Evidence map›Paper›PMID 35569850›Full record

ReviewAnalytica chimica acta2022

Applied tutorial for the design and fabrication of biomicrofluidic devices by resin 3D printing.

Hannah B Musgrove, Megan A Catterton, Rebecca R Pompano

Abstract readReview
In one paragraph

Review in Analytica chimica acta, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.

0numbers the graph read from it
0cells of the map it votes in
32citing 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

32 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

3 authors.

Hannah B MusgroveDepartment of Chemistry, University of Virginia. Charlottesville, VA, USA.
Megan A CattertonDepartment of Chemistry, University of Virginia. Charlottesville, VA, USA.
Rebecca R PompanoDepartment of Chemistry, University of Virginia. Charlottesville, VA, USA. Electronic address: rrp2z@virginia.edu.

Funding

Modeling immunity with a hybrid lymph node tissue-chipR01AI131723 · NIAID · UNIVERSITY OF VIRGINIA · PI POMPANO, REBECCA R · 2018 to 2022
$2.3M
A user-friendly microchip for rapid optimization of protein conjugation reactionsR03EB028043 · NIBIB · UNIVERSITY OF VIRGINIA · PI POMPANO, REBECCA R · 2019 to 2020
$143k
NIAID NIH HHS R01 AI131723NIBIB NIH HHS R03 EB028043
6 · The paper itself

Abstract

Resin 3D printing, especially digital light processing (DLP) printing, is a promising rapid fabrication method for bio-microfluidic applications such as clinical tests, lab-on-a-chip devices, and sensor integrated devices. The benefits of 3D printing lead many to believe this fabrication method will accelerate the use of microfluidics, but there are a number of potential obstacles to overcome for bioanalytical labs to fully utilize this technology. For commercially available printing materials, this includes challenges in producing prints with the print resolution and mechanical stability required for a particular design, along with cytotoxic components within many photopolymerizing resins and low optical compatibility for imaging experiments. Potential solutions to these problems are scattered throughout the literature and rarely available in head-to-head comparisons. Therefore, we present here a concise guide to the principles of resin 3D printing most relevant for fabrication of bioanalytical microfluidic devices. Intended to quickly orient labs that are new to 3D printing, the tutorial includes the results of selected systematic tests to inform resin selection, strategies for design optimization, and improvement of biocompatibility of resin 3D printed bio-microfluidic devices.

Indexed as

Lab-On-A-Chip DevicesPrinting, Three-DimensionalMicrofluidicsCell cultureDigital light processingMicrofluidic fabricationPhotopolymerizable resinsSLAStereolithography

Identifiers

PMID35569850
PMCPMC9454328

What OpenQuestion holds

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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.