Evidence map›Paper›PMID 29258802›Full record

ArticleTicks and tick-borne diseases2018

A molecular algorithm to detect and differentiate human pathogens infecting Ixodes scapularis and Ixodes pacificus (Acari: Ixodidae).

Christine B Graham, Sarah E Maes, Andrias Hojgaard, Amy C Fleshman, Sarah W Sheldon, Rebecca J Eisen

Abstract read
In one paragraph

Article in Ticks and tick-borne diseases, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.

0numbers the graph read from it
0cells of the map it votes in
33citing papers in PubMed
1.8field-weighted citation impact, top 17% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

33 citing papers in PubMed, 45 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 1 institution in 1 country.

Christine B GrahamDivision of Vector-Borne Diseases, Centers for Disease Control and Prevention, 3156 Rampart Rd., Fort Collins, CO 80521, United States. Electronic address: hyb4@cdc.gov.
Sarah E MaesDivision of Vector-Borne Diseases, Centers for Disease Control and Prevention, 3156 Rampart Rd., Fort Collins, CO 80521, United States.
Andrias HojgaardDivision of Vector-Borne Diseases, Centers for Disease Control and Prevention, 3156 Rampart Rd., Fort Collins, CO 80521, United States.
Amy C FleshmanDivision of Vector-Borne Diseases, Centers for Disease Control and Prevention, 3156 Rampart Rd., Fort Collins, CO 80521, United States.
Sarah W SheldonDivision of Vector-Borne Diseases, Centers for Disease Control and Prevention, 3156 Rampart Rd., Fort Collins, CO 80521, United States.
Rebecca J EisenDivision of Vector-Borne Diseases, Centers for Disease Control and Prevention, 3156 Rampart Rd., Fort Collins, CO 80521, United States.
Centers for Disease Control and Prevention · US

Funding

Intramural CDC HHS CC999999
6 · The paper itself

Abstract

The incidence and geographic range of tick-borne illness associated with Ixodes scapularis and Ixodes pacificus have dramatically increased in recent decades. Anaplasmosis, babesiosis, and Borrelia spirochete infections, including Lyme borreliosis, account for tens of thousands of reported cases of tick-borne disease every year. Assays that reliably detect pathogens in ticks allow investigators and public health agencies to estimate the geographic distribution of human pathogens, assess geographic variation in their prevalence, and evaluate the effectiveness of prevention strategies. As investigators continue to describe new species within the Borrelia burgdorferi sensu lato complex and to recognize some Ixodes-borne Borrelia species as human pathogens, assays are needed to detect and differentiate these species. Here we describe an algorithm to detect and differentiate pathogens in unfed I. scapularis and I. pacificus nymphs including Anaplasma phagocytophilum, Babesia microti, Borrelia burgdorferi sensu stricto, Borrelia mayonii, and Borrelia miyamotoi. The algorithm comprises 5 TaqMan real-time polymerase chain reaction assays and 3 sequencing protocols. It employs multiple targets for each pathogen to optimize specificity, a gene target for I. scapularis and I. pacificus to verify tick-derived DNA quality, and a pan-Borrelia target to detect Borrelia species that may emerge as human disease agents in the future. We assess the algorithm's sensitivity, specificity, and performance on field-collected ticks.

Indexed as

AlgorithmsArachnid VectorsIxodesAnaplasma phagocytophilumAnimalsBabesiaNymphReal-Time Polymerase Chain ReactionSequence Analysis, DNAAnaplasma phagocytophilumBabesia microtiBorreliaIxodesReal-time PCR

Identifiers

PMID29258802
PMCPMC6452875
OpenAlexW2774056824

What OpenQuestion holds

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Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.