Evidence map›Paper›PMID 11018803›Full record

ArticleJournal of clinical laboratory analysis2000

Evaluation of Henes-PCR assay for Mycobacterium detection in different clinical specimens from patients with or without tuberculosis-associated HIV infection.

S M Hernandez Abanto, M H Hirata, R D Hirata, E M Mamizuka, M Schmal, S Hoshino-Shimizu

Abstract read
In one paragraph

Article in Journal of clinical laboratory analysis, 2000. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
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2citing papers in PubMed, 1 pooled it
–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

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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 synthesis or guideline pooled it.

  1. Pooled it
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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

6 authors.

S M Hernandez AbantoDepartment of Clinical and Toxicological Analyses, Faculty of Pharmaceutical Sciences, São Paulo University, São Paulo, Brazil.
M H Hirata
R D Hirata
E M Mamizuka
M Schmal
S Hoshino-Shimizu

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The need for early diagnosis of tuberculosis, particularly in HIV-infected patients, requires the development of diagnosis methods that have a high sensitivity and specificity, as does the nucleic acid-based technology. With the purpose of improving the detection of mycobacterium in different clinical samples, we proposed and evaluated an assay based on nucleic acid-amplification: heminested-PCR (Henes-PCR). The procedure was designed to identify Mycobacterium spp., M. tuberculosis complex (MTC), and M. avium complex (MAC), although it has the potential to include more primers for the identification of other species. Analytical and clinical evaluation of Henes-PCR was performed by analysis of reference strains and 356 clinical specimens from 246 patients with pulmonary and meningitis tuberculosis and unrelated infections, including 142 HIV-infected individuals. Ninety-three percent (199) positive and 100% (143) negative results were obtained in specimens from patients with tuberculosis and non-tuberculosis infection, respectively. The overall sensitivity of Henes-PCR was 93.4%, specificity was 100%, positive and negative predictive values were 100 and 91.1%, respectively. Sensitivity and negative predictive value of Henes-PCR were significantly higher than culture procedure for microscopy-negative specimens. Even though frequency of HIV infection was higher in patients with tuberculosis, diagnostic parameters of Henes-PCR were similar between HIV-positive and HIV-negative patients. MTB was identified in 194 (98%) specimens while MAC was detected in 5 (2%) specimens. These findings suggest that Henes-PCR is a useful test for rapid detection of mycobacterium in clinically suspected cases of tuberculosis with smear-negative results.

Indexed as

HIV InfectionsHumansMycobacteriumPolymerase Chain ReactionSensitivity and SpecificityTuberculosis

Identifiers

PMID11018803
PMCPMC6807795

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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.