Evidence map›Paper›PMID 35464135›Full record

ReviewBiomicrofluidics2022

Recent advances in the understanding of alveolar flow.

Jun Dong, Yue Yang, Yonggang Zhu

Open access · greenAbstract readReview
In one paragraph

Review in Biomicrofluidics, 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
2.1field-weighted citation impact, top 12% 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, 15 citations in OpenAlex.

  1. Physiological Modeling of the Vascularized Human Lung Organoid.American journal of respiratory cell and molecular biology · 2025
    Article
  2. Article
  3. Review
  4. Hypertonic treatment of acute respiratory distress syndrome.Frontiers in bioengineering and biotechnology · 2023
    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 1 institution in 1 country.

Jun DongCenter for Microflows and Nanoflows, School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen, Guangdong 518055, China.ORCID 0000-0002-4959-6605
Yue YangCenter for Microflows and Nanoflows, School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen, Guangdong 518055, China.ORCID 0000-0002-2395-286X
Yonggang ZhuCenter for Microflows and Nanoflows, School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen, Guangdong 518055, China.ORCID 0000-0002-5419-9508
Harbin Institute of Technology · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Understanding the dynamics of airflow in alveoli and its effect on the behavior of particle transport and deposition is important for understanding lung functions and the cause of many lung diseases. The studies on these areas have drawn substantial attention over the last few decades. This Review discusses the recent progress in the investigation of behavior of airflow in alveoli. The information obtained from studies on the structure of the lung airway tree and alveolar topology is provided first. The current research progress on the modeling of alveoli is then reviewed. The alveolar cell parameters at different generation of branches, issues to model real alveolar flow, and the current numerical and experimental approaches are discussed. The findings on flow behavior, in particular, flow patterns and the mechanism of chaotic flow generation in the alveoli are reviewed next. The different flow patterns under different geometrical and flow conditions are discussed. Finally, developments on microfluidic devices such as lung-on-a-chip devices are reviewed. The issues of current devices are discussed.

Identifiers

PMID35464135
PMCPMC9010052
OpenAlexW4224141924

What OpenQuestion holds

Textmetadata
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.