Reprint
Microfluidics for Cells and Other Organisms
Edited by
October 2019
200 pages
- ISBN978-3-03921-562-1 (Paperback)
- ISBN978-3-03921-563-8 (PDF)
This book is a reprint of the Special Issue Microfluidics for Cells and Other Organisms that was published in
Chemistry & Materials Science
Engineering
Physical Sciences
Summary
Microfluidics-based devices play an important role in creating realistic microenvironments in which cell cultures can thrive. They can, for example, be used to monitor drug toxicity and perform medical diagnostics, and be in a static-, perfusion- or droplet-based device. They can also be used to study cell-cell, cell-matrix or cell-surface interactions. Cells can be either single cells, 3D cell cultures or co-cultures. Other organisms could include bacteria, zebra fish embryo, C. elegans, to name a few.
Format
- Paperback
License
© 2019 by the authors; CC BY-NC-ND license
Keywords
instrumentation; microfluidic flow cytometry; intracellular proteins; absolute quantification; cells-in-gels-in-paper; cancer metastasis; cell motility; cancer stem cell; drug resistance; laminar flows; paracrine signaling; co-culture; microfluidic device; target cell-specific binding molecules; screening; adherent cells; pneumatic microvalve; cell homogenous dispersion structure; bacterial concentration; capacitively coupled contactless conductivity detection (C4D); capillary; E. coli; printed-circuit-board (PCB); microfluidics; single-cell manipulation; single-cell analysis; micropipette aspiration; microfluidics; single-cell mechanics; Wheatstone bridge; cbNIPD; fnRBC; capture efficiency; microfluidics; nanostructure; on-chip cell incubator; periodic hydrostatic pressure; periodic pressure; time-lapse observation; cell growth; simultaneous multiple chamber observation; microfluidics; 3D printing; zebrafish embryo; embryogenesis; sample preparation; nucleic acid; DNA; RNA; microscopy; microfluidics; microfabrication; biomedical engineering; microfluidics; 3D flow focusing; 3D particle focusing; particle/cell imaging; bioMEMS; unsupervised learning; neural networks; variational inference; n/a