Evidence map›Paper›PMID 40119224›Full record

ReviewEuropean journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology2025

A review of historical landmarks and pioneering technologies for the diagnosis of Hepatitis C Virus (HCV).

Kajal Sharma, Meesala Krishna Murthy

Abstract readReviewHistorical Article
PubMed Publisher
In one paragraph

Review in European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
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

2 citing papers in PubMed.

  1. Article
  2. Review
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

2 authors.

Kajal SharmaDepartment of Allied Health Sciences, Chitkara School of Health Sciences, Chitkara University, Rajpura, Punjab, 140401, India.
Meesala Krishna MurthyDepartment of Allied Health Sciences, Chitkara School of Health Sciences, Chitkara University, Rajpura, Punjab, 140401, India. krishnameesala6@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSince the progress of hepatitis C Virus (HCV) infection to chronic liver disease and finally cirrhosis and hepatocellular carcinoma, HCV infection has become a worldwide challenge to public health.

resultsThe progression from liver biopsy and antibody based test to the current time in the advancements in HCV diagnostic method is reviewed in this analysis with detailed discussion of enzyme immunoassay (EIAs), nucleic acid tests (NATs) and genotyping in enhancing accuracy of HCV detection. Next generation sequencing (NGS) and point of care testing (POCT) provided fast and economical diagnostic solutions. However, as promising diagnostic tools, Artificial Intelligence (AI) as well as Machine Learning (ML) can only be used in well-resourced environments, whereas Rapid Diagnostic Tests (RDTs) are advantageous for low and middle income countries.

conclusionThis review discusses some of the future challenges that face lowering of diagnostic costs in low resource settings and promoting early detection, some of which can be addressed by microfluidic platforms. Research in this area is far from over, and past and ongoing research has tremendous potential to access new technology for a myriad of purposes in the course of HCV control and global HCV elimination.

Indexed as

Diagnostic Tests, RoutineHepacivirusHepatitis CHigh-Throughput Nucleotide SequencingHistory, 20th CenturyHistory, 21st CenturyHumansArtificial intelligence and machine learningDiagnosisEnzyme immunoassaysHepatitis C VirusNext-generation sequencing

Identifiers

What Socratic holds

Textmetadata
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 Socratic graph.