Immuno-informatics based approaches to design a novel multi epitope-based vaccine for immune response reinforcement against Leptospirosis

Leptospirosis is known as a zoonotic disease of global importance originated from infection with the spirochete bacterium Leptospira. Although several leptospirosis vaccines have been tested, the vaccination is relatively unsuccessful in clinical application despite decades of research. Therefore, this study was conducted to construct a novel multi-epitope based vaccine against leptospirosis by using Hap1, LigA, LAg42, SphH and HSP58 antigens. T cell and IFN gamma epitopes were predicted from these antigens. In addition, to induce strong CD4+ helper T lymphocytes (HTLs) responses, Pan HLA DR-binding epitope (PADRE) and helper epitopes selected from Tetanus toxin fragment C (TTFrC) were applied. Moreover, for boosting immune response, Heparin-Binding Hemagglutinin (HBHA), a novel TLR4 agonist was added to the construct as an adjuvant. Finally, selected epitopes were linked together using EAAAK, GPGPG, AAY and HEYGAEALERAG linkers. Based on the predicted epitopes, a multi-epitope vaccine was construct with 490 amino acids. Physico-chemical properties, secondary and tertiary structures, stability, intrinsic protein disorder, solubility, and allergenicity of this vaccine construct were assessed by applying immunoinformatics analyses. A soluble, and non-allergic protein with a molecular weight of 53.476 kDa was obtained. Further analyses validated the stability of the chimeric protein and the predicted epitopes in the chimeric vaccine indicated strong potential to induce B-cell and T-cell mediated immune response. Therefore, immunoinformatics analysis showed that the modeled multi-epitope vaccine can properly stimulate the both T and B cells immune responses and could potentially be used for prophylactic or therapeutic usages

An evaluation of three DoE-guided meta-heuristic-based solution methods for a three-echelon sustainable distribution network

This article evaluates the efficiency of three meta-heuristic optimiser (viz. MOGA-II, MOPSO and NSGA-II)-based solution methods for designing a sustainable three-echelon distribution network. The distribution network employs a bi-objective location-routing model. Due to the mathematically NP-hard nature of the model a multi-disciplinary optimisation commercial platform, modeFRONTIER®, is adopted to utilise the solution methods. The proposed Design of Experiment (DoE)-guided solution methods are of two phased that solve the NP-hard model to attain minimal total costs and total CO2 emission from transportation. Convergence of the optimisers are tested and compared. Ranking of the realistic results are examined using Pareto frontiers and the Technique for Order Preference by Similarity to Ideal Solution approach, followed by determination of the optimal transportation routes. A case of an Irish dairy processing industry’s three-echelon logistics network is considered to validate the solution methods. The results obtained through the proposed methods provide information on open/closed distribution centres (DCs), vehicle routing patterns connecting plants to DCs, open DCs to retailers and retailers to retailers, and number of trucks required in each route to transport the products. It is found that the DoE-guided NSGA-II optimiser based solution is more efficient when compared with the DoE-guided MOGA-II and MOPSO optimiser based solution methods in solving the bi-objective NP-hard three-echelon sustainable model. This efficient solution method enable managers to structure the physical distribution network on the demand side of a logistics network, minimising total cost and total CO2 emission from transportation while satisfying all operational constraints

A game theory-based approach for modeling autonomous vehicle behavior in congested, urban lane-changing scenarios

Autonomous vehicles are expected to display human-like behavior, at least to the extent that their decisions can be intuitively understood by other road users. If this is not the case, the coexistence of manual and autonomous vehicles in a mixed environment might affect road user interactions negatively and might jeopardize road safety. To this end, it is highly important to design algorithms that are capable of analyzing human decision-making processes and of reproducing them. In this context, lane-change maneuvers have been studied extensively. However, not all potential scenarios have been considered, since most works have focused on highway rather than urban scenarios. We contribute to the field of research by investigating a particular urban traffic scenario in which an autonomous vehicle needs to determine the level of cooperation of the vehicles in the adjacent lane in order to proceed with a lane change. To this end, we present a game theory-based decision-making model for lane changing in congested urban intersections. The model takes as input driving-related parameters related to vehicles in the intersection before they come to a complete stop. We validated the model by relying on the Co-AutoSim simulator. We compared the prediction model outcomes with actual participant decisions, i.e., whether they allowed the autonomous vehicle to drive in front of them. The results are promising, with the prediction accuracy being 100% in all of the cases in which the participants allowed the lane change and 83.3% in the other cases. The false predictions were due to delays in resuming driving after the traffic light turned green. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.This work was supported by the Austrian Ministry for Climate Action, Environment, Energy, Mobility, Innovation, and Technology (BMK) Endowed Professorship for Sustainable Transport Logistics 4.0., IAV France S.A.S.U., IAV GmbH, Austrian Post AG, and the UAS Technikum Wien. It was additionally supported by the Zero Emission Roll-Out?Cold Chain Distribution_877493

Helicobacter pylori evasion strategies of the host innate and adaptive immune responses to survive and develop gastrointestinal diseases

Helicobacter pylori (H. pylori) is a bacterial pathogen that resides in more than half of the human population and has co-evolved with humans for more than 58,000 years. This bacterium is orally transmitted during childhood and is a key cause of chronic gastritis, peptic ulcers and two malignant cancers including MALT (mucosa-associated lymphoid tissue) lymphoma and adenocarcinoma. Despite the strong innate and adaptive immune responses, H. pylori has a long-term survival in the gastric mucosa. In addition to the virulence factors, survival of H. pylori is strongly influenced by the ability of bacteria to escape, disrupt and manipulate the host immune system. This bacterium can escape from recognition by innate immune receptors via altering its surface molecules. Moreover, H. pylori subverts adaptive immune response by modulation of effector T cell. In this review, we discuss the immune-pathogenicity of H. pylori by focusing on its ability to manipulate the innate and acquired immune responses to increase its survival in the gastric mucosa, leading up to gastrointestinal disorders. We also highlight the mechanisms that resulted to the persistence of H. pylori in gastric mucos

On the role of corticosterone in behavioral disorders, microbiota composition alteration and neuroimmune response in adult male mice subjected to maternal separation stress

Experiencing psychosocial adversities in early life such as maternal separation (MS) increases the risk of psychiatric disorders. Immune-inflammatory responses have imperative roles in the pathophysiology of psychiatric disorders. MS relatively changes the composition of intestinal microbiota leading to an overactivation of the hypothalamic-pituitary-adrenal (HPA) axis, and subsequently increases the corticosterone level. In this study, we aimed to evaluate the role of corticosterone in behavioral changes and microbiota modifications in a mouse model of MS afflicted neuroinflammatory response in the hippocampus. For this purpose, 180 min of MS stress was applied to mice at postnatal day (PND) 2-14 followed by behavioral tests including forced swimming test (FST), splash test, open field test (OFT) and elevated plus maze (EPM) at PND 50-52. For evaluating the role of corticosterone, mice were subjected to adrenalectomy. Using real-time RT-PCR, the expression of inflammatory genes was determined in the hippocampus and colon tissues. We found that MS provoked depressive- and anxiety-like behaviors in adult male mice. In addition, MS was able to active a neuroimmune response in the hippocampus, motivate inflammation and histopathologic changes in the colon tissue and modify the composition of gut microbiota as well. Interestingly, our findings showed that adrenalectomy (decline in the corticosterone level), could modulate the above-mentioned negative effects of MS. In conclusion, our results demonstrated that overactivation of HPA axis and the subsequent increased level of corticosterone could act, possibly, as the deleterious effects of MS on behavior, microbiota composition changes and activation of neuroimmune respons

Integrated low-carbon distribution system for the demand side of a product distribution supply chain: a DoE-guided MOPSO optimiser-based solution approach

This article contributes to distribution system literature on three inter-linked aspects viz. formulation of a novel integrated low-carbon/green distribution system for the demand side of a Supply Chain (SC) with a single product and multiple consumers, i.e. drop-off points, a novel and robust solution approach through a Design of Experiment (DoE)-guided Multiple-Objective Particle Swarm Optimisation (MOPSO) optimiser and exhaustive analysis of the solutions (i.e. prioritisation, ranking and scenario analysis). The total costs, CO2 emission and the traversed distances of the vehicles during transportation are optimised. The optimisation model for the strategic decision-making is formulated by effectively integrating the 0–1 mixed-integer programming with a green constraint based on Analytic Hierarchy Process. Due to the computationally NP-hard characteristic of the model, a systematic and technically robust DoE-guided solution approach is designed using a commercial solver – modeFRONTIER®. DoE guides the solution through the MOPSO optimiser in order to eliminate the un-realistic set of feasible and optimal solution sets. A popular multi-attribute decision-making approach, TOPSIS, evaluates the solutions found from the Pareto optimal solution space of the solver. Finally, decision-makers’ preferences are analysed for monitoring the changes in the controlling parameters with respect to the changes in the decisions. A scenario analysis of the events by considering alternative possible outcomes is also conducted. It is found that the implemented methodology successfully routes the vehicles with optimal costs and low-carbon emission thus contributing to greening the environment on the demand side of a SC network

Simultaneous monitoring of SARS-CoV-2, bacteria, and fungi in indoor air of hospital: a study on Hajar Hospital in Shahrekord, Iran

The novel SARS-CoV-2 outbreak was declared as pandemic by the World Health Organization (WHO) on March 11, 2020. Understanding the airborne route of SARS-CoV-2 transmission is essential for infection prevention and control. In this study, a total of 107 indoor air samples (45 SARS-CoV-2, 62 bacteria, and fungi) were collected from different wards of the Hajar Hospital in Shahrekord, Iran. Simultaneously, bacterial and fungal samples were also collected from the ambient air of hospital yard. Overall, 6 positive air samples were detected in the infectious 1 and infectious 2 wards, intensive care unit (ICU), computed tomography (CT) scan, respiratory patients' clinic, and personal protective equipment (PPE) room. Also, airborne bacteria and fungi were simultaneously detected in the various wards of the hospital with concentrations ranging from 14 to 106 CFU m(-3) and 18 to 141 CFU m(-3), respectively. The highest mean concentrations of bacteria and fungi were observed in respiratory patients' clinics and ICU wards, respectively. Significant correlation (p < 0.05) was found between airborne bacterial concentration and the presence of SARS-CoV-2, while no significant correlation was found between fungi concentration and the virus presence. This study provided an additional evidence about the presence of SARS-CoV-2 in the indoor air of a hospital that admitted COVID-19 patients. Moreover, it was revealed that the monitoring of microbial quality of indoor air in such hospitals is very important, especially during the COVID-19 pandemic, for controlling the nosocomial infections