A Meta-Analysis of Procedures to Change Implicit Measures

Using a novel technique known as network meta-analysis, we synthesized evidence from 492 studies (87,418 participants) to investigate the effectiveness of procedures in changing implicit measures, which we define as response biases on implicit tasks. We also evaluated these procedures’ effects on explicit and behavioral measures. We found that implicit measures can be changed, but effects are often relatively weak (|ds| \u3c .30). Most studies focused on producing short-term changes with brief, single-session manipulations. Procedures that associate sets of concepts, invoke goals or motivations, or tax mental resources changed implicit measures the most, whereas procedures that induced threat, affirmation, or specific moods/emotions changed implicit measures the least. Bias tests suggested that implicit effects could be inflated relative to their true population values. Procedures changed explicit measures less consistently and to a smaller degree than implicit measures and generally produced trivial changes in behavior. Finally, changes in implicit measures did not mediate changes in explicit measures or behavior. Our findings suggest that changes in implicit measures are possible, but those changes do not necessarily translate into changes in explicit measures or behavior

Oral Microbiome and Gingival Gene Expression of Inflammatory Biomolecules with Aging and Periodontitis

Although data describe the presence and increase of inflammatory mediators in the local environment in periodontitis vs. health in humans, details regarding how these responses evolve in the transition from health to disease, changes during disease progression, and features of a resolved lesion remain unknown. This study used a nonhuman primate model of ligature-induced periodontitis in young, adolescent, adult, and aged animals to document features of inflammatory response affected by age. Rhesus monkeys had ligatures tied and provided gingival tissue biopsy specimens at baseline, 0.5, 1, and 3 months of disease and at 5 months of the study, which was 2 months post-ligature removal for clinically resolved tissues. The transcriptome was assessed using microarrays for chemokine (n = 41), cytokine (n = 45), chemokine receptor (n = 21), cytokine receptor (n = 37), and lipid mediator (n = 31) genes. Limited differences were noted in healthy tissues for chemokine expression with age; however, chemokine receptor genes were decreased in young but elevated in aged samples. IL1A, IL36A, and IL36G cytokines were decreased in the younger groups, with IL36A elevated in aged animals. IL10RA/IL10RB cytokine receptors were altered with age. Striking variation in the lipid mediator genes in health was observed with nearly 60% of these genes altered with age. A specific repertoire of chemokine and chemokine receptor genes was affected by the disease process, predominated by changes during disease initiation. Cytokine/cytokine receptor genes were also elevated with disease initiation, albeit IL36B, IL36G, and IL36RN were all significantly decreased throughout disease and resolution. Significant changes were observed in similar lipid mediator genes with disease and resolution across the age groups. Examination of the microbiome links to the inflammatory genes demonstrated that specific microbes, including Fusobacterium, P. gingivalis, F. alocis, Pasteurellaceae, and Prevotella are most frequently significantly correlated. These correlations were generally positive in older animals and negative in younger specimens. Gene expression and microbiome patterns from baseline were distinctly different from disease and resolution. These results demonstrate patterns of inflammatory gene expression throughout the phases of the induction of a periodontal disease lesion. The patterns show a very different relationship to specific members of the oral microbiome in younger compared with older animals

Gingival Transcriptome of Innate Antimicrobial Factors and the Oral Microbiome with Aging and Periodontitis

The epithelial barrier at mucosal sites comprises an important mechanical protective feature of innate immunity, and is intimately involved in communicating signals of infection/tissue damage to inflammatory and immune cells in these local environments. A wide array of antimicrobial factors (AMF) exist at mucosal sites and in secretions that contribute to this innate immunity. A non-human primate model of ligature-induced periodontitis was used to explore characteristics of the antimicrobial factor transcriptome (n = 114 genes) of gingival biopsies in health, initiation and progression of periodontal lesions, and in samples with clinical resolution. Age effects and relationship of AMF to the dominant members of the oral microbiome were also evaluated. AMF could be stratified into 4 groups with high (n = 22), intermediate (n = 29), low (n = 18) and very low (n = 45) expression in healthy adult tissues. A subset of AMF were altered in healthy young, adolescent and aged samples compared with adults (e.g., APP, CCL28, DEFB113, DEFB126, FLG2, PRH1) and were affected across multiple age groups. With disease, a greater number of the AMF genes were affected in the adult and aged samples with skewing toward decreased expression, for example WDC12, PGLYRP3, FLG2, DEFB128, and DEF4A/B, with multiple age groups. Few of the AMF genes showed a \u3e2-fold increase with disease in any age group. Selected AMF exhibited significant positive correlations across the array of AMF that varied in health and disease. In contrast, a rather limited number of the AMF significantly correlated with members of the microbiome; most prominent in healthy samples. These correlated microbes were different in younger and older samples and differed in health, disease and resolution samples. The findings supported effects of age on the expression of AMF genes in healthy gingival tissues showing a relationship to members of the oral microbiome. Furthermore, a dynamic expression of AMF genes was related to the disease process and showed similarities across the age groups, except for low/very low expressed genes that were unaffected in young samples. Targeted assessment of AMF members from this large array may provide insight into differences in disease risk and biomolecules that provide some discernment of early transition to disease

Oral Microbial Biofilm Stimulation of Epithelial Cell Responses

Oral bacterial biofilms trigger chronic inflammatory responses in the host that can result in the tissue destructive events of periodontitis. However, the characteristics of the capacity of specific host cell types to respond to these biofilms remain ill-defined. This report describes the use of a novel model of bacterial biofilms to stimulate oral epithelial cells and profile select cytokines and chemokines that contribute to the local inflammatory environment in the periodontium. Monoinfection biofilms were developed with Streptococcus sanguinis, Streptococcus oralis, Streptococcus gordonii, Actinomyces naeslundii, Fusobacterium nucleatum, and Porphyromonas gingivalis on rigid gas-permeable contact lenses. Biofilms, as well as planktonic cultures of these same bacterial species, were incubated under anaerobic conditions with a human oral epithelial cell line, OKF4, for up to 24 h. Gro-1α, IL1α, IL-6, IL-8, TGFα, Fractalkine, MIP-1α, and IP-10 were shown to be produced in response to a range of the planktonic or biofilm forms of these species. P. gingivalis biofilms significantly inhibited the production of all of these cytokines and chemokines, except MIP-1α. Generally, the biofilms of all species inhibited Gro-1α, TGFα, and Fractalkine production, while F. nucleatum biofilms stimulated significant increases in IL-1α, IL-6, IL-8, and IP-10. A. naeslundii biofilms induced elevated levels of IL-6, IL-8 and IP-10. The oral streptococcal species in biofilms or planktonic forms were poor stimulants for any of these mediators from the epithelial cells. The results of these studies demonstrate that oral bacteria in biofilms elicit a substantially different profile of responses compared to planktonic bacteria of the same species. Moreover, certain oral species are highly stimulatory when in biofilms and interact with host cell receptors to trigger pathways of responses that appear quite divergent from individual bacteria

Gingival transcriptomic patterns of macrophage polarization during initiation, progression, and resolution of periodontitis.

Phenotypic and functional heterogeneity of macrophages is clearly a critical component of their effective functions in innate and adaptive immunity. This investigation hypothesized that altered profiles of gene expression in gingival tissues in health, disease, and resolution would reflect changes in macrophage phenotypes occurring in these tissues. The study used a nonhuman primate model to evaluate gene expression profiles as footprints of macrophage variation using a longitudinal experimental model of ligature-induced periodontitis in animals from 3 to 23 years of age to identify aging effects on the gingival environment. Significant differences were observed in distribution of expressed gene levels for M0, M1, and M2 macrophages in healthy tissues with the younger animals showing the least expression. M0 gene expression increased with disease in all but the aged group, while M1 was increased in adult and young animals, and M2 in all age groups, as early as disease initiation (within 0.5 months). Numerous histocompatibility genes were increased with disease, except in the aged samples. An array of cytokines/chemokines representing both M1 and M2 cells were increased with disease showing substantial increases with disease initiation (e.g. IL1A, CXCL8, CCL19, CCL2, CCL18), although the aged tissues showed a more limited magnitude of change across these macrophage genes. The analytics of macrophage genes at sites of gingival health, disease, and resolution demonstrated distinct profiles of host response interactions that may help model the disease mechanisms occurring with the formation of a periodontal lesion

Patient-Specific Variations in Biomarkers across Gingivitis and Periodontitis

This study investigates the use of saliva, as an emerging diagnostic fluid in conjunction with classification techniques to discern biological heterogeneity in clinically labelled gingivitis and periodontitis subjects (80 subjects; 40/group) A battery of classification techniques were investigated as traditional single classifier systems as well as within a novel selective voting ensemble classification approach (SVA) framework. Unlike traditional single classifiers, SVA is shown to reveal patient-specific variations within disease groups, which may be important for identifying proclivity to disease progression or disease stability. Salivary expression profiles of IL-1ß, IL-6, MMP-8, and MIP-1α from 80 patients were analyzed using four classification algorithms (LDA: Linear Discriminant Analysis [LDA], Quadratic Discriminant Analysis [QDA], Naïve Bayes Classifier [NBC] and Support Vector Machines [SVM]) as traditional single classifiers and within the SVA framework (SVA-LDA, SVA-QDA, SVA-NB and SVA-SVM). Our findings demonstrate that performance measures (sensitivity, specificity and accuracy) of traditional classification as single classifier were comparable to that of the SVA counterparts using clinical labels of the samples as ground truth. However, unlike traditional single classifier approaches, the normalized ensemble vote-counts from SVA revealed varying proclivity of the subjects for each of the disease groups. More importantly, the SVA identified a subset of gingivitis and periodontitis samples that demonstrated a biological proclivity commensurate with the other clinical group. This subset was confirmed across SVA-LDA, SVA-QDA, SVA-NB and SVA-SVM. Heatmap visualization of their ensemble sets revealed lack of consensus between these subsets and the rest of the samples within the respective disease groups indicating the unique nature of the patients in these subsets. While the source of variation is not known, the results presented clearly elucidate the need for novel approaches that accommodate inherent heterogeneity and personalized variations within disease groups in diagnostic characterization. The proposed approach falls within the scope of P4 medicine (predictive, preventive, personalized, and participatory) with the ability to identify unique patient profiles that may predict specific disease trajectories and targeted disease management

MARINE FISHES (CHONDRICHTHYES, HOLOCEPHALI, ACTINOPTERYGII) FROM THE UPPER CRETACEOUS (CAMPANIAN) RYBUSHKA FORMATION NEAR BELOE OZERO, SARATOV OBLAST, RUSSIA

A diverse fish paleofauna occurs in the upper Campanian portion of the Rybushka Formation exposed near Saratov city in the Saratov Oblast, Russia. Twenty taxa have been identified, including two holocephalans (Ischyodus bifurcatus and Amylodon karamysh), twelve elasmobranchs (Synechodus sp., Cederstroemia sp., Cretalamna cf. C. borealis, C. cf. C. sarcoportheta, Archaeolamna kopingensis, Eostriatolamia segedini, E. venusta, Pseudocorax laevis, Squalicorax kaupi, Squalicorax morphotype 1, Squalidae indet., and Squatirhina sp.), and six teleosts (Pachyrhizodus sp., Saurocephalus lanciformis, Paralbula casei, Enchodus cf. E. dirus, E. cf. E. gladiolus, and E. petrosus). Many of these taxa are new to the Campanian fish record of Russia, and the assemblage demonstrates that there is significant taxonomic overlap between the Rybushka Formation paleofauna and that of North America

Cross-Talk Between Clinical and Host-Response Parameters of Periodontitis in Smokers

Background and Objective Periodontal diseases are a major public health concern leading to tooth loss and have also been shown to be associated with several chronic systemic diseases. Smoking is a major risk factor for the development of numerous systemic diseases, as well as periodontitis. While it is clear that smokers have a significantly enhanced risk for developing periodontitis leading to tooth loss, the population varies regarding susceptibility to disease associated with smoking. This investigation focused on identifying differences in four broad sets of variables, consisting of: (i) host‐response molecules; (ii) periodontal clinical parameters; (iii) antibody responses to periodontal pathogens and oral commensal bacteria; and (iv) other variables of interest, in a population of smokers with (n = 171) and without (n = 117) periodontitis. Material and Methods Bayesian network structured learning (BNSL) techniques were used to investigate potential associations and cross‐talk between the four broad sets of variables. Results BNSL revealed two broad communities with markedly different topology between the populations of smokers, with and without periodontitis. Confidence of the edges in the resulting network also showed marked variations within and between the periodontitis and nonperiodontitis groups. Conclusion The results presented validated known associations and discovered new ones with minimal precedence that may warrant further investigation and novel hypothesis generation. Cross‐talk between the clinical variables and antibody profiles of bacteria were especially pronounced in the case of periodontitis and were mediated by the antibody response profile to Porphyromonas gingivalis