Evidence map›Paper›PMID 41726126›Full record

ArticleOpen medicine (Warsaw, Poland)2026

Neutrophil to lymphocyte ratio (NLR) and YKL-40 as potential markers for discriminating mycoplasma pneumoniae pneumonia from viral pneumonia in children.

Yu Wan, Jun Lv, Fei Jiang, Fei Fan

Abstract read
In one paragraph

Article in Open medicine (Warsaw, Poland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

4 authors.

Yu WanDepartment of Pediatrics, The Second People's Hospital of Changzhou, The Third Affiliated Hospital of Nanjing Medical University, Changzhou, China.
Jun LvDepartment of Pediatrics, The Second People's Hospital of Changzhou, The Third Affiliated Hospital of Nanjing Medical University, Changzhou, China.
Fei JiangDepartment of Pediatrics, The Second People's Hospital of Changzhou, The Third Affiliated Hospital of Nanjing Medical University, Changzhou, China.
Fei FanDepartment of Pediatrics, The Second People's Hospital of Changzhou, The Third Affiliated Hospital of Nanjing Medical University, Changzhou, China.ORCID https://orcid.org/0009-0001-1409-6301

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: Our research aimed to explore the potential indicators in discriminating mycoplasma pneumoniae pneumonia (MPP) from viral pneumonia (VP) in children. Methods: TaqMan PCR testing was used to detect respiratory pathogens in all pneumonia patients. The serum levels of IL-37 and YKL-40 were measured by ELISA. While the expression levels of YKL-40 and IL37 mRNA in PBMCs were detected by qRT-PCR. We calculated the neutrophil to lymphocyte ratio (NLR) and platelet to lymphocyte ratio (PLR) values from blood routine test results. This study had been registered in the Chinese Clinical Trials Registry System (MR-32-24-025608). Results: 170 patients were selected, including 85 MPP patients and 85 VP patients. In addition, 85 healthy control children were selected for comparison. The levels of serum YKL-40, IL-37 and their mRNA expressions in MPP patients were significantly higher than in VP patients and healthy control. Similarly, MPP patients had the highest levels of NLR and PLR. According to the ROC curve result, the combination of two indicators (YKL-40 and NLR) was the strongest predictor of MP vs. VP. Conclusions: These results indicate that YKL-40 and NLR constitute the critical combination of biomarkers useful for differentiating between MPP and VP in children.

Indexed as

interleukin‐37mycoplasma pneumoniae pneumoniaNLRviral pneumoniaYKL-40

Identifiers

PMID41726126
PMCPMC12917580

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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.