AIMC Topic: Rheology

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Artificial intelligence velocimetry reveals in vivo flow rates, pressure gradients, and shear stresses in murine perivascular flows.

Proceedings of the National Academy of Sciences of the United States of America
Quantifying the flow of cerebrospinal fluid (CSF) is crucial for understanding brain waste clearance and nutrient delivery, as well as edema in pathological conditions such as stroke. However, existing in vivo techniques are limited to sparse velocit...

Self-assembled pH-stable gellan/κ-carrageenan bigel: Rheological studies and viscosity prediction by neural network.

International journal of biological macromolecules
The current study focused on analysing and predicting the effect of physicochemical parameters on the rheological properties of the novel polysaccharide-based bigel. This is the first study to report a bigel fabricated entirely from polysaccharides a...

Cell deformability heterogeneity recognition by unsupervised machine learning from in-flow motion parameters.

Lab on a chip
Cell deformability is a well-established marker of cell states for diagnostic purposes. However, the measurement of a wide range of different deformability levels is still challenging, especially in cancer, where a large heterogeneity of rheological/...

Modeling consolidation of soft clay by developing a fractional differential constitutive model in conjunction with an intelligent displacement inversion method.

PloS one
Studying the constitutive relation of soft clays is of critical importance for fundamentally understanding their complex consolidation behavior. This study proposes a fractional differential constitutive model in conjunction with an intelligent displ...

Development and Validation of a Deep Learning System for Sound-based Prediction of Urinary Flow.

European urology focus
BACKGROUND: Uroflowmetry remains an important tool for the assessment of patients with lower urinary tract symptoms (LUTS), but accuracy can be limited by within-subject variation of urinary flow rates. Voiding acoustics appear to correlate well with...

Rapid Temperature-Dependent Rheological Measurements of Non-Newtonian Solutions Using a Machine-Learning Aided Microfluidic Rheometer.

Analytical chemistry
Biofluids such as synovial fluid, blood plasma, and saliva contain several proteins which impart non-Newtonian properties to the biofluids. The concentration of such protein macromolecules in biofluids is regarded as an important biomarker for the di...

Ultrasound deep learning for monitoring of flow-vessel dynamics in murine carotid artery.

Ultrasonics
Several arterial diseases are closely related with mechanical properties of the blood vessel and interactions of flow-vessel dynamics such as mean flow velocity, wall shear stress (WSS) and vascular strain. However, there is an opportunity to improve...

Cell Mechanics Based Computational Classification of Red Blood Cells Via Machine Intelligence Applied to Morpho-Rheological Markers.

IEEE/ACM transactions on computational biology and bioinformatics
Despite fluorescent cell-labelling being widely employed in biomedical studies, some of its drawbacks are inevitable, with unsuitable fluorescent probes or probes inducing a functional change being the main limitations. Consequently, the demand for a...

Machine learning enables design automation of microfluidic flow-focusing droplet generation.

Nature communications
Droplet-based microfluidic devices hold immense potential in becoming inexpensive alternatives to existing screening platforms across life science applications, such as enzyme discovery and early cancer detection. However, the lack of a predictive un...

Flow driven robotic navigation of microengineered endovascular probes.

Nature communications
Minimally invasive medical procedures, such as endovascular catheterization, have considerably reduced procedure time and associated complications. However, many regions inside the body, such as in the brain vasculature, still remain inaccessible due...