McGregor Colon (canoesummer2)

To study the peri-implant submucosal microbiome in relation to implant disease status, dentition status, smoking habit, gender, implant location, implant system, time of functional loading, probing pocket depth (PPD), and presence of bleeding on probing. Biofilm samples were collected from the deepest peri-implant site of 41 patients with paper points, and analysed using 16S rRNA gene pyrosequencing. We observed differences in microbial profiles by PPD, implant disease status, and dentition status. Microbiota in deep pockets included higher proportions of the genera Fusobacterium, Prevotella, and Anaeroglobus compared with shallow pockets that harboured more Rothia, Neisseria, Haemophilus, and Streptococcus. Peri-implantitis (PI) sites were dominated by Fusobacterium and Treponema compared with healthy implants and peri-implant mucositis, which were mostly colonized by Rothia and Streptococcus. Partially edentulous (PE) individuals presented more Fusobacterium, Prevotella, and Rothia, whereas fully edentulous individuals presented more Veillonella and Streptococcus. PPD, implant disease status, and dentition status may affect the submucosal ecology leading to variation in composition of the microbiome. Deep pockets, PI, and PE individuals were dominated by Gram-negative anaerobic taxa. PPD, implant disease status, and dentition status may affect the submucosal ecology leading to variation in composition of the microbiome. Deep pockets, PI, and PE individuals were dominated by Gram-negative anaerobic taxa. This study aimed to investigate the association between work patterns and periodontal disease. Data were collected from the Korea National Health and Nutrition Examination Survey between 2007 and 2012, and data from 22,508 subjects aged ≥19 years were included. An individual's work pattern was classified as either daytime or shift work. Sleep duration was categorized into three ranges ≤5, 6-8, and ≥9h/day. A multivariate logistic regression model was used to determine the adjusted odds ratio (OR) for CPI (Community Periodontal Index) ≥3. The CONTRAST statement was used to show the interaction effect of work patterns and sleep duration. The adjusted OR of shift work was 2.168 (CI 1.929-2.438, p < .0001). Participants who sleep ≤5 or ≥9h/day showed ORs 0.735 and 0.663, respectively (p=.0181). Interaction effect analysis revealed that the work pattern had a strong influence on periodontal condition when combined with the sleep amount. Shift workers with ≤5 or ≥ 9h of sleep showed significantly increased ORs for CPI ≥3 (2.1406 and 2.3251, respectively, p < .0001). Trastuzumab The ORs for daytime workers were comparable to the original values (≤5 0.7348, p=.0292; ≥9 0.6633, p=.0428). Altered sleep patterns caused by shift work have more influence on periodontal disease than sleep duration. Altered sleep patterns caused by shift work have more influence on periodontal disease than sleep duration.We propose a Bayesian hierarchical monitoring design for single-arm phase II clinical trials of cancer treatments that incorporates the information on the duration of response (DOR) into the monitoring rules. To screen a new treatment by evaluating its preliminary therapeutic effect, futility monitoring rules are commonly used in phase II clinical trials to make "go/no-go" decisions timely and efficiently. These futility monitoring rules are usually focused on a single outcome (eg, response rate), although a single outcome may not adequately determine the efficacy of the experimental treatment. For example, targeted agents with a long response duration but a similar response rate may be worth further evaluation in cancer research. To address this issue, we propose Bayesian hierarchical futility monitoring rules to consider both the response rate and duration. The first level of monitoring evaluates whether the response rate provides evidence th