Evidence map›Paper›PMID 36628207›Full record

ArticleAmerican journal of translational research2022

The diagnostic value of combined detection of microRNA-155, TNF-α and IL-37 for active pulmonary tuberculosis in the elderly.

Longmei Yu, Haiyang Fu, Haiyun Zhang

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In one paragraph

Article in American journal of translational research, 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
0.1field-weighted citation impact, top 53% 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

3 citing papers in PubMed, 1 citations in OpenAlex.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

3 authors at 1 institution in 1 country.

Longmei YuDepartment of Laboratory Medicine, Affiliated Nantong Hospital of Shanghai University (The Sixth People's Hospital of Nantong) Nantong 226001, Jiangsu, P. R. China.
Haiyang FuDepartment of Laboratory Medicine, Affiliated Nantong Hospital of Shanghai University (The Sixth People's Hospital of Nantong) Nantong 226001, Jiangsu, P. R. China.
Haiyun ZhangDepartment of Laboratory Medicine, Affiliated Nantong Hospital of Shanghai University (The Sixth People's Hospital of Nantong) Nantong 226001, Jiangsu, P. R. China.
Nantong University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo identify a panel of potential biomarkers consisting of microRNA-155 (miR-155), tumor necrosis factor-alpha (TNF-α), interleukin-37 (IL-37) for the diagnosis of active pulmonary tuberculosis (PTB) infection in elderly patients.

methodsThe serum expression of miR-155 and TNF-α in patients was measured by RT-qPCR, and the serum IL-37 level was determined by ELISA. The correlation between their expression and the features of active PTB patients was also analyzed. The AUCs of miR-155, TNF-α and IL-37 in diagnosing active PTB was calculated according to the ROC curves. The sensitivity, specificity and Youden's index of the three biomarkers alone or in combination for the active PTB diagnosis in the elderly were assessed.

resultsmiR-155, TNF-α, and IL-37 were overexpressed in the sera of elderly patients with active PTB. miR-155, TNF-α and IL-37 serum levels were enhanced in the elderly patients with pulmonary and extrapulmonary TB relative to those with PTB only, and in patients with active TB infection in both lungs compared to those with unilateral lung infection. The AUCs of miR-155, TNF-α and IL-37 for the diagnosis of active PTB were 0.7920, 0.8734 and 0.7398, respectively. The combination of these three improved the sensitivity of the diagnosis (84.78%).

conclusionmiR-155, TNF-α and IL-37 were overexpressed in elderly patients with active PTB. The combined detection of serum miR-155/TNF-α/IL-37 expression has potential to serve as a diagnostic tool for active PTB in the elderly.

Indexed as

active pulmonary tuberculosisbiomarkersIL-37MicroRNA-155TNF-α

Identifiers

PMID36628207
PMCPMC9827330
OpenAlexW4315619138

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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.