The coronavirus disease 2019 (COVID-19) outbreak was first identified in Wuhan, the capital of Hubei Province, China, in December 2019.¹ The disease spread rapidly from Wuhan to other cities. To contain this epidemic, Wuhan was locked down on January 23, 2020. Wenzhou, which has a population of 9.3 million and is located in southeastern China approximately 600 miles from Wuhan, is the most affected Chinese city outside of Hubei. Approximately 48 800 people traveled from Wuhan to Wenzhou from January 10 to 23, 2020. As of February 15, 2020, there were 502 confirmed cases of COVID-19 but no deaths reported in Wenzhou. To stop the spread of COVID-19 in Wenzhou, multiple community containment approaches were implemented beginning January 24, 2020, including quarantine, isolation, traffic control, and social distancing (Figure). This decision analytical model examined several key epidemiological features of imported COVID-19 cases in Wenzhou.

Regional lymph node metastasis (LNM) increases the risk of distant metastasis in papillary thyroid cancer (PTC) patients. However, it remains unclear how tumor cells in PTC patients with LNM evade immune system surveillance and proceed to colonize distant organs. Here, we comprehensively characterize the tumor-infiltrating immune cell landscape in PTC with LNM. LNM-related genes include multiple important soluble mediators such as CXCL6, IL37, MMP10, and COL11A1, along with genes involved in areas such as extracellular matrix organization and TLR regulation by endogenous ligands. In PTC without LNM, the tumor infiltration of activated dendritic cells and M0 macrophages showed increases from normal cells, but with yet greater increases and correspondingly worse prognosis in PTC with LNM. Conversely, the tumor infiltration of activated NK cells and eosinophils was decreased in PTC without LNM, as compared to normal cells, and yet further decreased in PTC with LNM, with such decreases associated with poor prognosis. We further demonstrate that mutations of driver genes in tumor cells influence the infiltration of surrounding immune cells in the tumor microenvironment (TME). Particularly, patients carrying TG mutations tend to show increased filtration of M2 macrophages and activated NK cells in the TME, whereas patients carrying HRAS mutations tend to show reduced filtration of M0 macrophages and show enhanced filtration of activated dendritic cells in the TME. These findings increase our understanding of the mechanisms of regional lymph node metastasis in PTC and its associated tumor microenvironment, potentially facilitating the development of personalized treatment regimens to combat immunotherapy failure.

Athena Dong, Xiaoqing Pan, Chien-Wei Lin, Yi-Wen Huang, Hayden Krause, Pan Pan, Arielle Baim, Michael J Thomas, Xiao Chen, Jianhua Yu, Laura Michaelis, Pengyuan Liu, Li-Shu Wang, Ehab Atallah. J Cancer Prev 2022;27:129-38. https://doi.org/10.15430/JCP.2022.27.2.129

Background: In intensive care unit (ICU), what thresholds of MAP variability are effective in distinguishing low- and high-risk patients for short-term mortality (in-hospital and 28-day) remains unclear.Methods: Fifteen thousand five hundred sixty adult subjects admitted to ICU at Beth Israel Deaconess Medical Center (Boston, USA) between 2001 and 2012 were included in this retrospective study from MIMIC-III database. MAP within the first 24 h after admission were collected. Quantiles of MAP variability from 10% to 90% with 10% increasement each were considered to divide study participants into two groups, either having coefficients of variation of MAP greater or less than the given threshold. The threshold of MAP variability was identified by maximizing the odds ratio associated with increased risk of short-term mortality (in-hospital and 28-day). Logistic regression and Cox regression models were further applied to evaluate the association between increased variability of MAP and short-term mortality (in-hospital and 28-day).Results: 90% quantile of MAP variability was determined as the threshold generating the largest odds ratio associated with the increased risk of short-term mortality. Increased MAP variability, especially over 90% of MAP variability, was associated with increased risk of in-hospital mortality (odds ratio: 2.351, 95% CI: 2.064–2.673), and 28-day mortality (hazard ratio: 2.064, 95% CI: 1.820–2.337).Conclusion: Increased MAP variability, especially over 90% of MAP variability, is associated with short-term mortality. Our proposed threshold of MAP variability may aid in the early identification of critically ill patients with a high risk of mortality.

Background: DNA methylation is an important epigenetic mechanism that may influence blood pressure (BP) regulation and hypertension risk. Obesity, a major lifestyle factor associated with hypertension, may interact with DNA methylation to affect BP. However, the indirect effect of DNA methylation on 24-h BP measurements mediated by obesity-related phenotypes such as BMI has not been investigated. Methods: Causal mediation analysis was applied to examine the mediating role of BMI in the relation between DNA methylation and 24-h BP phenotypes, including SBP, DBP and mean arterial blood pressure (MAP), in 281 African American participants. Results: Analysis of 38 215 DNA methylation regions, derived from 1 549 368 CpG sites across the genome, identified up to 138 methylation regions that were significantly associated with 24-h BP measurements through BMI mediation. Among them, 38 (19.2%) methylation regions were concurrently associated with SBP, DBP and MAP. Genes associated with BMI-mediated methylation regions are potentially involved in various chronic diseases such as coronary artery disease and renal disease, which are often caused or exacerbated by hypertension. Notably, three genes (CDH4, NOTCH1 and COLGALT1) showed both direct associations with 24-h BP measurements and indirect associations through BMI after adjusting for age and sex covariates. Conclusion: Our findings suggest that DNA methylation may contribute to the regulation of 24-h BP in African Americans both directly and indirectly through BMI mediation.

DNA methylation plays a crucial role in transcriptional regulation. Reduced representation bisulfite sequencing (RRBS) is a technique of increasing use for analyzing genome-wide methylation profiles. Many computational tools such as Metilene, MethylKit, BiSeq and DMRfinder have been developed to use RRBS data for the detection of the differentially methylated regions (DMRs) potentially involved in epigenetic regulations of gene expression. For DMR detection tools, as for countless other medical applications, P-values and their adjustments are among the most standard reporting statistics used to assess the statistical significance of biological findings. However, P-values are coming under increasing criticism relating to their questionable accuracy and relatively high levels of false positive or negative indications. Here, we propose a method to calculate E-values, as likelihood ratios falling into the null hypothesis over the entire parameter space, for DMR detection in RRBS data. We also provide the R package ‘metevalue’ as a user-friendly interface to implement E-value calculations into various DMR detection tools. To evaluate the performance of E-values, we generated various RRBS benchmarking datasets using our simulator ‘RRBSsim’ with eight samples in each experimental group. Our comprehensive benchmarking analyses showed that using E-values not only significantly improved accuracy, area under ROC curve and power, over that of P-values or adjusted P-values, but also reduced false discovery rates and type I errors. In applications using real RRBS data of CRL rats and a clinical trial on low-salt diet, the use of E-values detected biologically more relevant DMRs and also improved the negative association between DNA methylation and gene expression.