Analysis of the effectiveness of face-coverings on the death ratio of COVID-19 using machine learning.

The recent outbreak of the COVID-19 led to death of millions of people worldwide. To stave off the spread of the virus, the authorities in the US employed different strategies, including the mask mandate order issued by the states' governors. In the current work, we defined a parameter called average death ratio as the monthly average of the number of daily deaths to the monthly average number of daily cases. We utilized survey data to quantify people's abidance by the mask mandate order. Additionally, we implicitly addressed the extent to which people abide by the mask mandate order, which may depend on some parameters such as population, income, and education level. Using different machine learning classification algorithms, we investigated how the decrease or increase in death ratio for the counties in the US West Coast correlates with the input parameters. The results showed that for the majority of counties, the mask mandate order decreased the death ratio, reflecting the effectiveness of such a preventive measure on the West Coast. Additionally, the changes in the death ratio demonstrated a noticeable correlation with the socio-economic condition of each county. Moreover, the results showed a promising classification accuracy score as high as 90%.

A Bayesian approach to estimate the diffusion coefficient of Rhodamine 6G in breast cancer spheroids.

Multicellular spheroids have emerged as a robust platform to model tumor growth and are widely used for studying drug sensitivity. Diffusion is the main mechanism for transporting nutrients and chemotherapeutic drugs into spheroids, since they are typically avascular. In this study, the Bayesian inference was used to solve the inverse problem of determining the light attenuation coefficient and diffusion coefficient of Rhodamine 6G (R6G) in breast cancer spheroids, as a mock drug for the tyrosine kinase inhibitor, Neratinib. Four types of breast cancer spheroids were formed and the diffusion coefficient was estimated assuming a linear relationship between the intensity and concentration. The mathematical model used for prediction is the solution to the diffusion problem in spherical coordinates, accounting for the light attenuation. The Gaussian likelihood was used to account for the error between the measurements and model predictions. The Markov Chain Monte Carlo algorithm (MCMC) was used to sample from the posterior. The posterior predictions for the diffusion and light attenuation coefficients were provided. The results indicate that the diffusion coefficient values do not significantly vary across a HER2+ breast cancer cell line as a function of transglutaminase 2 levels, even in the presence of fibroblast cells. However, we demonstrate that different diffusion coefficient values can be ascertained from tumorigenic compared to nontumorigenic spheroids and from nonmetastatic compared to post-metastatic breast cancer cells using this approach. We also report agreement between spheroid radius, attenuation coefficient, and subsequent diffusion coefficient to give evidence of cell packing in self-assembled spheroids. The methodology presented here will allow researchers to determine diffusion in spheroids to decouple transport and drug penetration changes from biological resistivity.

Design and simulation of a microfluidic device for acoustic cell separation.

Experimental acoustic cell separation methods have been widely used to perform separation for different types of blood cells. However, numerical simulation of acoustic cell separation has not gained enough attention and needs further investigation since by using numerical methods, it is possible to optimize different parameters involved in the design of an acoustic device and calculate particle trajectories in a simple and low cost manner before spending time and effort for fabricating these devices. In this study, we present a comprehensive finite element-based simulation of acoustic separation of platelets, red blood cells and white blood cells, using standing surface acoustic waves (SSAWs). A microfluidic channel with three inlets, including the middle inlet for sheath flow and two symmetrical tilted angle inlets for the cells were used to drive the cells through the channel. Two interdigital transducers were also considered in this device and by implementing an alternating voltage to the transducers, an acoustic field was created which can exert the acoustic radiation force to the cells. Since this force is dependent to the size of the cells, the cells are pushed towards the midline of the channel with different path lines. Particle trajectories for different cells were obtained and compared with a theoretical equation. Two types of separations were observed as a result of varying the amplitude of the acoustic field. In the first mode of separation, white blood cells were sorted out through the middle outlet and in the second mode of separation, platelets were sorted out through the side outlets. Depending on the clinical needs and by using the studied microfluidic device, each of these modes can be applied to separate the desired cells.