English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Background Canine babesiosis is an important tick-borne disease in endemic regions. One of the relevant subspecies in Europe is  Babesia canis , and it can cause severe clinical signs such as hemolytic anemia. Apart from acute clinical symptoms dogs can also have a more chronic disease development or be asymptomatic carriers. Our objective was to identify readily available ADVIA hematology analyzer parameters suggestive of  B. canis  parasitemia in dogs and to formulate a predictive model. Methods A historical dataset of complete blood count data from an ADVIA hematology system with blood smear or PCR confirmed parasitemia cases was used to obtain a model by conventional statistics (CS) methods and machine learning (ML) using logistical regression and tree methods. Results Both methods identified that important parameters were platelet count, mean platelet volume and percentage large unstained cells. We were able to formulate a CS model and ML model to screen for  Babesia  parasitemia in dogs with a sensitivity of 84.6% (CS) and 100% (ML), a specificity of 97.7% (CS) and 95.7% (ML) and a positive likelihood ratio (LR+) of 36.78 (CS) and 23.2 (ML). Conclusions This study introduces two methods of screening for  B. canis  parasitemia on readily available data from ADVIA hematology systems. The algorithms can easily be introduced in laboratories that use these analyzers. When the algorithm marks a sample as ‘suggestive’ for  Babesia  parasitemia, the sample is approximately 37 times more likely to show  Babesia  merozoites on blood smear analysis. Graphical Abstract

parasites and vectors

Identification of parameters and formulation of a statistical and machine learning model to identify Babesia canis infections in dogs using available ADVIA hematology analyzer data