Evidence map›Paper›PMID 39649102›Full record

ArticleBiomicrofluidics2024

Mechanically mediated cargo delivery to cells using microfluidic devices.

Zhiyu Mao, Bori Shi, Jinbo Wu, Xinghua Gao

Abstract read
In one paragraph

Article in Biomicrofluidics, 2024. 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
–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

2 citing papers in PubMed.

  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.

Zhiyu MaoMaterials Genome Institute, Shanghai University, Shanghai 200444, China.ORCID https://orcid.org/0009-0004-5863-6017
Bori ShiMaterials Genome Institute, Shanghai University, Shanghai 200444, China.ORCID https://orcid.org/0000-0003-0089-4414
Jinbo Wu
Xinghua GaoMaterials Genome Institute, Shanghai University, Shanghai 200444, China.ORCID https://orcid.org/0000-0002-5244-0986

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Drug delivery technologies, which are a crucial area of research in the field of cell biology, aim to actively or passively deliver drugs to target cells to enhance therapeutic efficacy and minimize off-target effects. In recent years, with advances in drug development, particularly, the increasing demand for macromolecular drugs (e.g., proteins and nucleic acids), novel drug delivery technologies and intracellular cargo delivery systems have emerged as promising tools for cell and gene therapy. These systems include various viral- and chemical-mediated methods as well as physical delivery strategies. Physical methods, such as electroporation and microinjection, have shown promise in early studies but have not been widely adopted due to concerns regarding efficiency and cellular viability. Recently, microfluidic technologies have provided new opportunities for cargo delivery by allowing for precise control of fluid dynamic parameters to achieve efficient and safe penetration of cell membranes, as well as for foreign material transport. Microfluidics-based mechanical delivery methods utilize biophysical phenomena, such as cell constriction and fluid shear, and are associated with high throughput and high transfection efficiency. In this review, we summarize the latest advancements in microfluidic mechanical delivery technologies, and we discuss constriction- and fluid shear-induced delivery strategies. Furthermore, we explore the potential application of artificial intelligence in optimizing cargo delivery technologies, aiming to provide theoretical support and practical guidance for the future development of novel cellular drug delivery technologies.

Identifiers

PMID39649102
PMCPMC11624913

What OpenQuestion holds

Textmetadata
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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.