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Rapid detection of Lassa virus by reverse transcription-loop-mediated isothermal amplification

2010· article· en· W1483999325 on OpenAlexaff
Aiko Fukuma, Yohei Kurosaki, Yuko Morikawa, Allen Grolla, Heinz Feldmann, Jiro Yasuda

Bibliographic record

VenueMicrobiology and Immunology · 2010
Typearticle
Languageen
FieldMedicine
TopicViral Infections and Outbreaks Research
Canadian institutionsUniversity of ManitobaPublic Health Agency of Canada
FundersJapan Science and Technology AgencyNational Institute of Allergy and Infectious DiseasesJapan Society for the Promotion of ScienceNational Institutes of Health
KeywordsLassa virusLoop-mediated isothermal amplificationReverse Transcription Loop-mediated Isothermal AmplificationVirologySierra leoneBiologyReverse transcriptaseLassa feverArenavirusVirusMolecular biologyPrimer (cosmetics)RNAAntigenImmunologyChemistryGeneDNAGeneticsLymphocytic choriomeningitis

Abstract

fetched live from OpenAlex

In this study, a simple one-step reverse transcription-loop-mediated isothermal amplification (RT-LAMP) assay for rapid detection of Lassa virus (LASV) was established. The two primer sets were designed to detect LASV circulating in Sierra Leone and northeastern Nigeria. The RT-LAMP assay using these primer sets was able to detect 100 copies of the in vitro transcribed artificial LASV RNA within 25 min. The assay was also evaluated using intact viral RNA extracted from cell culture-propagated viruses and confirmed to be highly specific for LASV. The RT-LAMP assay developed in this study is rapid, simple, and highly specific for the detection of LASV, although its sensitivity is slightly lower than that of real-time RT-PCR. In addition, because the RT-LAMP assay does not require the use of sophisticated equipment, it would be advantageous for clinical diagnosis of LASV infection in developing countries. It might also be employed in cases of deliberate release during bioterrorism attacks or in epidemiological surveillance for disease outbreaks.

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 score0.088
Threshold uncertainty score0.490

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 head0.010
GPT teacher head0.246
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

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

Citations31
Published2010
Admission routes1
Has abstractyes

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