Gastric cancer is the fourth most common cancer and the second leading cause of cancer deaths worldwide. Chemotherapy is one of the major treatments for gastric cancer, but drug resistance limits the effectiveness of chemotherapy, which results in treatment failure. Resistance to chemotherapy can be present intrinsically before the administration of chemotherapy or it can develop during chemotherapy. The mechanisms of chemotherapy resistance in gastric cancer are complex and multifactorial. A variety of factors have been demonstrated to be involved in chemoresistance, including the reduced intracellular concentrations of drugs, alterations in drug targets, the dysregulation of cell survival and death signaling pathways, and interactions between cancer cells and the tumor microenvironment. This review focuses on the molecular mechanisms of chemoresistance in gastric cancer and on recent studies that have sought to overcome the underlying mechanisms of chemoresistance.

Biochemical adaptation is one of the basic functions that are widely implemented in biological systems for a variety of purposes such as signal sensing, stress response and homeostasis. The adaptation time scales span from milliseconds to days, involving different regulatory machineries in different processes. The adaptive networks with enzymatic regulation (ERNs) have been investigated in detail. But it remains unclear if and how other forms of regulation will impact the network topology and other features of the function. Here, we systematically studied three-node transcriptional regulatory networks (TRNs), with three different types of gene regulation logics. We found that the topologies of adaptive gene regulatory networks can still be grouped into two general classes: negative feedback loop (NFBL) and incoherent feed-forward loop (IFFL), but with some distinct topological features comparing to the enzymatic networks. Specifically, an auto-activation loop on the buffer node is necessary for the NFBL class. For IFFL class, the control node can be either a proportional node or an inversely-proportional node. Furthermore, the tunability of adaptive behavior differs between TRNs and ERNs. Our findings highlight the role of regulation forms in network topology, implementation and dynamics.

MIR17HG, located on chromosome 13, is a class of Pri-miRNAs that generates six miRNAs: miR-17, miR-18a, miR-19a, miR-20a, miR-19b-1 and miR-92-1. These miRNAs are ubiquitously overexpressed in diverse tumour types and exhibit complex biological links to tumour metastasis. We demonstrated that MIR17HG-derived miR-18a and miR-19a coordinately mediate gastric cancer cell metastasis by directly inhibiting SMAD2 expression and upregulating Wnt/β-catenin signalling. Based on previous studies, we hypothesised that an investigation of MIR17HG inhibition would be beneficial to clinical gastric cancer treatment, and systematically coupled bioinformatics analyses brought interferon regulatory factor-1 (IRF-1) to our attention. We then established stable clones in gastric cancer cells containing a doxycycline-inducible IRF-1 expression system and found that the expression of IRF-1 downregulates the embedded miRNAs of MIR17HG in gastric cancer cells and inhibits gastric cancer cell metastasis by attenuating Wnt/β-catenin signalling. Further rescue assays confirmed the crucial roles of miR-18a and miR-19a in the IRF-1-mediated inhibition of Wnt/β-catenin signalling. We also demonstrated that IRF-1 binds to the transcriptional site in the MIR17HG promoter and inhibits MIR17HG expression. Moreover, IFN-γ induced the IRF-1-mediated downregulation of MIR17HG in gastric cancer cells. Our hypothesis was supported by the results of immunohistochemistry analyses of clinical gastric cancer samples, and we also demonstrated the role of IRF-1 in inhibiting MIR17HG expression and tumour metastasis in vivo. We conclude that IRF-1 inhibits gastric cancer metastasis by downregulating MIR17HG-miR-18a/miR-19a axis expression and attenuating Wnt/β-catenin signalling.

Objectives To explore the diagnostic value of radiomics in differentiating between lung adenocarcinomas appearing as ground‐glass opacity nodules (GGO) with high‐ and low Ki‐67 expression levels. Materials and Methods From January 2018 to January 2021, patients with pulmonary GGO who received lung resection were evaluated for potential enrollment. The included GGOs were then randomly divided into a training cohort and a validation cohort with a ratio of 7:3. Logistic regression (LR), decision tree (DT), support vector machines (SVM), and adaboost (AB) were applied for radiomic model construction. Area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used to evaluate the diagnostic efficacy of the established models. Results Seven hundred and sixty‐nine patients with 769 GGOs were included in this study. Two hundred and forty‐five GGOs were confirmed to be of high Ki‐67 labeling index (LI). In the training cohort, gender, age, spiculation sign, pleural indentation sign, bubble sign, and maximum 2D diameter of the nodule were found to be significantly different between high‐ and low Ki‐67 LI groups (p < 0.05), and spiculation sign and maximum 2D diameter of the nodule were further confirmed to be risk factors for Ki‐67 LI. The radiomic model established using SVM exhibited an AUC of 0.731 in the validation cohort, which was higher than that of the clinical‐radiographic model (AUC = 0.675). Moreover, radiomic model combining both intra‐ and peri‐nodular features showed better diagnostic efficacy than using intra‐nodular features alone (AUC = 0.731 and 0.720, respectively). Conclusions The established radiomic model exhibited good diagnostic efficacy in differentiating between lung adenocarcinoma GGOs with high and low Ki‐67 LI, which was higher than the clinical‐radiographic model. Peri‐nodular radiomic features showed added benefits to the radiomic model. As a novel noninvasive method, radiomics have the potential to be applied in the preliminary classification of Ki‐67 expression level in lung adenocarcinoma GGOs.

In December 2019, a cluster of cases of acute respiratory illness, novel coronavirusinfected pneumonia, occurred in Wuhan, Hubei Province, China. The false-negative nasopharyngeal swabs of SARS-CoV-2 caused the delayed diagnosis of COVID-19 which hindered the prevention and control of the pandemic. The transmission risk of SARS-CoV-2 in negative nasopharyngeal swabs cases were little addressed previously. This study evaluated two clusters of COVID-19 in six patients. Four of six (66.7%) showed negative RNA of SARS-CoV-2 by nasopharyngeal swabs. All epidemiological, clinical and laboratory information was collected. The first cluster was a nosocomial infection of four health care providers at early January. One of them made sequential familial cluster of infection. All patients received either selfquarantined at home or were admitted to hospital for isolated treatment. All recovered and had anti-SARS-CoV-2 IgG and/or IgM positive (100%) for serological detection of SARS-CoV-2 at recovery stage. Our study provides a cautionary warning that negative results of nasopharyngeal swabs of suspected SARS-CoV-2 infection can increase the risk of nosocomial infection among health A

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The ongoing Coronavirus Disease 2019 , which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has posed a serious threat to human public health and global economy (Yang, Sha, et al., 2020). Given the unavailability of specific drugs for the treatment of this disease, timely public health interventions (e.g., travel restrictions, social distancing and wearing of facial masks) are one of the most effective ways to prevent and control the epidemic. On January 23, 2020, Wuhan, the capital city of Hubei Province, China, implemented an unprecedented lockdown measure to prohibit people and motor vehicles from entering and leaving the city. Then, this policy was extended to the entire province of Hubei and other cities in China. Studies indicate that these control measures have greatly reduced the spread of COVID-19 in China. The Wuhan shutdown resulted in the delayed arrival of COVID-19 in other cities by 2.91 days (Tian et al., 2020). If the implementation of public health interventions was delayed for 5 days, the extent of the epidemic would have tripled in Mainland China (Yang, Zeng, et al., 2020).Population mobility is one of the major drivers of epidemic transmission, accelerating the dispersion of virus in space (Kraemer et al., 2019). Through the migration of population across regions, the virus is exported from one city to another. The association between population mobility and epidemic dynamics has attracted wide attention of researchers. Different data sources that record people's mobile behaviors, such as mobile phone, social media and public transport, can be used for this study (Yang, Sha, et al., 2020). Kraemer et al. (2020) quantitatively evaluated the impact of population movement on COVID-19 epidemic. Through linear regression modeling, they observed that the population movement data can well explain the distribution of COVID-19 cases in China. Jia et al. (2020) used mobile-phone data to track population flow from Wuhan and then correlated them with COVID-19 cases of 296 cities in Mainland China by using multiplicative exponential models. The results show that population flow data can be used not only to predict

3D laparoscopy appears to reduce the performance time of laparoscopic colectomy when compared with 2D laparoscopy. Further studies are required to address the role of the 3D vision system in laparoscopic colectomy.