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Dendritic cells (DCs) play a key role in the initiation stage of an antigen-specific immune response. A variety of tumor-derived factors (TDFs) can suppress DC maturation and function, resulting in defects in the tumor-specific immune response. To identify unknown TDFs that may suppress DCs maturation and function, we established a high-throughput screening technology based on a human liver tumor T7 phage cDNA library and screened all of the proteins derived from hepatoma cells that potentially interact with immature DCs. Growth/differentiation factor-15 (GDF-15) was detected and chosen for further study. By incubation of DCs cultures with GDF-15, we demonstrate that GDF-15 can inhibit surface protrusion formation during DC maturation; suppress the membrane expression of CD83, CD86 and HLA-DR on DCs; enhance phagocytosis by DCs; reduce IL-12 and elevate TGF-β1 secretion by DCs; inhibit T cell stimulation and cytotoxic T lymphocyte (CTL) activation by DCs. By building tumor-bearing mouse models, we demonstrate that GDF-15 can inhibit the ability of DCs to stimulate a tumor-specific immune response in vivo. These results indicate that GDF-15 may be one of the critical molecules that inhibit DC maturation and function and are involved in tumor immune escape. Thus, GDF-15 may be a novel target in tumor immunotherapy.

BackgroundInjuries affect all age groups but have a particular impact on young people. To evaluate the incidence of non-fatal, unintentional, injuries among undergraduates in Wenzhou, China, assess the burden caused by these injuries, and explore the associated risk factors for unintentional injuries among these undergraduates, we conducted a college-based cross-sectional study.MethodsParticipants were selected by a multi-stage random sampling method, and 2,287 students were asked whether they had had an injury in the last 12 months; the location, cause, and consequences of the event. The questionnaire included demographic and socioeconomic characteristics, lifestyle habits, and the scale of type A behaviour pattern (TABP). Multivariate logistic regression models were used; crude odds ratios (ORs), adjusted ORs and their 95% confidence intervals (CIs) were estimated, with students having no injuries as the reference group.ResultsThe incidence of injuries among undergraduates in Wenzhou was 18.71 injuries per 100 person-years (95%CI: 17.12~20.31 injuries per 100 person-years). Falls were the leading cause of injury, followed by traffic injuries, and animal/insect bites. Male students were more likely to be injured than female students. Risk factors associated with unintentional injuries among undergraduates were: students majoring in non-medicine (adjusted OR: 1.53; 95% CI: 1.19-1.96); type A behaviour pattern (adjusted OR: 2.99; 95% CI: 1.45-6.14); liking sports (adjusted OR: 1.86; 95% CI: 1.41-2.45).ConclusionsInjuries have become a public health problem among undergraduates. Falls were the major cause of non-fatal injury. Therefore, individuals, families, schools and governments should promptly adopt preventive measures aimed at preventing and controlling morbidity due to non-fatal injury, especially among students identified to be at high-risk; such as male students with type A behaviour pattern who like sports.

The frequency of terahertz radar ranges from 0.1 THz to 10 THz, which is higher than that of microwaves. Multi-modal signals, including high-resolution range profile (HRRP) and Doppler signatures, can be acquired by the terahertz radar system. These two kinds of information are commonly used in automatic target recognition; however, dynamic gesture recognition is rarely discussed in the terahertz regime. In this paper, a dynamic gesture recognition system using a terahertz radar is proposed, based on multi-modal signals. The HRRP sequences and Doppler signatures were first achieved from the radar echoes. Considering the electromagnetic scattering characteristics, a feature extraction model is designed using location parameter estimation of scattering centers. Dynamic Time Warping (DTW) extended to multi-modal signals is used to accomplish the classifications. Ten types of gesture signals, collected from a terahertz radar, are applied to validate the analysis and the recognition system. The results of the experiment indicate that the recognition rate reaches more than 91%. This research verifies the potential applications of dynamic gesture recognition using a terahertz radar.

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