raman scattering are unique thanks to the high sensitivity and specificity achieved via surface-enhanced Raman scattering (SERS). SERS is used here to take advantage of characteristic vibrational frequency differences of O-antigens, thus allowing bacterial differentiation. Characteristic fundamental vibrational modes associated with the monosaccharide N-acetylglucosamine and deformations of the O-antigen chains provide the main spectroscopic differences between the O-antigens of E. coli O16 and S.typhimurium.

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 score0.004
Threshold uncertainty score0.200

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 integrity0.0000.000
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 head0.012
GPT teacher head0.298
Teacher spread0.286 · 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

Citations23
Published2010
Admission routes1
Has abstractyes

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