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In recent years, entity relation extraction has been a critical technique to help people analyze complex structured text data. However, there is no advanced research in food health and safety to help people analyze the complex concepts between food and human health and their relationships. This paper proposes an entity relation extraction method FHER for the few-shot learning in the food health and safety domain. For few-shot learning in the food health and safety domain, we propose three methods that effectively improve the performance of entity relationship extraction. The three methods are applied to the self-built data sets FH and MHD. The experimental results show that the method can effectively extract domain-related entities and their relations in a small sample size environment.Background Long non-coding RNAs (lncRNAs) are key regulators of pancreatic cancer development and are involved in ferroptosis regulation. LncRNA transcript levels serve as a prognostic factor for pancreatic cancer. Therefore, identifying ferroptosis-related lncRNAs (FRLs) with prognostic value in pancreatic cancer is critical. Methods In this study, FRLs were identified by combining The Cancer Genome Atlas (TCGA) and FerrDb databases. For training cohort, univariate Cox, Lasso, and multivariate Cox regression analyses were applied to identify prognosis FRLs and then construct a prognostic FRLs signature. Testing cohort and entire cohort were applied to validate the prognostic signature. Moreover, the nomogram was performed to predict prognosis at different clinicopathological stages and risk scores. A co-expression network with 76 lncRNA-mRNA targets was constructed. Results Univariate Cox analysis was performed to analyze the prognostic value of 193 lncRNAs. Furthermore, the least absolute shrinkage and selen clinical practice.PEG-Asparaginase (also known as Pegaspargase), along with glucocorticoids (predominantly prednisolone or dexamethasone) and other chemotherapeutic agents (such as cyclophosphamide, idarubicin, vincristine, cytarabine, methotrexate and 6-mercaptopurine) is the current standard treatment for acute lymphoblastic leukaemia in both children and adults. High doses of PEG-asparaginase are associated with side effects such as hepatotoxicity, pancreatitis, venous thrombosis, hypersensitivity reactions against the drug and severe hypertriglyceridemia. We report a case of a 28-year-old male who was normolipidemic at baseline and developed severe hypertriglyceridemia (triglycerides of 1793 mg/dl) following treatment with PEG-asparaginase for acute lymphoblastic leukaemia. Thorough genetic analysis was conducted to assess whether genetic variants could suggest a predisposition to this drug-induced metabolic condition. This genetic analysis showed the presence of a rare heterozygous missense variant c.11G > A-p.(Arg4Gln) in the APOC3 gene, classified as a variant of uncertain significance, as well as its association with four common single nucleotide polymorphisms (SNPs; c.*40C > G in APOC3 and c.*158T > C; c.162-43G > A; c.-3A > G in APOA5) related to increased plasma triglyceride levels. To our knowledge this is the first case that a rare genetic variant associated to SNPs has been related to the onset of severe drug-induced hypertriglyceridemia.Kidney renal clear cell carcinoma (KIRC) has high morbidity and gradually increased in recent years, and the rate of progression once relapsed is high. At present, owing to lack of effective prognosis predicted markers and post-recurrence drug selection guidelines, the prognosis of KIRC patients is greatly affected. Necroptosis is a regulated form of cell necrosis in a way that is independent of caspase. Induced necroptosis is considered an effective strategy in chemotherapy and targeted drugs, and it can also be used to improve the efficacy of immunotherapy. Herein, we quantified the necroptosis landscape of KIRC patients from The Cancer Genome Atlas (TCGA) database and divided them into two distinct necroptosis-related patterns (C1 and C2) through the non-negative matrix factorization (NMF) algorithm. Multi-analysis revealed the differences in clinicopathological characteristics and tumor immune microenvironment (TIME). Then, we constructed the NRG prognosis signature (NRGscore), which contained 10 NRGs (PLK1, APP, TNFRSF21, CXCL8, MYCN, TNFRSF1A, TRAF2, HSP90AA1, STUB1, and FLT3). We confirmed that NRGscore could be used as an independent prognostic marker for KIRC patients and performed excellent stability and accuracy. A nomogram model was also established to provide a more beneficial prognostic indicator for the clinic. We found that NRGscore was significantly correlated with clinicopathological characteristics, TIME, and tumor mutation burden (TMB) of KIRC patients. Moreover, NRGscore had effective guiding significance for immunotherapy, chemotherapy, and targeted drugs.[This corrects the article DOI 10.3389/fgene.2020.595477.].Epigenetic reprogramming is a hallmark of lymphomagenesis, however its role in reshaping the tumor microenvironment is still not well understood. Here we review the most common chromatin modifier mutations in B cell lymphoma and their effect on B cells as well as on T cell landscape. We will also discuss precision therapy strategies to reverse their aberrant signaling by targeting mutated proteins or counterbalance epigenetic mechanisms.The functional study on circRNAs has been increasing in the past decade due to its important roles in micro RNA sponge, protein coding, the initiation, and progression of diseases. The study of circRNA functions depends on the full-length sequences of circRNA, and current sequence assembly methods based on short reads face challenges due to the existence of linear transcript. Long reads produced by long-read sequencing techniques such as Nanopore technology can cover full-length sequences of circRNA and therefore can be used to evaluate the correctness and completeness of circRNA full sequences assembled from short reads of the same sample. Using long reads of the same samples, one from human and the other from mouse, we have comprehensively evaluated the performance of several well-known circRNA sequence assembly algorithms based on short reads, including circseq_cup, CIRI_full, and CircAST. Based on the F1 score, the performance of CIRI-full was better in human datasets, whereas in mouse datasets CircAST was better. In general, each algorithm was developed to handle special situations or circumstances. Our results indicated that no single assembly algorithm generated better performance in all cases. Therefore, these assembly algorithms should be used together for reliable full-length circRNA sequence reconstruction. After analyzing the results, we have introduced a screening protocol that selects out exonic circRNAs with full-length sequences consisting of all exons between back splice sites as the final result. After screening, CIRI-full showed better performance for both human and mouse datasets. The average F1 score of CIRI-full over four circRNA identification algorithms increased from 0.4788 to 0.5069 in human datasets, and it increased from 0.2995 to 0.4223 in mouse datasets.Ovarian cancer (OC) is the most lethal gynecological malignancy, in which chemoresistance is a crucial factor leading to the poor prognosis. Recently, immunotherapy has brought new light for the treatment of solid tumors. Hence, as a kind of immunologically active cancer, it is reasonably necessary to explore the potential mechanism between immune characteristics and cisplatin resistance in OC. Our study focused on the important role of cisplatin resistance-related lncRNAs on mediating the OC tumor immune microenvironment (TIME) using an integrative analysis based on the Cancer Genome Atlas (TCGA) database. First, the cisplatin resistance-related differentially expressed lncRNAs (DELs) and mRNAs (DEMs) were preliminarily screened to construct a DEL-DEM co-expression network. Next, the protein-protein interaction (PPI) network and pivot analysis were performed to reveal the relevance of these lncRNAs with tumor immune response. Second, the novel lncRNA CTD-2288O8.1 was identified as a key gene for the OC cispl OC, underscoring the potential mechanism of the TIME in conferring cisplatin resistance, which provided the research basis for further clinical treatment. CTD-2288O8.1 was identified to mediate cisplatin resistance and affect the response of immunotherapy, which could serve as a promising biomarker for guiding clinical treatment and improving prognosis in OC.Background Fat is a tissue that not just stores energy and plays a protective role; it is also a vital endocrine organ that generates and integrates signals to influence metabolism. Meanwhile, the excessive accumulation of lipids in adipose tissue can lead to metabolic disturbance and diseases. To date, the complicated molecular mechanisms of bovine adipose tissue are still unknown. This study aimed to identify key genes and functionally enriched pathways in various adipose tissue types. Results The RNAseq data of 264 samples were downloaded from Gene Expression Omnibus (GEO) and analyzed by weighted gene co-expression network analysis (WGCNA). We identified 19 modules that significantly associated with at least one adipose tissue type. The brown module from GSE39618 was most closely associated with intramuscular fat tissue, which contained 550 genes. These genes were significantly enriched in pathways that related to inflammation and disease, such as TNF signaling pathway, IL-17 signaling pathway, and NF-kappa B signaling pathway. The pink module (GSE39618) that contained 58 genes was most closely associated with omental fat tissue. The turquoise (GSE39618), blue (GSE116775), and yellow (GSE65125) module were most closely associated with subcutaneous fat tissue. Genes in these modules were significantly enriched in pathways related to fat metabolism, such as the PPAR signaling pathway, fatty acid metabolism and PI3K-Akt signaling pathway. At last, key genes for intramuscular fat (PTGS2 and IL6), omental fat (ARHGEF5 and WT1), and subcutaneous fat (KIT, QR6Q1, PKD2L1, etc.) were obtained and verified. In addition, it was found that IL10 and VCAM1 might be potential genes to distinguish adipose and muscle. Conclusion The study applied WGCNA to generate a landscape of adipose tissue and provide a basis for identifying potential pathways and hub genes of different adipose tissue types.Introduction Clear cell renal cell carcinoma (ccRCC) patients suffer from its high recurrence and metastasis rate, and a new prognostic risk score to predict individuals with high possibility of recurrence or metastasis is in urgent need. Autophagy has been found to have a dual influence on tumorigenesis. In this study we aim to analyze autophagy related genes (ATGs) and ccRCC patients and find a new prognostic risk score. Method Analyzing differential expression genes (DEGs) in TCGA-KIRC dataset, and took intersection with ATGs. Through lasso, univariate, and multivariate cox regression, DEGs were chosen, and the coefficients and expression levels of them were components constructing the formula of risk score. We analyzed mRNA expression of DEGs in tumor and normal tissue in ONCOMINE database and TCGA-KIRC dataset. The Human Protein Atlas (HPA) was used to analyze protein levels of DEGs. The protein-protein interaction (PPI) network was examined in STRING and visualized in cytoscape. Functional enrichment analysis was performed in RStudio.
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