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Record W2136575826 · doi:10.4269/ajtmh.13-0106

Application of a qPCR Assay with Melting Curve Analysis for Detection and Differentiation of Protozoan Oocysts in Human Fecal Samples from Dominican Republic

2013· article· en· W2136575826 on OpenAlexaff
Laura F. Lalonde, Julissa Reyes, Alvin A. Gajadhar

Bibliographic record

VenueAmerican Journal of Tropical Medicine and Hygiene · 2013
Typearticle
Languageen
FieldImmunology and Microbiology
TopicParasitic Infections and Diagnostics
Canadian institutionsCanadian Food Inspection Agency
Fundersnot available
KeywordsCryptosporidiumCyclospora cayetanensisCyclosporaFecesBiologyCryptosporidium parvumCoccidiaMicrobiologyPolymerase chain reactionAmpliconMelting curve analysisVirologyApicomplexaVeterinary medicineNested polymerase chain reactionParasite hostingGeneProtozoal disease

Abstract

fetched live from OpenAlex

A quantitative polymerase chain reaction assay with melt curve analysis (qPCR-MCA) was applied for the detection of protozoan oocysts in 501 human fecal samples collected in Dominican Republic. Samples were subjected to qPCR using universal coccidia primers targeting 18S rDNA to detect oocysts followed by MCA to identify oocyst species based on amplicon melting temperature. Putative positive samples were also tested by conventional PCR and microscopy. Cystoisospora belli (×3), Cryptosporidium parvum (×3), Cryptosporidium hominis (×5), Cryptosporidium meleagridis (×1), Cryptosporidium canis (×1), and Cyclospora cayetanensis (×9) were detected by qPCR-MCA and confirmed by sequencing. This assay consistently detected 10 copies of the cloned target fragment and can be considered more efficient and sensitive than microscopy flotation methods for detecting multiple species of oocysts in human feces. The qPCR-MCA is a reliable protozoan oocyst screening assay for use on clinical and environmental samples in public health, food safety and veterinary programs.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.200
Threshold uncertainty score0.368

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.014
GPT teacher head0.280
Teacher spread0.266 · 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 designObservational
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

Citations30
Published2013
Admission routes1
Has abstractyes

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