Evidence map›Paper›PMID 30322029›Full record

ReviewSensors (Basel, Switzerland)2018

HCV Detection, Discrimination, and Genotyping Technologies.

Shrikant Dashrath Warkad, Satish Balasaheb Nimse, Keum-Soo Song, Taisun Kim

Open access · goldAbstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
2.1field-weighted citation impact, top 11% 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

14 citing papers in PubMed, 36 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

4 authors at 1 institution in 1 country.

Shrikant Dashrath WarkadInstitute for Applied Chemistry and Department of Chemistry, Hallym University, Chuncheon 200-702, Korea. shrikant.warkad@hallym.ac.kr.
Satish Balasaheb NimseInstitute for Applied Chemistry and Department of Chemistry, Hallym University, Chuncheon 200-702, Korea. satish_nimse@hallym.ac.kr.ORCID 0000-0002-5137-0584
Keum-Soo SongInstitute for Applied Chemistry and Department of Chemistry, Hallym University, Chuncheon 200-702, Korea. hanlimsk@empal.com.
Taisun KimInstitute for Applied Chemistry and Department of Chemistry, Hallym University, Chuncheon 200-702, Korea. tskim@hallym.ac.kr.
Hallym University · KR

Funding

Hallym University HRF-201709-009Ministry of Trade, Industry & Energy (MOTIE), Korea Institute for Advancement of Technology (KIAT) through the Encouragement Program for The Industries of Economic Cooperation Region (Project No.: R0004191)
6 · The paper itself

Abstract

According to the World Health Organization (WHO), 71 million people were living with Hepatitis C virus (HCV) infection worldwide in 2015. Each year, about 399,000 HCV-infected people succumb to cirrhosis, hepatocellular carcinoma, and liver failure. Therefore, screening of HCV infection with simple, rapid, but highly sensitive and specific methods can help to curb the global burden on HCV healthcare. Apart from the determination of viral load/viral clearance, the identification of specific HCV genotype is also critical for successful treatment of hepatitis C. This critical review focuses on the technologies used for the detection, discrimination, and genotyping of HCV in clinical samples. This article also focuses on advantages and disadvantages of the reported methods used for HCV detection, quantification, and genotyping.

Indexed as

Biosensing TechniquesGenotyping TechniquesHepacivirusHepatitis CHumansNanoparticlesNanotechnologyNanotubes, CarbonNucleic Acid Amplification TechniquesQuantum DotsReal-Time Polymerase Chain ReactionReverse TranscriptionViral LoadNanotubes, CarbondetectiongenotypingHCVnucleic acidsquantificationRT-PCRviral loadviruses

Identifiers

PMID30322029
PMCPMC6210034
OpenAlexW2897595134

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.