Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related deaths worldwide, mainly due to the absence of effective diagnostic biomarkers and therapeutic targets. Therefore, novel molecular targets are urgently needed, in order to formulate novel therapeutic approaches for this devastating disease. In the present study, we demonstrated that diacylglycerol acyltransferase 2 (Dgat2) was downregulated in human HCC tissues compared with in matched normal tissues. Furthermore, its high expression was significantly associated with longer survival. In addition, Dgat2 overexpression significantly suppressed HCC cell proliferation. in vivo studies, we revealed that the weight and volume of the tumors derived from Balb/c nude mice was markedly decreased when using HCC cells overexpressing Dgat2. Mechanism analysis demonstrated that cell cycle-related gene expressions were significantly downregulated in HCC cells overexpressing Dgat2. Taken together, these data suggest that Dgat2 is an important regulator of HCC cell proliferation, and could represent a potential anticancer target and diagnostic biomarker for HCC.

Background: Single‐cell RNA sequencing is widely used in cancer research and organ development because of its powerful ability to analyze cellular heterogeneity. However, its application in cardiomyocytes is dissatisfactory mainly because the cardiomyocytes are too large and fragile to withstand traditional single‐cell approaches. Methods and Results: Through designing the isolation procedure of neonatal mouse cardiac cells, we provide detailed cellular atlases of the heart at single‐cell resolution across 4 different stages after birth. We have obtained 10 000 cardiomyocytes; to our knowledge, this is the most extensive reference framework to date. Moreover, we have discovered unexpected erythrocyte‐like cardiomyocyte–terminal cardiomyocytes, comprising more than a third of all cardiomyocytes. Only a few genes are highly expressed in these cardiomyocytes. They are highly differentiated cardiomyocytes that function as contraction pumps. In addition, we have identified 2 cardiomyocyte‐like conducting cells, lending support to the theory that the sinoatrial node pacemaker cells are specialized cardiomyocytes. Notably, we provide an initial blueprint for comprehensive interactions between cardiomyocytes and other cardiac cells. Conclusions: This mouse cardiac cell atlas improves our understanding of cardiomyocyte heterogeneity and provides a valuable reference in response to varying physiological conditions and diseases.

The increasing incidence rate and decreasing patients' five-year survival rate for endometrial cancer (EC) in recent decades highlight the necessity for further investigation of the molecular characteristics involved in cancer initiation and progression. In this study, we found that the pathways associated with mitotic cell cycle were enriched in primary EC samples versus normal endometrial samples through analyzing RNA-seq data of The Cancer Genome Atlas (TCGA). The messenger RNA (mRNA) expression of three activator E2Fs (E2F1, E2F2, and E2F3) and their target genes increased significantly in EC samples. Additionally, the high transcriptional activity of activator E2Fs was associated with poor survival, advanced clinical stage, high histologic grade, and aggressive histological type. We further demonstrated that E2Fs hyperactivation correlated with DNA hypomethylation and high cyclin-dependent kinase 4 (CDK4) expression. Moreover, abemaciclib, a selective CDK4 inhibitor, significantly inhibited the proliferation rates of human EC cell lines in vitro. And, abemaciclib also obviously inhibited EC cell growth in nude mice model. Collectively, our data suggest that the misregulation of CDK4/RB/E2Fs axis is associated with EC oncogenesis, and abemaciclib is a potential targeted drug for EC.

The COVID-19 vaccine has been administered to over 200 countries and regions. With the unprecedented vaccination scale and speed, vaccination correlated mental health issues should be paid precise attention to. This study aims to assess the association between socio-demographic factors and mental health following vaccination and to analyze the mediation effect of vaccine hesitancy. This study recruited 2112 individuals who took two doses of the COVID-19 vaccine in Shanghai. Structural equation modeling was performed to assess factors associated with anxiety and depression of the vaccinated individuals and the underlying mechanism. The results yielded that vaccine hesitancy partially mediated/suppressed the effect from gender and employment status to anxiety/depression and fully mediated the effects from education to anxiety/depression. This study advanced the understanding of mental health disparity among different socio-demographic groups after vaccination and the impact of vaccine hesitancy on the vaccinated population’s mental health. The finding offered insights into the possible mental vulnerability of people holding a hesitant attitude before vaccination and suggested that vaccine hesitancy played a crucial role in people’s mental health after vaccination. Health promotion programs can target vaccine hesitancy to prevent unfavorable mental health consequences among specific populations.

Detecting low-abundance mutations is very important for cancer diagnosis and treatment. Here we describe an improved targeted sequencing analysis that dramatically increases sequencing depth. Seven colorectal cancer (CRC) patients and seven healthy adults were enrolled in this study. We examined genetic mutations in tissue samples from the central and peripheral regions of tumors from the CRC patients and in blood cells from the healthy adults. We observed that each CRC carried larger numbers of mutations more than previously estimated. These included numerous deletion mutations in the tumor tissue. While the cellular morphology in the surrounding normal colonic tissues was healthy, these cells also carried many mutations. Similarly, the blood cells from the healthy donors carried numerous mutations. These findings shed new light on the processes of tumorigenesis and aging, and also present a potentially effective method for detecting low-abundance mutations for cancer diagnosis and targeted treatments.

Adaptive approaches, allowing for more flexible trial design, have been proposed for individually randomized trials to save time or reduce sample size. However, adaptive designs for cluster-randomized trials in which groups of participants rather than individuals are randomized to treatment arms are less common. Motivated by a cluster-randomized trial designed to assess the effectiveness of a machine-learning based clinical decision support system for physicians treating patients with depression, two Bayesian adaptive designs for cluster-randomized trials are proposed to allow for early stopping for efficacy at pre-planned interim analyses. The difference between the two designs lies in the way that participants are sequentially recruited. Given a maximum number of clusters as well as maximum cluster size allowed in the trial, one design sequentially recruits clusters with the given maximum cluster size, while the other recruits all clusters at the beginning of the trial but sequentially enrolls individual participants until the trial is stopped early for efficacy or the final analysis has been reached. The design operating characteristics are explored via simulations for a variety of scenarios and two outcome types for the two designs. The simulation results show that for different outcomes the design choice may be different. We make recommendations for designs of Bayesian adaptive cluster-randomized trial based on the simulation results.