Specialized Plant Metabolism Characteristics and Impact on Target Molecule Biotechnological Production.

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI linkPlant secondary metabolism evolved in the context of highly organized and differentiated cells and tissues, featuring massive chemical complexity operating under tight environmental, developmental and genetic control. Biotechnological demand for natural products has been continuously increasing because of their significant value and new applications, mainly as pharmaceuticals. Aseptic production systems of plant secondary metabolites have improved considerably, constituting an attractive tool for increased, stable and large-scale supply of valuable molecules. Surprisingly, to date, only a few examples including taxol, shikonin, berberine and artemisinin have emerged as success cases of commercial production using this strategy. The present review focuses on the main characteristics of plant specialized metabolism and their implications for current strategies used to produce secondary compounds in axenic cultivation systems. The search for consonance between plant secondary metabolism unique features and various in vitro culture systems, including cell, tissue, organ, and engineered cultures, as well as heterologous expression in microbial platforms, is discussed. Data to date strongly suggest that attaining full potential of these biotechnology production strategies requires being able to take advantage of plant specialized metabolism singularities for improved target molecule yields and for bypassing inherent difficulties in its rational manipulation

Vaccination with recombinant 4×M2e.HSP70c fusion protein as a universal vaccine candidate enhances both humoral and cell-mediated immune responses and decreases viral shedding against experimental challenge of H9N2 influenza in chickens

As cellular immunity is essential for virus clearance, it is commonly accepted that no adequate cellular immunity is achieved by all available inactivated HA-based influenza vaccines. Thus, an improved influenza vaccine to induce both humoral and cell-mediated immune responses is urgently required to control LPAI H9N2 outbreaks in poultry farms. M2e-based vaccines have been suggested and developed as a new generation of universal vaccine candidate against influenza A infection. Our previous study have shown that a prime-boost administration of recombinant 4 × M2e. HSP70c (r4M2e/H70c) fusion protein compared to conventional HA-based influenza vaccines provided full protection against lethal dose of influenza A viruses in mice. In the present study, the immunogenicity and protective efficacy of (r4M2e/H70c) was examined in chickens. The data reported herein show that protection against H9N2 viral challenge was significantly increased in chickens by injection of r4M2e/H70c compared with injection of conventional HA-based influenza vaccine adjuvanted with MF59 or recombinant 4 × M2e (r4M2e) without HSP70c. Oropharyngeal and cloacal shedding of the virus was detected in all of the r4M2e/H70c vaccinated birds at 2 days after challenge, but the titer was low and decreased rapidly to reach undetectable levels at 7 days after challenge. Moreover, comparison of protective efficacy against LPAI H9N2 in birds intramuscularly immunized with r4M2e/H70c likely represented the ability of the M2e-based vaccine in providing cross-protection against heterosubtypic H9N2 challenge and also allowed the host immune system to induce HA-homosubtype neutralizing antibody against H9N2 challenge. This protective immunity might be attributed to enhanced cell-mediated immunity, which is interpreted as increased lymphocytes proliferation, increased levels of Th1-type (IFN-γ) and Th2-type (IL-4) cytokines production and increased CD4+ to CD8+ ratios, resulting from the injection of four tandem repeats of the ectodomain of the conserved influenza matrix protein M2 (4×M2e) genetically fused to C-terminus of Mycobacterium tuberculosis HSP70 (mHSP70c). © 2014 Elsevier B.V

Vertical Transmission of Coronavirus Disease 19 (COVID-19) from Infected Pregnant Mothers to Neonates: A Review

Background: Since early December 2019, the Coronavirus Disease 19 (COVID-19) infection has been prevalent in China and eventually spread to other countries. There are a few published cases of COVID-19 occurring during pregnancy and due the possibility of mother-fetal vertical transmission, there is a concern that the fetuses may be at risk of congenital COVID-19. Methods: We reviewed the risk of vertical transmission of COVID-19 to the fetus of infected mothers by using data of published articles or official websites up to March 4, 2020. Results: A total of 31 infected pregnant mothers with COVID-19 were reported. No COVID-19 infection was detected in their neonates or placentas. Two mothers died from COVID-19-related respiratory complications after delivery. Conclusions: Currently, based on limited data, there is no evidence for intrauterine transmission of COVID-19 from infected pregnant women to their fetuses. Mothers may be at increased risk for more severe respiratory complications. © 2020, © 2020 Taylor & Francis Group, LLC

Proportion and mortality of Iranian diabetes mellitus, chronic kidney disease, hypertension and cardiovascular disease patients with COVID-19: a meta-analysis

Background: Currently, the number of patients with SARS-COV-2 infection has increased rapidly in Iran, but the risk and mortality of SARS-COV-2 infection in Iranian patients with diabetes mellitus (DM), chronic kidney disease (CKD), hypertension and cardiovascular diseases (CVDs) still not clear. The aim of this meta-analysis was to estimate the proportion and mortality of SARS-COV-2 in these patients. Methods: A comprehensive literature search was carried out in PubMed, Web of Sciences, Cochrane Library, EMBASE, CNKI, SciELO, and other databases to identify all relevant studies published up to 10 January, 2020. The proportion and mortality in the patients were assessed by odd ratio (OR) and the corresponding 95 confidence interval (95 CI). Results: A total of ten case-series including 11,755 cases with SARS-COV-2 infection and 942 deaths were selected. Among them, there were total of 791 DM patients with 186 deaths, 225 CKD patients with 45 deaths, 790 hypertension cases with 86 deaths, and 471 CVDs cases with 60 deaths. Pooled data revealed that the proportion of SARS-COV-2 infection in the patients with hypertension, DM, CVDs and CKD were 21.1 , 16.3 , 14.0 and 5.0 , respectively. Moreover, the SARS-COV-2 infection were associated with an increased risk of mortality in DM (OR = 0.549, CI 95 0.448�0.671, p � 0.001) and CKD (OR = 0.552, 95 CI 0.367�0.829, p = 0.004) patients, but not hypertension and CVDs. There was no publication bias. Conclusions: Our pooled data showed that the proportion of SARS-COV-2 infection was the highest in the Iranian patients with hypertension (21.1 ) followed by DM (16.3 ), CVDs (14.0 ) and CKD (5.0 ). Moreover, DM and CKD in patients with SARS-COV-2 infection were associated with a 0.549 and 0.552-fold increase in mortality, respectively. Clinicians in Iran should be aware of these findings, to identifying patients at higher risk and inform interventions to reduce the risk of death. Moreover, well-designed, large-scale and multicenter studies are needed to improve and validate our findings. © 2021, Springer Nature Switzerland AG

Normal parameter reduction algorithm in soft set based on hybrid binary particle swarm and biogeography optimizer

© 2019, Springer-Verlag London Ltd., part of Springer Nature. Existing classification techniques that are proposed previously for eliminating data inconsistency could not achieve an efficient parameter reduction in soft set theory, which effects on the obtained decisions. Meanwhile, the computational cost made during combination generation process of soft sets could cause machine infinite state, which is known as nondeterministic polynomial time. The contributions of this study are mainly focused on minimizing choices costs through adjusting the original classifications by decision partition order and enhancing the probability of searching domain space using a developed Markov chain model. Furthermore, this study introduces an efficient soft set reduction-based binary particle swarm optimized by biogeography-based optimizer (SSR-BPSO-BBO) algorithm that generates an accurate decision for optimal and sub-optimal choices. The results show that the decision partition order technique is performing better in parameter reduction up to 50%, while other algorithms could not obtain high reduction rates in some scenarios. In terms of accuracy, the proposed SSR-BPSO-BBO algorithm outperforms the other optimization algorithms in achieving high accuracy percentage of a given soft dataset. On the other hand, the proposed Markov chain model could significantly represent the robustness of our parameter reduction technique in obtaining the optimal decision and minimizing the search domain.Published versio

A novel dynamic framework to detect DDoS in SDN using metaheuristic clustering

© 2019 John Wiley & Sons, Ltd. Security is a crucial factor in the continuously evolving programmable networks. With the emergence of programmable networking terminals, the need to protect the networks has become mandatory. Software-defined networks (SDNs) provide programmable switches, thereby isolating the data plane from the control plane. Many security algorithms have been proposed to protect the network; however, they have failed to protect SDNs from attacks such as distributed denial of service (DDoS), jamming, and man-in-the-middle attacks. In this article, we only address the DDoS attack that prevails in SDNs. Isolation of the control plane from the data plane increases the probability of an attack on the data plane. Therefore, a framework that can handle the dynamic traffic and can protect the network from DDoS attacks is required. Our proposed whale optimization algorithm–based clustering for DDoS detection (WOA-DD) avoids the DDoS attacks using a metaheuristic approach by clustering the attack requests. We evaluated this algorithm for robustness in comparison with several existing solutions and found it to be safe under several conditions. The proposed attack request clustering is explored to check its feasibility with various machine learning approaches and found to be stable with the prevailing mechanisms. Analysis of the algorithm under varied conditions reveals that WOA-DD is robust, stable, and efficient against DDoS attacks