企业动态
  • 捷报!新合生物以8项全满分成绩通过2020年NCCL室间质评

    未来,新合生物将充分发挥专业技术优势,为肿瘤临床检测及治疗贡献力量。

  • 抗癌新征程|新合生物新抗原疫苗临床试验正式启动

    新合生物期待通过本项临床试验,进一步观察并确认基于肿瘤新抗原疫苗在治疗中晚期胃癌、食管癌的治疗效果,帮助受试者延长病情稳定性及生存期、提升生活质量。

  • The lysosomal Rag-Ragulator complex licenses RIPK1– and caspase-8–mediated pyroptosis by Yersinia

    Host cells initiate cell death programs to limit pathogen infection. Inhibition of transforming growth factor–β–activated kinase 1 (TAK1) by pathogenic Yersinia in macrophages triggers receptor-interacting serine-threonine protein kinase 1 (RIPK1)–dependent caspase-8 cleavage of gasdermin D (GSDMD) and inflammatory cell death (pyroptosis). A genome-wide CRISPR screen to uncover mediators of caspase-8–dependent pyroptosis identified an unexpected role of the lysosomal folliculin (FLCN)–folliculin-interacting protein 2 (FNIP2)–Rag-Ragulator supercomplex, which regulates metabolic signaling and the mechanistic target of rapamycin complex 1 (mTORC1). In response to Yersinia infection, Fas-associated death domain (FADD), RIPK1, and caspase-8 were recruited to Rag-Ragulator, causing RIPK1 phosphorylation and caspase-8 activation. Pyroptosis activation depended on Rag guanosine triphosphatase activity and lysosomal tethering of Rag-Ragulator but not mTORC1. Thus, the lysosomal metaboli

  • Clustering single-cell RNA-seq data with a model-based deep learning approach

    Single-cell RNA sequencing (scRNA-seq) promises to provide higher resolution of cellular differences than bulk RNA sequencing. Clustering transcriptomes profiled by scRNA-seq has been routinely conducted to reveal cell heterogeneity and diversity. However, clustering analysis of scRNA-seq data remains a statistical and computational challenge, due to the pervasive dropout events obscuring the data matrix with prevailing ‘false’ zero count observations.

  • MATHLA: a robust framework for HLA‑peptide binding prediction integrating bidirectional LSTM and multiple head attention mechanism

    Background: Accurate prediction of binding between class I human leukocyte antigen (HLA) and neoepitope is critical for target identification within personalized T-cell based immunotherapy.
    Results: We present a pan-allele HLA-peptide binding prediction framework-MATHLA which integrates bi-directional long short-term memory network and multiple head attention mechanism.
    Conclusion: Our method demonstrates the necessity of further development of deep learning algorithm in improving and interpreting HLA-peptide binding prediction in parallel to increasing the amount of high-quality HLA ligandome data.

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