Objectives: This study aimed to describe and compare health-related quality of life (QoL) as measured by the World Health Organization Quality of Life–BREF (WHOQoL-BREF) and the EuroQol-5 Dimensions (EQ-5D) among the Malaysian population, examining differences by sociodemographic characteristics including age, income, sex, ethnicity, educational level, and occupation. Methods: This cross-sectional study used data from 19,402 individuals collected as part of a health and demographic surveillance system survey conducted in the Segamat district of Malaysia in 2018–2019. Descriptive statistics and measures of central tendency were produced.Differences in QoL among demographic sub-groups were examined using the t-test and analysisof variance, while the correlations between the WHOQoL-BREF and EQ-5D were evaluated usingPearson correlation coefficients. Results: Based on complete case analysis (n = 19,129), the average scores for the 4 WHOQoLBREF domains were 28.2 (physical), 24.1 (psychological), 12.0 (social relationships), and 30.4 (environment). The percentages of participants not in full health for each EQ-5D dimension were 12.8% (mobility), 3.1% (self-care), 6.9% (usual activities), 20.9% (pain/discomfort), and 6.8% (anxiety/depression). Correlations between the 4 WHOQoL-BREF domains and the 5 EQ-5D dimensions were relatively weak, ranging from –0.06 (social relationships with self-care and pain/discomfort; p < 0.001) to –0.42 (physical with mobility; p < 0.001). Conclusion Although health-related QoL as measured by the WHOQoL-BREF and the EQ-5D are correlated, these 2 measures should not be considered interchangeable. The choice betweenthem should be guided by the specific research questions and the intended use of the data.

Objectives: This study aimed to describe and compare health-related quality of life (QoL) as measured by the World Health Organization Quality of Life–BREF (WHOQoL-BREF) and the EuroQol-5 Dimensions (EQ-5D) among the Malaysian population, examining differences by sociodemographic characteristics including age, income, sex, ethnicity, educational level, and occupation. Methods: This cross-sectional study used data from 19,402 individuals collected as part of a health and demographic surveillance system survey conducted in the Segamat district of Malaysia in 2018–2019. Descriptive statistics and measures of central tendency were produced.Differences in QoL among demographic sub-groups were examined using the t-test and analysisof variance, while the correlations between the WHOQoL-BREF and EQ-5D were evaluated usingPearson correlation coefficients. Results: Based on complete case analysis (n = 19,129), the average scores for the 4 WHOQoLBREF domains were 28.2 (physical), 24.1 (psychological), 12.0 (social relationships), and 30.4 (environment). The percentages of participants not in full health for each EQ-5D dimension were 12.8% (mobility), 3.1% (self-care), 6.9% (usual activities), 20.9% (pain/discomfort), and 6.8% (anxiety/depression). Correlations between the 4 WHOQoL-BREF domains and the 5 EQ-5D dimensions were relatively weak, ranging from –0.06 (social relationships with self-care and pain/discomfort; p < 0.001) to –0.42 (physical with mobility; p < 0.001). Conclusion Although health-related QoL as measured by the WHOQoL-BREF and the EQ-5D are correlated, these 2 measures should not be considered interchangeable. The choice betweenthem should be guided by the specific research questions and the intended use of the data.

Objectives: This study aimed to describe and compare health-related quality of life (QoL) as measured by the World Health Organization Quality of Life–BREF (WHOQoL-BREF) and the EuroQol-5 Dimensions (EQ-5D) among the Malaysian population, examining differences by sociodemographic characteristics including age, income, sex, ethnicity, educational level, and occupation. Methods: This cross-sectional study used data from 19,402 individuals collected as part of a health and demographic surveillance system survey conducted in the Segamat district of Malaysia in 2018–2019. Descriptive statistics and measures of central tendency were produced.Differences in QoL among demographic sub-groups were examined using the t-test and analysisof variance, while the correlations between the WHOQoL-BREF and EQ-5D were evaluated usingPearson correlation coefficients. Results: Based on complete case analysis (n = 19,129), the average scores for the 4 WHOQoLBREF domains were 28.2 (physical), 24.1 (psychological), 12.0 (social relationships), and 30.4 (environment). The percentages of participants not in full health for each EQ-5D dimension were 12.8% (mobility), 3.1% (self-care), 6.9% (usual activities), 20.9% (pain/discomfort), and 6.8% (anxiety/depression). Correlations between the 4 WHOQoL-BREF domains and the 5 EQ-5D dimensions were relatively weak, ranging from –0.06 (social relationships with self-care and pain/discomfort; p < 0.001) to –0.42 (physical with mobility; p < 0.001). Conclusion Although health-related QoL as measured by the WHOQoL-BREF and the EQ-5D are correlated, these 2 measures should not be considered interchangeable. The choice betweenthem should be guided by the specific research questions and the intended use of the data.

Objectives: This study aimed to describe and compare health-related quality of life (QoL) as measured by the World Health Organization Quality of Life–BREF (WHOQoL-BREF) and the EuroQol-5 Dimensions (EQ-5D) among the Malaysian population, examining differences by sociodemographic characteristics including age, income, sex, ethnicity, educational level, and occupation. Methods: This cross-sectional study used data from 19,402 individuals collected as part of a health and demographic surveillance system survey conducted in the Segamat district of Malaysia in 2018–2019. Descriptive statistics and measures of central tendency were produced.Differences in QoL among demographic sub-groups were examined using the t-test and analysisof variance, while the correlations between the WHOQoL-BREF and EQ-5D were evaluated usingPearson correlation coefficients. Results: Based on complete case analysis (n = 19,129), the average scores for the 4 WHOQoLBREF domains were 28.2 (physical), 24.1 (psychological), 12.0 (social relationships), and 30.4 (environment). The percentages of participants not in full health for each EQ-5D dimension were 12.8% (mobility), 3.1% (self-care), 6.9% (usual activities), 20.9% (pain/discomfort), and 6.8% (anxiety/depression). Correlations between the 4 WHOQoL-BREF domains and the 5 EQ-5D dimensions were relatively weak, ranging from –0.06 (social relationships with self-care and pain/discomfort; p < 0.001) to –0.42 (physical with mobility; p < 0.001). Conclusion Although health-related QoL as measured by the WHOQoL-BREF and the EQ-5D are correlated, these 2 measures should not be considered interchangeable. The choice betweenthem should be guided by the specific research questions and the intended use of the data.

Objectives: This study aimed to describe and compare health-related quality of life (QoL) as measured by the World Health Organization Quality of Life–BREF (WHOQoL-BREF) and the EuroQol-5 Dimensions (EQ-5D) among the Malaysian population, examining differences by sociodemographic characteristics including age, income, sex, ethnicity, educational level, and occupation. Methods: This cross-sectional study used data from 19,402 individuals collected as part of a health and demographic surveillance system survey conducted in the Segamat district of Malaysia in 2018–2019. Descriptive statistics and measures of central tendency were produced.Differences in QoL among demographic sub-groups were examined using the t-test and analysisof variance, while the correlations between the WHOQoL-BREF and EQ-5D were evaluated usingPearson correlation coefficients. Results: Based on complete case analysis (n = 19,129), the average scores for the 4 WHOQoLBREF domains were 28.2 (physical), 24.1 (psychological), 12.0 (social relationships), and 30.4 (environment). The percentages of participants not in full health for each EQ-5D dimension were 12.8% (mobility), 3.1% (self-care), 6.9% (usual activities), 20.9% (pain/discomfort), and 6.8% (anxiety/depression). Correlations between the 4 WHOQoL-BREF domains and the 5 EQ-5D dimensions were relatively weak, ranging from –0.06 (social relationships with self-care and pain/discomfort; p < 0.001) to –0.42 (physical with mobility; p < 0.001). Conclusion Although health-related QoL as measured by the WHOQoL-BREF and the EQ-5D are correlated, these 2 measures should not be considered interchangeable. The choice betweenthem should be guided by the specific research questions and the intended use of the data.

As a serine hydrolase, monoacylglycerol lipase (MAGL) is principally responsible for the metabolism of 2-arachidonoylglycerol (2-AG) in the central nervous system (CNS), leading to the formation of arachidonic acid (AA). Dysfunction of MAGL has been associated with multiple CNS disorders and symptoms, including neuroinflammation, cognitive impairment, epileptogenesis, nociception and neurodegenerative diseases. Inhibition of MAGL provides a promising therapeutic direction for the treatment of these conditions, and a MAGL positron emission tomography (PET) probe would greatly facilitate preclinical and clinical development of MAGL inhibitors. Herein, we design and synthesize a small library of fluoropyridyl-containing MAGL inhibitor candidates. Pharmacological evaluation of these candidates by activity-based protein profiling identified