Investigation of the hypothalamopituitary-adrenal axis by low-dose (1 mu g) adrenocorticotrophic hormone test and metyrapone test in patients with chronic fatigue syndrome

Chronic fatigue syndrome (CFS) is a disease characterized by debilitating fatigue of at least 6 months' duration. The pathophysiology and the localization of the underlying HPA axis disturbance are a matter of debate. Our aim was to evaluate the hypothalamopituitary-adrenal (HPA) axis by the 1 mu g adrenocorticotrophic hormone (ACTH) test and metyrapone test in patients with CFS and to compare the size of the adrenal glands of the patients with that of the control subjects. Twenty patients (14 females, 6 males) with CFS were included in the study. Fifteen healthy subjects (12 females, 3 males) served as matched controls. ACTH stimulation test was carried out by using 1 mu g ACTH, intravenously, as a bolus injection after an overnight fast, and blood samples for cortisol were drawn at 0, 30, and 60 minutes. Metyrapone at a dosage of 30 mg/kg was taken orally at 11:00 PM with a snack. The following morning, blood was sampled for serum 11-deoxycortisol between 8:00 and 9:00 AM. Peak cortisol responses to 1 mu g ACTH test were significantly lower in the CFS group (620.7 +/- 146.2 nmol/L) than in the control group (838.7 +/- 129.6 nmol/L) (P 0.05). We conclude that the perturbation of the HPA axis in CFS is characterized by underactivation of the HPAaxis

A Proposed Methodology for Risk Classification Using Fuzzy Group Decision Making and Fuzzy C-Means

Clean production and resource efficiency are two major concerns of contemporary manufacturing processes. The main reason is that the resources and environment are significant concerns for the future. The study proposes the assessment of risks in a butchery unit in a major retail company. The regular assessment using impact and probability requires concrete input from the relevant expert. The possible impact and probability are challenging to measure because of their vagueness in nature. The proposed study uses the aggregation of fuzzy opinions under group decisions to assess the impact and probability of each risk in the butchery unit for the first phase. The outputs of the first phase are the impact and probability values for each risk based on group decisions under fuzzy logic. The second phase involves converting global risk values to classified risk groups. In our study, Fuzzy-C-Means will be used to classify risks based on their importance to 3 groups. By applying classification, the responsible for the relevant actions can take preventive actions for the risks that deserve the most attention. The proposed methodology is applied to a real data set of risk analysis. Results of the research demonstrate that the use of fuzzy logic in the assessment of risk analysis shows a promising approach and is accepted as an improvement over the existing practice to define the risk and classify it. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG