Qiang Li

<b><i>Background:</i></b> Primary liver cancer, around 90% are hepatocellular carcinoma in China, is the fourth most common malignancy and the second leading cause of tumor-related death, thereby posing a significant threat to the life and health of the Chinese people. <b><i>Summary:</i></b> Since the publication of <i>Guidelines for Diagnosis and Treatment of Primary Liver Cancer (2017 Edition)</i> in 2018, additional high-quality evidence has emerged with relevance to the diagnosis, staging, and treatment of liver cancer in and outside China that requires the guidelines to be updated. The new edition <i>(2019 Edition)</i> was written by more than 70 experts in the field of liver cancer in China. They reflect the real-world situation in China regarding diagnosing and treating liver cancer in recent years. <b><i>Key Messages:</i></b> Most importantly, the new guidelines were endorsed and promulgated by the Bureau of Medical Administration of the National Health Commission of the People’s Republic of China in December 2019.

Systemic metabolic syndrome significantly increases the risk of morbidity and mortality in patients with non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatohepatitis (NASH). However, no effective therapeutic strategies are available, practically because our understanding of its complicated pathogenesis is poor. Here we identify the tripartite motif-containing protein 31 (Trim31) as an endogenous inhibitor of rhomboid 5 homolog 2 (Rhbdf2), and we further determine that Trim31 directly binds to Rhbdf2 and facilitates its proteasomal degradation. Hepatocyte-specific Trim31 ablation facilitates NAFLD-associated phenotypes in mice. Inversely, transgenic or ex vivo gene therapy-mediated Trim31 gain-of-function in mice with NAFLD phenotypes virtually alleviates severe deterioration and progression of steatohepatitis. The current findings suggest that Trim31 is an endogenous inhibitor of Rhbdf2 and downstream cascades in the pathogenic process of steatohepatitis and that it may serve as a feasible therapeutical target for the treatment of NAFLD/NASH and associated metabolic disorders.

Abstract Glioblastoma is the most aggressive and deadly brain cancer. There is growing interest to develop drugs that specifically target to glioblastoma tumor-initiating cells (TICs). However, the cost-effective production of large numbers of high quality glioblastoma TICs for drug discovery with current cell culturing technologies remains very challenging. Here, we report a new method that cultures glioblastoma TICs in microscale alginate hydrogel tubes (or AlgTubes). The AlgTubes allowed long-term culturing (~50 days, 10 passages) of glioblastoma TICs with high growth rate (~700-fold expansion/14 days), high cell viability and high volumetric yield (~3.0 × 10 8 cells/mL) without losing the stem cell properties, all offered large advancements over current culturing methods. This method can be applied for the scalable production of glioblastoma TICs at affordable cost for drug discovery.

Adjuvant interferon (IFN) therapy following curative treatment for hepatocellular carcinoma (HCC) has been extensively investigated; however, the clinical benefits with different hepatitis backgrounds remain unclear. Medline, Embase, PubMed and the Cochrane Library databases were searched to identify randomized trials and cohort studies that enrolled HCC patients who received curative surgery or ablation therapy followed by IFN and control subjects; the studies were required to include data on early or late recurrence and mortality rates of HCC. Hepatitis B virus (HBV) associated with HCC (HBV-HCC) and hepatitis C virus (HCV) associated with HCC (HCV-HCC) were separately analyzed and recurrence, mortality and clinicopathological factors were compared. A total of 14 studies (9 randomized trials and 5 cohort studies, including 1,385 patients in total) were eligible for meta-analysis. IFN was found to decrease mortality and early recurrence rates, but exerted no effect on late recurrence rate. The effect of IFN differed between HBV-HCC and HCV-HCC cases. In HCV-HCC, IFN significantly reduced mortality as well as recurrence rates. However, in HBV-HCC patients, IFN reduced mortality rather than recurrence rates, although it also reduced the recurrence rate in certain subgroups. In conclusion, the effect of adjuvant IFN on postoperative recurrence differed between HBV-HCC and HCV-HCC cases; therefore, different strategies with adjuvant IFN should be used to treat HCC with different hepatitis backgrounds.

Coronavirus disease (COVID-19) has caused a worldwide pandemic, putting millions of people's health and lives in jeopardy. Detecting infected patients early on chest computed tomography (CT) is critical in combating COVID-19. Harnessing uncertainty-aware consensus-assisted multiple instance learning (UC-MIL), we propose to diagnose COVID-19 using a new bilateral adaptive graph-based (BA-GCN) model that can use both 2D and 3D discriminative information in 3D CT volumes with arbitrary number of slices. Given the importance of lung segmentation for this task, we have created the largest manual annotation dataset so far with 7,768 slices from COVID-19 patients, and have used it to train a 2D segmentation model to segment the lungs from individual slices and mask the lungs as the regions of interest for the subsequent analyses. We then used the UC-MIL model to estimate the uncertainty of each prediction and the consensus between multiple predictions on each CT slice to automatically select a fixed number of CT slices with reliable predictions for the subsequent model reasoning. Finally, we adaptively constructed a BA-GCN with vertices from different granularity levels (2D and 3D) to aggregate multi-level features for the final diagnosis with the benefits of the graph convolution network's superiority to tackle cross-granularity relationships. Experimental results on three largest COVID-19 CT datasets demonstrated that our model can produce reliable and accurate COVID-19 predictions using CT volumes with any number of slices, which outperforms existing approaches in terms of learning and generalisation ability. To promote reproducible research, we have made the datasets, including the manual annotations and cleaned CT dataset, as well as the implementation code, available at https://doi.org/10.5281/zenodo.6361963.