Evidence map›Paper›PMID 36013309›Full record

ArticleLife (Basel, Switzerland)2022

Quantitative Evaluation of Very Low Levels of HIV-1 Reverse Transcriptase by a Novel Highly Sensitive RT-qPCR Assay.

Francesca Marino-Merlo, Valeria Stefanizzi, Agnese Ragno, Lucia Piredda, Sandro Grelli, Beatrice Macchi, Antonio Mastino

Abstract read
In one paragraph

Article in Life (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
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

7 authors.

Francesca Marino-MerloDepartment of Chemical, Biological, Pharmaceutical, and Environmental Sciences, University of Messina, 98166 Messina, Italy.ORCID 0000-0001-7898-583X
Valeria StefanizziDepartment of Chemical Science and Technology, University of Rome "Tor Vergata", 00133 Rome, Italy.ORCID 0000-0003-0516-5683
Agnese RagnoDepartment of Chemical, Biological, Pharmaceutical, and Environmental Sciences, University of Messina, 98166 Messina, Italy.
Lucia PireddaDepartment of Biology, University of Rome "Tor Vergata", 00133 Rome, Italy.
Sandro GrelliDepartment of Experimental Medicine, University of Rome "Tor Vergata", 00133 Rome, Italy.
Beatrice MacchiDepartment of Chemical Science and Technology, University of Rome "Tor Vergata", 00133 Rome, Italy.ORCID 0000-0003-0878-1799
Antonio MastinoThe Institute of Translational Pharmacology, Consiglio Nazionale delle Ricerche (C.N.R.), 00133 Rome, Italy.ORCID 0000-0002-0338-6450

Funding

Gilead Sciences s.r.l. Fellowship Program 2018Italian Ministry of University and Research, PRIN-Research Projects of National Relevance n. 2017M8R7N9_002
6 · The paper itself

Abstract

Based on previous experience in our laboratory, we developed a real-time reverse transcriptase (RT) quantitative PCR (RT-qPCR) assay for the assessment of very low levels of HIV-1 RT activity. The RNA, acting as a template for reverse transcription into cDNA by HIV-1 RT, consisted of a synthetic RNA ad hoc generated by in vitro transcription and included a coding sequence for HSV-1 gD (gD-RNA-synt). Different conditions of variables involved in the RT-qPCR reaction, notably different amounts of gD-RNA-synt, different mixes of the reaction buffer, and different dNTP concentrations, were tested to optimize the assay. The results indicated that the gD-RNA-synt-based RT assay, in its optimized formulation, could detect a specific cDNA reverse transcription even in the presence of 1 × 10

Indexed as

human immunodeficiency virusin vitro transcriptionquantitative PCR assayreverse transcriptase

Identifiers

PMID36013309
PMCPMC9410348

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Registered trials

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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.