Evidence map›Paper›PMID 37304577›Full record

ArticleMaterials today. Bio2023

Immunosuppressive dead cell as lung-targeting vehicle and cytokine absorption material for cytokine storm attenuation of pneumonia.

Tianyuan Ci, Yaoxuan Xiong, Jinniu Zhang, Jing Zang, Nianping Feng

Open access · goldAbstract read
In one paragraph

Article in Materials today. Bio, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.3field-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

6 citing papers in PubMed, 8 citations in OpenAlex.

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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

5 authors at 1 institution in 1 country.

Tianyuan CiSchool of Pharmacy, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China.
Yaoxuan XiongSchool of Pharmacy, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China.
Jinniu ZhangSchool of Pharmacy, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China.
Jing ZangSchool of Pharmacy, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China.
Nianping FengSchool of Pharmacy, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China.
Shanghai University of Traditional Chinese Medicine · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Effectively controlling cytokine storm is important to reduce the mortality of severe pneumonia. In this work a bio-functional dead cell was engineered by one-time quick shock of live immune cells in liquid nitrogen, and the obtained immunosuppressive dead cell could server as both lung-targeting vehicle and cytokine absorption material. After loading the anti-inflammatory drugs of dexamethasone (DEX) and baicalin (BAI), the drug-loaded dead cell (DEX&BAI/Dead cell) could first passively target to the lung after intravenous administration and quickly release the drugs under high shearing stress of pulmonary capillaries, realizing drug enrichment in the lung. Then, the immunosuppressive dead cell acted as the camouflage of normal immune cells with various cytokine receptors exposing on their surface, to "capture" the cytokines and further reduce the state of inflammation. With above formulation design, a synergic anti-inflammatory effect between drugs and carrier could be achieved. In a lipopolysaccharide-induced pneumonia mice model, this system could calm down the cytokine storm with high efficacy and elongate the survival of mice.

Indexed as

Cytokine stormDead cellImmunosuppressionLung targetingPneumonia

Identifiers

PMID37304577
PMCPMC10250915
OpenAlexW4378575361

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.