Search

Background: Automatic and accurate recognition of various biomedical named entities from literature is an important task of biomedical text mining, which is the foundation of extracting biomedical knowledge from unstructured texts into structured formats. Using the sequence labeling framework and deep neural networks to implement biomedical named entity recognition (BioNER) is a common method at present. However, the above method often underutilizes syntactic features such as dependencies and topology of sentences. Therefore, it is an urgent problem to be solved to integrate semantic and syntactic features into the BioNER model., Results: In this paper, we propose a novel biomedical named entity recognition model, named BioByGANS (BioBERT/SpaCy-Graph Attention Network-Softmax), which uses a graph to model the dependencies and topology of a sentence and formulate the BioNER task as a node classification problem. This formulation can introduce more topological features of language and no longer be only concerned about the distance between words in the sequence. First, we use periods to segment sentences and spaces and symbols to segment words. Second, contextual features are encoded by BioBERT, and syntactic features such as part of speeches, dependencies and topology are preprocessed by SpaCy respectively. A graph attention network is then used to generate a fusing representation considering both the contextual features and syntactic features. Last, a softmax function is used to calculate the probabilities and get the results. We conduct experiments on 8 benchmark datasets, and our proposed model outperforms existing BioNER state-of-the-art methods on the BC2GM, JNLPBA, BC4CHEMD, BC5CDR-chem, BC5CDR-disease, NCBI-disease, Species-800, and LINNAEUS datasets, and achieves F1-scores of 85.15%, 78.16%, 92.97%, 94.74%, 87.74%, 91.57%, 75.01%, 90.99%, respectively., Conclusion: The experimental results on 8 biomedical benchmark datasets demonstrate the effectiveness of our model, and indicate that formulating the BioNER task into a node classification problem and combining syntactic features into the graph attention networks can significantly improve model performance., (© 2022. The Author(s).)

Background: Although PD-L1 expression is a crucial predictive biomarker for immunotherapy, it can be influenced by many factors., Methods: A total of 248 Chinese patients with lung adenocarcinoma was retrospectively identified. Data for clinical features, gene alternations, signaling pathways and immune signatures was analyzed among negative expression group (TPS < 1%, n = 124), intermediate expression group (1% ≤ TPS < 50%, n = 93), and high expression group (TPS ≥ 50%, n = 38). Clinical outcomes among different expression groups were also evaluated from public database., Results: Firstly, high tumor mutation burden was significantly associated with high PD-L1 expression in these Chinese patients with lung adenocarcinoma. In addition, gene alternations including TP53, PRKDC, KMT2D, TET1 and SETD2 apparently occurred in high PD-L1 expression group. Moreover, pathway analysis showed that mutations involving in DDR pathway, TP53 pathway, cell-cycle pathway and NOTCH pathway were obviously varied among three PD-L1 expression groups. Besides, most of patients in high PD-L1 expression group from TCGA database were determined as high-grade immune subtypes (C2-C4), showing significant higher proportions of IFN-gamma, CD8+ T-cells, NK cells, NK CD56 dim cells, Th1 cells, Th2 cells (P < 0.0001). Moreover, SETD2 mutation slightly correlated with overall survival from MSKCC cohort (HR 1.92 [95%CI 0.90-4.10], P = 0.085), and the percentage of IFN-gamma was significantly higher in SETD2 mutant group than in wild-type group (P < 0.01)., Conclusions: This study illustrated in-depth genomic correlates of PD-L1 expression in Chinese lung adenocarcinoma patients and relevant immune signatures from public database, which might interpret more potential molecular mechanisms for immunotherapy in NSCLC., (© 2022. The Author(s).)

The evolution of microbial magnetoreception (or magnetotaxis) is of great interest in the fields of microbiology, evolutionary biology, biophysics, geomicrobiology, and geochemistry. Current genomic data from magnetotactic bacteria (MTB), the only prokaryotes known to be capable of sensing the Earth's geomagnetic field, suggests an ancient origin of magnetotaxis in the domain Bacteria Vertical inheritance, followed by multiple independent magnetosome gene cluster loss, is considered to be one of the major forces that drove the evolution of magnetotaxis at or above the class or phylum level, although the evolutionary trajectories at lower taxonomic ranks (e.g., within the class level) remain largely unstudied. Here we report the isolation, cultivation, and sequencing of a novel magnetotactic spirillum belonging to the genus Terasakiella ( Terasakiella sp. strain SH-1) within the class Alphaproteobacteria The complete genome sequence of Terasakiella sp. strain SH-1 revealed an unexpected duplication event of magnetosome genes within the mamAB operon, a group of genes essential for magnetosome biomineralization and magnetotaxis. Intriguingly, further comparative genomic analysis suggests that the duplication of mamAB genes is a common feature in the genomes of alphaproteobacterial MTB. Taken together, with the additional finding that gene duplication appears to have also occurred in some magnetotactic members of the Deltaproteobacteria , our results indicate that gene duplication plays an important role in the evolution of magnetotaxis in the Alphaproteobacteria and perhaps the domain Bacteria IMPORTANCE A diversity of organisms can sense the geomagnetic field for the purpose of navigation. Magnetotactic bacteria are the most primitive magnetism-sensing organisms known thus far and represent an excellent model system for the study of the origin, evolution, and mechanism of microbial magnetoreception (or magnetotaxis). The present study is the first report focused on magnetosome gene cluster duplication in the Alphaproteobacteria , which suggests the important role of gene duplication in the evolution of magnetotaxis in the Alphaproteobacteria and perhaps the domain Bacteria A novel scenario for the evolution of magnetotaxis in the Alphaproteobacteria is proposed and may provide new insights into evolution of magnetoreception of higher species., (Copyright © 2019 Du et al.)

There are two genetically distinct morphological types of multicellular magnetotactic prokaryotes (MMPs) in the intertidal zone of Lake Yuehu (China): ellipsoidal MMPs (eMMPs) and spherical MMPs (sMMPs). We studied the vertical distribution of both types of MMPs in the sediment at Lake Yuehu during 1 year. Both types of MMPs were observed at sediment depths ranging from 1 to 34 cm, depending on the seasons. The eMMPs distributed at depths of 2-34 cm during spring, 1-11 cm during summer, 2-21 cm during autumn and 9-32 cm during winter. The eMMP species Candidatus Magnetananas rongchenensis, with magnetite magnetosomes, dominated at all distribution depths. These results suggested that Ca. M. rongchenensis migrated vertically during four seasons. The vertical profiles of oxidation-reduction potential (ORP) in Lake Yuehu changed seasonally, and these changes coincided with the seasonal distribution of MMPs, suggesting that the ORP affected the vertical distribution of MMPs. In addition, high concentrations of ammonium and silicate were associated with low abundances of MMPs., (© 2018 Society for Applied Microbiology and John Wiley & Sons Ltd.)

Pathogenesis and genetic factors influencing predisposition to antituberculosis drug-induced hepatotoxicity (ATDH) are not clear. Polymorphism at the genetic locus of a drug and xenobiotic compound metabolizing enzyme, N-acetyltransferase type 2 (NAT2), is reported to be associated with the excess generation of toxic reactive metabolites. To date, many case-control studies have been carried out to investigate the relationship between the NAT2 polymorphisms and ATDH, but the results have been inconsistent. To investigate this inconsistency, a meta-analysis was performed. Databases including PubMed, Web of Science, EMBASE and CNKI were searched to find relevant studies. A total of 26 case-control studies, involving 1,198 cases and 2,921 controls were included. Overall, we found significant association between slow acetylator genotype of NAT2 and ATDH (OR = 3.10, 95% CI: 2.47-3.88, P < 10(-5)). Significant results were also found in East Asians, South Asians, Brazilians and Middle Eastern when stratified by ethnicity. However, no significant associations were found for Caucasians. This meta-analysis demonstrated that the slow acetylator genotype of NAT2 is a risk factor associated with increased ATDH susceptibility, but these associations vary in different ethnic populations.