Evidence map›Paper›PMID 37241596›Full record

ReviewMicromachines2023

Advances in Integration, Wearable Applications, and Artificial Intelligence of Biomedical Microfluidics Systems.

Xingfeng Ma, Gang Guo, Xuanye Wu, Qiang Wu, Fangfang Liu, Hua Zhang, Nan Shi, Yimin Guan

Abstract readReview
In one paragraph

Review in Micromachines, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
  4. Review
  5. Article
  6. Review
  7. Review
  8. Review
  9. Article
  10. Review
  11. Review
  12. Review
  13. Review
  14. Article
  15. Review
  16. Review
  17. 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

8 authors.

Xingfeng MaSchool of Communication and Information Engineering, Shanghai University, Shanghai 200000, China.
Gang GuoDepartment of Microelectronics, Shanghai University, Shanghai 200000, China.
Xuanye WuDepartment of Microelectronics, Shanghai University, Shanghai 200000, China.
Qiang WuShanghai Aure Technology Limited Company, Shanghai 200000, China.
Fangfang LiuShanghai Industrial μTechnology Research Institute, Shanghai 200000, China.
Hua ZhangShanghai Aure Technology Limited Company, Shanghai 200000, China.
Nan ShiShanghai Industrial μTechnology Research Institute, Shanghai 200000, China.
Yimin GuanDepartment of Microelectronics, Shanghai University, Shanghai 200000, China.

Funding

AviChina Industry & Technology (China) CEIEC-2021-ZM02-0188
6 · The paper itself

Abstract

Microfluidics attracts much attention due to its multiple advantages such as high throughput, rapid analysis, low sample volume, and high sensitivity. Microfluidics has profoundly influenced many fields including chemistry, biology, medicine, information technology, and other disciplines. However, some stumbling stones (miniaturization, integration, and intelligence) strain the development of industrialization and commercialization of microchips. The miniaturization of microfluidics means fewer samples and reagents, shorter times to results, and less footprint space consumption, enabling a high throughput and parallelism of sample analysis. Additionally, micro-size channels tend to produce laminar flow, which probably permits some creative applications that are not accessible to traditional fluid-processing platforms. The reasonable integration of biomedical/physical biosensors, semiconductor microelectronics, communications, and other cutting-edge technologies should greatly expand the applications of current microfluidic devices and help develop the next generation of lab-on-a-chip (LOC). At the same time, the evolution of artificial intelligence also gives another strong impetus to the rapid development of microfluidics. Biomedical applications based on microfluidics normally bring a large amount of complex data, so it is a big challenge for researchers and technicians to analyze those huge and complicated data accurately and quickly. To address this problem, machine learning is viewed as an indispensable and powerful tool in processing the data collected from micro-devices. In this review, we mainly focus on discussing the integration, miniaturization, portability, and intelligence of microfluidics technology.

Indexed as

artificial intelligenceintegrationmicrofluidicsminiaturization

Identifiers

PMID37241596
PMCPMC10222806

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.