Hepatitis B and circadian rhythm of the liver

The circadian rhythm in humans is determined by the central clock located in the hypothalamus’s suprachiasmatic nucleus, and it synchronizes the peripheral clocks in other tissues. Circadian clock genes and clock-controlled genes exist in almost all cell types. They have an essential role in many physiological processes, including lipid metabolism in the liver, regulation of the immune system, and the severity of infections. In addition, circadian rhythm genes can stimulate the immune response of host cells to virus infection. Hepatitis B virus (HBV) infection is the leading cause of liver disease and liver cancer globally. HBV infection depends on the host cell, and hepatocyte circadian rhythm genes are associated with HBV replication, survival, and spread. The core circadian rhythm proteins, REV-ERB and brain and muscle ARNTL-like protein 1, have a crucial role in HBV replication in hepatocytes. In addition to influencing the virus’s life cycle, the circadian rhythm also affects the pharmacokinetics and efficacy of antiviral vaccines. Therefore, it is vital to apply antiviral therapy at the appropriate time of day to reduce toxicity and improve the effectiveness of antiviral treatment. For these reasons, understanding the role of the circadian rhythm in the regulation of HBV infection and host responses to the virus provides us with a new perspective of the interplay of the circadian rhythm and anti-HBV therapy. Therefore, this review emphasizes the importance of the circadian rhythm in HBV infection and the optimization of antiviral treatment based on the circadian rhythm-dependent immune response.     

Management of immune-related adverse events resulting from immune checkpoint blockade

Introduction: Immune checkpoint inhibitors (ICI) are now a standard of care in the treatment of many cancers leading to durable responses in patients with metastatic disease. These agents are generally well tolerated but may lead to the occurrence of immune-related adverse events (irAEs). As any organ may be affected, clinicians should be aware of the broad range of clinical manifestations and symptoms and keep in mind that toxicities may occur late, at any point along a patient’s treatment course. Although the most common irAEs are rarely severe, some of them may be associated with great morbidity and even become life-threatening. The rate of occurrence, type and severity of irAEs may vary with the type of ICI; thus, grade 3 and 4 irAEs are reported in more than 55% of patients treated with the combination of ipilimumab 3 mg/kg and nivolumab 1 mg/kg.

Area covered: This review presents the management of irAEs resulting from checkpoint blockade, with a focus on rare irAEs.

Expert commentary: With the development of immuno-oncology and the expanding role of ICI, physicians have learnt to diagnose and treat most of the irAEs that can occur. This review provides an overview of current guidelines, previously published studies and our multidisciplinary team based practices.     

组织气氛在护士知识与行为间的中介效应:以脑卒中吞咽障碍护理为例

目的探究组织气氛在护士吞咽障碍知识水平与护理行为之间的中介效应。方法2019年6-7月,便利抽样法选取15个省市33所医院及医疗机构神经科、康复科具有吞咽障碍护理经验的231名护士为研究对象采用脑卒中吞咽障碍知识水平问卷、脑卒中吞咽障碍护理行为问卷及团队创新气氛量表对其进行调查。结果护士吞咽障碍知识水平总分为(56.69±13.50)分、护理行为得分为(140.65土32.02)分、组织气氛得分为(157.63±26.56)分。组织气氛与吞咽障碍知识水平(r=0.49,P<0.01)、护理行为(r=0.50,P<0.01)均呈正相关。组织气氛在知识水平和护理行为之间起中介作用,中介效应占总效应的83.18%。结论组织气氛在护士吞咽障碍知识水平与护理行为之间起中介作用,临床管理者可在提升护士吞咽障碍知识水平的基础上,通过提高组织气氛水平推进其护理行为。     

Papillary thyroid carcinoma with a high tumor mutation burden has a poor prognosis

BackgroundTumor mutation burden (TMB) as a prognostic marker for immunotherapy has shown prognostic value in many cancers. However, there is no systematic investigation on TMB in papillary thyroid carcinoma (PTC).MethodsBased on the somatic mutation data of 487 PTC patients from The Cancer Genome Atlas (TCGA), TMB was calculated, and we classified the samples into high-TMB (H-TMB) and low-TMB (L-TMB) groups. Bioinformatics methods were used to explore the characteristics and potential mechanism of TMB in PTC.ResultsHigh TMB predicts shorter progression-free survival (PFS) (P < 0.001). TMB was positively correlated with age, stage, tumor size, metastasis, the male sex and tall cell PTC. Compared to the L-TMB group, the H-TMB group presented with lower immune cell infiltration, a higher proportion of tumor-promoting immune cells (M0 macrophages, activated dendritic cells and monocytes) and a lower proportion of antitumor immune cells (M1 macrophages, CD8⁺ T cells and B cells). Additionally, the characteristics displayed by different TMB groups were not driven by critical driver mutations such as BRAF and RAS.ConclusionsPTC patients with high TMB have a worse prognosis. By stratifying PTC patients according to their TMB, advanced PTC patients who are candidates for immunotherapy could be selected.     

Interferon-induced Transmembrane Protein 3 Prevents Acute Influenza Pathogenesis in Mice

Objective Interferon-induced transmembrane protein 3(IFITM3) is an important member of the IFITM family. However, the molecular mechanisms underlying its antiviral action have not been completely elucidated. Recent studies on IFITM3, particularly those focused on innate antiviral defense mechanisms, have shown that IFITM3 affects the body's adaptive immune response. The aim of this study was to determine the contribution of IFITM3 proteins to immune control of influenza infection in vivo.Methods We performed proteomics, flow cytometry, and immunohistochemistry analysis and used bioinformatics tools to systematically compare and analyze the differences in natural killer(NK) cell numbers, their activation, and their immune function in the lungs of Ifitm3-/-and wild-type mice.Results Ifitm3-/-mice developed more severe inflammation and apoptotic responses compared to wild-type mice. Moreover, the NK cell activation was higher in the lungs of Ifitm3-/-mice during acute influenza infection.Conclusions Based on our results, we speculate that the NK cells are more readily activated in the absence of IFITM3, increasing mortality in Ifitm3-/-mice.     

Differentiating autoimmune pancreatitis from pancreatic ductal adenocarcinoma with CT radiomics features

PurposeThe purpose of this study was to determine whether computed tomography (CT)-based machine learning of radiomics features could help distinguish autoimmune pancreatitis (AIP) from pancreatic ductal adenocarcinoma (PDAC).Materials and MethodsEighty-nine patients with AIP (65 men, 24 women; mean age, 59.7 ± 13.9 [SD] years; range: 21–83 years) and 93 patients with PDAC (68 men, 25 women; mean age, 60.1 ± 12.3 [SD] years; range: 36–86 years) were retrospectively included. All patients had dedicated dual-phase pancreatic protocol CT between 2004 and 2018. Thin-slice images (0.75/0.5 mm thickness/increment) were compared with thick-slices images (3 or 5 mm thickness/increment). Pancreatic regions involved by PDAC or AIP (areas of enlargement, altered enhancement, effacement of pancreatic duct) as well as uninvolved parenchyma were segmented as three-dimensional volumes. Four hundred and thirty-one radiomics features were extracted and a random forest was used to distinguish AIP from PDAC. CT data of 60 AIP and 60 PDAC patients were used for training and those of 29 AIP and 33 PDAC independent patients were used for testing.ResultsThe pancreas was diffusely involved in 37 (37/89; 41.6%) patients with AIP and not diffusely in 52 (52/89; 58.4%) patients. Using machine learning, 95.2% (59/62; 95% confidence interval [CI]: 89.8–100%), 83.9% (52:67; 95% CI: 74.7–93.0%) and 77.4% (48/62; 95% CI: 67.0–87.8%) of the 62 test patients were correctly classified as either having PDAC or AIP with thin-slice venous phase, thin-slice arterial phase, and thick-slice venous phase CT, respectively. Three of the 29 patients with AIP (3/29; 10.3%) were incorrectly classified as having PDAC but all 33 patients with PDAC (33/33; 100%) were correctly classified with thin-slice venous phase with 89.7% sensitivity (26/29; 95% CI: 78.6–100%) and 100% specificity (33/33; 95% CI: 93–100%) for the diagnosis of AIP, 95.2% accuracy (59/62; 95% CI: 89.8–100%) and area under the curve of 0.975 (95% CI: 0.936–1.0).ConclusionsRadiomic features help differentiate AIP from PDAC with an overall accuracy of 95.2%.