Licochalcone A inhibits proliferation and promotes apoptosis of colon cancer cell by targeting programmed cell death-ligand 1 via the NF-κB and Ras/Raf/MEK pathways.

ETHNOPHARMACOLOGICAL RELEVANCE: Glycyrrhiza glabra L., a traditional medicinal, has a history of thousands of years. It is widely used in clinic and has been listed in Chinese Pharmacopoeia. Licochalcone A is a phenolic chalcone compound and a characteristic chalcone of Glycyrrhiza glabra L. It has many pharmacological activities, such as anti-cancer, anti-inflammatory, anti-viral and anti-angiogenic activities. AIM OF THE STUDY: In this study, we explored the anti-tumor activity and potential mechanism of licochalcone A in vitro and in vivo. MATERIALS AND METHODS: In vitro, the mechanism of licochalcone A at inhibiting PD-L1 expression was investigated by molecular docking, western blotting, RT-PCR, flow cytometry, immunofluorescence and immunoprecipitation assays. The co-culture model of T cells and tumor cells was used to detect the activity of cytotoxic T lymphocytes. Colony formation, EdU labelling and apoptosis assays were used to detect changes in cellular proliferation and apoptosis. In vivo, anti-tumor activity of licochalcone A was assessed in a xenograft model of HCT116 cells. RESULTS: In the present study, we found that licochalcone A suppressed the expression of programmed cell death ligand-1 (PD-L1), which plays a key role in regulating the immune response. In addition, licochalcone A inhibited the expressions of p65 and Ras. Immunoprecipitation experiment showed that licochalcone A suppressed the expression of PD-L1 by blocking the interaction between p65 and Ras. In the co-culture model of T cells and tumor cells, licochalcone A pretreatment enhanced the activity of cytotoxic T lymphocytes and restored the ability to kill tumor cells. In addition, we showed that licochalcone A inhibited cell proliferation and promoted cell apoptosis by targeting PD-L1. In vivo xenograft assay confirmed that licochalcone A inhibited the growth of tumor xenografts. CONCLUSION: In general, these results reveal the previously unknown properties of licochalcone A and provide new insights into the anticancer mechanism of this compound.

A Knowledge Intensive Approach to Mapping Clinical Narrative to LOINC.

Many natural language processing systems are being applied to clinical text, yet clinically useful results are obtained only by honing a system to a particular context. We suggest that concentration on the information needed for this processing is crucial and present a knowledge intensive methodology for mapping clinical text to LOINC. The system takes published case reports as input and maps vital signs and body measurements and reports of diagnostic procedures to fully specified LOINC codes. Three kinds of knowledge are exploited: textual, ontological, and pragmatic (including information about physiology and the clinical process). Evaluation on 4809 sentences yielded precision of 89% and recall of 93% (F-score 0.91). Our method could form the basis for a system to provide semi-automated help to human coders.

Adapting Semantic Natural Language Processing Technology to Address Information Overload in Influenza Epidemic Management.

Explosion of disaster health information results in information overload among response professionals. The objective of this project was to determine the feasibility of applying semantic natural language processing (NLP) technology to addressing this overload. The project characterizes concepts and relationships commonly used in disaster health-related documents on influenza pandemics, as the basis for adapting an existing semantic summarizer to the domain. Methods include human review and semantic NLP analysis of a set of relevant documents. This is followed by a pilot-test in which two information specialists use the adapted application for a realistic information seeking task. According to the results, the ontology of influenza epidemics management can be described via a manageable number of semantic relationships that involve concepts from a limited number of semantic types. Test users demonstrate several ways to engage with the application to obtain useful information. This suggests that existing semantic NLP algorithms can be adapted to support information summarization and visualization in influenza epidemics and other disaster health areas. However, additional research is needed in the areas of terminology development (as many relevant relationships and terms are not part of existing standardized vocabularies), NLP, and user interface design.