Evidence map›Paper›PMID 37969470›Full record

ReviewBiomicrofluidics2023

Recent advances in droplet sequential monitoring methods for droplet sorting.

Yukun He, Yi Qiao, Lu Ding, Tianguang Cheng, Jing Tu

Abstract readReview
In one paragraph

Review in Biomicrofluidics, 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. 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

5 authors.

Yukun HeState Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China.ORCID https://orcid.org/0000-0003-2800-2748
Yi QiaoState Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China.ORCID https://orcid.org/0000-0002-1371-6775
Lu DingState Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China.ORCID https://orcid.org/0009-0005-8564-7501
Tianguang ChengState Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China.ORCID https://orcid.org/0009-0003-4574-6105
Jing TuState Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China.ORCID https://orcid.org/0000-0002-1310-5915

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Droplet microfluidics is an attractive technology to run parallel experiments with high throughput and scalability while maintaining the heterogeneous features of individual samples or reactions. Droplet sorting is utilized to collect the desired droplets based on droplet characterization and in-droplet content evaluation. A proper monitoring method is critical in this process, which governs the accuracy and maximum frequency of droplet handling. Until now, numerous monitoring methods have been integrated in the microfluidic devices for identifying droplets, such as optical spectroscopy, mass spectroscopy, electrochemical monitoring, and nuclear magnetic resonance spectroscopy. In this review, we summarize the features of various monitoring methods integrated into droplet sorting workflow and discuss their suitable condition and potential obstacles in use. We aim to provide a systematic introduction and an application guide for choosing and building a droplet monitoring platform.

Identifiers

PMID37969470
PMCPMC10645479

What OpenQuestion holds

Textmetadata
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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.