A robust quantitative approach for laser speckle contrast imaging perfusion analysis revealed anomalies in the brain blood flow in offspring mice of preeclampsia.

Microcirculation analysis of the brain cortex is challenging because surface perfusion varies rapidly in small space-time regions and is bone protected. The laser speckle contrast imaging (LSCI) technique allows analyzing in vivo brain vascular perfusion generating a large amount of data that requires sophisticated data analytics, making researchers invest much effort in processing. Our research question was whether the reduced placental perfusion model (RUPP) of preeclampsia (PE) was associated with impaired blood perfusion in the offspring's brains. We aimed to develop a robust numerical approach that mainly consisted of applying a signal-processing tool for calculating optimal segmentation and piece-wise fits of the offspring's brain perfusion signals obtained from the LSCI technique. We combined this tool with the usual statistical analysis, implementing both in Matlab software. We performed brain perfusion measurements from offspring (five days postnatal, P5) of control pregnant dams (sham, n = 13) and of RUPP dams (RUPP, n = 7) using the Pericam® PSI-HR system at a basal condition and after thermal stimuli (warm and cold). We found that pups of RUPP mice exhibited significant differences in perfusion and vascular response to thermal stimuli compared to the sham mice. These differences were associated with high data variability in the Sham group, while in the RUPP group, perfusion looks "stiffer." Data also suggest sex-dimorphism in the vascular response since female pups in the Sham group but not male pups showed statistically significant differences in response to the warm stimulus. Again, this sex-related difference was absent in pups of RUPP mice. In conclusion, we present a robust quantitative approach for LSCI measurements that revealed anomalies in the brain blood flow in offspring of the RUPP model of PE.

Forecasting COVID-19 Chile' second outbreak by a generalized SIR model with constant time delays and a fitted positivity rate.

The COVID-19 disease has forced countries to make a considerable collaborative effort between scientists and governments to provide indicators to suitable follow-up the pandemic's consequences. Mathematical modeling plays a crucial role in quantifying indicators describing diverse aspects of the pandemic. Consequently, this work aims to develop a clear, efficient, and reproducible methodology for parameter optimization, whose implementation is illustrated using data from three representative regions from Chile and a suitable generalized SIR model together with a fitted positivity rate. Our results reproduce the general trend of the infected's curve, distinguishing the reported and real cases. Finally, our methodology is robust, and it allows us to forecast a second outbreak of COVID-19 and the infection fatality rate of COVID-19 qualitatively according to the reported dead cases.