Xin Yin

Sepsis is a systemic inflammatory disease with infection, and autophagy has been shown to play an important role in sepsis. This review summarizes the main regulatory mechanisms of autophagy in sepsis and its latest research. Recent studies have shown that autophagy can regulate innate immune processes and acquired immune processes, and the regulation of autophagy in different immune cells is different. Mitophagy can select damaged mitochondria and remove it to deal with oxidative stress damage. The process of mitophagy is regulated by other factors. Non-coding RNA is also an important factor in the regulation of autophagy. In addition, more and more studies in recent years have shown that autophagy plays different roles in different organs. It tends to be protective in the lungs, heart, kidneys, and brain, and tends to be damaging in skeletal muscle. We also mentioned that some drugs can regulate autophagy. The process of modulating autophagy through drug intervention appears to be a new potential hope for the treatment of sepsis.

BACKGROUND: Spinal cord injury (SCI), including immediate mechanical injury and secondary injury, is associated with the inflammatory response, apoptosis and oxidative stress in response to traumatic injury. Tanshinone IIA (TIIA) is one of the major extracts obtained from Salvia miltiorrhiza BUNGE, which has anti-inflammatory and anti-apoptotic effects on many diseases. However, little is known about the effects of TIIA treatment on SCI. Therefore, the aim of the present study is to evaluate the pharmacological action of TIIA on secondary damage and the underlying mechanisms of experimental SCI in rats. METHODOLOGY/PRINCIPAL FINDINGS: SCI was generated using a weight drop device on the dorsal spinal cord via a two-level T9-T11 laminectomy. SCI in rats resulted in severe trauma, characterized by locomotor disturbance, edema, neutrophil infiltration, the production of astrocytes and inflammatory mediators, apoptosis and oxidative stress. TIIA treatment (20 mg/kg, i.p.) after SCI induced significant effects: (1) improved motor function (Basso, Beattie and Bresnahan scores), (2) reduced the degree of tissue injury (histological score), neutrophil infiltration (myeloperoxidase activity) and the expression of astrocytes, (3) inhibited the activation of SCI-related pathways, such as NF-κB and MAPK signaling pathways, (4) decreased the production of pro-inflammatory cytokines (TNF-α, IL-1β, and IL-6) and iNOS, (5) reduced apoptosis (TUNEL staining, and Bcl-2 and caspase-3 expression) and (6) reversed the redox state imbalance. CONCLUSIONS/SIGNIFICANCE: The results clearly show that TIIA has a prominent protective effect against SCI through inhibiting the inflammatory response and apoptosis in the spinal cord tissue after SCI.

Breast cancer is one of the leading causes of cancer-associated mortality in women worldwide and has become a major public health problem. Although the definitive cause of breast cancer is not known, many genes sensitive to breast cancer have been detected using advanced technologies. Our study identified 3301 differentially expressed lncRNAs and mRNAs between tumor and normal samples from The Cancer Genome Atlas database. Based on the gene expression analysis and clinical traits as well as weighted gene co-expression network analysis, the co-expression Brown module was found to be key for breast cancer prognosis. A total of 453 genes in the Brown module were used for functional enrichment, protein-protein interaction analysis, lncRNA-miRNA-mRNA ceRNA network, and lncRNA-RNA binding protein-mRNA network construction. GRM4, SSTR2, PARD6B, PRR15, COX6C, and lncRNA DSCAM-AS1 were the hub genes according to protein-protein interaction, lncRNA-miRNA-mRNA and lncRNA-RNA binding protein-mRNA network. Their high expression was found to be correlated with breast cancer development, according to multiple databases. In conclusion, this study provides a framework of the co-expression gene modules of breast cancer and identifies several important biomarkers in breast cancer development and prognosis.

Background: To contain the outbreak of coronavirus disease 2019 (COVID-19) in China, many unprecedented intervention measures are adopted by the government. However, these measures may interfere in the normal medical service. We sought to model the trend of COVID-19 and estimate the restoration of operational capability of metropolitan medical service in China. Methods: Real-time data of COVID-19 and population mobility data were extracted from open sources. SEIR (Susceptible, Exposed, Infectious, Recovered) and neural network models (NNs) were built to model disease trends in Wuhan, Beijing, Shanghai and Guangzhou. Combined with public transportation data, Autoregressive Integrated Moving Average (ARIMA) model was used to estimate the accumulated demands for nonlocal hospitalization during the epidemic period in Beijing, Shanghai and Guangzhou. Results: The number of infected people and deaths would increase by 45% and 567% respectively, given that the government only has implemented traffic control in Wuhan without additional medical professionals. The epidemic of Wuhan (measured by cumulative confirmed cases) was predicted to reach turning point at the end of March and end in later April, 2020. The outbreak in Beijing, Shanghai and Guangzhou was predicted to end at the end of March and the medical service could be fully back to normal in middle of April. During the epidemic, the number of nonlocal inpatient hospitalizations decreased by 69.86%, 57.41% and 66.85% in Beijing, Shanghai and Guangzhou respectively. After the end of epidemic, medical centers located in these metropolises may face 58,799 (95% CI 48926-67,232) additional hospitalization needs in the first month. Conclusion: The COVID-19 epidemic in China has been effectively contained and medical service across the country is expected to return to normal in April. However, the huge unmet medical needs for other diseases could result in massive migration of patients and their families, bringing tremendous challenges for medical service in major metropolis and disease control for the potential asymptomatic virus carrier.

It is well known that noncoding RNAs (ncRNAs) cannot encode proteins, but they can play important regulatory roles in tumors by combining with proteins, RNAs, and DNAs. As more and more studies reveal the important roles and underlying mechanisms of long noncoding RNAs (lncRNAs) and circular RNAs (circRNAs) in cancer, their huge application potential in cancer therapy cannot be ignored. For example, lncRNAs can be involved in tumor-related signal transduction pathways, cell cycle control, DNA damage, epigenetic regulation, and microRNA control. A group of studies confirmed that abnormal expression of lncRNAs can affect cancer progression. Furthermore, as covalently closed continuous circular ncRNAs, many recent studies have shown that circRNAs have regulatory effects and other important biological significances in cancer. Interestingly, circRNAs were found to have translational functions. This has greatly attracted people's attention to circRNAs research. In this review, we introduce the important roles of lncRNAs and circRNAs in some representative cancers, respectively. Furthermore, we focus on the biological functions and important clinical therapeutic implications of lnRNAs and circRNAs in neuroblastoma. Our review also focuses on providing rationale and relevant references for novel biomarkers for neuroblastoma diagnosis, prognosis, and treatment.