Search

BackgroundDeterioration of self-reported oral health and decline in cognitive function are two main adverse health outcomes experienced by the older adults. Little evidence was found on the psychosocial mechanism between self-reported oral health and cognitive function. This study explores the association between self-reported oral health and cognitive function and examines the mediating effect of life satisfaction among the community-dwelling elderly in Jinan, China.MethodsA total of 512 older individuals (60+) were included in the study. Cognitive function was assessed using the Chinese version of the Mini-Mental State Examination scale (MMSE), and self-reported oral health was measured using the Chinese version of the Geriatric Oral Health Assessment Index (GOHAI). Pearson correlation analysis was used to determine the relationship between self-reported oral health, life satisfaction, and cognitive function. Multivariate linear regression analysis was conducted to explore the possible effect of covariates. Structural equation modelling and Bootstrap analyses were conducted to verify the mediating role of life satisfaction.ResultsThe mean MMSE score was 25.65 ± 4.42. Better self-reported oral health was significantly associated with a higher level of life satisfaction, and those with higher life satisfaction experienced better cognitive function. Age, educational level and source of living expenses were found to be cofounding variables. Life satisfaction partially mediated the effect of self-reported oral health on cognitive function (95% confidence interval [CI]: 0.010 to 0.075). The mediating effect of life satisfaction accounted for 24% of the total effect.ConclusionThe level of cognitive function was relatively high. Self-reported oral health was positively associated with cognitive function, and the mediating effect of life satisfaction was proven to exist in the community-dwelling older individuals. Early screening for oral diseases and a greater focus on life satisfaction are recommended.

Additional file 1: Supplementary Table 1. Measurements of the variables in this study.

Additional file 3: Supplemental Table 1. The percentage of chromosome coverage (%) by ROHs in each breed. Supplemental Table 2. Candidate genes located in genomic regions with a high frequency of ROHs. Supplemental Table 3. Pig QTLs located in genomic regions with a high frequency of ROHs. Supplemental Table 4. The summary of Sample information for each breed.

Low cost disposable three-electrode was prepared with screen-printed method. Modification with AuNPs(gold nano-particles)@FLGS(few-layer graphene sheet) was studied to improve its electrochemical properties. FLGS was obtained by sonicating expanded graphite in the DMF solvent, and AuNPs @FLGS composite was prepared with chemical reaction methods where PVP(Poly-vingl pyrrolidone) was added to improve their shape and distribution of AuNPs on FLGS. The surface morphologies of samples were observed with SEM and their electrochemical properties were characterized by CV curves. Results show that the modification with AuNPs@FLGS can greatly improve the electrochemical response of electrodes. The ethanol dehydrogenase (ADH), cohydrogenase enzyme (NAD+) and electron transfer mediator (potassium ferricyanide) are immobilized on the modified electrode for ethanol detection. This biosensor exhibits a fast and steady amperometric response to different ethanol concentration.

In this article, carbon nanotubes were grown on free standing multi-layer graphene sheet to form three dimensional carbon (3D-carbon) materials, and then used as support of Fe2O3 nanoparticles. Ultrasmall Fe2O3 nanoparticles were uniformly deposited on the surface of the 3D-carbon. Due to the synergistic effect of Fe2O3 nanoparticles and 3D-carbon, the electrochemical properties are higher than that of bare Fe2O3 material and multi-layer graphene sheet supported Fe2O3. Reversible capacity 528 mAh g-1 is maintained at a high current density of 500 mA g−1 after 50 cycles. This composite shows a stable cycling and high rate performance.

Clinical diagnosis of acute infectious diseases during the early stages of infection is critical to administering the appropriate treatment to improve the disease outcome. We present a data driven analysis of the human cellular response to respiratory viruses including influenza, respiratory syncytia virus, and human rhinovirus, and compared this with the response to the bacterial endotoxin, Lipopolysaccharides (LPS). Using an anomaly detection framework we identified pathways that clearly distinguish between asymptomatic and symptomatic patients infected with the four different respiratory viruses and that accurately diagnosed patients exposed to a bacterial infection. Connectivity pathway analysis comparing the viral and bacterial diagnostic signatures identified host cellular pathways that were unique to patients exposed to LPS endotoxin indicating this type of analysis could be used to identify host biomarkers that can differentiate clinical etiologies of acute infection. We applied the Multivariate State Estimation Technique (MSET) on two human influenza (H1N1 and H3N2) gene expression data sets to define host networks perturbed in the asymptomatic phase of infection. Our analysis identified pathways in the respiratory virus diagnostic signature as prognostic biomarkers that triggered prior to clinical presentation of acute symptoms. These early warning pathways correctly predicted that almost half of the subjects would become symptomatic in less than forty hours post-infection and that three of the 18 subjects would become symptomatic after only 8 hours. These results provide a proof-of-concept for utility of anomaly detection algorithms to classify host pathway signatures that can identify presymptomatic signatures of acute diseases and differentiate between etiologies of infection. On a global scale, acute respiratory infections cause a significant proportion of human co-morbidities and account for 4.25 million deaths annually. The development of clinical diagnostic tools to distinguish between acute viral and bacterial respiratory infections is critical to improve patient care and limit the overuse of antibiotics in the medical community. The identification of prognostic respiratory virus biomarkers provides an early warning system that is capable of predicting which subjects will become symptomatic to expand our medical diagnostic capabilities and treatment options for acute infectious diseases. The host response to acute infection may be viewed as a deterministic signaling network responsible for maintaining the health of the host organism. We identify pathway signatures that reflect the very earliest perturbations in the host response to acute infection. These pathways provide a monitor the health state of the host using anomaly detection to quantify and predict health outcomes to pathogens.