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Related Concept Videos

Deformation of Member under Multiple Loadings01:11

Deformation of Member under Multiple Loadings

When a rod is made of different materials or has various cross-sections, it must be divided into parts that meet the necessary conditions for determining the deformation. These parts are each characterized by their internal force, cross-sectional area, length, and modulus of elasticity. These parameters are then used to compute the deformation of the entire rod.
In the case of a member with a variable cross-section, the strain is not constant but depends on the position. The deformation of an...
Temperature Dependent Deformation01:12

Temperature Dependent Deformation

In a nonhomogeneous rod made up of steel and brass, restrained at both ends and subjected to a temperature change, several steps are involved in calculating the stress and compressive load. Due to the problem's static indeterminacy, one end support is disconnected, allowing the rod to experience the temperature change freely. Next, an unknown force is applied at the free end, triggering deformations in the rod's steel and brass portions. These deformations are then calculated and added together...

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Related Experiment Video

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A Microfluidic Technique to Probe Cell Deformability
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Cell deformability heterogeneity recognition by unsupervised machine learning from in-flow motion parameters.

Maria Isabella Maremonti1, David Dannhauser1, Valeria Panzetta1

  • 1Interdisciplinary Research Centre on Biomaterials (CRIB) and Dipartimento di Ingegneria Chimica, dei Materiali e della Produzione Industriale, Università degli Studi di Napoli "Federico II", Piazzale Tecchio 80, 80125 Naples, Italy. david.dannhauser@unina.it.

Lab on a Chip
|November 18, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces simple in-flow motion parameters to measure cell deformability, crucial for cancer diagnostics. These parameters effectively identify varying cell mechanical properties, offering a versatile and cost-effective method.

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Area of Science:

  • Biophysics
  • Cell Biology
  • Biomedical Engineering

Background:

  • Cell deformability is a key diagnostic marker for cell states.
  • Measuring diverse cell deformability, especially in heterogeneous cancers, remains challenging.
  • A simple, versatile, and cost-effective method for recognizing variable cell mechanical properties is needed.

Purpose of the Study:

  • To introduce novel in-flow motion parameters for identifying cell deformability heterogeneity.
  • To correlate these motion parameters with specific rheological/mechanical properties.
  • To validate a new method for cell mechanical property recognition.

Main Methods:

  • Measured cell deformability via distinct in-flow motions: rolling (R), tumbling (T), swinging (S), and tank-treading (TT).
  • Utilized unsupervised machine learning (principal component analysis - PCA) on motion and structural parameters.
  • Identified dominant features: local cell velocity (V_Cell/V_Avg), equilibrium position (Y_Eq), and orientation angle variation (Δφ).

Main Results:

  • In-flow motion parameters effectively distinguished between different cell deformability levels.
  • PCA revealed V_Cell/V_Avg, Y_Eq, and Δφ as dominant features for classifying cell mechanical properties.
  • Established a clear correlation between motion regimes (R to TT) and a wide spectrum of cell rheological/mechanical properties.

Conclusions:

  • Simple motion parameters can recognize cell deformability heterogeneity.
  • The developed method directly measures cell rheological/mechanical properties.
  • This approach offers a cost-effective and versatile tool for cell mechanical characterization.