Serum Copeptin Levels in Adult Patients with a Migraine Attack: A Cross-Sectional Study

Aim:This study investigated the potential role of serum copeptin, a mediator of acute pain via sympathetic stress stimulation, as a biomarker of varying degrees of migraine-related disability. Specifically, we aimed to analyze whether the serum copeptin level can be used to differentiate migraine types (e.g., with and without aura).Methods:The study population included 80 consecutively consenting adult patients who had migraine attacks and attended the emergency department from June 2020 through November 2020, as well as 80 age- and sex-matched healthy controls. Using the Migraine Disability Assessment Scale (MIDAS), the same medical professional assessed each patient’s level of headache-related disability. Based on their MIDAS scores, the patients were separated into four groups: no disability (score 0-5; group MIDAS-I); mild disability (score 6-10; group MIDAS-II); moderate impairment (score 11-20; group MIDAS-III); and severe disability (score >20; group MIDAS-IV). There were also two categories of migraineurs: those with auras and those without auras. Upon admission, comparisons were made between the groups’ serum copeptin values.Results:In comparison to the control group, the patient group’s serum copeptin levels were noticeably higher (2113.30±206.20 vs. 1383.40±488.40; p<0.001). The study of the receiving operator’s characteristics showed that the cut-off copeptin level was 1898.5 pg/mL, with 90% sensitivity and 82.4% specificity for distinguishing patients from controls. There were no noticeable differences in the mean serum copeptin levels between the patient groups when compared by MIDAS score. Additionally, patients with and without aura did not differ notably in terms of mean serum copeptin levels. (2118.70±211.60 vs. 2071.10±160.40).Conclusion:Serum copeptin levels may be used as a diagnostic aid to help anticipate migraine-related headache attacks when combined with clinical signs and symptoms

An artificial neural network based simulation metamodeling approach for dual resource constrained assembly line

The main objective of this study is to find the optimum values of design and operational parameters related to worker flexibility in a Dual Resource Constrained (DRC) assembly line considering the performance measures of Hourly Production Rate (HPR), Throughput Time (TT) and Number of Worker Transfers (NWT). We used Artificial Neural Networks (ANN) as a simulation metamodel to estimate DRC assembly line performances for all possible alternatives. All alternatives were evaluated with respect to a utility function which consists of weighted sum of normalized performance measures

An artificial neural network based simulation metamodeling approach for dual resource constrained assembly line

The main objective of this study is to find the optimum values of design and operational parameters related to worker flexibility in a Dual Resource Constrained (DRC) assembly line considering the performance measures of Hourly Production Rate (HPR), Throughput Time (TT) and Number of Worker Transfers (NWT). We used Artificial Neural Networks (ANN) as a simulation metamodel to estimate DRC assembly line performances for all possible alternatives. All alternatives were evaluated with respect to a utility function which consists of weighted sum of normalized performance measures. © Springer-Verlag Berlin Heidelberg 2006

A simulation based fuzzy goal programming model for cell formation

Cell formation decisions are made based on several factors such as machining times, utilization, workload, alternative routings, capacities, operation sequences. Most of the traditional Cell Formation procedures ignore the existence of stochastic production requirements and alternative routes. In this study, a simulation based Fuzzy Goal Programming model is proposed for solving cell formation problems considering stochastic production requirements and alternative routes. A tabu search based solution methodology is used for solution

Determining the parameters of dual-card kanban system: an integrated multicriteria and artificial neural network methodology

In this study, we proposed a methodology for determining the design parameters of kanban systems. In this methodology, a backpropagation neural network is used in order to generate simulation meta-models, and a multi-criteria decision making technique (TOPSIS) is employed to evaluate kanban combinations. In order to reflect the decision maker's point of view, different weight structures are used to find the optimum design parameters. The proposed methodology is applied to a case problem and the results are presented. We also performed several experiments on different types of problems to show the effectiveness of the methodology