scholarly journals Light-driven biological actuators to probe the rheology of 3D microtissues

2022 ◽  
Author(s):  
Adrien Méry ◽  
Artur Ruppel ◽  
Jean Révilloud ◽  
Martial Balland ◽  
Giovanni Cappello ◽  
...  
Keyword(s):  
Real Time ◽  
Biological Tissues ◽  
External Loading ◽  
Tissue Elasticity ◽  
Mechanical Perturbation ◽  
Dynamic Regulation ◽  
Internal Cell ◽  
Spatio Temporal ◽  
And Function ◽  
External Stimuli

The mechanical properties of biological tissues are key to the regulation of their physical integrity and function. Although the application of external loading or biochemical treatments allows to estimate these properties globally, it remains problematic to assess how such external stimuli compare with internal, cell-generated contractions. Here we engineered 3D microtissues composed of optogenetically-modified fibroblasts encapsulated within collagen. Using light to control the activity of RhoA, a major regulator of cellular contractility, we induced local mechanical perturbation within 3D fibrous microtissues, while tracking in real time microtissue stress and strain. We thus investigated the dynamic regulation of light-induced, local contractions and their spatio-temporal propagation in microtissues. By comparing the evolution of stresses and strains upon stimulation, we demonstrated the potential of our technique for quantifying tissue elasticity and viscosity, before examining the possibility of using light to map local anisotropies in mechanically heterogeneous microtissues. Altogether, our results open an avenue to non-destructively chart the rheology of 3D tissues in real time, using their own constituting cells as internal actuators.

Online Measurement of Glucose Consumption from HepG2 Cells Using an Integrated Bioreactor and Enzymatic Assay

2018 ◽  
Author(s):  
Anna Adams ◽  
Radha Krishna Murthy Bulusu ◽  
Nikita Mukhitov ◽  
Jose Mendoza-Cortes ◽  
Michael Roper
Keyword(s):  
Real Time ◽  
Glucose Concentration ◽  
Hepg2 Cells ◽  
Glucose Consumption ◽  
Microfluidic System ◽  
Structure And Function ◽  
Online Measurement ◽  
Fluorescent Assay ◽  
And Function ◽  
Integrated Bioreactor

In this work, we developed a microfluidic bioreactor for optimizing growth and maintaining structure and function of HepG2, and when desired, the device could be removed and the extracellular output from the bioreactor combined with enzymatic glucose reagents into a droplet-based microfluidic system. The intensity of the resulting fluorescent assay product in the droplets was measured, and was directly correlated to glucose concentration, allowing the effect of insulin on glucose consumption in the HepG2 cells to be observed and quantified online and in near real-time.

Online Measurement of Glucose Consumption from HepG2 Cells Using an Integrated Bioreactor and Enzymatic Assay

2018 ◽  
Author(s):  
Anna Adams ◽  
Radha Krishna Murthy Bulusu ◽  
Nikita Mukhitov ◽  
Jose Mendoza-Cortes ◽  
Michael Roper
Keyword(s):  
Real Time ◽  
Glucose Concentration ◽  
Hepg2 Cells ◽  
Glucose Consumption ◽  
Microfluidic System ◽  
Structure And Function ◽  
Online Measurement ◽  
Fluorescent Assay ◽  
And Function ◽  
Integrated Bioreactor

In this work, we developed a microfluidic bioreactor for optimizing growth and maintaining structure and function of HepG2, and when desired, the device could be removed and the extracellular output from the bioreactor combined with enzymatic glucose reagents into a droplet-based microfluidic system. The intensity of the resulting fluorescent assay product in the droplets was measured, and was directly correlated to glucose concentration, allowing the effect of insulin on glucose consumption in the HepG2 cells to be observed and quantified online and in near real-time.

scholarly journals Linear Response of General Observables in Spiking Neuronal Network Models

Entropy ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. 155
Author(s):  
Bruno Cessac ◽  
Ignacio Ampuero ◽  
Rodrigo Cofré
Keyword(s):  
Linear Response ◽  
Network Connectivity ◽  
Network Models ◽  
General Context ◽  
Collective Effect ◽  
Neuronal Dynamics ◽  
The Past ◽  
Spatio Temporal ◽  
External Stimuli ◽  
Spike Dynamics

We establish a general linear response relation for spiking neuronal networks, based on chains with unbounded memory. This relation allow us to predict the influence of a weak amplitude time dependent external stimuli on spatio-temporal spike correlations, from the spontaneous statistics (without stimulus) in a general context where the memory in spike dynamics can extend arbitrarily far in the past. Using this approach, we show how the linear response is explicitly related to the collective effect of the stimuli, intrinsic neuronal dynamics, and network connectivity on spike train statistics. We illustrate our results with numerical simulations performed over a discrete time integrate and fire model.

scholarly journals Serial Ultrasonographic and Real-Time Elastosonographic Assessment of the Ovine Common Calcaneal Tendon, after an Experimentally Induced Tendinopathy

2021 ◽  
Vol 8 (4) ◽  
pp. 54
Author(s):  
Daniele Serrani ◽  
Antonella Volta ◽  
Franco Cingolani ◽  
Luca Pennasilico ◽  
Caterina Di Bella ◽  
...  
Keyword(s):  
Real Time ◽  
Statistical Significance ◽  
Healing Process ◽  
Tendon Healing ◽  
Tissue Elasticity ◽  
Calcaneal Tendon ◽  
Non Invasive ◽  
Type Ia ◽  
Single Operator ◽  
Experimentally Induced

Real-time elastosonography (RTE) is a recently described, non-invasive, ultrasonographic technique developed to assess tissue elasticity. The main aim of this study was to investigate the ultrasonographic and elastosonographic appearance of the common calcaneal tendon (CCT) in an ovine model, and to monitor the progression of tendon healing after an experimentally-induced tendinopathy. Sound tendons were initially evaluated (T0) with a caliper and by a single operator with ultrasound. Ultrasonographic and elastosonographic images were then acquired. Subsequently, ultrasound-guided tendon lesions were induced by injecting 500 IU of Type IA collagenases proximally to the calcaneal tuberosity. Caliper measurement, ultrasonography and elastosonography were then repeated at 15 (T1), 30 (T2) and 60 (T3) days. Clinically measured width of the tendon, ultrasonographic thickness and width and percentage of hard (Elx-t%hrd) and soft (Elx-t%sft) tissue were recorded. Statistical analysis was performed on the data collected; statistical significance was set at p < 0.05. Intra-class correlation coefficient (ICC) revealed good (0.68) repeatability of elastosonographic evaluation of the CCT. The tendon width was significantly increased when comparing T0 with T1–2 and decreased when comparing T1–2 with T3. Ultrasound-assessed thickness was significantly increased between T0–T1 and decreased between T1-T2–3. Elx-t%hrd was significantly decreased at T1–2–3 and Elx-t%sft was significantly increased at T1–2–3. In conclusion, the ovine CCT is a highly stiff structure that undergoes a severe loss of stiffness during the healing process. Thickness and width of the tendon increased during the first 30 days and then reduced progressively along the subsequent 30 days. Ultrasonographic appearance of the tendon remained severely abnormal and the tendon showed severely reduced elastic proprieties 60 days after lesion induction.

Passthrough+

2020 ◽  
Vol 3 (1) ◽  
pp. 1-17
Author(s):  
Gaurav Chaurasia ◽  
Arthur Nieuwoudt ◽  
Alexandru-Eugen Ichim ◽  
Richard Szeliski ◽  
Alexander Sorkine-Hornung
Keyword(s):  
Real Time ◽  
Video Encoding ◽  
View Synthesis ◽  
Performance Constraints ◽  
Trade Offs ◽  
Stereoscopic View ◽  
Target User ◽  
Spatio Temporal ◽  
And Performance ◽  
3D Scene

We present an end-to-end system for real-time environment capture, 3D reconstruction, and stereoscopic view synthesis on a mobile VR headset. Our solution allows the user to use the cameras on their VR headset as their eyes to see and interact with the real world while still wearing their headset, a feature often referred to as Passthrough. The central challenge when building such a system is the choice and implementation of algorithms under the strict compute, power, and performance constraints imposed by the target user experience and mobile platform. A key contribution of this paper is a complete description of a corresponding system that performs temporally stable passthrough rendering at 72 Hz with only 200 mW power consumption on a mobile Snapdragon 835 platform. Our algorithmic contributions for enabling this performance include the computation of a coarse 3D scene proxy on the embedded video encoding hardware, followed by a depth densification and filtering step, and finally stereoscopic texturing and spatio-temporal up-sampling. We provide a detailed discussion and evaluation of the challenges we encountered, as well as algorithm and performance trade-offs in terms of compute and resulting passthrough quality.;AB@The described system is available to users as the Passthrough+ feature on Oculus Quest. We believe that by publishing the underlying system and methods, we provide valuable insights to the community on how to design and implement real-time environment sensing and rendering on heavily resource constrained hardware.

scholarly journals Machine learning estimation of tissue optical properties

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Brett H. Hokr ◽  
Joel N. Bixler
Keyword(s):  
Neural Network ◽  
Optical Properties ◽  
Biological Tissues ◽  
Homogeneous Layer ◽  
In Vivo Measurement ◽  
Tissue Optical Properties ◽  
The Neural Network ◽  
Spatio Temporal ◽  
Fully Connected

AbstractDynamic, in vivo measurement of the optical properties of biological tissues is still an elusive and critically important problem. Here we develop a technique for inverting a Monte Carlo simulation to extract tissue optical properties from the statistical moments of the spatio-temporal response of the tissue by training a 5-layer fully connected neural network. We demonstrate the accuracy of the method across a very wide parameter space on a single homogeneous layer tissue model and demonstrate that the method is insensitive to parameter selection of the neural network model itself. Finally, we propose an experimental setup capable of measuring the required information in real time in an in vivo environment and demonstrate proof-of-concept level experimental results.

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