Min Kyung Chu

Individual bacteria and shifts in microbiome composition are associated with human disease, including cancer. To unravel the connections underlying oral bacterial dysbiosis and oral squamous cell carcinoma (OSCC), cancer lesion samples and anatomically matched normal samples were obtained from the same patients. We then profiled the bacteria within OSCC lesion surface samples at the species level using next-generation sequencing to comprehensively investigate bacterial community composition and functional genes in these samples. Significantly greater bacterial diversity was observed in the cancer samples than in the normal samples. Compared with previous studies, we identified many more taxa demonstrating remarkably different distributions between the groups. In particular, a group of periodontitis-correlated taxa, including Fusobacterium, Dialister, Peptostreptococcus, Filifactor, Peptococcus, Catonella and Parvimonas, was significantly enriched in OSCC samples. Additionally, several operational taxonomic units (OTUs) associated with Fusobacterium were highly involved in OSCC and demonstrated good diagnostic power. Our study revealed drastic changes in surface bacterial communities of OSCC. The findings enrich knowledge of the association between oral bacterial communities and oral cancer.

Introduction: One of the most common sleep disorders, insomnia is a significant public health concern. Several psychiatric disorders, such as anxiety disorders and depression, have shown strong relationships with insomnia. However, the clinical impact of the combination of these two conditions on insomnia severity and sleep quality remains unknown. We investigated the relationship between sleep disturbance and psychiatric comorbidities in subjects with high risk for insomnia. Methods: We analyzed data from a nation-wide cross-sectional survey of Korean adults aged 19~69 years conducted from November 2011 to January 2012. The survey was performed via face-to-face interviews using a structured questionnaire. We used the insomnia severity index (ISI) to evaluate insomnia and defined respondents with ISI scores of ≥10 as subject having a high risk for insomnia. To diagnose anxiety and depression, we used the Goldberg anxiety scale (GAS) and Patient Health Questionnaire-9 (PHQ-9), respectively. Results: Of the 2,762 respondents, 290 (10.5%) were classified as subjects with high risk for insomnia; anxiety (odds ratio [OR], 9.8; 95% confidence interval [CI]. 7.3 - 13.1) and depression (OR, 19.7; 95% CI ,13.1 - 29.6) were more common in this population than in participants without insomnia. Of the participants with insomnia, 152 (52.4%) had neither anxiety nor depression, 63 (21.7 %) only had anxiety, 21 (7.2%) only had depression, and 54 (18.6%) had both anxiety and depression. The group with both anxiety and depression was associated with worse scores on sleep-related scales than the other groups (high ISI, Pittsburgh Sleep Quality Index [PSQI], and Epworth Sleepiness Scale). The relationship between outcome measures (ISI and PSQI) and psychiatric problems was significant only when anxiety and depression were present. The PSQI has a significant mediation effect on the relationship between psychiatric comorbidities and insomnia severity. Conclusion: Among the respondents with insomnia, psychiatric comorbidities may have a negative impact on daytime alertness, general sleep quality, and insomnia severity, especially when the two conditions are present at the same time. Clinicians should therefore consider psychiatric comorbidities when treat insomnia.

BACKGROUND: Various stimuli can trigger migraines in susceptible individuals. We examined migraine trigger factors by using a smartphone headache diary application. METHOD: Episodic migraineurs who agreed to participate in our study downloaded smartphone headache diary application, which was designed to capture the details regarding headache trigger factors and characteristics for 3 months. The participants were asked to access the smartphone headache diary application daily and to confirm the presence of a headache and input the types of trigger factors. RESULTS: Sixty-two participants kept diary entries until the end of the study. The diary data for 4,579 days were analyzed. In this data set, 1,099 headache days (336 migraines, 763 non-migraine headaches) were recorded; of these, 772 headache events had with trigger factors, and 327 events did not have trigger factors. The common trigger factors that were present on headache days included stress, fatigue, sleep deprivation, hormonal changes, and weather changes. The likelihood of a headache trigger was 57.7% for stress, 55.1% for sleep deprivation, 48.5% for fatigue, and 46.5% for any trigger. The headaches with trigger factors were associated with greater pain intensity (p<0.001), headache-related disability (p<0.001), abortive medication use (p = 0.02), and the proportion of migraine (p < 0.001), relative to those without trigger factors. Traveling (odd ratios [OR]: 6.4), hormonal changes (OR: 3.5), noise (OR: 2.8), alcohol (OR: 2.5), overeating (OR: 2.4), and stress (OR:1.8) were significantly associated with migraines compared to non-migraine headaches. The headaches that were associated with hormonal changes or noise were more often migraines, regardless of the preventive medication. The headaches due to stress, overeating, alcohol, and traveling were more often migraines without preventive medication, but it was not evident with preventive medication. CONCLUSION: Smartphone headache diary application is an effective tool to assess migraine trigger factors. The headaches with trigger factors had greater severity or migraine features. The type of triggers and the presence of preventive medication influenced the headache characteristics; hence, an investigation of trigger factors would be helpful in understanding migraine occurrences.

The change in the feeding system can greatly improve the growth performance of the yak ( Bos grunniens ), an important livestock species in the plateau region. Here, we comprehensively compared the effects of different feeding systems on the growth performance and ruminal development of yaks, and investigated the effects of ruminal microorganisms and metabolites using the 16S rRNA gene sequencing and liquid chromatograph–mass spectrometer (LC-MS) technologies. We found that compared to traditional grazing feeding, house feeding significantly improved the growth performance (such as average daily gain and net meat weight) and rumen development of the yaks. At the genus level, the abundance of Rikenellaceae RC9 Gut group , Christensenellaceae R-7 group , Lachnospiraceae NK3A20 group , Ruminococcaceae UCG-014 , and Prevotellaceae UCG-003 showed significant differences and was closely related to rumen development in the two distinct feeding systems. Also, metabolomics revealed that the change in the feeding system significantly affected the concentration and metabolic pathways of the related rumen metabolites. The metabolites with significant differences were significantly enriched in purine metabolism (xanthine, adenine, inosine, etc.), tyrosine metabolism (L-tyrosine, dopaquinone, etc.), phenylalanine metabolism (dihydro-3-caumaric acid, hippuric acid, etc.), and cAMP signaling pathway [acetylcholine, (-)-epinephrine, etc.]. This study scientifically support the house fattening feeding system for yaks. Also, our results provide new insights into the composition and function of microbial communities that promote ruminal development and in general growth of the yaks.

Study Objectives: To determine if weekend catch-up sleep (CUS) impacts body mass index (BMI) in the general population. Methods: A stratified random sample (2156 subjects; age 19-82 years old, 43.0 ± 14.5; 1183 male) from the general population was evaluated, in 2010, using face-to-face interviews about sociodemographic characteristics, height, weight, habitual sleep duration, and time-in-bed at night on weekdays and weekend, sleep-related profiles, mood and anxiety scales, and comorbid-medical conditions. Weekend CUS was identified when nocturnal sleep extension occurred over the weekend, and this was quantified. Average sleep duration, BMI, and chronotype were determined. The association of BMI with the presence and the amount of weekend CUS was analyzed, independent of average sleep duration, chronotype, and sociodemographic factors. Results: BMI and average sleep duration was 23.0 ± 3.0 kg/m2 and 7.3 ± 1.2 hours, respectively. The weekend CUS group consisted of 932 subjects (43.2%) who slept longer on weekend than weekdays by 1.8 ± 1.1 hours. Weekend CUS subjects had a significantly lower BMI (22.8 ± 0.19 kg/m2) than the non-CUS (23.1 ± 0.19 kg/m2) group, after adjustment for age, sex, average sleep duration, chronotype, other sociodemographic factors, and anxiety/mood status (p = .01) The relationship between weekend CUS and BMI was dose-dependent (p = 0.02): Every additional hour of weekend CUS was associated with a decrease of 0.12 kg/m2 in BMI (95% confidence interval, -0.23 to -0.02). Conclusions: Weekend sleep extension may have biological protective effects in preventing sleep-restriction induced or related obesity. The results suggest a simple population-level strategy to minimize effects of sleep loss.