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Colon cancer is one of the most common malignancies causing death worldwide. It is well known that the cells of the tumor microenvironment contribute to the progression and prognosis of colon cancer. However, the gene alterations and potential remodeling mechanisms in the tumor microenvironment of colon cancer remain largely unknown. In this study, immune scores from the ESTIMATE algorithm were used to discriminate between patients with high or low immune-cell infiltration. There were 42 immune differentially expressed genes (DEGs) of prognostic value identified in this study. Among them, KCNJ5 is a key factor in promoting M2 macrophage recruitment and tumor immune infiltration in colon cancer. These findings may provide novel insights for decoding the complicated interplay between cancer cells and the tumor microenvironment as well as for developing new avenues for therapeutic intervention in colon cancer.Knocking down delta-5-desaturase (D5D) expression by D5D small interfering RNA (siRNA) has been reported that could redirect the cyclooxygenase-2 (COX-2)-catalyzed dihomo-γ-linolenic acid (DGLA) peroxidation from producing prostaglandin E2 to 8-hydroxyoctanoic acid (8-HOA), resulting in the inhibition of colon and pancreatic cancers. However, the effect of D5D siRNA on lung cancer is still unknown. In this study, by incorporating epithelial cell adhesion molecule (EpCAM) aptamer and validated D5D siRNA into the innovative three-way junction (3WJ) RNA nanoparticle, target-specific accumulation and D5D knockdown were achieved in the lung cancer cell and mouse models. By promoting the 8-HOA formation from the COX-2-catalyzed DGLA peroxidation, the 3WJ-EpCAM-D5D siRNA nanoparticle inhibited lung cancer growth in vivo and in vitro. As a potential histone deacetylases inhibitor, 8-HOA subsequently inhibited cancer proliferation and induced apoptosis via suppressing YAP1/TAZ nuclear translocation and expression. Therefore, this 3WJ-RNA nanoparticle could improve the targeting and effectiveness of D5D siRNA in lung cancer therapy.Long noncoding RNAs (lncRNAs) have recently been implicated in many pathophysiological cardiovascular processes, including vascular remodeling and atherosclerosis. However, the functional role of lncRNAs in the differentiation, proliferation, and apoptosis of vascular smooth muscle cells (VSMCs) is largely unknown. SMS 201-995 cell line In this study, differentially expressed lncRNAs in synthetic and contractile human VSMCs were screened using RNA sequencing. Among the seven selected lncRNAs, the expression of MSC-AS1, MBNL1-AS1, and GAS6-AS2 was upregulated, whereas the expression of NR2F1-AS1, FUT8-AS1, FOXC2-AS1, and CTD-2207P18.2 was reduced upon VSMC differentiation. We focused on the NR2F1-AS1 and FOXC2-AS1 lncRNAs and showed that their knockdown significantly reduced the expression of smooth muscle contractile marker genes (ACTA2, CNN1, and TAGLN). Furthermore, FOXC2-AS1 was found to regulate cell proliferation and apoptosis through Akt/mTOR signaling, and affect Notch signaling, which is a key regulator of the contractile phenotype of VSMCs. Taken together, we identified novel lncRNAs involved in VSMC proliferation and differentiation and FOXC2-AS1 as a multifunctional regulator for vascular homeostasis and associated diseases.Protein-protein interactions (PPIs) are pivotal for cellular functions and biological processes. In the past years, computational methods using amino acid sequences and gene ontology (GO) annotations of proteins for prioritizing PPIs have provided important references for biological experiments in the wet lab. Despite the current success, sequence information and ontological annotation in semantic representation have not been integrated into current methods. We propose a deep-learning-based PPI prediction methodology conjointly featuring sequence information and GO annotation. First, we adopt a word-embedding tool, the NCBI-blueBERT model pre-trained on PubMed, to map the GO terms into their semantic vectors. Then, the GO semantic vectors and protein sequence vector serve as the input of the proposed inception recurrent neural network (RNN) attention network (IRAN). The IRAN captures the spatial relationship and the potential sequential feature of the protein sequence and ontological annotation semantics. The extensive experimental results on 12 benchmarks demonstrate that our method achieves superiority over state-of-the-art baselines. In the yeast dataset of a binary PPI prediction, our method improved the performance with the Matthews correlation coefficient increasing from 94.2% to 98.2% and the accuracy from 97.1% to 98.2%. The analogous results were also obtained in other comparison evaluations.[This corrects the article DOI 10.1016/j.omtn.2018.06.005.].In reference to the announcement of the pandemic of the new coronavirus 2019-(nCoV), all educational institutions in the Republic of Kazakhstan have switched to online learning (OL). The purpose of this study was to investigate the mental state of the medical students switching to OL in comparison with the mental state of the students who had traditional learning (TL). A repeated questionnaire-based cross-sectional study was conducted among medical students ranging from 1st year to 5th year at Astana Medical University in the 2019-2020 academic year. The first study was conducted during the TL (October-November 2019, N = 619), and the second study was conducted during the OL period (April 2020, N = 798). Burnout syndrome, depression, anxiety, somatic symptoms, and satisfaction with academic performance have been studied. The findings revealed that prevalence of the burnout syndrome, depression, anxiety, and somatic symptoms decreased after transitioning from TL to OL. However, during the OL period, the prevalence of colleague-related burnout increased, which tells us about the negative impact of OL on students’ communication and interpersonal relationships. The most common depression and anxiety symptoms, dissatisfaction with academic performance were among students who indicated a decrease in academic performance during OL. Students who lived alone during the quarantine were more prone to depression during OL. In conclusion, during the quarantine period after the transition from TL to OL, the mental health state of medical students improved, despite the severe conditions of the pandemic.

The online version contains supplementary material available at 10.1007/s40670-020-01165-y.

The online version contains supplementary material available at 10.1007/s40670-020-01165-y.