Evidence map›Paper›PMID 35465249›Full record

ArticleInfection and drug resistance2022

Detection of

Ying Liu, Huanhuan Zhu, Yinan Zheng

Open access · goldAbstract read
In one paragraph

Article in Infection and drug resistance, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.4field-weighted citation impact, top 19% 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

4 citing papers in PubMed, 9 citations in OpenAlex.

  1. Observational
  2. Article
  3. Molecular Analysis of Dihydropteroate Synthase Gene Mutations inInternational journal of molecular sciences · 2023
    Article
  4. 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

3 authors at 2 institutions in 1 country.

Ying LiuDepartment of Pediatrics, Peking University Shenzhen Hospital, Shenzhen, 518036, People's Republic of China.
Huanhuan ZhuPediatric Intensive Care Unit, GuangDong Women And Children Hospital, Guangzhou, Guangdong, 511442, People's Republic of China.
Yinan ZhengPediatric Intensive Care Unit, GuangDong Women And Children Hospital, Guangzhou, Guangdong, 511442, People's Republic of China.
Guangdong Province Women and Children Hospital · CNPeking University Shenzhen Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: This study aimed to investigate the characteristics of the non-human immunodeficiency virus (HIV) pneumocystis pneumonia (PCP) via the microbial composition of Methods: The clinical characteristics of 16 infants with non-HIV PCP (the PCP group) and 33 infants with severe pneumonia (the control group) who were hospitalized at the same time in the PICU were analyzed retrospectively. Using metagenomic next-generation sequencing (mNGS), the bronchoalveolar lavage fluid (BALF) of the two groups was analyzed, and the microbial results and clinical data were compared. Results: Compared with the control group, the infants in the PCP group had a lower incidence of cough (25% vs 78.8%; Conclusion: Non-HIV PCP has a higher incidence in PICU infants with severe pneumonia, especially those with underlying diseases or who are immunocompromised, which are clinically difficult to treat. A BALF analysis using mNGS is helpful for early and clear diagnoses. It also helps to clarify the distribution of pathogenic and lower respiratory tract colonizing bacteria in infants with severe pneumonia.

Indexed as

infantslower respiratory tract microorganismsmetagenomic next-generation sequencingnon-HIV Pneumocystis jirovecii pneumoniapathogen detectionsevere pneumonia

Identifiers

PMID35465249
PMCPMC9020507
OpenAlexW4223894098

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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 OpenQuestion graph.