Investigation of UV-treated mesenchymal stem cells in an in vitro wound model.

This study examines the effects of ultraviolet-induced adipose tissue-derived mesenchymal stem cells and their supernatants on wound healing regarding cell viability, percentage of wound healing, released cytokine, and growth factors. It has been reported in previous studies that mesenchymal stem cells are resistant to ultraviolet light and have a protective effect on skin cells against ultraviolet-induced damage. At the same time, there are many studies in the literature about the positive effects of cytokines and growth factors secreted by mesenchymal stem cells. Based on this information, the effects of ultraviolet-induced adipose-derived stem cells and supernatants containing their secreted cytokines and growth factors on an in vitro two-dimensional wound model created with two different cell lines were investigated in this study. It was determined from the results that the highest cell viability and the least apoptotic staining were 100 mJ in mesenchymal stem cells (**p < 0.01). Furthermore, analysis of cytokines and growth factors collected from supernatants also supported 100 mJ as the optimal ultraviolet dose. It was observed that cells treated with ultraviolet and their supernatants significantly increased cell viability and wound-healing rate over time compared to other groups. In conclusion, with this study, it has been shown that adipose-derived stem cells exposed to ultraviolet light can have an important use in wound healing, both with their potential and with the more cytokines and growth factors they secrete. However, further analysis and animal experiments should be performed before clinical use.

Comparison of the different isoforms of vitamin e against amyloid beta-induced neurodegeneration.

Neurodegeneration is the progressive loss of structure or function of neurons. Amyloid beta oligomers and aggregates have been linked to neurodegeneration. While previous studies have suggested that dietary α-tocopherol intake can prevent amyloid beta aggregation and protect the brain against neurotoxicity, other research, however, indicated that tocotrienol forms might be used as an alternate agent against this kind of degeneration. In the presented research, we compared the in vitro protective effects of α-tocopherol and α-tocotrienol. In this context, we formed an in vitro neurodegeneration model with primary isolated neurons and measured α-tocopherol's and α-tocotrienol's protective effects. As a result, α-tocopherol and α-tocotrienol prevent the degeneration of neurons. Moreover, α-tocopherol and α-tocotrienol regulated the neuron's calcium channels mechanism by decreasing the expression of the calcium channel alpha 1C subunit. We also observed that the amount of amyloid beta accumulation in the extracellular matrix decreased with the application of these isoforms. In specific time points, α-tocopherol and α-tocotrienol differ in terms of protective effects. In conclusion, it could be interpreted that, in more extended periods, α-tocotrienol could be a significant protective agent against amyloid beta-induced neurodegeneration, and it can be used as an alternative to other protective agents, especially α-tocopherol.

Application of Multi-Criteria Decision analysis including sustainable indicators for prioritization of public transport system.

Paratransit modes dominate the public transportation industry in the majority of developing countries, especially those in Sub-Saharan Africa. The number of these modes has increased rapidly over the years. Authorities have not yet been able to provide a better alternative for passengers, whose demand is ever-growing. In this research, the main modes of public transport in a Sub-Saharan African city, Kampala, have been studied. Here we used multi-criteria decision-making methods to find out which among the four major public transport modes in Kampala, Uganda, are best from a transportation operator's perspective. The evaluation of alternatives was conducted using Analytic Network Process (ANP) and Elimination Et Choice Translating Reality (ELECTRE III) methods. Both the ANP and the ELECTRE III analysis results demonstrate that alternative "Coaster" is the most appropriate alternative from a public transport operator's point of view. A sensitivity analysis consisting of four different scenarios was considered in the changing of weighting percentages (for benefit, cost, and risk clusters) in the ANP and the ELECTRE III analyses, to show the robustness of the ranking results. According to the sensitivity analysis results, there was no change in the final ranking. The findings of this paper are crucial to policymakers within the transportation sectors of developing countries. Integr Environ Assess Manag 2022;18:25-38. © 2021 SETAC.

An environmental life cycle comparison of various sandwich composite panels for railway passenger vehicle applications.

This study reports the results of a comparison made using life cycle assessment (LCA) analysis of the environmental impact of nine different sandwich material models (SMs). The objective is to reveal whether the candidate materials considered for a railway passenger vehicle (conventional or high-speed train) are green/environmentally friendly or not. For this aim, life cycle approach enables to take into account the light weighting gain without disregarding the environmental impact of manufacturing process. These SMs are designed as combinations of existing traditional and candidate materials, such as steel, aluminium, carbon/glass fibre-reinforced plastics (CFRP/GFRP), aluminium honeycomb, and polymer foam core. The environmental performance of these nine different models has been calculated via the LCA analysis with CML-IA v.3.0 impact assessment methodology in a SimaPro 8.5.0. The system boundaries in the LCA analysis include "cradle to grave" process of sandwich composite materials in the railway passenger vehicle. The functional unit was selected as "one product of SM" for each configuration; besides, this panel has a lifetime span of 25 years at 400,000 vehicle-km per year in the vehicle operation. The results show that the use-phase, which dominates the environmental impact of the SMs of the railway passenger vehicle car body, is itself largely affected by electricity generation. In particular, the mass reduction in the models also achieved a reduction in environmental impact over its lifetime, mainly owing to decreased energy consumption. Another important finding regarding the manufacture of certain models (such as CFRP and GFRP) for lightweight design, is that assessment, based solely on mass reduction, may not always have better environmental performance or be reliable due to the manufacturing impact.

Decision-making for the selection of different leachate treatment/management methods: the ANP and PROMETHEE approaches.

Municipal solid waste (MSW) landfill leachate is a highly contaminated liquid effluent. Leachate has a complex nature that needs to be appropriately treated before being discharged into the environment. There are various options for leachate treatment. Deciding which option should be applied is a complex process, since it depends on many factors that need to provide a balance between the technical, economic, and environmental aspects of sustainability. Multi-criteria decision-making (MCDM) methods are useful techniques to solve complex problems that cannot be easily solved. In this study, MCDM techniques are used for an evaluation of four different leachate treatment options: recirculation of leachate to a landfill site (A1), combined treatment with municipal wastewater (A2), anaerobic and aerobic sequential treatment (A3), and advanced leachate treatment based on membrane processes (A4). The selection of the most appropriate one, based on the criteria, analytic network process (ANP), and preference ranking organization method for enrichment evaluations (PROMETHEE) methods, was applied as MCDM techniques using the Super Decisions software and D-Sight software, respectively. Both the ANP and the PROMETHEE analysis results demonstrate that option A2 is the most appropriate for all of the decision-makers.

Hierarchical Spectral Consensus Clustering for Group Analysis of Functional Brain Networks.

A central question in cognitive neuroscience is how cognitive functions depend on the integration of specialized widely distributed brain regions. In recent years, graph theoretical methods have been used to characterize the structure of the brain functional connectivity. In order to understand the organization of functional connectivity networks, it is important to determine the community structure underlying these complex networks. Moreover, the study of brain functional networks is confounded by the fact that most neurophysiological studies consists of data collected from multiple subjects; thus, it is important to identify communities representative of all subjects. Typically, this problem is addressed by averaging the data across subjects which omits the variability across subjects or using voting methods, which requires a priori knowledge of cluster labels. In this paper, we propose a hierarchical consensus spectral clustering approach to address these problems. Furthermore, new information-theoretic criteria are introduced for selecting the optimal community structure. The proposed framework is applied to electroencephalogram data collected during a study of error-related negativity to better understand the community structure of functional networks involved in the cognitive control.

Multiple subject analysis of functional brain network communities through co-regularized spectral clustering.

In recent years, the human brain has been characterized as a complex network composed of segregated modules linked by short path lengths. In order to understand the organization of the network, it is important to determine these modules underlying the functional brain networks. However, the study of these modules is confounded by the fact that most neurophysiological studies consist of data collected from multiple subjects. Typically, this problem is addressed by either averaging the data across subjects which omits the variability across subjects or using consensus clustering methods which treats all subjects equally irrespective of outliers in the data. In this paper, we adapt a recently introduced co-regularized multiview spectral clustering approach to address these problems. The proposed framework is applied to EEG data collected during a study of error-related negativity (ERN) to better understand the functional networks involved in cognitive control and to compare between the network structure between error and correct responses.