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  1. 5601

    Development of genomic phenotype and immunophenotype of acute respiratory distress syndrome using autophagy and metabolism-related genes by Feiping Xia, Hui Chen, Hui Chen, Yigao Liu, Lili Huang, Shanshan Meng, Jingyuan Xu, Jianfeng Xie, Guozheng Wang, Fengmei Guo

    Published 2023-10-01
    “…The mean risk score was determined to be 2.231332, and based on this score, patients were classified into high-risk and low-risk groups. 371 differential genes in high- and low-risk groups were analyzed. ITGAM, TYROBP, ITGB2, SPI1, PLEK, FGR, MPO, S100A12, HCK, and MYC were identified as hub genes. …”
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    Article
  2. 5602

    Spatial Prediction of Landslide Susceptibility Using Logistic Regression (LR), Functional Trees (FTs), and Random Subspace Functional Trees (RSFTs) for Pengyang County, China by Hui Shang, Lixiang Su, Wei Chen, Paraskevas Tsangaratos, Ioanna Ilia, Sihang Liu, Shaobo Cui, Zhao Duan

    Published 2023-10-01
    “…Next, we extracted 15 landslide conditioning factors, including the slope angle, elevation, profile curvature, plan curvature, slope aspect, TWI (topographic wetness index), TPI (topographic position index), distance to roads and rivers, NDVI (normalized difference vegetation index), rainfall, land use, lithology, SPI (stream power index), and STI (sediment transport index), from the spatial database. …”
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  3. 5603

    Identification of RNA Transcript Makers Associated With Prognosis of Kidney Renal Clear Cell Carcinoma by a Competing Endogenous RNA Network Analysis by Qiwei Yang, Qiwei Yang, Weiwei Chu, Wei Yang, Yanqiong Cheng, Chuanmin Chu, Xiuwu Pan, Jianqing Ye, Jianwei Cao, Sishun Gan, Xingang Cui, Xingang Cui

    Published 2020-10-01
    “…Seven mRNA targets of miRNA-21 (FASLG, FGF1, TGFBI, ALX1, SLC30A10, ADCY2, and ABAT) and 12 mRNAs targets of miRNA-155 (STXBP5L, SCG2, SPI1, C12orf40, TYRP1, CTHRC1, TDO2, PTPRQ, TRPM8, ERMP1, CD36, and ST9SIA4) also acted as prognostic biomarkers for KIRC patients.ConclusionWe screened numerous novel prognosis-related RNA markers for KIRC patients by a ceRNA network analysis, providing deeper understandings of prognostic values of RNA transcripts for KIRC.…”
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  4. 5604

    Identification of QTLs Conferring Agronomic and Quality Traits in Hexaploid Wheat by Jun MA, Cai-ying ZHANG, Gui-jun YAN, Chun-ji LIU

    Published 2012-09-01
    “…Data of a multiple environmental test was employed to genetically dissect quantitative trait loci (QTL) for agronomic traits such as plant height (PH), spike length (SL), spikelet per spike (SPI), grain number per spike (GNS) and thousand grains weight (TGW) and for quality traits including grain protein content (GPC), gluten content (GC), grain hardness (GH), falling number (FN) and sedimentation value (SV). …”
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  5. 5605

    Degradation of epigallocatechin and epicatechin gallates by a novel tannase TanHcw from Herbaspirillum camelliae by Jia Lei, Yong Zhang, Xuechen Ni, Xuejing Yu, Xingguo Wang

    Published 2021-10-01
    “…TanHcw was a secretary enzyme with a Sec/SPI signal peptide of 48 amino acids at the N-terminus, and it catalyzed the degradation of tannin, methyl gallate (MG), epigallocatechin-3-gallate (EGCG) and epicatechin-3-gallate (ECG). …”
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  6. 5606

    Whole-genome sequencing of extensively drug-resistant Salmonella enterica serovar Typhi clinical isolates from the Peshawar region of Pakistan by Mah Noor Mumtaz, Muhammad Irfan, Sami Siraj, Aslam Khan, Hizbullah Khan, Muhammad Imran, Ishtiaq Ahmad Khan, Asifullah Khan

    Published 2024-02-01
    “…Prophage and Salmonella Pathogenicity Island (SPI) analysis identified intact prophages and eight SPIs involved in Salmonella's invasion and replication within host cells. …”
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    Article
  7. 5607

    Bioinformatics analysis of potential pathogenesis and risk genes of immunoinflammation-promoted renal injury in severe COVID-19 by Zhimin Chen, Zhimin Chen, Caiming Chen, Caiming Chen, Fengbin Chen, Ruilong Lan, Guo Lin, Yanfang Xu, Yanfang Xu, Yanfang Xu

    Published 2022-08-01
    “…A further protein-protein interaction (PPI) network analysis screened 15 PPI-hub genes: ALOX5, CD38, GSF3R, LGR, RPR1, HCK, ITGAX, LYN, MAPK3, NCF4, SELP, SPI1, WAS, TLR2 and TLR4. Single-cell sequencing analysis indicated that PPI-hub genes were mainly distributed in neutrophils, macrophages, and dendritic cells. …”
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  8. 5608

    Co-expression modules construction by WGCNA and identify potential hub genes and regulation pathways of postpartum depression by Zhifang Deng, Wei Cai, Jue Liu, Aiping Deng, Yuan Yang, Jie Tu, Cheng Yuan, Han Xiao, Wenqi Gao

    Published 2021-11-01
    “…Eight genes (HNRNPA2B1, IL10, RAD51, UBA52, NHP2, RPL13A, FBL, SPI1) were identified as “real” hub genes from cross-validation data of the three modules and DEGs, and possessed diagnostic value in PPD. …”
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    Article
  9. 5609

    Basic Symptoms Are Associated With Age in Patients With a Clinical High-Risk State for Psychosis: Results From the PRONIA Study by Helene Walger, Linda A. Antonucci, Alessandro Pigoni, Alessandro Pigoni, Rachel Upthegrove, Raimo K. R. Salokangas, Rebekka Lencer, Katharine Chisholm, Katharine Chisholm, Anita Riecher-Rössler, Theresa Haidl, Eva Meisenzahl, Marlene Rosen, Stephan Ruhrmann, Joseph Kambeitz, Lana Kambeitz-Ilankovic, Peter Falkai, Anne Ruef, Jarmo Hietala, Christos Pantelis, Stephen J. Wood, Stephen J. Wood, Stephen J. Wood, Paolo Brambilla, Alessandro Bertolino, Stefan Borgwardt, Nikolaos Koutsouleris, Frauke Schultze-Lutter, Frauke Schultze-Lutter

    Published 2020-11-01
    “…BS and the BS criteria, “Cognitive Disturbances” (COGDIS) and “Cognitive-perceptive BS” (COPER), were assessed with the “Schizophrenia Proneness Instrument, Adult version” (SPI-A). Using logistic regressions, prevalence rates of perceptive and cognitive BS, and of COGDIS and COPER, as well as the impact of social and role functioning on the association between age and BS were studied in three age groups (15–18 years, 19–23 years, 24–40 years). …”
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  10. 5610
  11. 5611
  12. 5612
  13. 5613

    پهنه‌بندی حساسیت خطر سیل در حوضه آبخیز رودخانه کشکان با استفاده از دو مدل WOE و EBF by فهیمه آزادی, سید حسن صدوق, منیژه قهرودی, هیمن شهابی

    Published 2020-05-01
    “…هر دو مدل 14 فاکتور مؤثر در ایجاد سیل را مورد توجه قرار داده‌اند که عبارتند از: شیب، جهت شیب، زمین‌شناسی، جنس خاک، کاربری اراضی، شاخص رطوبت توپوگرافی (TWI)، توان آبراهه (SPI)، بارش، فاصله از رودخانه، فاصله از جاده، پوشش گیاهی (NDVI)، انحنای شیب (Curvatior)، تراکم آبراهه و مدل ارتفاعی رقومی منطقه. …”
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  14. 5614
  15. 5615

    Identification of Potential Key Genes and Regulatory Markers in Essential Thrombocythemia Through Integrated Bioinformatics Analysis and Clinical Validation by Wang J, Wu Y, Uddin MN, Chen R, Hao JP

    Published 2021-07-01
    “…Besides, we identified 4 TFs (SPI1, IRF4, SRF, and AR) as master transcriptional regulators that were associated with regulating the DEGs in ET. …”
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  16. 5616

    Prognostic analysis and validation of diagnostic marker genes in patients with osteoporosis by Xing Wang, Zhiwei Pei, Ting Hao, Jirigala Ariben, Siqin Li, Wanxiong He, Xiangyu Kong, Jiale Chang, Zhenqun Zhao, Baoxin Zhang

    Published 2022-10-01
    “…The interaction network was constructed between the hub genes and miRNAs, transcription factors, RNA binding proteins, and drugs.ResultsA total of 40 DEGs, eight OP-related differential genes, six OP diagnostic marker genes, four OP key diagnostic marker genes, and ten hub genes (TNF, RARRES2, FLNA, STXBP2, EGR2, MAP4K2, NFKBIA, JUNB, SPI1, CTSD) were identified. RT-qPCR results revealed a total of eight genes had significant differential expression between osteoporosis patients and control samples. …”
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  17. 5617
  18. 5618
  19. 5619
  20. 5620