東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Linked to FindBook

Google Book

Amazon

博客來

Validating Diagnostic Markers That May Predict the Outcome of Ebola Virus Disease Patients.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Validating Diagnostic Markers That May Predict the Outcome of Ebola Virus Disease Patients./
Author:	Lazo, Jocelyn Ginette Perez .
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
Description:	282 p.
Notes:	Source: Dissertations Abstracts International, Volume: 83-04, Section: B.
Contained By:	Dissertations Abstracts International83-04B.
Subject:	Tumor necrosis factor-TNF. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28671113
ISBN:	9798544200123

Validating Diagnostic Markers That May Predict the Outcome of Ebola Virus Disease Patients.
Lazo, Jocelyn Ginette Perez .

Validating Diagnostic Markers That May Predict the Outcome of Ebola Virus Disease Patients. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 282 p.

Source: Dissertations Abstracts International, Volume: 83-04, Section: B.

Thesis (Ph.D.)--The University of Liverpool (United Kingdom), 2020.

This item must not be sold to any third party vendors.

Ebola virus disease (EVD) is one of the deadliest viral infections in humans, with case fatality rates recorded between 50 to 70%. Despite the ongoing efforts to control disease transmission using a recently licensed vaccine, no licensed treatments and prognostic tests are available that could improve the outcome of acute EVD patients and survivors. In the search for prognostic tools, different markers have been proposed. However, to date Ebola virus (EBOV) viral load measured as Ct value is currently the most reliable predictor of the clinical outcome, particularly in the context of defining survivors (Ct > 22) or fatal cases (Ct < 20). However, the viral load cannot predict accurately the clinical outcome in patients with Ct values between this range since the outcome is approximately equal between survival and a fatal infection. In a previous study 10 genes were identified by machine-learning of transcriptome data from EBOV patients as being potential prognostic markers. This study investigates whether this set of host-based response markers can predict the outcome of infection at the acute phase specially in situations where viral load gives little predictive value. Quantitative reverse transcription PCR (RT-qPCR) assays were developed for each gene transcripts and analysed in 39 clinical samples collected by the European Mobile Laboratory during the 2013-16 West Africa outbreak. A subset of the gene transcripts was used to generate machine learning models with or without EBOV Ct values to predict the outcome of a second group of 64 EVD patient samples using a blind-coded approach. The best discriminating model had a greater overall predictive accuracy (68.4%) compared to a model that only used EBOV Ct as a predictor variable (54.3%). These findings were ratified in a larger sample size, the prediction performance of the models was enhanced when a subset of the gene transcripts was combined with EBOV Ct (90-100%) than EBOV Ct alone (87%). Furthermore, a multiplex RT-qPCR assay for a subset of the gene transcripts was developed. This study proposes a novel approach that highly predictsthe risk of Ebola virus disease mortality at the time of diagnosis, which could be used to speed up the triage and clinical management of patients in future outbreaks.

ISBN: 9798544200123Subjects--Topical Terms:

3560383
Tumor necrosis factor-TNF.

Validating Diagnostic Markers That May Predict the Outcome of Ebola Virus Disease Patients.
LDR:03347nmm a2200301 4500 001 2349417
005 20220920134310.5
008 241004s2020 ||||||||||||||||| ||eng d
020 $a 9798544200123
035 $a (MiAaPQ)AAI28671113
035 $a (MiAaPQ)Liverpool_3120794
035 $a AAI28671113
040 $a MiAaPQ $c MiAaPQ
100 1 $a Lazo, Jocelyn Ginette Perez . $3 3688829
245 1 0 $a Validating Diagnostic Markers That May Predict the Outcome of Ebola Virus Disease Patients.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2020
300 $a 282 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-04, Section: B.
500 $a Advisor: Hiscox, Julian;Blake, Neil;Ng, Lisa.
502 $a Thesis (Ph.D.)--The University of Liverpool (United Kingdom), 2020.
506 $a This item must not be sold to any third party vendors.
520 $a Ebola virus disease (EVD) is one of the deadliest viral infections in humans, with case fatality rates recorded between 50 to 70%. Despite the ongoing efforts to control disease transmission using a recently licensed vaccine, no licensed treatments and prognostic tests are available that could improve the outcome of acute EVD patients and survivors. In the search for prognostic tools, different markers have been proposed. However, to date Ebola virus (EBOV) viral load measured as Ct value is currently the most reliable predictor of the clinical outcome, particularly in the context of defining survivors (Ct > 22) or fatal cases (Ct < 20). However, the viral load cannot predict accurately the clinical outcome in patients with Ct values between this range since the outcome is approximately equal between survival and a fatal infection. In a previous study 10 genes were identified by machine-learning of transcriptome data from EBOV patients as being potential prognostic markers. This study investigates whether this set of host-based response markers can predict the outcome of infection at the acute phase specially in situations where viral load gives little predictive value. Quantitative reverse transcription PCR (RT-qPCR) assays were developed for each gene transcripts and analysed in 39 clinical samples collected by the European Mobile Laboratory during the 2013-16 West Africa outbreak. A subset of the gene transcripts was used to generate machine learning models with or without EBOV Ct values to predict the outcome of a second group of 64 EVD patient samples using a blind-coded approach. The best discriminating model had a greater overall predictive accuracy (68.4%) compared to a model that only used EBOV Ct as a predictor variable (54.3%). These findings were ratified in a larger sample size, the prediction performance of the models was enhanced when a subset of the gene transcripts was combined with EBOV Ct (90-100%) than EBOV Ct alone (87%). Furthermore, a multiplex RT-qPCR assay for a subset of the gene transcripts was developed. This study proposes a novel approach that highly predictsthe risk of Ebola virus disease mortality at the time of diagnosis, which could be used to speed up the triage and clinical management of patients in future outbreaks.
590 $a School code: 0722.
650 4 $a Tumor necrosis factor-TNF. $3 3560383
650 4 $a Viruses. $3 571474
650 4 $a Fatalities. $3 3681792
650 4 $a Monkeys & apes. $3 3681351
650 4 $a Kinases. $3 3558077
650 4 $a Epidemics. $3 588395
650 4 $a Virology. $3 642304
690 $a 0720
710 2 $a The University of Liverpool (United Kingdom). $3 1684840
773 0 $t Dissertations Abstracts International $g 83-04B.
790 $a 0722
791 $a Ph.D.
792 $a 2020
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28671113