Human umbilical cord mesenchymal stem cell-derived exosomes alleviate the severity of experimental autoimmune encephalomyelitis and enhance lag-3 expression on foxp3 + CD4 + T cells.

BACKGROUND: Multiple sclerosis (MS) is a complex autoimmune disease that affects the central nervous system, causing inflammation, demyelination, and neurodegeneration. Understanding the dysregulation of Tregs, dynamic cells involved in autoimmunity, is crucial in comprehending diseases like MS. However, the role of lymphocyte-activation gene 3 (Lag-3) in MS remains unclear. METHODS: In this study, we explore the potential of exosomes derived from human umbilical cord mesenchymal stem cells (hUMSCs-Exs) as an immune modulator in experimental autoimmune encephalomyelitis (EAE), a model for MS. RESULTS: Using flow cytometry, our research findings indicate that groups receiving treatment with hUMSC-Exs revealed a significant increase in Lag-3 expression on Foxp3 + CD4 + T cells. Furthermore, cell proliferation conducted on spleen tissue samples from EAE mice using the CFSE method exposed to hUMSC-Exs yielded relevant results. CONCLUSIONS: These results suggest that hUMSCs-Exs could be a promising anti-inflammatory agent to regulate T-cell responses in EAE and other autoimmune diseases. However, further research is necessary to fully understand the underlying mechanisms and Lag-3's precise role in these conditions.

How to Weigh Values in Value Sensitive Design: A Best Worst Method Approach for the Case of Smart Metering.

Proactively including the ethical and societal issues of new technologies could have a positive effect on their acceptance. These issues could be captured in terms of values. In the literature, the values stakeholders deem important for the development of technology have often been identified. However, the relative ranking of these values in relation to each other have not been studied often. The best worst method is proposed as a possible method to determine the weights of values, hence it is used in an evaluative fashion. The applicability of the method is tested by applying it to the case of smart meters, one of the main components of the smart grid. The importance of values is examined for three dimensions of acceptance namely sociopolitical, market, and household acceptance.

Evaluating firms' R&D performance using best worst method.

Since research and development (R&D) is the most critical determinant of the productivity, growth and competitive advantage of firms, measuring R&D performance has become the core of attention of R&D managers, and an extensive body of literature has examined and identified different R&D measurements and determinants of R&D performance. However, measuring R&D performance and assigning the same level of importance to different R&D measures, which is the common approach in existing studies, can oversimplify the R&D measuring process, which may result in misinterpretation of the performance and consequently fallacy R&D strategies. The aim of this study is to measure R&D performance taking into account the different levels of importance of R&D measures, using a multi-criteria decision-making method called Best Worst Method (BWM) to identify the weights (importance) of R&D measures and measure the R&D performance of 50 high-tech SMEs in the Netherlands using the data gathered in a survey among SMEs and from R&D experts. The results show how assigning different weights to different R&D measures (in contrast to simple mean) results in a different ranking of the firms and allow R&D managers to formulate more effective strategies to improve their firm's R&D performance by applying knowledge regarding the importance of different R&D measures.

Measuring efficiency of university-industry Ph.D. projects using best worst method.

A collaborative Ph.D. project, carried out by a doctoral candidate, is a type of collaboration between university and industry. Due to the importance of such projects, researchers have considered different ways to evaluate the success, with a focus on the outputs of these projects. However, what has been neglected is the other side of the coin-the inputs. The main aim of this study is to incorporate both the inputs and outputs of these projects into a more meaningful measure called efficiency. A ratio of the weighted sum of outputs over the weighted sum of inputs identifies the efficiency of a Ph.D. PROJECT: The weights of the inputs and outputs can be identified using a multi-criteria decision-making (MCDM) method. Data on inputs and outputs are collected from 51 Ph.D. candidates who graduated from Eindhoven University of Technology. The weights are identified using a new MCDM method called Best Worst Method (BWM). Because there may be differences in the opinion of Ph.D. candidates and supervisors on weighing the inputs and outputs, data for BWM are collected from both groups. It is interesting to see that there are differences in the level of efficiency from the two perspectives, because of the weight differences. Moreover, a comparison between the efficiency scores of these projects and their success scores reveals differences that may have significant implications. A sensitivity analysis divulges the most contributing inputs and outputs.

Commitment to and preparedness for sustainable supply chain management in the oil and gas industry.

Our current dependency on the oil and gas (O&G) industry for economic development and social activities necessitates research into the sustainability of the industry's supply chains. At present, studies on sustainable supply chain management (SSCM) practices in the industry do not include firm-internal factors that affect the sustainability strategies employed by different functional areas of its supply chains. Our study aims to address this gap by identifying the relevant internal factors and exploring their relationship with SSCM strategies. Specifically, we discuss the commitment to and preparedness for sustainable practices of companies that operate in upstream and downstream O&G supply chain. We study the impact of these factors on their sustainability strategies of four key supply chain functions: supplier management, production management, product stewardship and logistics management. The analyses of data collected through a survey among 81 companies show that management preparedness may enhance sustainable supply chain strategies in the O&G industry more than commitment does. Among the preparedness measures, management of supply chain operational risks is found to be vital to the sustainability of all supply chain functions except for production management practices. The findings also highlight the central importance of supplier and logistics management to the achievement of sustainable O&G supply chains. Companies must also develop an organizational culture that encourages, for example, team collaboration and proactive behaviour to finding innovative sustainability solutions in order to translate commitment to sustainable practices into actions that can produce actual difference to their SSCM practices.