Prevalence of anti-sperm antibodies, risk factors associated and their impact on spermatobioscopy in infertile men

Background: The first immunological correlation with male infertility was reported in 1954 by Wilson and Rumke with the identification of anti-sperm antibodies. The prevalence of anti-sperm antibodies in infertile men varies from 9%-36%, the main cause being the loss of the blood-testicular barrier and otherwise the association with chronic inflammation. It has been shown that immune infertility is found in 15% of patients with varicocele.Methods: A transversal comparative study was carried out with 360 infertile men who were tested for anti-sperm antibodies between January 2011 and July 2018. Two groups were integrated; Group 1, infertile men with positive anti-sperm antibodies >50%, group 2, infertile men with negative anti-sperm <50%. Seminogram parameters were evaluated according to the WHO 5th edition and associated risk factors with anti-sperm antibodies.Results: 360 infertile men were evaluated during the study, 42 were excluded because they did not meet the inclusion criteria, the prevalence of anti-sperm antibodies was 14.5%. Group 1; n=46 (14.5%) and group 2, n=272 (85.5%), the clinical characteristics and the hormonal profile were compared at study admission without significant difference. There was a significant decrease in progressive motility in group 1 (38.7±23.8) vs group 2 (50.1±18.9) p=0.03. Analyzing the risk factors, varicocele was found to be significant 23.7%, OR 2.14 (1.27-3.61) p=0.004 as well as retractable testicle 26.4%, OR 2.13 (1.23-3.70) p= 0.008.Conclusions: The affectation of motility was confirmed, which leads to the suspect varicocele and retractable testicle as risk factors

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

Optimization methods for electric power systems: An overview

Power systems optimization problems are very difficult to solve because power systems are very large, complex, geographically widely distributed and are influenced by many unexpected events. It is therefore necessary to employ most efficient optimization methods to take full advantages in simplifying the formulation and implementation of the problem. This article presents an overview of important mathematical optimization and artificial intelligence (AI) techniques used in power optimization problems. Applications of hybrid AI techniques have also been discussed in this article

Microbial Risk Assessment of Pathogens in Water

Water ManagementCivil Engineering and Geoscience