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J Endocr Soc ; 6(Suppl 1):A409, 2022.
Article in English | PubMed Central | ID: covidwho-2119819


HM15136 is a novel long-acting glucagon analogue with an extended half-life. In vivo efficacy studies of HM15136 in animal models showed its therapeutic potential in obesity, and treatment requiring hypoglycemia. We enrolled an obese and overweight subjects without diabetes (study Part 1) and with diabetes (study Part 2) in a randomized, double-blind, placebo-controlled study to assess the safety, pharmacokinetics, and pharmacodynamics of multiple subcutaneous doses of HM15136 for 12 weeks (NCT04167553). In Part 1, a total of 36 non-diabetic subjects randomly received HM15136 or its matching placebo in a ratio of 9: 3 in 3 cohorts (0.02, 0.04, and 0.06 mg/kg). The baseline mean age was 37.5 years, BMI was 33.9 kg/m2, and Fasting Plasma Glucose (FPG) was 92.3 mg/dL. The FPG increased with escalating doses of HM15136. The mean (SD) changes from baseline in FPG at week 12 were -1.0 (13.0) mg/dL for 0.02 mg/kg, 12.0 (10.7) mg/dL for 0.04 mg/kg, 17.9 (16.9) mg/dL for 0.06 mg/kg vs. 0.6 (6.5) mg/dL for placebo group. The FPG had returned to baseline at 3 weeks after study drug discontinuation. The presence of Anti-Drug Antibodies (ADAs) was confirmed in 5 subjects (18.5%) but dose-dependency was not observed. One (1) out of 5 ADA positive subjects had neutralizing ADA activity with no cross reactivity to endogenous glucagon. The most frequent Treatment Related Adverse Event (TRAE) was injection site erythema (11.1%), the frequency of TRAEs was not dose dependent. Throughout Part 1 and Part 2, all TRAEs were mild except for moderate hyperglycemia in patients with diabetes. Part 2 was completed earlier than planned due to the impact of COVID-19 and discontinued subjects due to the hyperglycemia. Part 2 was not included in this presentation because the interpretability of the data was limited. In conclusion, HM15136 was safe and well tolerated in non-diabetic obese subjects during the 12-week treatment at various dose levels. Treatment with HM15136 showed a dose dependent blood glucose increase. These results suggest future development opportunities for the management of treatment requiring hypoglycemia. A phase 2, Proof-of-Concept study in patients with congenital hyperinsulinism is currently ongoing (NCT04732416).Presentation: Sunday, June 12, 2022 12:30 p.m. - 2:30 p.m.

International Social Work ; 2022.
Article in English | Web of Science | ID: covidwho-2098153


This study utilizes the Technology Acceptance Model by exploring the relationship between user acceptance attitude and actual usage behaviors of technological tools in telesupervision among supervisors in international societies. Specifically, the age of supervisors is examined to see whether it mediates the relationship between acceptance attitude and usage behavior. Survey data were collected from 194 supervisors in international societies using online Survey Monkey. The results indicated a significant relationship between user acceptance attitude and actual usage behaviors of technological tools in telesupervision. Implications of these findings for supervision training and further telesupervision development are discussed.

Computers, Materials and Continua ; 68(2):2621-2632, 2021.
Article in English | Scopus | ID: covidwho-1215885


The analysis of large time-series datasets has profoundly enhanced our ability to make accurate predictions in many fields. However, unpredictable phenomena, such as extreme weather events or the novel coronavirus 2019 (COVID-19) outbreak, can greatly limit the ability of time-series analyses to establish reliable patterns. The present work addresses this issue by applying uncertainty analysis using a probability distribution function, and applies the proposed scheme within a preliminary study involving the prediction of power consumption for a single hotel in Seoul, South Korea based on an analysis of 53,567 data items collected by the Korea Electric Power Corporation using robotic process automation. We first apply Facebook Prophet for conducting time-series analysis. The results demonstrate that the COVID-19 outbreak seriously compromised the reliability of the time-series analysis. Then, machine learning models are developed in the TensorFlow framework for conducting uncertainty analysis based on modeled relationships between electric power consumption and outdoor temperature. The benefits of the proposed uncertainty analysis for predicting the electricity consumption of the hotel building are demonstrated by comparing the results obtained when considering no uncertainty, aleatory uncertainty, epistemic uncertainty, and mixed aleatory and epistemic uncertainty. The minimum and maximum ranges of predicted electricity consumption are obtained when using mixed uncertainty. Accordingly, the application of uncertainty analysis using a probability distribution function greatly improved the predictive power of the analysis compared to time-series analysis. © This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.