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Abstract Background Obstructive sleep apnoea (OSA) is commonly associated with insulin resistance (IR) and dyslipidaemia. Apolipoprotein E (APOE) plays important roles in lipid metabolism. The study aimed to disentangle the multifactorial relationships between IR and APOE based on a large-scale population with OSA. Methods A total of 5,591 participants who underwent polysomnography for OSA diagnosis were finally enrolled. We collected anthropometric, fasting biochemical and polysomnographic data for each participant. Linear regression analysis was performed to evaluate the relationships between APOE, IR, and sleep breathing-related parameters. Logistic regression, restricted cubic spline (RCS) and mediation analyses were used to explore relationships between APOE and IR in patients with OSA. Results Increasing OSA severity was associated with greater obesity, more obvious dyslipidaemia, and higher levels of APOE and IR. APOE was positively correlated with the apnoea-hypopnoea index (AHI), oxygen desaturation index (ODI) and microarousal index (MAI) even after adjusting for age, sex, body mass index, and smoking and drinking levels (β = 0.107, β = 0.102, β = 0.075, respectively, all P

BackgroundThe molecular effects of intermediate and long-term exposure to air pollution and temperature, such as those on extracellular microRNA (ex-miRNA) are not well understood but may have clinical consequences.ObjectivesTo assess the association between exposure to ambient air pollution and temperature and ex-miRNA profiles.MethodsOur study population consisted of 734 participants in the Normative Aging Study (NAS) between 1999 and 2015. We used high-resolution models to estimate four-week, eight-week, twelve-week, six-month, and one-year moving averages of PM2.5, O3, NO2, and ambient temperature based on geo-coded residential addresses. The outcome of interest was the extracellular microRNA (ex-miRNA) profile of each participant over time. We used a longitudinal quantile regression approach to estimate the association between the exposures and each ex-miRNA. Results were corrected for multiple comparisons and ex-miRNAs that were still significantly associated with the exposures were further analyzed using KEGG pathway analysis and Ingenuity Pathway Analysis.ResultsWe found 151 significant associations between levels of PM2.5, O3, NO2, and ambient temperature and 82 unique ex-miRNAs across multiple quantiles. Most of the significant results were associations with intermediate-term exposure to O3, long-term exposure to PM2.5, and both intermediate and long-term exposure to ambient temperature. The exposures were most often associated with the 75th and 90th percentile of the outcomes. Pathway analyses of significant ex-miRNAs revealed their involvement in biological pathways involving cell function and communication as well as clinical diseases such as cardiovascular disease, respiratory disease, and neurological disease.ConclusionOur results show that intermediate and long-term exposure to all our exposures of interest were associated with changes in the ex-miRNA profile of study participants. Further studies on environmental risk factors and ex-miRNAs are warranted.

BackgroundWhile the health effects of air pollution and temperature are widely studied, the molecular effects are poorly understood. Extracellular microRNAs (ex-miRNAs) have the potential to serve as diagnostic or prognostic biomarkers and/or to act as intercellular signaling molecules that mediate the effects of environmental exposures on health outcomes.MethodsWe examined the relationship between short-term exposure to air pollution and ambient temperature and the ex-miRNA profiles of participants in the Normative Aging Study (NAS) from 1999 to 2015. Our exposures were defined as same-day, two-day, three-day, one-week, two-week, and three-week moving averages of PM2.5, NO2, O3, and temperature which were derived from high-resolution spatio-temporal models. The ex-miRNA profiles of the subjects were obtained during follow-up visits. We analyzed the data using a longitudinal quantile regression model adjusted for individual covariates, batch effects, and time trends. We adjusted for multiple comparisons using a false discovery rate (FDR) correction. Ex-miRNAs that were significantly associated with exposures were further investigated using pathway analyses.ResultsWe found that all the examined exposures were associated with changes in ex-miRNA profiles in our study, particularly PM2.5 which was responsible for most of the statistically significant results. We found 110 statistically significant exposure-outcome relationships that revealed associations with the levels of 52 unique ex-miRNAs. Pathway analyses showed these ex-miRNAs have been linked to target mRNAs, genes, and biological mechanisms that could affect virtually every organ system, and as such may be linked to multiple clinical disease presentations such as cardiovascular disease, respiratory disease, and neurological disease.ConclusionsAir pollution and temperature exposures were significantly associated with alterations in the ex-miRNA profiles of NAS subjects with possible biological consequences.

While associations between short-term exposure to fine particulate matter (PM2.5) and risk of hospitalization are well documented and evidence suggests that such associations change over time, it is unclear whether these temporal changes exist in understudied less-urban areas or differ by sub-population. We analyzed daily time-series data of 968 continental U.S. counties for 2000-2016, with cause-specific hospitalization from Medicare claims and population-weighted PM2.5 concentrations originally estimated at 1km × 1 km from a hybrid model. Circulatory and respiratory hospitalizations were categorized based on primary diagnosis codes at discharge. Using modified Bayesian hierarchical modelling, we evaluated the temporal trend in association between PM2.5 and hospitalizations and whether disparities in this trend exist across individual-level characteristics (e.g., sex, age, race, and Medicaid eligibility as a proxy for socio-economic status) and urbanicity. Urbanicity was categorized into three levels by county-specific percentage of urban population based on urban rural delineation from the U.S. Census. In this cohort with understudied less-urban areas without regulatory monitors, we still found positive association between circulatory and respiratory hospitalization and short-term exposure to PM2.5, with higher effect estimates towards the end of study period. Consistent with current literature, we identified significant disparity in associations by race, socioeconomic status and urbanicity. We found that the percentage change in circulatory hospitalization rate per 10 μg/m3 increase in PM2.5 was higher in the 2008-2016 time period compared to the 2000-2007 period by 0.33% (95% posterior credible interval 0.22, 0.44%), 0.52% (0.33, 0.69%), and 0.67% (0.53, 0.83%) for low, medium and high tertiles of urban areas, respectively. We also observed significant differences in temporal trends of associations across socioeconomic status, sex, and age, indicating a possible widening in disparity of PM2.5-related health burden. This study raises the importance of considering environmental justice issues in PM2.5-related health impacts with respect to how associations may change over time.

Although research indicates health and well-being benefits of greenspace, little is known regarding how greenspace may influence adaptation to health risks from heat, particularly how these risks change over time. Using daily hospitalization rates of Medicare beneficiaries ≥65 years for 2000-2016 in 40 U.S. Northeastern urban counties, we assessed how temperature-related hospitalizations from cardiovascular causes (CVD) and heat stroke (HS) changed over time. We analyzed effect modification of those temporal changes by Enhanced Vegetation Index (EVI), approximating greenspace. We used a two-stage analysis including a generalized additive model and meta-analysis. Results showed that relative risk (RR) (per 1 °C increase in lag0-3 temperature) for temperature-HS hospitalization was higher in counties with the lowest quartile EVI (RR = 2.7, 95% CI: 2.0, 3.4) compared to counties with the highest quartile EVI (RR = 0.40, 95% CI: 0.14, 1.13) in the early part of the study period (2000-2004). RR of HS decreased to 0.88 (95% CI: 0.31, 2.53) in 2013-2016 in counties with the lowest quartile EVI. RR for HS changed over time in counties in the highest quartile EVI, with RRs of 0.4 (95% CI: -0.7, 1.4) in 2000-2004 and 2.4 (95% CI: 1.6, 3.2) in 2013-2016. Findings suggest that adaptation to heat-health associations vary by greenness. Greenspace may help lower risks from heat but such health risks warrant continuous local efforts such as heat-health plans.

Epidemiologic studies have found associations between fine particulate matter (PM2.5) exposure and adverse health effects using exposure models that incorporate monitoring data and other relevant information. Here, we use nine PM2.5 concentration models (i.e., exposure models) that span a wide range of methods to investigate i) PM2.5 concentrations in 2011, ii) potential changes in PM2.5 concentrations between 2011 and 2028 due to on-the-books regulations, and iii) PM2.5 exposure for the U.S. population and four racial/ethnic groups. The exposure models included two geophysical chemical transport models (CTMs), two interpolation methods, a satellite-derived aerosol optical depth-based method, a Bayesian statistical regression model, and three data-rich machine learning methods. We focused on annual predictions that were regridded to 12-km resolution over the conterminous U.S., but also considered 1-km predictions in sensitivity analyses. The exposure models predicted broadly consistent PM2.5 concentrations, with relatively high concentrations on average over the eastern U.S. and greater variability in the western U.S. However, differences in national concentration distributions (median standard deviation: 1.00 μg m-3) and spatial distributions over urban areas were evident. Further exploration of these differences and their implications for specific applications would be valuable. PM2.5 concentrations were estimated to decrease by about 1 μg m-3 on average due to modeled emission changes between 2011 and 2028, with decreases of more than 3 μg m-3 in areas with relatively high 2011 concentrations that were projected to experience relatively large emission reductions. Agreement among models was closer for population-weighted than uniformly weighted averages across the domain. About 50% of the population was estimated to experience PM2.5 concentrations less than 10 μg m-3 in 2011 and PM2.5 improvements of about 2 μg m-3 due to modeled emission changes between 2011 and 2028. Two inequality metrics were used to characterize differences in exposure among the four racial/ethnic groups. The metrics generally yielded consistent information and suggest that the modeled emission reductions between 2011 and 2028 would reduce absolute exposure inequality on average.

BackgroundChronic exposures to particulate matter with an aerodynamic diameter < 2.5 μm (PM2.5) and ozone pollution can affect respiratory function. ARDS, an often lethal respiratory failure, is most common among older adults. However, few epidemiology studies have investigated an association between air pollution and the risk of ARDS.MethodsThis observational study was conducted to estimate air pollution exposures at the ZIP code level and hospital admissions with ARDS among US Medicare beneficiaries aged ≥ 65 years from 2000 to 2012. A two-pollutant generalized linear mixed model, adjusting for sex, age, race, median household income, smoking, and weather, was applied.ResultsThere were a total of 1,164,784 hospital admissions with ARDS in the cohort. Increases of 1 µg/m3 in annual average PM2.5 and of 1 parts per billion in annual average ozone were associated with increases in annual hospital admission rates for ARDS of 0.72% (95% CI, 0.62-0.82) and 0.15% (95% CI, 0.08-0.22), respectively. In low-pollution regions (annual average PM2.5 level < 12 µg/m3 and annual average ozone level < 45 parts per billion), the same annual increase in PM2.5 and ozone were associated with increases in annual hospital admission rates for ARDS of 1.50% (95% CI, 1.27-1.72) and 0.27% (95% CI, 0.16-0.38).ConclusionsLong-term exposures to PM2.5 and ozone were associated with increased risk of ARDS among older adults in the United States, including exposures below current annual US National Ambient Air Quality Standards.

Abstract Background Temporal envelope cues are conveyed by cochlear implants (CIs) to hearing loss patients to restore hearing. Although CIs could enable users to communicate in clear listening environments, noisy environments still pose a problem. To improve speech-processing strategies used in Chinese CIs, we explored the relative contributions made by the temporal envelope in various frequency regions, as relevant to Mandarin sentence recognition in noise. Methods Original speech material from the Mandarin version of the Hearing in Noise Test (MHINT) was mixed with speech-shaped noise (SSN), sinusoidally amplitude-modulated speech-shaped noise (SAM SSN), and sinusoidally amplitude-modulated (SAM) white noise (4 Hz) at a + 5 dB signal-to-noise ratio, respectively. Envelope information of the noise-corrupted speech material was extracted from 30 contiguous bands that were allocated to five frequency regions. The intelligibility of the noise-corrupted speech material (temporal cues from one or two regions were removed) was measured to estimate the relative weights of temporal envelope cues from the five frequency regions. Results In SSN, the mean weights of Regions 1–5 were 0.34, 0.19, 0.20, 0.16, and 0.11, respectively; in SAM SSN, the mean weights of Regions 1–5 were 0.34, 0.17, 0.24, 0.14, and 0.11, respectively; and in SAM white noise, the mean weights of Regions 1–5 were 0.46, 0.24, 0.22, 0.06, and 0.02, respectively. Conclusions The results suggest that the temporal envelope in the low-frequency region transmits the greatest amount of information in terms of Mandarin sentence recognition for three types of noise, which differed from the perception strategy employed in clear listening environments.

Exposure to ambient fine particulate matter (PM2.5) is a major global health concern. Quantitative estimates of attributable mortality are based on disease-specific hazard ratio models that incorporate risk information from multiple PM2.5 sources (outdoor and indoor air pollution from use of solid fuels and secondhand and active smoking), requiring assumptions about equivalent exposure and toxicity. We relax these contentious assumptions by constructing a PM2.5-mortality hazard ratio function based only on cohort studies of outdoor air pollution that covers the global exposure range. We modeled the shape of the association between PM2.5 and nonaccidental mortality using data from 41 cohorts from 16 countries-the Global Exposure Mortality Model (GEMM). We then constructed GEMMs for five specific causes of death examined by the global burden of disease (GBD). The GEMM predicts 8.9 million [95% confidence interval (CI): 7.5-10.3] deaths in 2015, a figure 30% larger than that predicted by the sum of deaths among the five specific causes (6.9; 95% CI: 4.9-8.5) and 120% larger than the risk function used in the GBD (4.0; 95% CI: 3.3-4.8). Differences between the GEMM and GBD risk functions are larger for a 20% reduction in concentrations, with the GEMM predicting 220% higher excess deaths. These results suggest that PM2.5 exposure may be related to additional causes of death than the five considered by the GBD and that incorporation of risk information from other, nonoutdoor, particle sources leads to underestimation of disease burden, especially at higher concentrations.

Abstract Background The anatomical dimensions of the lumbar dural sac determine the sensory block level of spinal anesthesia; however, whether they show the same predictive value during continuous epidural anesthesia (CEA) remains undetermined. We designed the present study to verify the efficacy of the anatomical dimensions of the lumbar dural sac in predicting the sensory block level during labor analgesia. Methods A total of 122 parturients with singleton pregnancies requesting labor analgesia were included in this study. The lumbar dural sac diameter (DSD), lumbar dural sac length (DSL), lumbar dural sac surface area (DSA), and lumbar dural sac volume (DSV) were measured with an ultrasound color Doppler diagnostic apparatus. CEA was performed at the L2-L3 interspace. After epidural cannulation, an electronic infusion pump containing 0.08% ropivacaine and sufentanil 0.4 μg/ml was connected. The sensory block level was determined with alcohol-soaked cotton, a cotton swab, and a pinprick. The analgesic efficacy of CEA was determined with a visual analog scale (VAS). The parturients were divided into two groups, “ideal analgesia” and “nonideal analgesia,” and the groups were compared by t test. Pearson’s correlation was performed to evaluate the association between the anatomical dimensions of the lumbar dural sac and sensory block level. Multiple linear regression analysis was used to create a model for predicting the sensory block level. Results In the ideal analgesia group, the height, DSL, DSA, DSV and DSD were significantly smaller, and the body mass index (BMI) was significantly larger (P

ObjectivesStudies have shown that obstructive sleep apnea (OSA) is inextricably linked with cardiovascular diseases (CVD). However, the roles of certain common sleep disturbances, such as low slow-wave sleep, excessive daytime sleepiness and short sleep duration, in the pathogenesis and progression of CVD in patients with OSA have not been determined. Therefore, we conducted a large cross-sectional study to explore the effect of low slow-wave sleep, excessive daytime sleepiness and short sleep duration on the risk of CVD in patients with OSA.MethodsSubjects were consecutively enrolled to participate in the sleep center of Shanghai Jiao Tong University Affiliated Sixth People’s Hospital. All OSA patients were diagnosed by standard polysomnography, while controls were all simple snorers. A total of 4,475 participants were strictly recruited. The Framingham Risk Score were employed to assess the 10-year risk of CVD, and logistic regression was used to measure the association between sleep disturbances and the moderate-to-high CVD risk.ResultsIn the whole cohort, OSA, excessive daytime sleepiness, and low slow-wave sleep were all risk factors for the moderate-to-high 10-year CVD risk (odds ratio [OR] = 3.012, 95% confidence interval [CI] 2.418–3.751; OR = 1.407, 95% CI: 1.228–1.613, and OR = 0.973,95% CI: 0.967–0.980), but sleep duration did not contribute significantly to that risk. Whether in patients with OSA and controls, low SWS (

ObjectivesA huge population, especially the elderly, suffers from otogenic vertigo. However, the multi-modal vestibular network changes, secondary to periphery vestibular dysfunction, have not been fully elucidated. We aim to identify potential microstate electroencephalography (EEG) signatures for otogenic vertigo in this study.Materials and MethodsPatients with recurrent otogenic vertigo and age-matched healthy adults were recruited. We performed 256-channel EEG recording of all participants at resting state. Neuropsychological questionnaires and vestibular function tests were taken as a measurement of patients’ symptoms and severity. We clustered microstates into four classes (A, B, C, and D) and identified their dynamic and syntax alterations of them. These features were further fed into a support vector machine (SVM) classifier to identify microstate signatures for vertigo.ResultsWe compared 40 patients to 45 healthy adults, finding an increase in the duration of Microstate A, and both the occurrence and time coverage of Microstate D. The coverage and occurrence of Microstate C decreased significantly, and the probabilities of non-random transitions between Microstate A and D, as well as Microstate B and C, also changed. To distinguish the patients, the SVM classifier, which is built based on these features, got a balanced accuracy of 0.79 with a sensitivity of 0.78 and a specificity of 0.8.ConclusionThere are several temporal dynamic alterations of EEG microstates in patients with otogenic vertigo, especially in Microstate D, reflecting the underlying process of visual-vestibular reorganization and attention redistribution. This neurophysiological signature of microstates could be used to identify patients with vertigo in the future.

The backfill construction of a utility tunnel is as important as its main structure construction. While the existing research on utility tunnels focuses on the construction of the main structure, rare attention has been paid to the backfill construction. In this paper, with a practical comprehensive utility tunnel project as the background, a variety of different backfill construction schemes were designed, and the corresponding numerical simulations were performed with the finite element analysis software Midas. The influence of different backfill construction schemes on the side wall, roof, and floor of the utility tunnel was systematically studied and analyzed, and the displacement and stress variation curves in the process of utility tunnel backfill were obtained. Based on the simulation results and the actual engineering situation, the optimal schemes for utility tunnel backfill construction were determined. The results show that the displacement of the side wall of the utility tunnel increased first and then decreased with increasing backfill soil in the unilateral backfill mode, and there was little displacement in the bilateral backfill mode. The shear stress of the side wall of the utility tunnel in both the unilateral and bilateral backfill modes gradually increased with the backfill process. Within the region 1 m above the roof of the utility tunnel, backfill modes had no effect on the final stress and displacement values, but the layering method made a difference. Accordingly, bilateral backfill mode is suggested for the region below the roof of the utility tunnel, while in the case of region 1 m above the roof, backfilling is recommended to be layered according to height rather than position.

Objectives: Mandarin-speaking users of cochlear implants (CI) perform poorer than their English counterpart. This may be because present CI speech coding schemes are largely based on English. This study aims to evaluate the relative contributions of temporal envelope (E) cues to Mandarin phoneme (including vowel, and consonant) and lexical tone recognition to provide information for speech coding schemes specific to Mandarin.Design: Eleven normal hearing subjects were studied using acoustic temporal E cues that were extracted from 30 continuous frequency bands between 80 and 7,562 Hz using the Hilbert transform and divided into five frequency regions. Percent-correct recognition scores were obtained with acoustic E cues presented in three, four, and five frequency regions and their relative weights calculated using the least-square approach.Results: For stimuli with three, four, and five frequency regions, percent-correct scores for vowel recognition using E cues were 50.43–84.82%, 76.27–95.24%, and 96.58%, respectively; for consonant recognition 35.49–63.77%, 67.75–78.87%, and 87.87%; for lexical tone recognition 60.80–97.15%, 73.16–96.87%, and 96.73%. For frequency region 1 to frequency region 5, the mean weights in vowel recognition were 0.17, 0.31, 0.22, 0.18, and 0.12, respectively; in consonant recognition 0.10, 0.16, 0.18, 0.23, and 0.33; in lexical tone recognition 0.38, 0.18, 0.14, 0.16, and 0.14.Conclusion: Regions that contributed most for vowel recognition was Region 2 (502–1,022 Hz) that contains first formant (F1) information; Region 5 (3,856–7,562 Hz) contributed most to consonant recognition; Region 1 (80–502 Hz) that contains fundamental frequency (F0) information contributed most to lexical tone recognition.

Purpose: To evaluate the association of prior to intensive care unit (ICU) statin use with the clinical outcomes in critically ill patients with acute kidney injury (AKI).Materials and Methods: Patients with AKI were selected from the Medical Information Mart for Intensive Care IV (version 1.0) database for this retrospective observational study. The primary outcome was 30-day intensive care unit (ICU) mortality. A 30-day in-hospital mortality and ICU length of stay (LOS) were considered as secondary outcomes. Comparison of mortality between pre-ICU statin users with non-users was conducted by the multivariate Cox proportional hazards model. Comparison of ICU LOS between two groups was implemented by multivariate linear model. Three propensity score methods were used to verify the results as sensitivity analyses. Stratification analyses were conducted to explore whether the association between pre-ICU statin use and mortality differed across various subgroups classified by sex and different AKI stages.Results: We identified 3,821 pre-ICU statin users and 9,690 non-users. In multivariate model, pre-ICU statin use was associated with reduced 30-day ICU mortality rate [hazard ratio (HR) 0.68 (0.59, 0.79); p < 0.001], 30-day in-hospital mortality rate [HR 0.64 (0.57, 0.72); p < 0.001] and ICU LOS [mean difference −0.51(−0.79, −0.24); p < 0.001]. The results were consistent in three propensity score methods. In subgroup analyses, pre-ICU statin use was associated with decreased 30-day ICU mortality and 30-day in-hospital mortality in both sexes and AKI stages, except for 30-day ICU mortality in AKI stage 1.Conclusion: Patients with AKI who were administered statins prior to ICU admission might have lower mortality during ICU and hospital stay and shorter ICU LOS.