Surface Modification of Silicone Rubber by Ion Implantation to Improve Biocompatibility

Silicone Rubber (SR) and SR-based materials have been used as medical tissue implants in the field of plastic surgery for many years, but there are still some reports of adverse reactions to long-term implants. In our study, three types of carbon ion silicone rubber were obtained by implanting three doses of carbon ions. Then, the surface characteristics, the antibacterial adhesion properties and in vivo host responses were evaluated. These study shown that ion implantation change the surface roughness and zeta potential of virgin SR; it also inhibit bacterial adhesion. At the same time, ion implantation enhance the cell proliferation, adhesion and tissue compatibility. These data indicate that carbon ion implanted silicone rubber exhibits good antibacterial adhesion properties, cytocompatibility and triggers thinner and weaker tissue capsules. In addition, according to the surface characteristics, we speculate that high surface roughness and high zeta potential may be the main factors that induce the unique biocompatibility of carbon ion implanted silicone rubber. In this chapter, we will review these results above and propose that ion implantation should be considered for further investigation and application, and carbon ion silicone rubber could be a better biomaterial to decrease silicone rubber–initiated complications

Preparation, Characterization, and Preliminary Biocompatibility Evaluation of Carbon Ion-Implanted Silicone Rubber

Silicone rubber (SR) is a common soft tissue filler material used in plastic surgery. However, it suffers from poor biocompatibility. Previous studies have found that the ion implantation technology can be used to improve the biocompatibility of metal materials. However, it is not clear whether it can improve the biocompatibility of polymer materials. In this study, carbon ion SR was prepared by carbon ion implantation. After that, the characteristics of ion implanted SR were investigated. Then, Escherichia coli was utilized to test the antibacterial ability of the carbon ion implanted SR. Besides, the dermal fibroblasts were used to evaluate the cytocompatibility. From the results, carbon ion implantation had no significant effect on the hardness, tensile strength and elongation at break of SR. At the same time, there was no significant change in the surface morphology of SR. But the results show that the surface nano-morphology, surface element composition, hydrophobic and ζ potential of the surface of SR changed significantly. The changes further mediated the lower adhesion of bacteria and enhanced biocompatibility. In conclusion, the carbon ion implantation technology can improve the surface properties of silicone rubber, and further improve its biocompatibility

Sevoflurane ameliorates doxorubicin-induced myocardial injury by affecting the phosphorylation states of proteins in PI3K/Akt/mTOR signaling pathway

  Background: The effect of sevoflurane on the doxorubicin-induced myocardial injury was explored by investigating the phosphorylation states of proteins in phosphatidylinositol 3-kinase (PI3K)/Akt/mam­malian target of rapamycin (mTOR) signaling pathway. Methods: Myocardial injury rat models were induced by doxorubicin and evenly assigned into five groups according to different treatment: Doxorubicin group (DG, 200-μL saline solution), sevoflurane group (SevG, inhaled with 2.4% sevoflurane for 2 h), LY294002 group (LYG, Akt inhibitor, 0.3 mg/kg in 200-μL Dimethyl Sulfoxide [DMSO]), solvent DMSO control group (SG) and autophagy inhibitor 3-methyladenine (3-MA) group (MG, 30 mg/kg in 200-μL DMSO). The healthy rats were assigned to a contro1 group (CG, 200-μL saline solution). Myocardial apoptosis was detected by terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) assay. The concentration of cardiac troponin I (cTnI) was detected by ELISA. The levels of total Akt (t-Akt), phosphorylated Akt (p-Akt), mammalian target of rapamycin (mTOR), phosphorylated-mTOR (p-mTOR) and autophagy marker LC3-II was detected by Western Blot. The experiments were also repeated at the cell level. Results: Terminal deoxynucleotidyl transferase dUTP nick end labeling analysis showed that the ap­optosis rates were high in DG and SG, reached the highest level in LYG, reduced in SevG and MG, and reached the lowest level in CG. The levels of p-Akt p-mTOR were low in groups DG and SG, reached the lowest level in LYG, increased in SevG and MG, and reached the highest level in CG. In contrast, LC3-II expression, apoptosis index and serum cTnI concentration were high in DG and SG, reached the highest level in LYG, reduced in SevG and MG, and reached the lowest level in CG (p < 0.05). Cell experiment showed similar results as with animal experiments. Conclusions: Sevoflurane ameliorates myocardial injury by affecting the phosphorylation states of the proteins in PI3K/Akt/mTOR signaling pathway and reducing the injury biomarker. (Cardiol J 2017; 24, 4: 409–418

Weight of Evidence Method and Its Applications and Development

AbstractThe development and applications about the weight of evidence technology in recent years are reviewed. This paper introduced the improved weight of evidence in remote sensing image processing and in different fields of application. Summary its constraints and existent problems. Look forward to the weight of evidence for the practical application

Hair cluster detection model based on dermoscopic images

Introduction: Hair loss has always bothered many people, with numerous individuals potentially facing the issue of sparse hair.Methods: Due to a scarcity of accurate research on detecting sparse hair, this paper proposes a sparse hair cluster detection model based on improved object detection neural network and medical images of sparse hair under dermatoscope to optimize the evaluation of treatment outcomes for hair loss patients. A new Multi-Level Feature Fusion Module is designed to extract and fuse features at different levels. Additionally, a new Channel-Space Dual Attention Module is proposed to consider both channel and spatial dimensions simultaneously, thereby further enhancing the model’s representational capacity and the precision of sparse hair cluster detection.Results: After testing on self-annotated data, the proposed method is proven capable of accurately identifying and counting sparse hair clusters, surpassing existing methods in terms of accuracy and efficiency.Discussion: Therefore, it can work as an effective tool for early detection and treatment of sparse hair, and offer greater convenience for medical professionals in diagnosis and treatment

Agro-ecological suitability assessment of Chinese Medicinal Yam under future climate change

Chinese Medicinal Yam (CMY) has been prescribed as medicinal food for thousand years in China by Traditional Chinese Medicine (TCM) practitioners. Its medical benefits include nourishing the stomach and spleen to improve digestion, replenishing lung and kidney, etc., according to the TCM literature. As living standard rises and public health awareness improves in recent years, the potential medicinal benefits of CMY have attracted increasing attention in China. It has been found that the observed climate change in last several decades, together with the change in economic structure, has driven significant shift in the pattern of the traditional CMY planting areas. To identify suitable planting area for CMY in the near future is critical for ensuring the quality and supply quantity of CMY, guiding the layout of CMY industry, and safeguarding the sustainable development of CMY resources for public health. In this study, we first collect 30-year records of CMY varieties and their corresponding phenology and agro-meteorological observations. We then consolidate these data and use them to enrich and update the eco-physiological parameters of CMY in the agro-ecological zone (AEZ) model. The updated CMY varieties and AEZ model are validated using the historical planting area and production under observed climate conditions. After the successful validation, we use the updated AEZ model to simulate the potential yield of CMY and identify the suitable planting regions under future climate projections in China. This study shows that regions with high ecological similarity to the genuine and core producing areas of CMY mainly distribute in eastern Henan, southeastern Hebei, and western Shandong. The climate suitability of these areas will be improved due to global warming in the next 50 years, and therefore, they will continue to be the most suitable CMY planting regions

Maintaining Rice Production while Mitigating Methane and Nitrous Oxide Emissions from Paddy Fields in China: Evaluating Tradeoffs by Using Coupled Agricultural Systems Models

China is the largest rice producing and consuming country in the world, accounting for more than 25% of global production and consumption. Rice cultivation is also one of the main sources of anthropogenic methane (CH4) and nitrous oxide (N2O) emissions. The challenge of maintaining food security while reducing greenhouse gas emissions is an important tradeoff issue for both scientists and policy makers. A systematical evaluation of tradeoffs requires attention across spatial scales and over time in order to characterize the complex interactions across agricultural systems components. We couple three well-known models that capture different key agricultural processes in order to improve the tradeoff analysis. These models are the DNDC biogeochemical model of soil denitrification-decomposition processes, the DSSAT crop growth and development model for decision support and agro-technology analysis, and the regional AEZ crop productivity assessment tool based on agro-ecological analysis. The calibration of eco-physiological parameters and model evaluation used the phenology and management records of 1981-2010 at nine agro-meteorological stations spanning the major rice producing regions of China. The eco-physiological parameters were calibrated with the GLUE optimization algorithms of DSSAT and then converted to the counterparts of DNDC. The upscaling of DNDC was carried out within each cropping zone as classified by AEZ. The emissions of CH4 and N2O associated with rice production under different management scenarios were simulated with the DNDC at each site and also each 1010 km grid-cell across each cropping zone. Our results indicate that it is feasible to maintain rice yields while reducing CH4 and N2O emissions through careful management changes. Our simulations indicated that a reduction of fertilizer applications by 5-35% and the introduction of midseason drainage across the nine study sites resulted in reduced CH4 emission by 17-40% and N2O emission by 12-60%, without negative consequences on rice yield

A Cross-scale Model Coupling Approach to Simulate the Risk-reduction Effect of Natural Adaptation on Soybean Production under Climate Change

This study establishes a procedure to couple Decision Support System for Agrotechnology Transfer (DSSAT) and China Agro-ecological Zone model (AEZ-China). This procedure enables us to quantify the effects of two natural adaptation measures on soybean production in China, concern on which has been growing owing to the rapidly rising demand for soybean and the foreseen global climate change. The parameters calibration and mode verification are based on the observation records of soybean growth at 13 agro-meteorological observation stations in Northeast China and Huang-Huai-Hai Plain over 1981–2011. The calibration of eco-physiological parameters is based on the algorithms of DSSAT that simulate the dynamic bio-physiological processes of crop growth in daily time-step. The effects of shifts in planting day and changes in the length of growth cycle (LGC) are evaluated by the speedy algorithms of AEZ. Results indicate that without adaptation, climate change from the baseline 1961-1990 to the climate of 2050s as specified in the Providing REgional Climate for Impacts Studies-A1B would decrease the potential yield of soybean. By contrast, simulations of DSSAT using AEZ-recommended cultivars with adaptive LGC and also the corresponding adaptive planting dates show that the risk of yield loss could be fully or partially mitigated across majority of grid-cells in the major soybean growing areas

Improved Cross Entropy Method for Well-Being Evaluation of Composite Generation and Transmission Systems

Well-Being analysis is an approach that integrates deterministic criteria with probabilistic methods, and it plays a crucial role in the operational planning of power systems. However, assessing the Well-Being of composite generation and transmission systems presents a formidable challenge, characterized by significant computational burdens and sluggish processing speeds. To tackle this issue, we embarked on an effort to enhance the computational efficiency of Well-Being assessment by employing the cross-entropy method (CEM). Nonetheless, our experimental pursuits revealed that the conventional employment of CEM for Well-Being assessment can lead to protracted convergence of the marginal index. To overcome this limitation, we introduce an enhanced multi-objective cross-entropy method (MCEM) that integrates weight factors, thereby ensuring an accelerated convergence rate for both the risk and marginal indices. To validate the effectiveness and advancement of our proposed MCEM approach, we conduct a comprehensive comparative analysis using the IEEE RTS79 and MRTS79 test systems as case studies. We contrast our method with the conventional MCS and CEM approaches, conducting a thorough examination of the computational performance of MCEM. This comprehensive comparative study unequivocally confirms the efficacy and progressive nature of the MCEM framework presented in this paper