Background and Objective: There is a bidirectional relationship between periodontal disease and type‐2 diabetes mellitus (DM). Inflammatory mediators may negatively affect glycemic control, and ...increased glucose levels and resultant glycation end‐products may alter the host response against bacterial infection. However, no agreement has been reached regarding the effect of DM on periodontal subgingival microbiota. Therefore, the purpose of the present study was to compare the subgingival biodiversity in deep periodontal pockets of subjects with chronic periodontitis and either uncontrolled type‐2 diabetes or no diabetes using 16S rRNA gene cloning and sequencing.
Material and methods: Twelve subjects with uncontrolled type‐2 diabetes (glycated hemoglobin > 8%) and eleven nondiabetic subjects presenting severe and generalized chronic periodontitis were selected. Subgingival biofilm from periodontal pockets > 5 mm were assessed using the 16S rRNA gene cloning and sequencing technique.
Results: Significant differences were observed in subgingival microbiota between diabetic and nondiabetic subjects. Diabetic subjects presented higher percentages of total clones of TM7, Aggregatibacter, Neisseria, Gemella, Eikenella, Selenomonas, Actinomyces, Capnocytophaga, Fusobacterium, Veillonella and Streptococcus genera, and lower percentages of Porphyromonas, Filifactor, Eubacterium, Synergistetes, Tannerella and Treponema genera than nondiabetic individuals (p < 0.05). Moreover, some phylotypes, such as Fusobacterium nucleatum, Veillonella parvula, V. dispar and Eikenella corrodens were detected significantly more often in diabetic subjects than in nondiabetic subjects (p < 0.05).
Conclusion: Subjects with uncontrolled type‐2 diabetes and chronic periodontitis presented significant dissimilarities in subgingival biodiversity compared with nondiabetic subjects.
Khat is a stimulating agent used by many people in the Horn of Africa and the Arabian peninsula. Khat chewing is a known cardiovascular risk factor and is thought to cause vasoconstriction, systemic ...hypertension, and thrombogenicity. A 33-year-old Somalian man initially presented with loss of neurological function of the left arm, hazy vision, and headache. He smokes tobacco and chews two bundles of khat a week for more than 10 years. His ECG on admission showed a Q wave in V1 and V2 and 2 mm ST-elevations in V1, V2, and V3 and a terminal negative T wave in I, aVL, V2, V3, and V4, consistent with a recent, evolving anterior infarction. A noncontrast enhanced CT of the brain showed ischemia in the right middle cerebral artery vascular territory. An MRI showed recent ischemia in the vascular territory of the posterior division of the right middle cerebral artery. Coronary angiography showed a 70% stenosis with haziness of the proximal left anterior descending artery. Diagnostic tests and imaging are consistent with recent myocardial infarction in the LAD vascular territory because of coronary spasm and cerebral infarction in the middle cerebral artery vascular territory probably related to khat chewing.
Proactive, that is, unsolicited, prosociality is a key component of our hyper-cooperation, which in turn has enabled the emergence of various uniquely human traits, including complex cognition, ...morality and cumulative culture and technology. However, the evolutionary foundation of the human prosocial sentiment remains poorly understood, largely because primate data from numerous, often incommensurable testing paradigms do not provide an adequate basis for formal tests of the various functional hypotheses. We therefore present the results of standardized prosociality experiments in 24 groups of 15 primate species, including humans. Extensive allomaternal care is by far the best predictor of interspecific variation in proactive prosociality. Proactive prosocial motivations therefore systematically arise whenever selection favours the evolution of cooperative breeding. Because the human data fit this general primate pattern, the adoption of cooperative breeding by our hominin ancestors also provides the most parsimonious explanation for the origin of human hyper-cooperation.
Many species use tools, but the mechanisms underpinning the behaviour differ between species and even among individuals within species, depending on the variants performed. When considering tool use ...‘as adaptation’, an important first step is to understand the contribution made by fixed phenotypes as compared to flexible mechanisms, for instance learning. Social learning of tool use is sometimes inferred based on variation between populations of the same species but this approach is questionable. Specifically, alternative explanations cannot be ruled out because population differences are also driven by genetic and/or environmental factors. To better understand the mechanisms underlying routine but non-universal (i.e. habitual) tool use, we suggest focusing on the ontogeny of tool use and individual variation within populations. For example, if tool-using competence emerges late during ontogeny and improves with practice or varies with exposure to social cues, then a role for learning can be inferred. Experimental studies help identify the cognitive and developmental mechanisms used when tools are used to solve problems. The mechanisms underlying the route to tool-use acquisition have important consequences for our understanding of the accumulation in technological skill complexity over the life course of an individual, across generations and over evolutionary time.
A new procedure is proposed for clustering attribute value data. When used in conjunction with conventional distance-based clustering algorithms this procedure encourages those algorithms to detect ...automatically subgroups of objects that preferentially cluster on subsets of the attribute variables rather than on all of them simultaneously. The relevant attribute subsets for each individual cluster can be different and partially (or completely) overlap with those of other clusters. Enhancements for increasing sensitivity for detecting especially low cardinality groups clustering on a small subset of variables are discussed. Applications in different domains, including gene expression arrays, are presented.
In meta-analysis, heterogeneity often exists between studies. Knowledge about study features (i.e., moderators) that can explain the heterogeneity in effect sizes can be useful for researchers to ...assess the effectiveness of existing interventions and design new potentially effective interventions. When there are multiple moderators, they may amplify or attenuate each other’s effect on treatment effectiveness. However, in most meta-analysis studies, interaction effects are neglected due to the lack of appropriate methods. The method meta-CART was recently proposed to identify interactions between multiple moderators. The analysis result is a tree model in which the studies are partitioned into more homogeneous subgroups by combinations of moderators. This paper describes the R-package
metacart
, which provides user-friendly functions to conduct meta-CART analyses in R. This package can fit both fixed- and random-effects meta-CART, and can handle dichotomous, categorical, ordinal and continuous moderators. In addition, a new look ahead procedure is presented. The application of the package is illustrated step-by-step using diverse examples.
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