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Jing-jing Li

Publications and source records attributed to Jing-jing Li.

3 recordsLinked to original sources

Effect of leukemia inhibitory factor on embryonic stem cell differentiation: implications for supporting neuronal differentiation.

AIM: Leukemia inhibitory factor (LIF), a pleiotropic cytokine, has been used extensively in the maintenance of mouse embryonic stem cell pluripotency. In this current work, we examined the effect of the LIF signaling pathway in embryonic stem (ES) cell differentiation to a neural fate. METHODS: In the presence of LIF (1000 U/mL), the production of neuronal cells derived from embryoid bodies (EB) was tested under various culture conditions. Inhibition of the LIF pathway was examined with specific inhibitors. The effects of cell apoptosis and proliferation on neural differentiation were examined. ES cell differentiation into three-germ layers was compared. RESULTS: Under various culture conditions, neuronal differentiation was increased in the presence of LIF. Blocking the LIF-activated STAT3 signaling pathway with specific inhibitors abolished the neuronal differentiation of ES cells, whereas inhibition of the LIF-activated MEK signaling pathway impaired the differentiation of ES cells toward a glial fate. LIF suppressed cell apoptosis and promoted cell proliferation during ES cell differentiation. LIF inhibited the differentiation of ES cells to both mesoderm and extraembryonic endoderm fates, but enhanced the determination of neural progenitors. CONCLUSION: These results suggest that LIF plays a positive role during the differentiation of ES cells into neuronal cells.

Animals↗

Predicting protein interaction sites from residue spatial sequence profile and evolution rate.

This paper proposes a novel method that can predict protein interaction sites in heterocomplexes using residue spatial sequence profile and evolution rate approaches. The former represents the information of multiple sequence alignments while the latter corresponds to a residue's evolutionary conservation score based on a phylogenetic tree. Three predictors using a support vector machines algorithm are constructed to predict whether a surface residue is a part of a protein-protein interface. The efficiency and the effectiveness of our proposed approach is verified by its better prediction performance compared with other models. The study is based on a non-redundant data set of heterodimers consisting of 69 protein chains.

Algorithms↗

[The reliability of ischemic stroke subtype classification using the TOAST criteria].

OBJECTIVE: The aim of this study was to assess the reliability of the classification of acute cerebral infarction based on the TOAST system. METHODS: 300 patients with ischemic stroke were recruited into this study. Ischemic stroke was classified into five subtypes according on clinical manifestations, MRI, MRA and DSA. The investigators classified all the patients into five etiologic subtypes. The data was analyzed by the statistics software of SPSS 11.5. RESULTS: The value of Kappa about the classification's reliability was 0.8, P = 0.028. CONCLUSIONS: The reliability for the classification of acute cerebral was excellent. This value could be improved with the development of the diagnosis tools and the physicians' recognition.

Antifibrinolytic Agents↗