Erratum to: Nanoparticles Impact the Expression of the Genes Involved in Biofilm Formation in S. aureus, a Model Antimicrobial-Resistant Species

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Nanoparticles Impact the Expression of the Genes Involved in Biofilm Formation in S. aureus, a Model Antimicrobial-Resistant Species

Background:     Infection with resistant bacteria are still reported in hospitals despite the routine cleaning of hospital surfaces. Presence of drug-resistant microbes in the on environment of hospitals and on medical equipment is indicative of the need for control measures which could impact the emergence of such microbes. In addition, biofilms are increasingly associated with human infections and it necessitates careful considerations on usage of a diverse range of medical devices, such as catheters, implants and pacemakers in hospitals.  Methods:      This study was designed to compare the effect of silver, ZnO nanoparticles and curcumin on drug-resistant Gram-positive and Gram-negative bacteria which were already isolated from different wards of the hospital. The MIC value were determined for silver, curcumin and ZnO nanoparticles. As the second step, the expression level of the genes involved in biofilm formation in S. aureus, including icaA, icaD, fnbA and fnbB, was studied to analyze the physiological reaction to controlled concentrations of such nanoparticles using RT-qPCR assessments. Results:     In this study, a total of 172 bacterial isolates were recovered from clinical and environmental samples (96 and 76 isolates, respectively). API-20 test revealed that these isolates belonged to 8 species. All antimicrobial resistant isolates were susceptible to the metal oxide nanoparticles. The results of q-PCR in this study showed that the expression of icaA and icaD genes in the presence of silver, curcumin and zinc nanoparticles were not significantly reduced compared to the control samples. But, exposure to nanoparticles reduced the expression of fnbA and fnbB genes from 0.46 to 0.06. Conclusion:  The results of our study showed that nanoparticles are highly effective on antibiotics- resistant isolates and these compounds can be used in the treatment of resistant bacteria. In addition, this study also demonstrates the promising potential of using nanoparticles as anti-biofilm formation agents

Developing a resilient supply chain in complex product systems through investment in reliability and cooperative contracts

In recent years, finding mitigation strategies for supply chain disruptions has become one of the most critical challenges for businesses. This issue is crucial for complex product industries because of their role in the modern economy, few suppliers, and their need for high investment in research and development (R&D). This paper studies a resilient supply chain in complex product systems to overcome its specific challenges through supplier reliability enhancement and cooperative contracts. Utilising a game theoretic approach and analytical models, this paper aims to improve the supply chain performance from the resilience perspective while considering R&D investment, supplier learning effect, buyer fairness concern, and market sensitivity to the product’s technology. Investment in supplier reliability enhancement with different contracts is proposed to mitigate disruption risks for a two-echelon supply chain. Analytical mathematical models have been developed, and a simulation approach has been used in optimisation. The results show how proposed contracts effectively increase supply chain performance from financial and resilience perspectives. Moreover, the market sensitivity to the product’s technological level and the sensitivity to the price could adversely affect performance. The buyer’s fairness concern also improves the profit loss while decreasing the service level slightly

Economic pricing of complex products in a competitive closed-loop supply chain network under uncertainty: A case study of CoPS industry

The development of technology, globalization of the economy and the unpredictable behavior of customers have eventuated in a dynamic and competitive environment in the complex product systems (CoPS) market. Besides, CoPS economic pricing is one of the key factors that dramatically reduces production costs and increases competitiveness. In this regard, this paper unveils a hybrid data envelopment analysis (DEA)-fuzzy mathematical model for economic pricing of CoPS in a competitive closed-loop supply chain network under uncertainty. In the first stage, different CoPS suppliers are evaluated exploiting a DEA model based on a set of economic, technical, and geographical criteria. The advantage of this evaluation is choosing appropriate suppliers, and reducing the complexity of the original model. Next, using a robust optimization model, the strategic and tactical decisions are simultaneously determined, providing a fully optimal solution to the model. In the concerned model, the costs and capacities of facilities are considered to be hemmed in by uncertainty. Eventually, to evaluate the proposed approach, a case study is conducted to derive the important managerial results. The numerical results corroborate that the presented robust model is capable of providing a stable structure under different realizations

A Novel Robust Network Data Envelopment Analysis Approach for Performance Assessment of Mutual Funds under Uncertainty

Mutual fund (MF) is one of the applicable and popular tools in investment market. The aim of this paper is to propose an approach for performance evaluation of mutual fund by considering internal structure and financial data uncertainty. To reach this goal, the robust network data envelopment analysis (RNDEA) is presented for extended two-stage structure. In the RNDEA method, leader-follower (non-cooperative game) and robust optimization approaches are applied in order to modeling network data envelopment analysis (NDEA) and dealing with uncertainty, respectively. The proposed RNDEA approach is implemented for performance assessment of 15 mutual funds. Illustrative results show that presented method is applicable and effective for performance evaluation and ranking of MFs in the presence of uncertain data. Also, the results reveal that the discriminatory power of robust NDEA approach is more than the discriminatory power of deterministic NDEA models

Optimizing whole supply chain benefit versus buyer's benefit through supplier selection

A number of mathematical models have been developed for modeling the supplier selection problem. Most of these models have considered the buyer's viewpoint and maximized only the buyer's benefit. This does not necessarily lead to an optimal situation for all members of a supply chain. Co-ordination models have been presented to optimize the benefits of all the members and alignment of decisions between entities of a supply chain. In this paper, the issue of coordination between one buyer and multiple potential suppliers in the supplier selection process has been considered. On the other hand, in the objective function of the model, the total cost of the supply chain is minimized rather than only the buyer's cost. The total cost of the supply chain includes the buyer's cost and suppliers' costs. The model has been solved by applying mixed-integer nonlinear programming. Finally, the proposed method is illustrated by a numerical example.Supplier selection Supply chain coordination Nonlinear programming