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Molecular Typing of<i>Borrelia burgdorferi</i>

2014· article· en· W1767592711 on OpenAlexaff
Guiqing Wang, Dionysios Liveris, Priyanka Mukherjee, Sabrina Jungnick, Gabriele Margos, Ira Schwartz

Bibliographic record

VenueCurrent Protocols in Microbiology · 2014
Typearticle
Languageen
FieldImmunology and Microbiology
TopicVector-borne infectious diseases
Canadian institutionsUniversity of Calgary
FundersNational Institute of Arthritis and Musculoskeletal and Skin DiseasesNational Institute of Allergy and Infectious DiseasesNational Institutes of Health
KeywordsBorrelia burgdorferiBiologyBorreliaLyme diseaseSensuTypingMicrobiologySpirochaetaceaeMultilocus sequence typingVirologyGeneticsGenotypeGenusGeneZoology

Abstract

fetched live from OpenAlex

Borrelia burgdorferi sensu lato is a group of spirochetes belonging to the genus Borrelia in the family of Spirochaetaceae. The spirochete is transmitted between reservoirs and hosts by ticks of the family Ixodidae. Infection with B. burgdorferi in humans causes Lyme disease or Lyme borreliosis. Currently, 20 Lyme disease-associated Borrelia species and more than 20 relapsing fever-associated Borrelia species have been described. Identification and differentiation of different Borrelia species and strains is largely dependent on analyses of their genetic characteristics. A variety of molecular techniques have been described for Borrelia isolate speciation, molecular epidemiology, and pathogenicity studies. In this unit, we focus on three basic protocols, PCR-RFLP-based typing of the rrs-rrlA and rrfA-rrlB ribosomal spacer, ospC typing, and MLST. These protocols can be employed alone or in combination for characterization of B. burgdorferi isolates or directly on uncultivated organisms in ticks, mammalian host reservoirs, and human clinical specimens.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.016
GPT teacher head0.298
Teacher spread0.282 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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

Citations61
Published2014
Admission routes1
Has abstractyes

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