Circ_0084188 promotes colorectal cancer progression by sponging miR-654-3p and regulating kruppel-like factor 12.

To investigate the biological role and mechanism of circ_0084188 in colorectal cancer (CRC). Real-time quantitative polymerase chain reaction and western blot assay were used to detect RNA levels and protein levels in CRC cell lines (HCT116 and SW480), respectively. Cell proliferation was evaluated by Cell Counting Kit-8 assay, 5-ethynyl-2'-deoxyuridine assay, and colony formation assays. Cell apoptosis was determined using flow cytometry. Cell migration and invasion were measured by transwell assay. Sphere formation efficiency was determined by sphere formation assay. The interaction between microRNA-654-3p (miR-654-3p) and circ_0084188 or Kruppel-like factor 12 (KLF12) was confirmed by a dual-luciferase reporter, RNA immunoprecipitation and RNA pull-down assays. Xenograft in CRC mice model was utilized for exploring the role of circ_0084188 in vivo.Circ_0084188 was overexpressed in CRC tissues and cells. Circ_0084188 silencing suppressed cell proliferation, migration, invasion, and stemness and induced apoptosis in CRC cells. Circ_0084188 acted as a sponge for miR-654-3p, and circ_0084188 regulated CRC cell behaviors via sponging miR-654-3p. Moreover, KLF12 was a target of miR-654-3p, and miR-654-3p overexpression inhibited the malignant behaviors of CRC cells by downregulating KLF12. Mechanically, circ_0084188 sponged miR-654-3p to regulate KLF12 expression in CRC cells. In addition, circ_0084188 downregulation inhibited tumor growth in vivo.Circ_0084188 knockdown might repress CRC progression partially via regulating the miR-654-3p/KLF12 axis, providing a novel insight into the pathogenesis of CRC.

Common mtDNA variations at C5178a and A249d/T6392C/G10310A decrease the risk of severe COVID-19 in a Han Chinese population from Central China.

BACKGROUND: Mitochondria have been shown to play vital roles during severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and coronavirus disease 2019 (COVID-19) development. Currently, it is unclear whether mitochondrial DNA (mtDNA) variants, which define mtDNA haplogroups and determine oxidative phosphorylation performance and reactive oxygen species production, are associated with COVID-19 risk. METHODS: A population-based case-control study was conducted to compare the distribution of mtDNA variations defining mtDNA haplogroups between healthy controls (n = 615) and COVID-19 patients (n = 536). COVID-19 patients were diagnosed based on molecular diagnostics of the viral genome by qPCR and chest X-ray or computed tomography scanning. The exclusion criteria for the healthy controls were any history of disease in the month preceding the study assessment. MtDNA variants defining mtDNA haplogroups were identified by PCR-RFLPs and HVS-I sequencing and determined based on mtDNA phylogenetic analysis using Mitomap Phylogeny. Student's t-test was used for continuous variables, and Pearson's chi-squared test or Fisher's exact test was used for categorical variables. To assess the independent effect of each mtDNA variant defining mtDNA haplogroups, multivariate logistic regression analyses were performed to calculate the odds ratios (ORs) and 95% confidence intervals (CIs) with adjustments for possible confounding factors of age, sex, smoking and diseases (including cardiopulmonary diseases, diabetes, obesity and hypertension) as determined through clinical and radiographic examinations. RESULTS: Multivariate logistic regression analyses revealed that the most common investigated mtDNA variations (> 10% in the control population) at C5178a (in NADH dehydrogenase subunit 2 gene, ND2) and A249d (in the displacement loop region, D-loop)/T6392C (in cytochrome c oxidase I gene, CO1)/G10310A (in ND3) were associated with a reduced risk of severe COVID-19 (OR = 0.590, 95% CI 0.428-0.814, P = 0.001; and OR = 0.654, 95% CI 0.457-0.936, P = 0.020, respectively), while A4833G (ND2), A4715G (ND2), T3394C (ND1) and G5417A (ND2)/C16257a (D-loop)/C16261T (D-loop) were related to an increased risk of severe COVID-19 (OR = 2.336, 95% CI 1.179-4.608, P = 0.015; OR = 2.033, 95% CI 1.242-3.322, P = 0.005; OR = 3.040, 95% CI 1.522-6.061, P = 0.002; and OR = 2.890, 95% CI 1.199-6.993, P = 0.018, respectively). CONCLUSIONS: This is the first study to explore the association of mtDNA variants with individual's risk of developing severe COVID-19. Based on the case-control study, we concluded that the common mtDNA variants at C5178a and A249d/T6392C/G10310A might contribute to an individual's resistance to developing severe COVID-19, whereas A4833G, A4715G, T3394C and G5417A/C16257a/C16261T might increase an individual's risk of developing severe COVID-19.

Leveraging text skeleton for de-identification of electronic medical records.

BACKGROUND: De-identification is the first step to use these records for data processing or further medical investigations in electronic medical records. Consequently, a reliable automated de-identification system would be of high value. METHODS: In this paper, a method of combining text skeleton and recurrent neural network is proposed to solve the problem of de-identification. Text skeleton is the general structure of a medical record, which can help neural networks to learn better. RESULTS: We evaluated our method on three datasets involving two English datasets from i2b2 de-identification challenge and a Chinese dataset we annotated. Empirical results show that the text skeleton based method we proposed can help the network to recognize protected health information. CONCLUSIONS: The comparison between our method and state-of-the-art frameworks indicates that our method achieves high performance on the problem of medical record de-identification.

The panorama of the diversity of H5 subtype influenza viruses.

To elucidate the global diversity of H5 influenza viruses from a dynamic view, haemagglutinin (HA) sequences of 170 isolates were selected and analyzed in this study. Our results showed that H5 influenza isolates could be divided into two distinct lineages that circulated in the Eastern Hemisphere and the Western Hemisphere, respectively. This may be due to the separate migration routes and habitats of birds in the two hemispheres. The two distinct lineages, having existed at least for decades, possibly began divergence in 1850s. Each of the two distinct HA lineages could be further divided into some sublineages, but there was little correlation between the minor lineages and their isolation places, isolation time, neuraminidase subtypes, host species or virulence. The panorama of the diversity of H5 influenza viruses presented here integrated all known H5 epidemics including the current severe H5N1 avian epidemics in the Eastern Hemisphere and suggested that H5 virulent viruses could originate from multiple sublineages and associate with multiple NA subtypes. Our study provided a framework for the studies on the evolution and epidemiology of H5 influenza viruses.

A preliminary panorama of the diversity of N1 subtype influenza viruses.

N1 subtype influenza viruses have caused many epidemics and even a few pandemics in humans, pigs and fowls including 1918 human H1N1 pandemic, which killed 20-50 million people and the current avian H5N1 pandemic in the Eastern Hemisphere, which has caused great economic losses and posed a severe threat to human public health. To elucidate the whole diversity of N1 influenza viruses from a dynamic view, 202 neuraminidase (NA) sequences of N1 subtype influenza isolates were selected and analyzed in this study. Our results showed that N1 influenza isolates could be divided into three distinct lineages (Human, Classic Swine and Avian), which largely circulated in the humans, pigs and fowls respectively, though viruses in the Avian lineage could infect mammals and even there was a sublineage in the Avian lineage wholly isolated from pigs. The Avian lineage and the Human lineage, which have existed at least for decades, possibly began divergence around in 1890 through regression analysis. Both of the Human and Avian lineages could be further divided into some sublineages, and the correlation between these lineages (or sublineages) and their isolation places, isolation time, hemagglutinin (HA) subtypes, host species, virulence, or epidemics were discussed. The panorama of the diversity of N1 influenza viruses presented in this study provided a framework for the studies on the evolution and epidemiology of N1 influenza viruses.