The Mediating Effects of Self-Esteem on Anxiety and Emotion Regulation.

Individuals with anxiety disorders maladaptively appraise interpersonal threat cues leading to inaccurate interpretations of the self and others. However, little is known about the factors that mediate this association, therefore, the main aim of this study was to examine the relationship between state and trait anxiety, self-esteem, and emotion regulation strategies: reappraisal and suppression. Young adults aged between 18-26 years participated in the study. They completed a set of self-reports measuring emotion regulation, self-esteem, state-trait anxiety, and positive and negative attributes. Participants also completed an experimental task, using the dot-probe paradigm, which measures threat bias and response inhibition. The findings showed that trait and state anxiety predicted suppression, reappraisal, and internalising problems, and is linked to response inhibition. Importantly, low self-esteem, significantly mediated the relationship between increased anxiety and suppression. Taken together, these results show specific associations between emotion regulation and anxiety, highlighting the significant impact of self-esteem in young adults.

Quality of Life Assessment After Maxillomandibular Advancement Surgery for Obstructive Sleep Apnea.

PURPOSE: Although maxillomandibular advancement (MMA) surgery is highly efficacious for the management of obstructive sleep apnea (OSA), little information exists regarding the subjective effect of this treatment modality. The present study was undertaken to investigate the effect of MMA on patient-perceived quality of life (QOL) in OSA. PATIENTS AND METHODS: A retrospective cohort study of patients treated with MMA for OSA from May 2010 to April 2015 was performed. The primary outcome measure was a change in the QOL detected using the Ottawa Sleep Apnea Questionnaire (OSA-Q), which assesses the MMA-related changes in QOL with a 5-point Likert scale. The secondary outcome measure was a change in the apnea hypopnea index (AHI). RESULTS: Twenty-two patients participated in the present study. The mean maxillary and mandibular advancement were 8.36 and 11.08 mm, respectively. The AHI decreased from 42.4 to 6.9 events per hour postoperatively (P < .001). The QOL improved significantly after MMA (OSA-Q score 3.98 ± 0.35; P < 001). The sleep quality (4.35 ± 0.63), daytime function (4.13 ± 0.46), physical health (4.19 ± 0.45), mental and emotional health (4.02 ± 0.55), and sexual health (3.78 ± 0.62) categories all improved postoperatively (P < .001). The MMA-related side effects did not adversely affect the QOL. CONCLUSIONS: MMA for OSA significantly improves patient's subjective overall QOL, with few MMA-related side effects.

Linear and volumetric airway changes after maxillomandibular advancement for obstructive sleep apnea.

PURPOSE: Maxillomandibular advancement (MMA) surgery is a well-established treatment of obstructive sleep apnea (OSA). Although many studies have assessed the efficacy of MMA in treating OSA, very few studies have quantified the magnitude of its changes to airway morphology. Therefore, the present study investigated the linear and volumetric morphologic changes that occur in the pharyngeal airway after treatment of OSA using MMA. MATERIALS AND METHODS: A retrospective cohort study of patients with OSA treated from May 2010 to February 2014 was performed. Each patient underwent preoperative clinical and fiberoptic nasopharyngoscopic examinations. Pre- and postoperative polysomnograms, lateral cephalograms, and cone-beam computed tomography scans were acquired. The radiographic images were used to determine the linear and volumetric airway measurements. The time and magnitude of skeletal movement were used as the independent variables. The dependent variables included assessment of success or cure, apnea hypopnea index (AHI), cephalometric changes, Epworth score, rapid eye movement sleep, body mass index, and various airway morphologic parameters. RESULTS: A total of 15 patients (13 men and 2 women) participated in the present study. The surgical success and cure rate was 73.33% and 40.00%, respectively. Statistically significant improvements were found in the airway total volume, minimal cross-sectional area, anteroposterior and lateral dimensions, airway index, airway length, posterior airway space morphology, AHI, and Epworth sleepiness score. CONCLUSIONS: MMA is a highly successful surgical treatment of OSA that improves airway morphology and sleep quality. MMA results in a shorter and broader airway and associated improvements in the AHI.

Pharyngeal airway morphology in healthy individuals and in obstructive sleep apnea patients treated with maxillomandibular advancement: a comparative study.

OBJECTIVE: This study investigated the differences in airway morphology between control patients and those with obstructive sleep apnea (OSA) treated with maxillomandibular advancement (MMA) to gain better insight into the beneficial effects of MMA on airway morphology and OSA severity. STUDY DESIGN: This retrospective case-control study included preoperative radiographic data gathered for all patients; postoperative radiographic data were gathered for the OSA group. Statistical analysis, including the Student t test, and simple linear regression was performed to identify differences in cephalometric and airway variables among the three groups and to associate airway morphology to disease severity. RESULTS: Twenty-four patients (12 with OSA; 12 controls) treated at the same clinic were included in this study. Statistically significant differences versus control values were found for preoperative total airway volume, postoperative airway length, and both pre- and postoperative airway minimum cross-sectional areas. In general, the untreated OSA airway was anatomically compromised in comparison with controls, whereas the treated airway showed significant morphologic improvements, comparable with the control group values. CONCLUSIONS: MMA produces statistically significant airway improvements for OSA patients, producing airway morphology comparable with that of the controls. However, some degree of residual OSA may still exist. Therefore, factors other than static airway morphology contribute to OSA pathogenesis.

Evaluation of 1H NMR metabolic profiling using biofluid mixture design.

A strategy for evaluating the performance of quantitative spectral analysis tools in conditions that better approximate background variation in a metabonomics experiment is presented. Three different urine samples were mixed in known proportions according to a {3, 3} simplex lattice experimental design and analyzed in triplicate by 1D (1)H NMR spectroscopy. Fifty-four urinary metabolites were subsequently quantified from the sample spectra using two methods common in metabolic profiling studies: (1) targeted spectral fitting and (2) targeted spectral integration. Multivariate analysis using partial least-squares (PLS) regression showed the latent structure of the spectral set recapitulated the experimental mixture design. The goodness-of-prediction statistic (Q(2)) of each metabolite variable in a PLS model was calculated as a metric for the reliability of measurement, across the sample compositional space. Several metabolites were observed to have low Q(2) values, largely as a consequence of their spectral resonances having low s/n or strong overlap with other sample components. This strategy has the potential to allow evaluation of spectral features obtained from metabolic profiling platforms in the context of the compositional background found in real biological sample sets, which may be subject to considerable variation. We suggest that it be incorporated into metabolic profiling studies to improve the estimation of matrix effects that confound accurate metabolite measurement. This novel method provides a rational basis for exploiting information from several samples in an efficient manner and avoids the use of multiple spike-in authentic standards, which may be difficult to obtain.

Acetaminophen dosing of humans results in blood transcriptome and metabolome changes consistent with impaired oxidative phosphorylation.

UNLABELLED: The diagnosis and management of drug-induced liver injury (DILI) is hindered by the limited utility of traditional clinical chemistries. It has recently been shown that hepatotoxicants can produce compound-specific changes in the peripheral blood (PB) transcriptome in rodents, suggesting that the blood transcriptome might provide new biomarkers of DILI. To investigate in humans, we used DNA microarrays as well as serum metabolomic methods to characterize changes in the transcriptome and metabolome in serial PB samples obtained from six healthy adults treated with a 4-g bolus dose of acetaminophen (APAP) and from three receiving placebo. Treatment did not cause liver injury as assessed by traditional liver chemistries. However, 48 hours after exposure, treated subjects showed marked down-regulation of genes involved in oxidative phosphorylation/mitochondrial function that was not observed in the placebos (P < 1.66E-19). The magnitude of down-regulation was positively correlated with the percent of APAP converted to the reactive metabolite N-acetyl-p-benzoquinone-imide (NAPQI) (r= 0.739;P= 0.058). In addition, unbiased analysis of the serum metabolome revealed an increase in serum lactate from 24 to 72 hours postdosing in the treated subjects alone (P< 0.005). Similar PB transcriptome changes were observed in human overdose patients and rats receiving toxic doses. CONCLUSION: The single 4-g APAP dose produced a transcriptome signature in PB cells characterized by down-regulation of oxidative phosphorylation genes accompanied by increased serum lactate. Similar gene expression changes were observed in rats and several patients after consuming hepatotoxic doses of APAP. The timing of the changes and the correlation with NAPQI production are consistent with mechanisms known to underlie APAP hepatoxicity. These studies support the further exploration of the blood transcriptome for biomarkers of DILI.

Evaluating low-intensity unknown signals in quantitative proton NMR mixture analysis.

Analytical analyses of highly complex mixtures, such as biofluids or liquid food products, often give rise to signals for unknown compounds, particularly for compounds at low concentration. Here we compare two conventional chemometric approaches for NMR spectral analysis ("spectral binning" and "high-resolution analysis") with a novel library-based method ("targeted profiling of unknowns", TPU). The three methods were applied to a proton NMR spectral data set of ultrafiltered mouse serum typical of those examined in metabolomics/metabonomics studies. The advantages of high-resolution analysis of typical NMR peaks have been well described previously, and as a result we examined low intensity unknowns peaks (LIUPs). A total of 25 LIUPs were assessed based on their significance to multivariate statistical analysis of the data set using the TPU method. The linearity of NMR signals at low incremental concentration changes (< 10 microM) was determined by titration of endogenously occurring metabolites into filtered mouse serum. Carbon-13 decoupling of the NMR spectra was used to ensure isotope-satellite peaks were eliminated. Four peaks were noted as significant to separation between arthritic and diseased animals. The conventional spectral methods were hampered by baseline noise or overlap with high concentration metabolites and were not able to identify these LIUPs reliably. In general, conventional methods, particularly high-resolution analysis, are recommended for peaks with moderate signal-to-noise. The TPU method is recommended for peaks with low signal-to-noise or when compression of spectral data with high fidelity is desirable, such as integration of NMR data into cross-platform studies.

Leveraging latent information in NMR spectra for robust predictive models.

A significant challenge in metabolomics experiments is extracting biologically meaningful data from complex spectral information. In this paper we compare two techniques for representing 1D NMR spectra: "Spectral Binning" and "Targeted Profiling". We use simulated 1D NMR spectra with specific characteristics to assess the quality of predictive multivariate statistical models built using both data representations. We also assess the effect of different variable scaling techniques on the two data representations. We demonstrate that models built using Targeted Profiling are not only more interpretable than Spectral Binning models, but are more robust with respect to compound overlap, and variability in solution conditions (such as pH and ionic strength). Our findings from the synthetic dataset were validated using a real-world dataset.

Targeted profiling: quantitative analysis of 1H NMR metabolomics data.

Extracting meaningful information from complex spectroscopic data of metabolite mixtures is an area of active research in the emerging field of "metabolomics", which combines metabolism, spectroscopy, and multivariate statistical analysis (pattern recognition) methods. Chemometric analysis and comparison of 1H NMR1 spectra is commonly hampered by intersample peak position and line width variation due to matrix effects (pH, ionic strength, etc.). Here a novel method for mixture analysis is presented, defined as "targeted profiling". Individual NMR resonances of interest are mathematically modeled from pure compound spectra. This database is then interrogated to identify and quantify metabolites in complex spectra of mixtures, such as biofluids. The technique is validated against a traditional "spectral binning" analysis on the basis of sensitivity to water suppression (presaturation, NOESY-presaturation, WET, and CPMG), relaxation effects, and NMR spectral acquisition times (3, 4, 5, and 6 s/scan) using PCA pattern recognition analysis. In addition, a quantitative validation is performed against various metabolites at physiological concentrations (9 microM-8 mM). "Targeted profiling" is highly stable in PCA-based pattern recognition, insensitive to water suppression, relaxation times (within the ranges examined), and scaling factors; hence, direct comparison of data acquired under varying conditions is made possible. In particular, analysis of metabolites at low concentration and overlapping regions are well suited to this analysis. We discuss how targeted profiling can be applied for mixture analysis and examine the effect of various acquisition parameters on the accuracy of quantification.