Evidence map›Paper›PMID 38089836›Full record

ArticleAsian journal of pharmaceutical sciences2023

Preliminary delivery efficiency prediction of nanotherapeutics into crucial cell populations in bone marrow niche.

Huijuan Chen, Anzhi Hu, Mengdi Xiao, Shiyi Hong, Jing Liang, Quanlong Zhang, Yang Xiong, Mancang Gu, Chaofeng Mu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Asian journal of pharmaceutical sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

9 authors.

Huijuan ChenSchool of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou 310053, China.
Anzhi HuSchool of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou 310053, China.
Mengdi XiaoSchool of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou 310053, China.
Shiyi HongSchool of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou 310053, China.
Jing LiangCenter for Synthetic Biochemistry, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
Quanlong ZhangSchool of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou 310053, China.
Yang XiongSchool of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou 310053, China.
Mancang GuSchool of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou 310053, China.
Chaofeng MuSchool of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou 310053, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Several crucial stromal cell populations regulate hematopoiesis and malignant diseases in bone marrow niches. Precise regulation of these cell types can remodel niches and develop new therapeutics. Multiple nanocarriers have been developed to transport drugs into the bone marrow selectively. However, the delivery efficiency of these nanotherapeutics into crucial niche cells is still unknown, and there is no method available for predicting delivery efficiency in these cell types. Here, we constructed a three-dimensional bone marrow niche composed of three crucial cell populations: endothelial cells (ECs), mesenchymal stromal cells (MSCs), and osteoblasts (OBs). Mimetic niches were used to detect the cellular uptake of three typical drug nanocarriers into ECs/MSCs/OBs

Indexed as

Bone marrow niche mimickingBone marrow stromal cellsDrug delivery predictionNanotherapeutics

Identifiers

PMID38089836
PMCPMC10711387

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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.