Autophagy and cancer treatment: four functional forms of autophagy and their therapeutic applications / 浙江大学学报（英文版）（B辑：生物医学和生物技术）

Cancer is the leading cause of death worldwide. Drugs play a pivotal role in cancer treatment, but the complex biological processes of cancer cells seriously limit the efficacy of various anticancer drugs. Autophagy, a self-degradative system that maintains cellular homeostasis, universally operates under normal and stress conditions in cancer cells. The roles of autophagy in cancer treatment are still controversial because both stimulation and inhibition of autophagy have been reported to enhance the effects of anticancer drugs. Thus, the important question arises as to whether we should try to strengthen or suppress autophagy during cancer therapy. Currently, autophagy can be divided into four main forms according to its different functions during cancer treatment: cytoprotective (cell survival), cytotoxic (cell death), cytostatic (growth arrest), and nonprotective (no contribution to cell death or survival). In addition, various cell death modes, such as apoptosis, necrosis, ferroptosis, senescence, and mitotic catastrophe, all contribute to the anticancer effects of drugs. The interaction between autophagy and these cell death modes is complex and can lead to anticancer drugs having different or even completely opposite effects on treatment. Therefore, it is important to understand the underlying contexts in which autophagy inhibition or activation will be beneficial or detrimental. That is, appropriate therapeutic strategies should be adopted in light of the different functions of autophagy. This review provides an overview of recent insights into the evolving relationship between autophagy and cancer treatment.

The characteristics of multi-source mobility datasets and how they reveal the luxury nature of social distancing in the U.S. during the COVID-19 pandemic

This study reveals the human mobility from various sources and the luxury nature of social distancing in the U.S during the COVID-19 pandemic by highlighting the disparities in mobility dynamics from lower-income and upper-income counties. We collect, process, and compute mobility data from four sources: 1) Apple mobility trend reports, 2) Google community mobility reports, 3) mobility data from Descartes Labs, and 4) Twitter mobility calculated via weighted distance. We further design a Responsive Index (RI) based on the time series of mobility change percentages to quantify the general degree of mobility-based responsiveness to COVID-19 at the U.S. county level. We find statistically significant positive correlations in the RI between either two data sources, revealing their general similarity, albeit with varying Pearsons r coefficients. Despite the similarity, however, mobility from each source presents unique and even contrasting characteristics, in part demonstrating the multifaceted nature of human mobility. The positive correlation between RI and income at the county level is significant in all mobility datasets, suggesting that counties with higher income tend to react more aggressively in terms of reducing more mobility in response to the COVID-19 pandemic. Most states present a positive difference in RI between their upper-income and lower-income counties, where diverging patterns in time series of mobility changes percentages can be found. To our best knowledge, this is the first study that cross-compares multi-source mobility datasets. The findings shed light on not only the characteristics of multi-source mobility data but also the mobility patterns in tandem with the economic disparity. HighlightsO_LIHuman mobility data provide valuable insight into how we adjust our travel behaviors during the COVID-19 pandemic. C_LIO_LIHuman mobility records from Descartes Labs, Apple, Google, and Twitter are compared. C_LIO_LIMulti-source mobility datasets well capture the general impact of COVID-19 pandemic on mobility in the U.S. but present unique and even contrasting characteristics C_LIO_LIThe proposed responsive index quantifies the level of mobility-based reaction in response to the COVID-19 pandemic C_LIO_LIAll selected mobility datasets suggest a statistically significant positive correlation between the responsive index and median income at the U.S. county level. C_LI

Racial segregation, testing sites access, and COVID-19 incidence rate in Massachusetts, USA

The U.S. has merely 4% of the world population but 25% of the worlds COVID-19 cases. Massachusetts has been in the leading position of total cases since the outbreak in the U.S. Racial residential segregation is a fundamental cause of racial disparities in health. Moreover, disparities of access to health care have a large impact on COVID-19 cases. Thus, this study estimates racial segregation and disparities in testing sites access and employs economic, demographic, and transportation variables at the city/town level in Massachusetts. Spatial regression models are applied to evaluate the relationships between COVID-19 incidence rate and related variables. This is the first study to apply spatial analysis methods across neighborhoods in the U.S. to examine the COVID-19 incidence rate. The findings are: 1) residential segregations of Hispanic and Non-Hispanic Black/African Americans have a significantly positive association with COVID-19 incidence rate, indicating the higher susceptibility of COIVD-19 infections among minority; 2) The Black has the shortest drive time to testing sites, followed by Hispanic, Asian, and Whites. The drive time to testing sites is significantly negatively associated with the COVID-19 incidence rate, implying the importance of testing location being accessed by all populations; 3) Poverty rate and road density are significant explanatory variables. Importantly, overcrowding represented by more than one person per room is a significant variable found to be positively associated with COVID-19 incidence rate, suggesting the effectiveness of social distancing for reducing infection; 4) Different from previous studies, elderly population rate is not statistically significant with incidence rate because the elderly population in Massachusetts is less distributed in the hot spot regions of COVID-19 infections. The findings in this study provide useful insights for policymakers to propose new strategies to contain the COVID-19 transmissions in Massachusetts.