Background: The stimulatory effects of caffeine contribute to poor sleep quality. However, the relationship between caffeinated beverages and sleep quality, considering frequency or types of caffeinated beverages, were not extensively studied. Methods: Data were collected from 160 urban workers (75 men [46.9%] aged 20–69 years; with an average age of 41.8±12.3 years) using a structured, self-administered online questionnaire. Sleep quality, time, satisfaction; types and frequency of caffeinated beverages (number of cups per week; Q1: 0 cup, Q4: 14 or more cups per week), demographics, and health behaviors were asked. Sleep quality were evaluated using the Korean version of the Pittsburgh Sleep Quality Index (PSQI-K). Multiple regression analysis was conducted on the association between the frequency of caffeinated beverages consumption and sleep quality. Results: The most frequently consumed beverages were unsweetened coffee (22.8%) and the most common time for caffeine was between 12 pm to 5 pm (58.2%). The average sleep quality score based on the PSQI-K was 6.0±2.0 overall, 5.3±1.6 in Q1, and 6.6±2.2 in Q4 (frequent caffeinated beverage drinkers), indicating poorer sleep quality in Q4 (P=0.022). In Q1, 13.3% rated their sleep quality as ‘very good,’ while in Q4, only 2.5% gave the same rating. Poor sleep quality was significantly associated with the frequency of caffeinated beverages per week (β=0.232, P=0.004) and self-reported stress level (β=0.256, P=0.002). Conclusions Frequent consumption of caffeinated beverages appears to be associated with poor sleep quality among urban workers. While reducing caffeine intake may contribute to improvements in sleep quality as a health promoting behavior, this hypothesis requires validation through future studies employing personalized intervention approaches.

Background: The stimulatory effects of caffeine contribute to poor sleep quality. However, the relationship between caffeinated beverages and sleep quality, considering frequency or types of caffeinated beverages, were not extensively studied. Methods: Data were collected from 160 urban workers (75 men [46.9%] aged 20–69 years; with an average age of 41.8±12.3 years) using a structured, self-administered online questionnaire. Sleep quality, time, satisfaction; types and frequency of caffeinated beverages (number of cups per week; Q1: 0 cup, Q4: 14 or more cups per week), demographics, and health behaviors were asked. Sleep quality were evaluated using the Korean version of the Pittsburgh Sleep Quality Index (PSQI-K). Multiple regression analysis was conducted on the association between the frequency of caffeinated beverages consumption and sleep quality. Results: The most frequently consumed beverages were unsweetened coffee (22.8%) and the most common time for caffeine was between 12 pm to 5 pm (58.2%). The average sleep quality score based on the PSQI-K was 6.0±2.0 overall, 5.3±1.6 in Q1, and 6.6±2.2 in Q4 (frequent caffeinated beverage drinkers), indicating poorer sleep quality in Q4 (P=0.022). In Q1, 13.3% rated their sleep quality as ‘very good,’ while in Q4, only 2.5% gave the same rating. Poor sleep quality was significantly associated with the frequency of caffeinated beverages per week (β=0.232, P=0.004) and self-reported stress level (β=0.256, P=0.002). Conclusions Frequent consumption of caffeinated beverages appears to be associated with poor sleep quality among urban workers. While reducing caffeine intake may contribute to improvements in sleep quality as a health promoting behavior, this hypothesis requires validation through future studies employing personalized intervention approaches.

Background: The stimulatory effects of caffeine contribute to poor sleep quality. However, the relationship between caffeinated beverages and sleep quality, considering frequency or types of caffeinated beverages, were not extensively studied. Methods: Data were collected from 160 urban workers (75 men [46.9%] aged 20–69 years; with an average age of 41.8±12.3 years) using a structured, self-administered online questionnaire. Sleep quality, time, satisfaction; types and frequency of caffeinated beverages (number of cups per week; Q1: 0 cup, Q4: 14 or more cups per week), demographics, and health behaviors were asked. Sleep quality were evaluated using the Korean version of the Pittsburgh Sleep Quality Index (PSQI-K). Multiple regression analysis was conducted on the association between the frequency of caffeinated beverages consumption and sleep quality. Results: The most frequently consumed beverages were unsweetened coffee (22.8%) and the most common time for caffeine was between 12 pm to 5 pm (58.2%). The average sleep quality score based on the PSQI-K was 6.0±2.0 overall, 5.3±1.6 in Q1, and 6.6±2.2 in Q4 (frequent caffeinated beverage drinkers), indicating poorer sleep quality in Q4 (P=0.022). In Q1, 13.3% rated their sleep quality as ‘very good,’ while in Q4, only 2.5% gave the same rating. Poor sleep quality was significantly associated with the frequency of caffeinated beverages per week (β=0.232, P=0.004) and self-reported stress level (β=0.256, P=0.002). Conclusions Frequent consumption of caffeinated beverages appears to be associated with poor sleep quality among urban workers. While reducing caffeine intake may contribute to improvements in sleep quality as a health promoting behavior, this hypothesis requires validation through future studies employing personalized intervention approaches.

Background: The stimulatory effects of caffeine contribute to poor sleep quality. However, the relationship between caffeinated beverages and sleep quality, considering frequency or types of caffeinated beverages, were not extensively studied. Methods: Data were collected from 160 urban workers (75 men [46.9%] aged 20–69 years; with an average age of 41.8±12.3 years) using a structured, self-administered online questionnaire. Sleep quality, time, satisfaction; types and frequency of caffeinated beverages (number of cups per week; Q1: 0 cup, Q4: 14 or more cups per week), demographics, and health behaviors were asked. Sleep quality were evaluated using the Korean version of the Pittsburgh Sleep Quality Index (PSQI-K). Multiple regression analysis was conducted on the association between the frequency of caffeinated beverages consumption and sleep quality. Results: The most frequently consumed beverages were unsweetened coffee (22.8%) and the most common time for caffeine was between 12 pm to 5 pm (58.2%). The average sleep quality score based on the PSQI-K was 6.0±2.0 overall, 5.3±1.6 in Q1, and 6.6±2.2 in Q4 (frequent caffeinated beverage drinkers), indicating poorer sleep quality in Q4 (P=0.022). In Q1, 13.3% rated their sleep quality as ‘very good,’ while in Q4, only 2.5% gave the same rating. Poor sleep quality was significantly associated with the frequency of caffeinated beverages per week (β=0.232, P=0.004) and self-reported stress level (β=0.256, P=0.002). Conclusions Frequent consumption of caffeinated beverages appears to be associated with poor sleep quality among urban workers. While reducing caffeine intake may contribute to improvements in sleep quality as a health promoting behavior, this hypothesis requires validation through future studies employing personalized intervention approaches.

Frequency-lowering (FL) algorithms improve the audibility of high-frequency sounds by shifting inaudible high-frequency components to audible low-frequency regions. However, the FL algorithm has yielded mixed findings. This study involved two experiments. The first experiment investigated whether objective and subjective auditory outcomes would be enhanced by activating frequency composition (Fcomp), which is a type of FL technique. The second experiment determined whether auditory training with Fcomp activation would provide perceptual benefits to older hearing aid users. Subjects and Methods: Twelve older hearing aid users with moderate to profound high-frequency sensorineural hearing loss participated in this study. In Experiment I, all participants received a 4-week adaptation period to Fcomp before the measurement, and then the influence of Fcomp was evaluated. In Experiment II, among the 12 participants in Experiment I, five received 8-week phoneme-based auditory training while activating Fcomp, whereas the remaining seven did not receive training but continued to use Fcomp as non-trained controls. Results: In Experiment I, 4-week passive accli­matization period to Fcomp did not improve speech-in-quiet recognition or self-perceived sound quality. In Experiment II, active pho­neme-based training enhanced recognition of consonants and words as well as better speech-quality ratings for trained participants. The consecutive use of Fcomp did not lead to any differences for non-trained participants. Conclusions: Overall, phoneme-based auditory training would allow older hearing aid users to relearn frequency-lowered speech sounds and reduce phonetic confusion. However, the analytical training approach did not lead to transfer to sentence recognition or overall satisfaction with the hearing aids.

Purpose: This study aims to identify risk factors for falls or fractures in patients with stroke. Methods: In this secondary data analysis study, data were extracted from the 2020 patient sample dataset provided by the Health Insurance Review and Assessment Service (HIRA). The independent variables were categorized into 4 characteristics: 3 general characteristics, 13 diagnostic characteristics, 8 medication characteristics, and 5 treatment characteristics. The data were analyzed using SAS for Windows version 9.4 software. The frequencies of the independent variables were examined, and x 2 tests were conducted to determine whether there were significant differences between falls or fractures and the independent variables. To identify the risk factors for falls or fractures, Cox proportional hazards regression analysis was performed to determine hazard ratios. Results: Among 13,629 patients with stroke, 925 (6.78%) experienced falls or fractures. Eight risk factors for falls or fractures in patients with stroke were identified: elderly patients aged 75 years and above, degenerative arthritis, osteoporosis, opioid medication, antidepressant medication, fluid treatment, a history of surgery, and no specialized rehabilitation therapy Conclusion It is necessary to develop fall prevention education programs based on the identified eight risk factors, and to conduct studies to validate their effectiveness.

Major depressive disorder is a leading cause of disability in more than 280 million people worldwide. Monoamine-based antidepressants are currently used to treat depression, but delays in treatment effects and lack of responses are major reasons for the need to develop faster and more efficient antidepressants. Studies show that ketamine (KET), a PCP analog, produces antidepressant effects within a few hours of administration that lasts up to a week. However, the use of KET has raised concerns about side effects, as well as the risk of abuse. 4 -F-PCP analog is a novel PCP analog that is also an NMDA receptor antagonist, structurally similar to KET, and might potentially elicit similar antidepressant effects, however, there has been no study on this subject yet. Herein, we investigate whether 4-F-PCP displays antidepressant effects and explored their potential therapeutic mechanisms. 4-F-PCP at 3 and 10 mg/kg doses showed antidepressant-like effects and repeated treatments maintained its effects. Furthermore, treatment with 4-F-PCP rescued the decreased expression of proteins most likely involved in depression and synaptic plasticity. Changes in the excitatory amino acid transporters (EAAT2, EAAT3, EAAT4) were also seen following drug treatment. Lastly, we assessed the possible side effects of 4-F-PCP after long-term treatment (up to 21 days). Results show that 4-F-PCP at 3 mg/kg dose did not alter the cognitive function of mice. Overall, current findings provide significant i

HemoHIM was used as a Korean traditional medicine for anti-inflammatory and antioxidant effects. However, there is no study on the effect of HemoHIM on fatigue. We examined the potential use of HemoHIM to determine whether it can induce anti-fatigue effects. Mice were administered with HemoHIM and VEH for 14 days. On the last day of treatment, mice were subjected to behavioral tests. Subsequently, their plasma and muscle were collected after the treadmill test to measure lactate, lactate dehydrogenase (LDH), ammonia, corticosterone, glycogen, and creatine kinase (CK). We found that HemoHIM moderately increased the running time (s) in the treadmill and mobility duration in the cold swimming tests. In addition, the VEH group showed a significant increase in lactate, LDH, and corticosterone levels in the plasma compared to the group that did not perform the test. However, this was moderately reduced in HemoHIM treatment. Moreover, the HemoHIMtreated group showed significant differences in LDH and glycogen levels, and showed significantly different CK levels in the muscle. HemoHIM is considered to be effective in improving fatigue, given the duration of cold swimming or running time on a treadmill. Also, HemoHIM treatment resulted in reduced concentrations of blood and muscle parameter analysis.

Previous reports have demonstrated that genetic mechanisms greatly mediate responses to drugs of abuse, including methamphetamine (METH). The circadian gene Period 2 (Per2 ) has been previously associated with differential responses towards METH in mice. While the behavioral consequences of eliminating Per2 have been illustrated previously, Per2 overexpression has not yet been comprehensively described; although, Per2 -overexpressing (Per2 OE) mice previously showed reduced sensitivity towards METH-induced addiction-like behaviors. To further elucidate this distinct behavior of Per2 OE mice to METH, we identified possible candidate biomarkers by determining striatal differentially expressed genes (DEGs) in both drug-naïve and METH-treated Per2 OE mice relative to wild-type (WT), through RNA sequencing. Of the several DEGs in drug naïve Per2 OE mice, we identified six genes that were altered after repeated METH treatment in WT mice, but not in Per2 OE mice. These results, validated by quantitative real-time polymerase chain reaction, could suggest that the identified DEGs might underlie the previously reported weaker METH-induced responses of Per2 OE mice compared to WT. Gene network analysis also revealed that Asic3, Hba-a1, and Rnf17 are possibly associated with Per2 through physical interactions and predicted correlations, and might potentially participate in addiction. Inhibiting the functional protein of Asic3 prior to METH administration resulted in the partial reduction of METH-induced conditioned place preference in WT mice, supporting a possible involvement of Asic3 in METH-induced reward.Although encouraging further investigations, our findings suggest that these DEGs, including Asic3, may play significant roles in the lower sensitivity of Per2 OE mice to METH.

Drug addiction influences most communities directly or indirectly. Increasing studies have reported the relationship between circadian-related genes and drug addiction.Per2disrupted mice exhibited more vulnerable behavioral responses against some drugs including methamphetamine (METH). However, its roles and mechanisms are still not clear. Transcriptional profiling analysis in Per2 knockout (KO) mice may provide a valuable tool to identify potential genetic involvement and pathways in enhanced behavioral responses against drugs. To explore the potential genetic involvement, we examined common differentially expressed genes (DEGs) in the striatum of drug naïve Per2 KO/wild-type (WT) mice, and before/after METH treatment in Per2 KO mice, but not in WT mice. We selected 9 common DEGs (Ncald, Cpa6, Pklr, Ttc29, Cbr2, Egr2, Prg4, Lcn2, and Camsap2) based on literature research. Among the common DEGs, Ncald, Cpa6, Pklr, and Ttc29 showed higher expression levels in drug naïve Per2 KO mice than in WT mice, while they were downregulated in Per2 KO mice after METH treatment. In contrast, Cbr2, Egr2, Prg4, Lcn2, and Camsap2 exhibited lower expression levels in drug naïve Per2 KO mice than in WT mice, while they were upregulated after METH treatment in Per2 KO mice. qRT-PCR analyses validated the expression patterns of 9 target genes before/after METH treatment in Per2 KO and WT mice. Although further research is required to deeply understand the relationship and roles of the 9 target genes in drug addiction, the findings from the present study indicate that the target genes might play important roles in drug addiction.