Evidence map›Paper›PMID 38764823›Full record

ArticleWorld journal of gastrointestinal oncology2024

Immunomodulation of adipose-derived mesenchymal stem cells on peripheral blood mononuclear cells in colorectal cancer patients with COVID-19.

Jun-Feng Wang, Xiao-Xia Yang, Jian Zhang, Yan Zheng, Fu-Qing Zhang, Xiao-Feng Shi, Yu-Liang Wang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in World journal of gastrointestinal oncology, 2024. 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
–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

3 citing papers in PubMed.

  1. Review
  2. Article
  3. 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

7 authors.

Jun-Feng WangDepartment of Colorectal Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin 300060, China.
Xiao-Xia YangDepartment of Neurology, Tianjin First Center Hospital, Tianjin 300192, China.
Jian ZhangProsthodontics Studio, Tianjin Stomatological Hospital, Tianjin 300041, China.
Yan ZhengDepartment of Clinical Laboratory Medicine, Tianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin 300211, China.
Fu-Qing ZhangDepartment of Clinical Laboratory Medicine, Tianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin 300211, China.
Xiao-Feng ShiDepartment of Emergency, Tianjin First Central Hospital, Tianjin 300192, China.
Yu-Liang WangDepartment of Clinical Laboratory Medicine, Tianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin 300211, China. wang_yu_l@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAccumulating evidence has shown that adipose tissue-derived mesenchymal stem cells (ADSCs) are an effective therapeutic approach for managing coronavirus disease 2019 (COVID-19); however, further elucidation is required to determine their underlying immunomodulatory effect on the mRNA expression of T helper cell-related transcription factors (TFs) and cytokine release in peripheral blood mononuclear cells (PBMCs).

aimTo investigate the impact of ADSCs on the mRNA expression of TFs and cytokine release in PBMCs from colorectal cancer (CRC) patients with severe COVID-19 (CRC

methodsPBMCs from CRC

resultsCompared with PBMCs-C, PBMCs-C

conclusionThe present

Indexed as

Adipose-derived mesenchymal stem cellsColorectal cancerCOVID-19ImmunomodulationT helper cell

Identifiers

PMID38764823
PMCPMC11099452

What OpenQuestion holds

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Registered trials

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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.