Evidence map›Paper›PMID 32702793›Full record

ReviewZhongguo fei ai za zhi = Chinese journal of lung cancer2020

[Advances in Exosomes in the Pathogenesis and Diagnosis of Lung Cancer].

Huanhuan Bi, Dunqiang Ren, Jun Zhang, Hongmei Wang

Open access · greenAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Zhongguo fei ai za zhi = Chinese journal of lung cancer, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.1field-weighted citation impact, top 55% 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

2 citing papers in PubMed, 1 citations in OpenAlex.

  1. Review
  2. 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 at 1 institution in 1 country.

Huanhuan BiDepartment of Respiratory and Critical Care Medcine, The Affiliated Hospital of Qingdao University, Qingdao 266000, China.
Dunqiang RenDepartment of Respiratory and Critical Care Medcine, The Affiliated Hospital of Qingdao University, Qingdao 266000, China.
Jun ZhangDepartment of Respiratory and Critical Care Medcine, The Affiliated Hospital of Qingdao University, Qingdao 266000, China.
Hongmei WangDepartment of Respiratory and Critical Care Medcine, The Affiliated Hospital of Qingdao University, Qingdao 266000, China.
Qingdao University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The incidence of lung cancer is high worldwide, and lung cancer is the leading cause of death from malignant tumors in both men and women. Early diagnosis of lung cancer can significantly improve the patient's prognosis. Therefore, searching for specific markers to assist in the early diagnosis of lung cancer is urgent question. Exosomes are nano-sized microvesicles and contain various biomaterial, including nucleic acids, proteins, and lipids. Exosomes are important carriers of these biomaterial, serve important roles in intracellular communications and signal transduction among tissues. Due to its unique enrichment mechanism, it has the stability and specificity as a biomarker. Exosomes are not only involved in the formation of tumor microenvironment and new blood vessels in lung cancer, but also involved in chemotherapy, targeted therapy response and prognosis assessment. Many research advances bring new hope for prolonging the survival of lung cancer patients. This article reviews the value of exosome specific protein and microRNA (miRNA) in lung cancer in the diagnosis and prognosis of lung cancer.

Indexed as

CarcinogenesisExosomesHumansLung NeoplasmsPrognosisBiomarkerDiagnosisExosomesLung neoplasmsPrognosis

Identifiers

PMID32702793
PMCPMC7406446
OpenAlexW3045922913

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.