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RISS 인기검색어
- 검색키워드
- 저자 : Jingbo Xia (검색결과 5 건)
- 무료
- 기관 내 무료
- 유료
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CDH17 nanobodies facilitate rapid imaging of gastric cancer and efficient delivery of immunotoxin
Jingbo Ma,Xiaolong Xu,Chunjin Fu,Peng Xia,Ming Tian,Liuhai Zheng,Kun Chen,Xiaolian Liu,Yilei Li,Le Yu,Qinchang Zhu,Yangyang Yu,Rongrong Fan,Haibo Jiang,Zhifen Li,Chuanbin Yang,Chengchao Xu,Ying Long,J 한국생체재료학회 2022 생체재료학회지 Vol.26 No.4
Background: It is highly desirable to develop new therapeutic strategies for gastric cancer given the low survival rate despite improvement in the past decades. Cadherin 17 (CDH17) is a membrane protein highly expressed in cancers of digestive system. Nanobody represents a novel antibody format for cancer targeted imaging and drug delivery. Nanobody targeting CHD17 as an imaging probe and a delivery vehicle of toxin remains to be explored for its theragnostic potential in gastric cancer. Methods: Naïve nanobody phage library was screened against CDH17 Domain 1-3 and identified nanobodies were extensively characterized with various assays. Nanobodies labeled with imaging probe were tested in vitro and in vivo for gastric cancer detection. A CDH17 Nanobody fused with toxin PE38 was evaluated for gastric cancer inhibition in vitro and in vivo. Results: Two nanobodies (A1 and E8) against human CDH17 with high affinity and high specificity were successfully obtained. These nanobodies could specifically bind to CDH17 protein and CDH17-positive gastric cancer cells. E8 nanobody as a lead was extensively determined for tumor imaging and drug delivery. It could efficiently co-localize with CDH17-positive gastric cancer cells in zebrafish embryos and rapidly visualize the tumor mass in mice within 3 h when conjugated with imaging dyes. E8 nanobody fused with toxin PE38 showed excellent anti-tumor effect and remarkably improved the mice survival in cell-derived (CDX) and patient-derived xenograft (PDX) models. The immunotoxin also enhanced the anti-tumor effect of clinical drug 5-Fluorouracil. Conclusions: The study presents a novel imaging and drug delivery strategy by targeting CDH17. CDH17 nanobodybased immunotoxin is potentially a promising therapeutic modality for clinical translation against gastric cancer.
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A review of drug knowledge discovery using BioNLP and tensor or matrix decomposition
Gachloo, Mina,Wang, Yuxing,Xia, Jingbo Korea Genome Organization 2019 Genomics & informatics Vol.17 No.2
Prediction of the relations among drug and other molecular or social entities is the main knowledge discovery pattern for the purpose of drug-related knowledge discovery. Computational approaches have combined the information from different sources and levels for drug-related knowledge discovery, which provides a sophisticated comprehension of the relationship among drugs, targets, diseases, and targeted genes, at the molecular level, or relationships among drugs, usage, side effect, safety, and user preference, at a social level. In this research, previous work from the BioNLP community and matrix or matrix decomposition was reviewed, compared, and concluded, and eventually, the BioNLP open-shared task was introduced as a promising case study representing this area.
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A review of drug knowledge discovery using BioNLP and tensor or matrix decomposition
Mina Gachloo,Yuxing Wang,Jingbo Xia 한국유전체학회 2019 Genomics & informatics Vol.17 No.2
Prediction of the relations among drug and other molecular or social entities is the main knowledge discovery pattern for the purpose of drug-related knowledge discovery. Computational approaches have combined the information from different sources and levels for drug-related knowledge discovery, which provides a sophisticated comprehension of the relationship among drugs, targets, diseases, and targeted genes, at the molecular level, or relationships among drugs, usage, side effect, safety, and user preference, at a social level. In this research, previous work from the BioNLP community and matrix or matrix decomposition was reviewed, compared, and concluded, and eventually, the BioNLP open-shared task was introduced as a promising case study representing this area.
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OryzaGP 2021 update: a rice gene and protein dataset for named-entity recognition
Larmande, Pierre,Liu, Yusha,Yao, Xinzhi,Xia, Jingbo Korea Genome Organization 2021 Genomics & informatics Vol.19 No.3
Due to the rapid evolution of high-throughput technologies, a tremendous amount of data is being produced in the biological domain, which poses a challenging task for information extraction and natural language understanding. Biological named entity recognition (NER) and named entity normalisation (NEN) are two common tasks aiming at identifying and linking biologically important entities such as genes or gene products mentioned in the literature to biological databases. In this paper, we present an updated version of OryzaGP, a gene and protein dataset for rice species created to help natural language processing (NLP) tools in processing NER and NEN tasks. To create the dataset, we selected more than 15,000 abstracts associated with articles previously curated for rice genes. We developed four dictionaries of gene and protein names associated with database identifiers. We used these dictionaries to annotate the dataset. We also annotated the dataset using pretrained NLP models. Finally, we analysed the annotation results and discussed how to improve OryzaGP.
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LitCovid-AGAC: cellular and molecular level annotation data set based on COVID-19
Ouyang, Sizhuo,Wang, Yuxing,Zhou, Kaiyin,Xia, Jingbo Korea Genome Organization 2021 Genomics & informatics Vol.19 No.3
Currently, coronavirus disease 2019 (COVID-19) literature has been increasing dramatically, and the increased text amount make it possible to perform large scale text mining and knowledge discovery. Therefore, curation of these texts becomes a crucial issue for Bio-medical Natural Language Processing (BioNLP) community, so as to retrieve the important information about the mechanism of COVID-19. PubAnnotation is an aligned annotation system which provides an efficient platform for biological curators to upload their annotations or merge other external annotations. Inspired by the integration among multiple useful COVID-19 annotations, we merged three annotations resources to LitCovid data set, and constructed a cross-annotated corpus, LitCovid-AGAC. This corpus consists of 12 labels including Mutation, Species, Gene, Disease from PubTator, GO, CHEBI from OGER, Var, MPA, CPA, NegReg, PosReg, Reg from AGAC, upon 50,018 COVID-19 abstracts in LitCovid. Contain sufficient abundant information being possible to unveil the hidden knowledge in the pathological mechanism of COVID-19.
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