Association of Obstructive Sleep Apnea-Hypopnea Syndrome with hearing loss: A systematic review and meta-analysis.

Objective: This study seeks to investigate the relationship between Obstructive Sleep Apnea-Hypopnea Syndrome (OSAHS) and hearing impairment by meta-analysis. Methods: Cochrane Library, PubMed, Embase, Web of Science and other databases are searched from their establishment to July 1st, 2022. Literature on the relationship between OSAHS and hearing loss is collected, and two researchers independently perform screening, data extraction and quality evaluation on the included literature. Meta-analysis is performed using RevMan 5.4.1 software. According to the heterogeneity between studies, a random-effects model or fixed-effects model is used for meta-analysis. Results: A total of 10 articles are included, with 7,867 subjects, 1,832 in the OSAHS group and 6,035 in the control group. The meta-analysis shows that the incidence of hearing impairment in the OSAHS group is higher than in the control group (OR = 1.38; 95% CI 1.18-1.62, Z = 4.09, P < 0.001), and the average hearing threshold of OSAHS patients is higher than that of the control group (MD = 5.89; 95% CI 1.87-9.91, Z = 2.87, P = 0.004). After stratifying the included studies according to hearing frequency, the meta-analysis shows that the OSAHS group has a higher threshold of 0.25, and the response amplitudes at frequencies 2, 4, 6, and 8 kHz are all higher than those of the control group. Conclusion: Compared with the control group, the OSAHS group has a higher incidence of hearing loss, mainly high-frequency hearing loss. Thus, OSAHS is closely associated with and a risk factor for hearing loss.

Application value of joint STOP-Bang questionnaire and Epworth Sleepiness Scale in screening for obstructive sleep apnea.

Objective: This paper evaluates the application value of the STOP-Bang questionnaire combined with the Epworth Sleepiness Scale (ESS) in screening for obstructive sleep apnea (OSA) in the population. Method: Thousand-six hundred seventy-one patients with suspected OSA who visited the Sleep Medicine Center of the First Affiliated Hospital of Guangzhou Medical University from August 2017 to August 2020 were monitored by overnight polysomnography (PSG) after completing the ESS scale and STOP-Bang questionnaire. The sensitivity, specificity, positive predictive value, negative predictive value and receiver operating characteristic (ROC) curves of the two scales were calculated, and the accuracy in predicting OSA of the STOP-Bang questionnaire combined with ESS was analyzed. Results: With Apnea Hypopnea Index (AHI) cutoffs of ≥5, ≥15 and ≥30 events/h, the areas under the ROC curve scored by STOP-Bang were 0.724, 0.703 and 0.712, and those of ESS were 0.632, 0.634 and 0.695; the diagnostic odds ratio (DOR) values of STOP-Bang for OSA, moderate to severe OSA, and severe OSA were 3.349, 2.651 and 3.189, and those of ESS were 2.665, 2.279 and 3.289. The STOP-Bang score of three was used as the cut-off point for OSA diagnosis with higher sensitivity and lower specificity, while ESS had higher specificity. STOP-Bang (≥3) combined with ESS significantly improved its specificity for predicting OSA. Conclusion: The STOP-Bang questionnaire is a simple and effective new tool for screening patients for OSA, while a STOP-Bang score of ≥3 combined with ESS can further improve its specificity. Thus, we suggest further screening with ESS after a STOP-Bang score of ≥3 in suspected patients.

A meta-analysis of the relationship between polycystic ovary syndrome and sleep disturbances risk.

Objective: A meta-analysis is used to explore the relationship between polycystic ovary syndrome (PCOS) and the risk of Sleep disturbances. Method: Cochrane Library, PubMed, Embase, and Web of Science databases are searched by computer from their establishment to 1 May 2022. Review Manager 5.4 software is used for the meta-analysis. Results: A total of nine articles are included, with 1,107 subjects. The results show that PCOS is positively associated with the risk of Sleep disturbances. Comparing with the "PCOS group" (experimental group) with the "NON-PCOS group" (control group), the incidence of Sleep disturbances is higher (OR = 11.24, 95% CI: 2.00-63.10, Z = 2.75, p = 0.006); the Pittsburgh Sleep Quality Index (PSQI) scores of the PCOS group is higher than that of the NON-PCOS group (MD = 0.78, 95% CI: 0.32-1.25, Z = 3.30, p = 0.001); the Epworth Sleepiness Scale (ESS) scores of the PCOS group is higher than that of the NON-PCOS group (MD = 2.49, 95% CI: 0.80-4.18, Z = 2.88, p = 0.004); Apnea hypopnea index (AHIs) in the PCOS group are higher than those in the NON-PCOS group (MD = 2.68, 95% CI: 1.07-4.28, Z = 3.27, p = 0.001); the sleep efficiency of the PCOS group is lower than that of the NON-PCOS group (MD = -5.16, 95% CI: 9.39--0.93, Z = 2.39, p = 0.02); the sleep onset latency of the PCOS group is higher than that of the NON-PCOS group (MD = 2.45, 95% CI: 1.40-3.50, Z = 4.57, p < 0.001); and the Rapid Eyes Movement (REM) sleep in the PCOS group is higher than that in the NON-PCOS group (MD = 17.19, 95% CI: 11.62-55.76, Z = 6.05, p < 0.001). The studies included in each analysis have publication biases of different sizes. After subgroup analysis and sensitivity analysis, the heterogeneity of each study in the meta-analysis is reduced, the bias is reduced accordingly, and the stability of the results can be maintained. Conclusion: PCOS is positively associated with the risk of Sleep disturbances. In order to reduce such risk, attention should be paid to the role of PCOS management, and PCOS prevention and treatment should be actively carried out.

Effects of psychological intervention on empathy fatigue in nurses: A meta-analysis.

Objective: The purpose of this meta-analysis is to systematically assess the effects of psychological intervention on empathy fatigue among nursing staff. Method: Five electronic databases are searched separately from their establishment to April 8th, 2022. The research team independently performs paper selection, quality assessment, data extraction and analysis for all included studies. PRISMA guidelines are used to report this meta-analysis. Results: A total of seven randomized controlled trials (RCTs) covering 513 nursing staff are included. The meta-analysis results show that the empathy fatigue score (SMD = -0.22, 95% CI: -0.42~-0.02, P = 0.03) and burnout (SMD = -0.37, 95% CI: -0.56~-0.19, P < 0.001) are lower than the control group. The empathy satisfaction score of the psychological intervention group is higher than that of the control group (SMD = 0.45, 95% CI: 0.27-0.63, P < 0.001). The differences are statistically significant (P < 0.05). Subgroup analysis finds significant heterogeneity in the impact of different departments on psychological intervention at ≥6 weeks (I 2 = 71%, P = 0.01) and <6 weeks (I 2 = 0%, P = 0.75) (P = 0.05). Different departments also show significant heterogeneity in the effects of psychological intervention: ICU (I 2 = 73%, P = 0.02), pediatric (I 2 = 53%, P = 0.14) and other departments (I 2 = 0%, P = 0.63). The differences are statistically significant (P = 0.0007). Besides, the results show that both mindfulness intervention (SMD = 0.50, 95% CI: 0.24-0.77, P = 0.0002) and other interventions (SMD = 0.41, 95% CI: 0.16-0.65, P = 0.001) are statistically significant difference in the level of empathy satisfaction between the psychological intervention group and the control group. Conclusion: Psychological intervention has a coordinated improvement effect on empathy fatigue, empathy satisfaction and burnout, and can also improve the quality of life of nursing staff.

Meta-Analysis of Relationship of Sleep Quality and Duration With Risk of Diabetic Retinopathy.

Objective: A meta-analysis is used to explore the relationship of sleep quality and duration with the risk of diabetic retinopathy (DR). Method: Cochrane Library, PubMed, Embase, and other databases are searched from their establishment to April 2022. Literature on the relationship of sleep quality and duration with DR risk published in various databases is collected, and two researchers independently screen the literature, extract data, and evaluate the quality of the included articles. The meta-analysis is performed with Review Manage 5.4.1 software. Results: A total of 7 articles are selected, including 4,626 subjects. The results show a strong correlation between sleep quality and DR risk. When comparing the sleep quality scores of "DR" (experimental group) and "NO DR" (control group), the Pittsburgh sleep quality index(PSQI) score of the DR group is significantly higher than that of the NO DR group (MD = 2.85; 95% confidence interval [CI] 1.92, 3.78, P<0.001), while the ESS score of the DR group is also significantly higher than that of the NO DR group (MD = 1.17; 95% confidence interval [CI] 0.14 to 2.30, P=0.04), so the sleep quality score of the DR group is higher than that of the NO DR group in both the PSQI and ESS scores, which confirms that low sleep quality is a risk factor for DR. Long sleep duration is also associated with the risk of developing DR; the number of adverse events (DR prevalence) is higher for "long sleep duration" than "normal sleep duration" [OR = 1.83, 95%CI 1.36-2.47, P < 0.001], suggesting that long sleep duration can cause increased DR risk. Short sleep duration is also associated with the occurrence of DR [OR = 1.49, 95%CI 1.15-1.94), P = 0.003] and can increase DR risk. Conclusion: Sleep quality and duration (including long and short sleep duration) are significantly associated with DR. To reduce DR risk, sleep intervention should be actively carried out, lifestyle changes should be made, and attention should be paid to the role of DR management.

Analysis on urban scaling characteristics of China's relatively developed cities.

China is undergoing rapid urbanization, but the speed and stage of urban development are quite heterogeneous among different regions and city types. Understanding the urban scaling characteristics of China's relatively developed cities is important for addressing environmental and social challenges. Within the scope of 114 third-tier-and-above Chinese cities, the research calculate the scaling parameters of various urban development variables with respect to urban population and urban GRP in different city types based on urban scaling quantitative models. Also, univariate and multivariate regression analyses were performed on the factors affecting urban electricity consumption. The research results show that the urban scaling characteristics of Chinese cities differ between different types of cities, industrial cities show unique scaling features compared to commercial cities and mixed-economy cities. Additionally, urban electricity consumption is found to be closely related to urban population, urban construction land area and street lamp number. The results can help different types of cities make targeted policies and provide insights for reducing resource consumption during the urbanization process.

Statistical regression modeling for energy consumption in wastewater treatment.

Wastewater treatment is one of critical issues faced by water utilities, and receives more and more attentions recently. The energy consumption modeling in biochemical wastewater treatment was investigated in the study via a general and robust approach based on Bayesian semi-parametric quantile regression. The dataset was derived from a municipal wastewater treatment plant, where the energy consumption of unit chemical oxygen demand (COD) reduction was the response variable of interest. Via the proposed approach, the comprehensive regression pictures of the energy consumption and truly influencing factors, i.e., the regression relationships at lower, median and higher energy consumption levels were characterized respectively. Meanwhile, the proposals for energy saving in different cases were also facilitated specifically. First, the lower level of energy consumption was closely associated with the temperature of influent wastewater, and the chroma-rich wastewater also showed helpful in the execution of energy saving. Second, at median energy consumption level, the COD-rich wastewater played a determinative role in the reduction of energy consumption, while the higher quality of treated water led to slightly energy intensive. Third, the higher level of energy consumption was most likely to be attributed to the relatively high temperature of wastewater and total nitrogen (TN)-rich wastewater, and both of the factors were preferably to be avoided to alleviate the burden of energy consumption. The study provided an efficient approach to controlling the energy consumption of wastewater treatment in the perspective of statistical regression modeling, and offered valuable suggestions for the future energy saving.

Development of a method for comprehensive water quality forecasting and its application in Miyun reservoir of Beijing, China.

Water quality forecasting is an essential part of water resource management. Spatiotemporal variations of water quality and their inherent constraints make it very complex. This study explored a data-based method for short-term water quality forecasting. Prediction of water quality indicators including dissolved oxygen, chemical oxygen demand by KMnO4 and ammonia nitrogen using support vector machine was taken as inputs of the particle swarm algorithm based optimal wavelet neural network to forecast the whole status index of water quality. Gubeikou monitoring section of Miyun reservoir in Beijing, China was taken as the study case to examine effectiveness of this approach. The experiment results also revealed that the proposed model has advantages of stability and time reduction in comparison with other data-driven models including traditional BP neural network model, wavelet neural network model and Gradient Boosting Decision Tree model. It can be used as an effective approach to perform short-term comprehensive water quality prediction.

Water quality assessment in Qu River based on fuzzy water pollution index method.

A fuzzy improved water pollution index was proposed based on fuzzy inference system and water pollution index. This method can not only give a comprehensive water quality rank, but also describe the water quality situation with a quantitative value, which is convenient for the water quality comparison between the same ranks. This proposed method is used to assess water quality of Qu River in Sichuan, China. Data used in the assessment were collected from four monitoring stations from 2006 to 2010. The assessment results show that Qu River water quality presents a downward trend and the overall water quality in 2010 is the worst. The spatial variation indicates that water quality of Nanbashequ section is the pessimal. For the sake of comparison, fuzzy comprehensive evaluation and grey relational method were also employed to assess water quality of Qu River. The comparisons of these three approaches' assessment results show that the proposed method is reliable.

Descriptive Characteristics of Surface Water Quality in Hong Kong by a Self-Organising Map.

In this study, principal component analysis (PCA) and a self-organising map (SOM) were used to analyse a complex dataset obtained from the river water monitoring stations in the Tolo Harbor and Channel Water Control Zone (Hong Kong), covering the period of 2009-2011. PCA was initially applied to identify the principal components (PCs) among the nonlinear and complex surface water quality parameters. SOM followed PCA, and was implemented to analyze the complex relationships and behaviors of the parameters. The results reveal that PCA reduced the multidimensional parameters to four significant PCs which are combinations of the original ones. The positive and inverse relationships of the parameters were shown explicitly by pattern analysis in the component planes. It was found that PCA and SOM are efficient tools to capture and analyze the behavior of multivariable, complex, and nonlinear related surface water quality data.

An Enhanced K-Means Algorithm for Water Quality Analysis of The Haihe River in China.

The increase and the complexity of data caused by the uncertain environment is today's reality. In order to identify water quality effectively and reliably, this paper presents a modified fast clustering algorithm for water quality analysis. The algorithm has adopted a varying weights K-means cluster algorithm to analyze water monitoring data. The varying weights scheme was the best weighting indicator selected by a modified indicator weight self-adjustment algorithm based on K-means, which is named MIWAS-K-means. The new clustering algorithm avoids the margin of the iteration not being calculated in some cases. With the fast clustering analysis, we can identify the quality of water samples. The algorithm is applied in water quality analysis of the Haihe River (China) data obtained by the monitoring network over a period of eight years (2006-2013) with four indicators at seven different sites (2078 samples). Both the theoretical and simulated results demonstrate that the algorithm is efficient and reliable for water quality analysis of the Haihe River. In addition, the algorithm can be applied to more complex data matrices with high dimensionality.

Water Environmental Capacity Analysis of Taihu Lake and Parameter Estimation Based on the Integration of the Inverse Method and Bayesian Modeling.

An integrated approach using the inverse method and Bayesian approach, combined with a lake eutrophication water quality model, was developed for parameter estimation and water environmental capacity (WEC) analysis. The model was used to support load reduction and effective water quality management in the Taihu Lake system in eastern China. Water quality was surveyed yearly from 1987 to 2010. Total nitrogen (TN) and total phosphorus (TP) were selected as water quality model variables. Decay rates of TN and TP were estimated using the proposed approach. WECs of TN and TP in 2011 were determined based on the estimated decay rates. Results showed that the historical loading was beyond the WEC, thus, reduction of nitrogen and phosphorus input is necessary to meet water quality goals. Then WEC and allowable discharge capacity (ADC) in 2015 and 2020 were predicted. The reduction ratios of ADC during these years were also provided. All of these enable decision makers to assess the influence of each loading and visualize potential load reductions under different water quality goals, and then to formulate a reasonable water quality management strategy.

Forecasting of dissolved oxygen in the Guanting reservoir using an optimized NGBM (1,1) model.

An optimized nonlinear grey Bernoulli model was proposed by using a particle swarm optimization algorithm to solve the parameter optimization problem. In addition, each item in the first-order accumulated generating sequence was set in turn as an initial condition to determine which alternative would yield the highest forecasting accuracy. To test the forecasting performance, the optimized models with different initial conditions were then used to simulate dissolved oxygen concentrations in the Guanting reservoir inlet and outlet (China). The empirical results show that the optimized model can remarkably improve forecasting accuracy, and the particle swarm optimization technique is a good tool to solve parameter optimization problems. What's more, the optimized model with an initial condition that performs well in in-sample simulation may not do as well as in out-of-sample forecasting.

Water quality assessment in the Harbin reach of the Songhuajiang River (China) based on a fuzzy rough set and an attribute recognition theoretical model.

A large number of parameters are acquired during practical water quality monitoring. If all the parameters are used in water quality assessment, the computational complexity will definitely increase. In order to reduce the input space dimensions, a fuzzy rough set was introduced to perform attribute reduction. Then, an attribute recognition theoretical model and entropy method were combined to assess water quality in the Harbin reach of the Songhuajiang River in China. A dataset consisting of ten parameters was collected from January to October in 2012. Fuzzy rough set was applied to reduce the ten parameters to four parameters: BOD5, NH3-N, TP, and F. coli (Reduct A). Considering that DO is a usual parameter in water quality assessment, another reduct, including DO, BOD5, NH3-N, TP, TN, F, and F. coli (Reduct B), was obtained. The assessment results of Reduct B show a good consistency with those of Reduct A, and this means that DO is not always necessary to assess water quality. The results with attribute reduction are not exactly the same as those without attribute reduction, which can be attributed to the α value decided by subjective experience. The assessment results gained by the fuzzy rough set obviously reduce computational complexity, and are acceptable and reliable. The model proposed in this paper enhances the water quality assessment system.

Using discriminant analysis to assess polycyclic aromatic hydrocarbons contamination in Yongding New River.

Yongding New River has been polluted by polycyclic aromatic hydrocarbons (PAHs) which are carcinogenic and mutagenic. In three periods (the abundant water period, mean water period, dry water period), ten sites (totally 30 samples) in Yongding New River were clustered into four categories by hierarchical cluster analysis (hierarchical CA). In the same cluster, the samples had the same approximate contamination situation. In order to eliminate the dimensional differences, the data in each sample, containing 16 kinds of PAHs, were standardized with normal standardization and maximum difference standardization. According to the results of the cubic clustering criterion, pseudo F, and pseudo t (2) (PST2), the proper number of clustering for the 30 samples is 4. Before conducting hierarchical CA and K-means cluster analysis on the samples, we used principal component analysis to obtain another group data set. This data set was composed of the principal component scores which are uncorrelated variables. Hierarchical CA and K-means cluster analysis were used to classify the two data sets into four categories. With the classification results of hierarchical CA and K-means cluster analysis, discriminant analysis is applied to determine which method was better for normalization of the original data and which one was proper to cluster the samples and establish discriminant functions so that a new sample can be grouped into the right categories.

Water quality evaluation of Haihe River with fuzzy similarity measure methods.

Fuzzy similarity measures, which are used to judge the closeness of two fuzzy sets, are presented to evaluate the water quality of the Haihe River. Based on the membership functions and coefficient of variation as the weights, four fuzzy similarity measures (including Lattice similarity measure, Hamming similarity measure, Euclidean similarity measure and the max-min similarity measure) are used to classify the 299 samples into the proper water quality standard ranks. The results are compared with the traditional distance discriminant methods. The calculation of two traditional distance discriminant methods (both Euclidean distance and absolute value distance) is also based on the use of coefficients of variation as the weights. Without the Lattice similarity measure, for this method loses some information, the correct assignment of samples classified into the same water quality ranks is 75.92% with the other three similarity measures and two distance discriminant methods. This result shows the reliability of the five methods. Only considering the three similarity measures, there were only 1.01% of the samples that did not classify to the same ranks, while the corresponding ratio of the two distance discriminant methods was 5.69%. The results of leave-one-out cross validation show that more than 88% of the samples are classified to the proper ranks, which demonstrates that the similarity measures are suitable to evaluate the water quality of the Haihe River.

Synthesis and biological evaluation of a series of 6,7-dimethoxy-1-(3,4-dimethoxybenzyl)-2-substituted tetrahydroisoquinoline derivatives.

Multidrug resistance in cancer is a major cause of failure in cancer chemotherapy. In search of new compounds with strong reversal activity and simple molecular structure, we have synthesized a series of compounds in which different substituents were linked to the 2-position of the 6,7-dimethoxy-1-(3,4-dimethoxybenzyl)- tetrahydroisoquinoline system. Compounds were analyzed for their cytotoxicity by MTT in K562 cell line in vitro, all of the derivatives exhibited little cytotoxic activity. In the meantime, these compounds were evaluated by MTT in K562/A02 cell line in vitro, 6e, 6h and 7c exhibited similar or more potent activities than verapamil with the IC50 values at 0.66, 0.65 and 0.96µM, and with the ratio factor of 24.13, 24.50 and 16.59, respectively.

5-Fluoro-1H-indole-3-carb-oxy-lic acid.

In the title compound, C(9)H(6)FNO(2), the carboxyl group is twisted slightly away from the indole-ring plane [dihedral angle = 7.39 (10)°]. In the crystal, carboxyl inversion dimers linked by pairs of O-H⋯O hydrogen bonds generate R(2) (2)(8) loops and N-H⋯O hydrogen bonds connect the dimers into (10[Formula: see text]) sheets.

Water quality evaluation based on improved fuzzy matter-element method.

For natural water, method of water quality evaluation based on improved fuzzy matter-element evaluation method is presented. Two important parts are improved, the weights determining and fuzzy membership functions. The coefficient of variation of each indicator is used to determine the weight instead of traditional calculating superscales method. On the other hand, fuzzy matter-elements are constructed, and normal membership degrees are used instead of traditional trapezoidal ones. The composite fuzzy matter-elements with associated coefficient are constructed through associated transformation. The levels of natural water quality are determined according to the principle of maximum correlation. The improved fuzzy matter-element evaluation method is applied to evaluate water quality of the Luokou mainstream estuary at the first ten weeks in 2011 with the coefficient of variation method determining the weights. Water quality of Luokou mainstream estuary is dropping from level I to level II. The results of the improved evaluation method are basically the same as the official water quality. The variation coefficient method can reduce the workload, and overcome the adverse effects from abnormal values, compared with the traditional calculating superscales method. The results of improved fuzzy matter-element evaluation method are more credible than the ones of the traditional evaluation method. The improved evaluation method can use information of monitoring data more scientifically and comprehensively, and broaden a new evaluation method for water quality assessment.

Synthesis and evaluation of furoxan-based nitric oxide-releasing derivatives of tetrahydroisoquinoline as anticancer and multidrug resistance reversal agents.

Multidrug resistance in tumor cells poses a major obstacle to efficient chemotherapy. Several types of agents have been recognized as multidrug resistance inhibitors, among which the tetrahydroisoquinolines is the most studied. In current study 16 furoxan-based nitric oxide-releasing derivatives of tetrahydroisoquinoline were synthesized. Their cytotoxic activities and effects in reversing multidrug resistance have been evaluated. The results revealed that these compounds had moderate cytotoxic effects. Compounds 7a-f, 7h, and 7l showed higher cytotoxicities than the rest, but lower than adriamycin on K562 cell line. Compounds 7d, 7f, and 7l exhibited potent MDR reversal activities on K562/A02 cell line. The accumulation assay indicated that compounds 7d, 7f, and 7l significantly increased the intracellular accumulation of rhodamine123 in K562/A02 cells. Furthermore, these three compounds produced high concentrations of NO in K562/A02 cells. Potentially, the high concentrations of NO produced by NO donor moieties will lead to an increased cytotoxicity to K562/A02 cells. Our results suggested that compounds 7d, 7f, and 7l had anticancer effects, as well as multidrug resistance reversal effects.