کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
5545873 | 1555642 | 2017 | 6 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Prediction of lymph node parasite load from clinical data in dogs with leishmaniasis: An application of radial basis artificial neural networks
ترجمه فارسی عنوان
پیش بینی بار انگل لنفاوی از داده های بالینی در سگ های مبتلا به لیشمانیوز: استفاده از شبکه های عصبی مصنوعی بر اساس شعاعی
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
کلمات کلیدی
موضوعات مرتبط
علوم زیستی و بیوفناوری
علوم کشاورزی و بیولوژیک
علوم دامی و جانورشناسی
چکیده انگلیسی
Quantification of Leishmania infantum load via real-time quantitative polymerase chain reaction (qPCR) in lymph node aspirates is an accurate tool for diagnostics, surveillance and therapeutics follow-up in dogs with leishmaniasis. However, qPCR requires infrastructure and technical training that is not always available commercially or in public services. Here, we used a machine learning technique, namely Radial Basis Artificial Neural Network, to assess whether parasite load could be learned from clinical data (serological test, biochemical markers and physical signs). By comparing 18 different combinations of input clinical data, we found that parasite load can be accurately predicted using a relatively small reference set of 35 naturally infected dogs and 20 controls. In the best case scenario (use of all clinical data), predictions presented no bias or inflation and an accuracy (i.e., correlation between true and predicted values) of 0.869, corresponding to an average error of ±38.2 parasites per unit of volume. We conclude that reasonable estimates of L. infantum load from lymph node aspirates can be obtained from clinical records when qPCR services are not available.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Veterinary Parasitology - Volume 234, 30 January 2017, Pages 13-18
Journal: Veterinary Parasitology - Volume 234, 30 January 2017, Pages 13-18
نویسندگان
Rafaela Beatriz Pintor Torrecilha, Yuri Tani Utsunomiya, LuÃs Fábio da Silva Batista, Anelise Maria Bosco, Cáris Maroni Nunes, Paulo César Ciarlini, Márcia Dalastra Laurenti,