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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In this work we study numerical construction of optimal clinical diagnostictests for detecting sporadic Creutzfeldt-Jakob disease (sCJD). A cerebrospinalfluid sample (CSF) from a suspected sCJD patient is subjected to a processwhich initiates the aggregation of a protein present only in cases of sCJD.This aggregation is indirectly observed in real-time at regular intervals, sothat a longitudinal set of data is constructed that is then analysed forevidence of this aggregation. The best existing test is based solely on thefinal value of this set of data, which is compared against a threshold toconclude whether or not aggregation, and thus sCJD, is present. This testcriterion was decided upon by analysing data from a total of 108 sCJD andnon-sCJD samples, but this was done subjectively and there is no supportingmathematical analysis declaring this criterion to be exploiting the availabledata optimally. This paper addresses this deficiency, seeking to validate orimprove the test primarily via support vector machine (SVM) classification.Besides this, we address a number of additional issues such as i) earlystopping of the measurement process, ii) the possibility of detecting theparticular type of sCJD and iii) the incorporation of additional patient datasuch as age, sex, disease duration and timing of CSF sampling into theconstruction of the test.

Optimal diagnostic tests for sporadic Creutzfeldt-Jakob disease based on support vector machine classification of RT-QuIC data