Image-guided simulation in comparison with laser speckle contrast imaging for full-field observation of blood flow in a microvasculature model

Revisiting hemodynamics and blood oxygenation in a microfluidic microvasculature replica

The complexity of microvascular circulation has led to the development of advanced imaging techniques and biomimetic models. This study developed a multifaceted microfluidic-based microdevice as an in vitro model of microvasculature to replicate important geometric and functional features of in vivo perfusion in mice. The microfluidic device consisted of a microchannel for blood perfusion, mirroring the natural hierarchical branching vascular structures found in mice. Additionally, the device incorporated a steady gradient of oxygen (O2) which diffused through the polydimethylsiloxane (PDMS) layer, allowing for dynamic blood oxygenation. The assembled multi-layered microdevice was accompanied by a dual-modal imaging system that combined laser speckle contrast imaging (LSCI) and intrinsic signal optical imaging (ISOI) to visualize full-field blood flow distributions and blood O2 profiles. By closely reproducing in vivo blood perfusion and oxygenation conditions, this microvasculature model, in conjunction with numerical simulation results, can provide quantitative information on physiologically relevant hemodynamics and key O2 transport parameters that are not directly measurable in traditional animal studies.

Blood Flow Prediction in Multi-Exposure Speckle Contrast Imaging Using Conditional Generative Adversarial Network

Purpose Blood perfusion is an important physiological parameter that can be quantitatively assessed using various imaging techniques. Blood flow prediction in laser speckle contrast imaging is important for medical diagnosis, drug development, tissue engineering, biomedical research, and continuous monitoring. Deep learning is a new and promising approach for predicting blood flow whenever the condition varies, but it comes with a high learning cost for real-world scenarios with a variable flow value derived from multi-exposure laser speckle contrast imaging (MECI) data. A generative adversarial network (GAN) is presented in this research for the reliable prediction of blood flows in diverse scenarios in MECI. Method We suggested a time-efficient approach using a low frame rate camera that can be used to predict blood flow in MECI data by using conditional GAN architecture. Our approach is implemented by extending our work to the entire flow as well as the specific region of interest (ROI) in the flow. Results Results show that conditional GAN exhibits improved generalization ability to predict blood flow in MECI when compared to classifications-based deep learning approaches with an accuracy of 98.5% with a relative mean error of 1.57% for the whole field and 7.53% for a specific ROI. Conclusion The conditional GAN is very effective in predicting blood flows in MECI, entirely or within ROI, compared with other deep learning approaches.

A microfluidic system for viability determination of microalgae upon disinfectant treatment under continuous flow

The extensive use of quaternary ammonium compounds (QACs) has raised concerns regarding their environmental fate and potential risks to the ecosystem. As sensitive pollution indicators, green microalgae could readily monitor the aquatic toxicity of QACs as reflective of the changes in cell viability. Recent microfluidic-based systems have been designed for environmental biomonitoring and ecotoxicity studies while overall information of cell viability cannot be directly visualized under flowing conditions. In the present study, we developed a multifunctional microfluidic platform with the integration of analytical techniques including laser speckle contrast imaging and fluorescence spectroscopy for monitoring algal activity in response to QAC treatment. The biocidal efficiency of a representative QAC benzalkonium bromide (BAB) on a typical aquatic algae Chlorella vulgaris was determined by collecting the bio-speckles and chlorophyll autofluorescence in real-time, where dose-dependent and time-dependent decrease of algal growth was found with the increase of BAB concentration and interaction time. The integrated system was capable of rapid detection of the aquatic toxicity of QACs along with macroscopical visualization of algal activities under flowing conditions in time-course, which could be extended to future implementation for broad ecotoxicity analysis of versatile environmental samples.