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Gastric and colorectal cancers (GC and CRC) have poor prognosis and are resistant to chemo- and/or radiotherapy. In the present study, the prophylactic effects of dendritic cell (DC) vaccination are evaluated on disease progression and clinical benefits in a group of 54 GC and CRC patients treated with DC immunotherapy combined with cytokine-induced killer (CIK) cells after surgery with or without chemo-radiotherapy. DCs were prepared from the mononuclear cells isolated from patients using IL-2/GM-CSF and loaded with tumor antigens; CIK cells were prepared by incubating peripheral blood lymphocytes with IL-2, IFN-γ, and CD3 antibodies. The DC/CIK therapy started 3 days after low-dose chemotherapy and was repeated 3-5 times in 2 weeks as one cycle with a total of 188.3 ± 79.8 × 10(6) DCs and 58.8 ± 22.3 × 10(8) CIK cells. Cytokine levels in patients' sera before and after treatments were measured and the follow-up was conducted for 98 months to determine disease-free survival (DFS) and overall survival (OS). The results demonstrate that all cytokines tested were elevated with significantly higher levels of IFN-γ and IL-12 in both GC and CRC cohorts of DC/CIK treated patients. By Cox regression analysis, DC/CIK therapy reduced the risk of post-operative disease progression (p<0.01) with an increased OS (<0.01). These results demonstrate that in addition to chemo- and/or radiotherapy, DC/CIK immunotherapy is a potential effective approach in the control of tumor growth for post-operative GC and CRC patients.

Recurrence of breast cancer after radiotherapy may be partly explained by the presence of radioresistant cells. Thus, it would be desirable to develop an effective therapy against radioresistant cells. In this study, we demonstrated the intense antitumor activity of cytokine-induced killer cells against MCF-7 and radioresistant MCF-7 cells, as revealed by cytokine-induced killer–mediated cytotoxicity, tumor cell proliferation, and tumor invasion. Radioresistant MCF-7 cells were more susceptible to cytokine-induced killer cell killing. The stronger cytotoxicity of cytokine-induced killer cells against radioresistant MCF-7 cells was dependent on the expression of major histocompatibility complex class I polypeptide–related sequence A/B on radioresistant MCF-7 cells after exposure of cytokine-induced killer cells to sensitized targets. In addition, we demonstrated that cytokine-induced killer cell treatment sensitized breast cancer cells to chemotherapy via the downregulation of TK1, TYMS, and MDR1. These results indicate that cytokine-induced killer cell treatment in combination with radiotherapy and/or chemotherapy may induce synergistic antitumor activities and represent a novel strategy for breast cancer.

Response gene to complement 32 (RGC-32) is a cell cycle regulator involved in the proliferation, differentiation and migration of cells and has also been implicated in angiogenesis. Here we show that RGC-32 expression in macrophages is induced by IL-4 and reduced by LPS, indicating a link between RGC-32 expression and M2 polarization. We demonstrated that the increased expression of RGC-32 is characteristic of alternatively activated macrophages, in which this protein suppresses the production of pro-inflammatory cytokine IL-6 and promotes the production of the anti-inflammatory mediator TGF-β. Consistent with in vitro data, tumor-associated macrophages (TAMs) express high levels of RGC-32, and this expression is induced by tumor-derived ascitic fluid in an M-CSF- and/or IL-4-dependent manner. Collectively, these results establish RGC-32 as a marker for M2 macrophage polarization and indicate that this protein is a potential target for cancer immunotherapy, targeting tumor-associated macrophages.

Response gene to complement 32 （RGC-32） is a cell cycle regulator involved in the proliferation, differentiation and migration of cells and has also been implicated in angiogenesis. Here we show that RGC-32 expression in macrophages is induced by IL-4 and reduced by LPS, indicating a link between RGC-32 expression and M2 polarization. We demonstrated that the increased expression of RGC-32 is characteristic of alternatively activated macrophages, in which this protein suppresses the production of pro-inflammatory cytokine IL-6 and promotes the production of the anti-inflammatory mediator TGF-β. Consistent with in vitro data, tumor-associated macrophages （TAMs） express high levels of RGC-32, and this expression is induced by tumor-derived ascitic fluid in an M-CSF- and/or IL-4-dependent manner. Collectively, these results establish RGC-32 as a marker for M2 macrophage polarization and indicate that this protein is a potential target for cancer immunotherapy, targeting tumor-associated macrophages.

Gastric and colorectal cancers (GC and CRC) have poor prognosis and are resistant to chemo- and/or radiotherapy. In the present study, the prophylactic effects of dendritic cell (DC) vaccination are evaluated on disease progression and clinical benefits in a group of 54 GC and CRC patients treated with DC immunotherapy combined with cytokine-induced killer (CIK) cells after surgery with or without chemo-radiotherapy. DCs were prepared from the mononuclear cells isolated from patients using IL-2/GM-CSF and loaded with tumor antigens; CIK cells were prepared by incubating peripheral blood lymphocytes with IL-2, IFN- gamma , and CD3 antibodies. The DC/CIK therapy started 3 days after low-dose chemotherapy and was repeated 3-5 times in 2 weeks as one cycle with a total of 188.3 plus or minus 79.8106 DCs and 58.8 plus or minus 22.3108 CIK cells. Cytokine levels in patients' sera before and after treatments were measured and the follow-up was conducted for 98 months to determine disease-free survival (DFS) and overall survival (OS). The results demonstrate that all cytokines tested were elevated with significantly higher levels of IFN- gamma and IL-12 in both GC and CRC cohorts of DC/CIK treated patients. By Cox regression analysis, DC/CIK therapy reduced the risk of post-operative disease progression (p<0.01) with an increased OS (<0.01). These results demonstrate that in addition to chemo- and/or radiotherapy, DC/CIK immunotherapy is a potential effective approach in the control of tumor growth for post-operative GC and CRC patients.

Adiponectin is an adipocyte-derived circulating protein with beneficial effects on injured livers. Adiponectin-deficient (adipo(−/−)) mice develop enhanced liver fibrosis, suggesting that adiponectin could be a therapeutic target for liver injury. In the present study, we investigated the protective role of ADP355, an adiponectin-based active short peptide, in thioacetamide (TAA)-induced acute injury and chronic liver fibrosis in mice. ADP355 remarkably reduced TAA-induced necroinflammation and liver fibrosis. ADP355 treatment increased liver glycogen, decreased serum alanine transaminase and alkaline phosphatase activity and promoted body weight gain, hyper-proliferation and hypo-apoptosis. In addition, ADP355 administration suppressed the TAA-induced activation of hepatic stellate cells and macrophages in the liver. These were associated with the inactivation of TGF-β1/SMAD2 signaling and the promotion of AMPK and STAT3 signaling. Sensitivity of adipo(−/−) mice to chronic liver injury was decreased with ADP355. In conclusion, ADP355 could mimic adiponectin’s action and may be suitable for the preclinical or clinical therapy of chronic liver injury.

Under the impacts of climate variability and human activities, there is violent fluctuation for streamflow in the large basins in China. Therefore, it is crucial to separate the impacts of climate variability and human activities on streamflow fluctuation for better water resources planning and management. In this study, the Three Rivers Headwater Region (TRHR) was chosen as the study area. Long-term hydrological data for the TRHR were collected in order to investigate the changes in annual runoff during the period of 1956–2012. The nonparametric Mann–Kendall test, moving t test, Pettitt test, Mann–Kendall–Sneyers test, and the cumulative anomaly curve were used to identify trends and change points in the hydro-meteorological variables. Change point in runoff was identified in the three basins, which respectively occurred around the years 1989 and 1993, dividing the long-term runoff series into a natural period and a human-induced period. Then, the hydrologic sensitivity analysis method was employed to evaluate the effects of climate variability and human activities on mean annual runoff for the human-induced period based on precipitation and potential evapotranspiration. In the human-induced period, climate variability was the main factor that increased (reduced) runoff in LRB and YARB (YRB) with contribution of more than 90 %, while the increasing (decreasing) percentage due to human activities only accounted for less than 10 %, showing that runoff in the TRHR is more sensitive to climate variability than human activities. The intra-annual distribution of runoff shifted gradually from a double peak pattern to a single peak pattern, which was mainly influenced by atmospheric circulation in the summer and autumn. The inter-annual variation in runoff was jointly controlled by the East Asian monsoon, the westerly, and Tibetan Plateau monsoons.

Objective Tongue segmentation as a basis for automated tongue recognition studies in Chinese medicine, which has defects such as network degradation and inability to obtain global features, which seriously affects the segmentation effect. This article proposes an improved model RTC_TongueNet based on DeepLabV3, which combines the improved residual structure and transformer and integrates the ECA (Efficient Channel Attention Module) attention mechanism of multiscale atrous convolution to improve the effect of tongue image segmentation. Methods In this paper, we improve the backbone network based on DeepLabV3 by incorporating the transformer structure and an improved residual structure. The residual module is divided into two structures and uses different residual structures under different conditions to speed up the frequency of shallow information mapping to deep network, which can more effectively extract the underlying features of tongue image; introduces ECA attention mechanism after concat operation in ASPP (Atrous Spatial Pyramid Pooling) structure to strengthen information interaction and fusion, effectively extract local and global features, and enable the model to focus more on difficult-to-separate areas such as tongue edge, to obtain better segmentation effect. Results The RTC_TongueNet network model was compared with FCN (Fully Convolutional Networks), UNet, LRASPP (Lite Reduced ASPP), and DeepLabV3 models on two datasets. On the two datasets, the MIOU (Mean Intersection over Union) and MPA (Mean Pixel Accuracy) values of the classic model DeepLabV3 were higher than those of FCN, UNet, and LRASPP models, and the performance was better. Compared with the DeepLabV3 model, the RTC_TongueNet network model increased MIOU value by 0.9% and MPA value by 0.3% on the first dataset; MIOU increased by 1.0% and MPA increased by 1.1% on the second dataset. RTC_TongueNet model performed best on both datasets. Conclusion In this study, based on DeepLabV3, we apply the improved residual structure and transformer as a backbone to fully extract image features locally and globally. The ECA attention module is combined to enhance channel attention, strengthen useful information, and weaken the interference of useless information. RTC_TongueNet model can effectively segment tongue images. This study has practical application value and reference value for tongue image segmentation.

The effects of degradation of alpine wetland meadow on soil respiration (Rs) and the sensitivity of Rs to temperature (Q 10 ) were measured in the Napa Lake region of Shangri-La on the southeastern edge of the Qinghai-Tibet Plateau. Rs was measured for 24 h during each of three different stages of the growing season on four different degraded levels. The results showed: (1) peak Rs occurred at around 5:00 p.m., regardless of the degree of degradation and growing season stage, with the maximum Rs reaching 10.05 μmol·m −2 ·s −1 in non-degraded meadows rather than other meadows; (2) the daily mean Rs value was 7.14–7.86 μmol·m −2 ·s −1 during the mid growing season in non-degraded meadows, and declined by 48.4–62.6% when degradation increased to the severely degraded level; (3) Q 10 ranged from 7.1–11.3 in non-degraded meadows during the mid growing season, 5.5–8.0 and 6.2–8.2 during the early and late growing seasons, respectively, and show a decline of about 50% from the non-degraded meadows to severely degraded meadows; (4) Rs was correlated significantly with soil temperature at a depth of 0–5 cm (p < 0.05) on the diurnal scale, but not at the seasonal scale; (5) significant correlations were found between Rs and soil organic carbon (SOC), between biomass and SOC, and between Q 10 and Rs (p < 0.05), which indicates that biomass and SOC potentially impact Q 10 . The results suggest that vegetation degradation impact both Rs and Q 10 significantly. Also, we speculated that Q 10 of alpine wetland meadow is probable greater at the boundary region than inner region of the Qinghai-Tibet Plateau, and shoule be a more sensitive indicator in the studying of climate change in this zone.

Objective Tongue segmentation as a basis for automated tongue recognition studies in Chinese medicine, which has defects such as network degradation and inability to obtain global features, which seriously affects the segmentation effect. This article proposes an improved model RTC_(T)ongueNet based on DeepLabV3, which combines the improved residual structure and transformer and integrates the ECA (Efficient Channel Attention Module) attention mechanism of multiscale atrous convolution to improve the effect of tongue image segmentation. Methods In this paper, we improve the backbone network based on DeepLabV3 by incorporating the transformer structure and an improved residual structure. The residual module is divided into two structures and uses different residual structures under different conditions to speed up the frequency of shallow information mapping to deep network, which can more effectively extract the underlying features of tongue image; introduces ECA attention mechanism after concat operation in ASPP (Atrous Spatial Pyramid Pooling) structure to strengthen information interaction and fusion, effectively extract local and global features, and enable the model to focus more on difficult-to-separate areas such as tongue edge, to obtain better segmentation effect. Results The RTC_(T)ongueNet network model was compared with FCN (Fully Convolutional Networks), UNet, LRASPP (Lite Reduced ASPP), and DeepLabV3 models on two datasets. On the two datasets, the MIOU (Mean Intersection over Union) and MPA (Mean Pixel Accuracy) values of the classic model DeepLabV3 were higher than those of FCN, UNet, and LRASPP models, and the performance was better. Compared with the DeepLabV3 model, the RTC_(T)ongueNet network model increased MIOU value by 0.9% and MPA value by 0.3% on the first dataset; MIOU increased by 1.0% and MPA increased by 1.1% on the second dataset. RTC_(T)ongueNet model performed best on both datasets. Conclusion In this study, based on DeepLabV3, we apply the improved residual structure and transformer as a backbone to fully extract image features locally and globally. The ECA attention module is combined to enhance channel attention, strengthen useful information, and weaken the interference of useless information. RTC_(T)ongueNet model can effectively segment tongue images. This study has practical application value and reference value for tongue image segmentation.