Evidence map›Paper›PMID 37258538›Full record

ArticleScientific reports2023

Design and evaluation of a skin-on-a-chip pumpless microfluidic device.

Marjan Mohamadali, Ali Ghiaseddin, Shiva Irani, Mohammad Amir Amirkhani, Mostafa Dahmardehei

Abstract read
In one paragraph

Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing 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

21 citing papers in PubMed.

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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.

Marjan MohamadaliDepartment of Biology, Science and Research Branch, Islamic Azad University, Tehran, Iran.
Ali GhiaseddinDepartment of Chemistry, Michigan State University, East Lansing, MI, USA. a.ghiaseddin@gmail.com.
Shiva IraniDepartment of Biology, Science and Research Branch, Islamic Azad University, Tehran, Iran.
Mohammad Amir AmirkhaniSkin and Stem Cell Research Center, Tehran University of Medical Sciences, Tehran, Iran.
Mostafa DahmardeheiDepartment of Plastic and Reconstructive Surgery, Burn Research Center, Iran University of Medical Sciences, Tehran, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The development of microfluidic culture technology facilitates the progress of study of cell and tissue biology. This technology expands the understanding of pathological and physiological changes. A skin chip, as in vitro model, consisting of normal skin tissue with epidermis and dermis layer (full thickness) was developed. Polydimethylsiloxane microchannels with a fed-batched controlled perfusion feeding system were used to create a full-thick ex-vivo human skin on-chip model. The design of a novel skin-on-a-chip model was reported, in which the microchannel structures mimic the architecture of the realistic vascular network as nutrients transporter to the skin layers. Viabilities of full-thick skin samples cultured on the microbioreactor and traditional tissue culture plate revealed that a precise controlled condition provided by the microfluidic enhanced tissue viability at least for seven days. Several advantages in skin sample features under micro-scale-controlled conditions were found such as skin mechanical strength, water adsorption, skin morphology, gene expression, and biopsy longevity. This model can provide an in vitro environment for localizing drug delivery and transdermal drug diffusion studies. The skin on the chip can be a valuable in vitro model for representing the interaction between drugs and skin tissue and a realistic platform for evaluating skin reaction to pharmaceutical materials and cosmetic products.

Indexed as

EpidermisSkinHumansLab-On-A-Chip DevicesMicrofluidicsPerfusion

Identifiers

PMID37258538
PMCPMC10232512

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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.