The influence of environmental diplomacy, economic determinants and renewable energy consumption on environmental degradation: Empirical evidence of G20 countries.

This study examines how various environmental and economic variables contribute to environmental degradation. Industrialization, trade openness, and foreign direct investment are among the variables, as are environmental diplomacy, environmental diplomacy secure, and renewable energy consumption. Therefore, the data covers the years 1991-2020, and our sample includes all 19 countries and two groups (the European Union and the African Union). The research used the Pesaran CD test to determine cross-section dependency, CIPS and CADF test to determine stationarity, the Wald test for hetrodcedasasticity and the Wooldridge test for autocorrelation; therefore, VIF for multicollinearity, Durbin and Hausman to analyze the endogeneity. It also employed Westerlund's cointegration test to ensure cross-sectional dependence, Wald test for group-wise heteroscedasticity, Wooldridge test for autocorrelation, VIF for multicollinearity, and Durbin and Hausman for endogeneity. The two-step system generalized method of moments (GMM) is used to estimate the results and confirm the relationship between independent variables (Industrialization, trade openness, FDI, environmental diplomacy, secure environmental diplomacy, and renewable energy) and dependent variables (Environmental Degradation) in G20 countries. Therefore, Industrialization, trade openness, foreign direct investment, ecological diplomacy, and renewable energy consumption significantly impact ecological degradation. Environmental diplomacy is crucial to combat degradation and stimulate global collaboration. G20 nations enact strict environmental restrictions to tackle climate change and encourage economic growth.

Translating digital healthcare to enhance clinical management: a protocol for an observational study using a digital health technology system to monitor medication adherence and its effect on mobility in people with Parkinson's.

INTRODUCTION: In people with Parkinson's (PwP) impaired mobility is associated with an increased falls risk. To improve mobility, dopaminergic medication is typically prescribed, but complex medication regimens result in suboptimal adherence. Exploring medication adherence and its impact on mobility in PwP will provide essential insights to optimise medication regimens and improve mobility. However, this is typically assessed in controlled environments, during one-off clinical assessments. Digital health technology (DHT) presents a means to overcome this, by continuously and remotely monitoring mobility and medication adherence. This study aims to use a novel DHT system (DHTS) (comprising of a smartphone, smartwatch and inertial measurement unit (IMU)) to assess self-reported medication adherence, and its impact on digital mobility outcomes (DMOs) in PwP. METHODS AND ANALYSIS: This single-centre, UK-based study, will recruit 55 participants with Parkinson's. Participants will complete a range of clinical, and physical assessments. Participants will interact with a DHTS over 7 days, to assess self-reported medication adherence, and monitor mobility and contextual factors in the real world. Participants will complete a motor complications diary (ON-OFF-Dyskinesia) throughout the monitoring period and, at the end, a questionnaire and series of open-text questions to evaluate DHTS usability. Feasibility of the DHTS and the motor complications diary will be assessed. Validated algorithms will quantify DMOs from IMU walking activity. Time series modelling and deep learning techniques will model and predict DMO response to medication and effects of contextual factors. This study will provide essential insights into medication adherence and its effect on real-world mobility in PwP, providing insights to optimise medication regimens. ETHICS AND DISSEMINATION: Ethical approval was granted by London-142 Westminster Research Ethics Committee (REC: 21/PR/0469), protocol V.2.4. Results will be published in peer-reviewed journals. All participants will provide written, informed consent. TRIAL REGISTRATION NUMBER: ISRCTN13156149.

A prognostic assessment predicated by blood culture-based bacteria clustering from real-world evidence: Novel strategies and perspectives on prevention and management of sepsis.

Sepsis, a syndrome with disturbed host response to severe infection, is a critical health problem worldwide. It is urged to develop and update novel therapeutic strategies for improving the outcome of sepsis. In this study, we demonstrated that different bacteria clustering in sepsis patients may generate differences of prognosis results. We extracted all the sepsis patients from Medical Information Mart for Intensive Care IV 2.0 (MIMIC-IV 2.0) critical care data set according to certain standards and clinical score, a total of 2,339 patients were included in our study. Then we used multiple data analytics and machine learning methods to make all data deeply analyzed and elucidated. The results showed that the types of bacteria infected by patients with different ages, sex and race are different, the types of bacteria infected by patients with different SIRS values and GCS scores of the first day are different, and the severity of patients with different clusters is different, and most importantly, the survival rate of patients with different clusters also has this significant difference. We concluded prognostic assessment predicated by bacteria clustering might be a relatively potentially novel strategies and perspectives on prevention and management for sepsis in the future.

Quantitative assessment of selective degradation behavior of etoxazole in different classes of organisms by compound-specific isotope analysis.

In this paper, the stereoselective degradation and quantitative identification of chiral pesticide etoxazole in organisms with different classes of organisms (soil, chlorella algal fluid and mice) were carried out by compound-specific isotope analysis (CSIA). The degradation behavior and stable isotope fractionation effect of etoxazole in soil, chlorella and mice were investigated. The R-etoxazole degraded faster than S-etoxazole in different classes of organisms. The metabolites M1, M2 and M3 were detected in all three substrates. Biodegradation is the main factor for the change of stable isotope ratio of chiral pesticide etoxazole. Furthermore, the relationship between fractionation value of carbon isotope and residual concentration of etoxazole is established by Rayleigh equation, and the biodegradation rate of etoxazole could be calculated by using CSIA without measuring the concentration of etoxazole. Therefore, the use of CSIA can accurately assess the degradation behavior of pesticide pollution in the environment and provide a certain scientific evidence and technical support in the process of environmental remediation.

Giant pyroelectricity in nanomembranes.

Pyroelectricity describes the generation of electricity by temporal temperature change in polar materials1-3. When free-standing pyroelectric materials approach the 2D crystalline limit, how pyroelectricity behaves remained largely unknown. Here, using three model pyroelectric materials whose bonding characters along the out-of-plane direction vary from van der Waals (In2Se3), quasi-van der Waals (CsBiNb2O7) to ionic/covalent (ZnO), we experimentally show the dimensionality effect on pyroelectricity and the relation between lattice dynamics and pyroelectricity. We find that, for all three materials, when the thickness of free-standing sheets becomes small, their pyroelectric coefficients increase rapidly. We show that the material with chemical bonds along the out-of-plane direction exhibits the greatest dimensionality effect. Experimental observations evidence the possible influence of changed phonon dynamics in crystals with reduced thickness on their pyroelectricity. Our findings should stimulate fundamental study on pyroelectricity in ultra-thin materials and inspire technological development for potential pyroelectric applications in thermal imaging and energy harvesting.

A MULTITASK DEEP-LEARNING SYSTEM FOR ASSESSMENT OF DIABETIC MACULAR ISCHEMIA ON OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY IMAGES.

PURPOSE: We aimed to develop and test a deep-learning system to perform image quality and diabetic macular ischemia (DMI) assessment on optical coherence tomography angiography (OCTA) images. METHODS: This study included 7,194 OCTA images with diabetes mellitus for training and primary validation and 960 images from three independent data sets for external testing. A trinary classification for image quality assessment and the presence or absence of DMI for DMI assessment were labeled on all OCTA images. Two DenseNet-161 models were built for both tasks for OCTA images of superficial and deep capillary plexuses, respectively. External testing was performed on three unseen data sets in which one data set using the same model of OCTA device as of the primary data set and two data sets using another brand of OCTA device. We assessed the performance by using the area under the receiver operating characteristic curves with sensitivities, specificities, and accuracies and the area under the precision-recall curves with precision. RESULTS: For the image quality assessment, analyses for gradability and measurability assessment were performed. Our deep-learning system achieved the area under the receiver operating characteristic curves >0.948 and area under the precision-recall curves >0.866 for the gradability assessment, area under the receiver operating characteristic curves >0.960 and area under the precision-recall curves >0.822 for the measurability assessment, and area under the receiver operating characteristic curves >0.939 and area under the precision-recall curves >0.899 for the DMI assessment across three external validation data sets. Grad-CAM demonstrated the capability of our deep-learning system paying attention to regions related to DMI identification. CONCLUSION: Our proposed multitask deep-learning system might facilitate the development of a simplified assessment of DMI on OCTA images among individuals with diabetes mellitus at high risk for visual loss.

Understanding longevity in Hong Kong: a comparative study with long-living, high-income countries.

BACKGROUND: Since 2013, Hong Kong has sustained the world's highest life expectancy at birth-a key indicator of population health. The reasons behind this achievement remain poorly understood but are of great relevance to both rapidly developing and high-income regions. Here, we aim to compare factors behind Hong Kong's survival advantage over long-living, high-income countries. METHODS: Life expectancy data from 1960-2020 were obtained for 18 high-income countries in the Organisation for Economic Co-operation and Development from the Human Mortality Database and for Hong Kong from Hong Kong's Census and Statistics Department. Causes of death data from 1950-2016 were obtained from WHO's Mortality Database. We used truncated cross-sectional average length of life (TCAL) to identify the contributions to survival differences based on 263 million deaths overall. As smoking is the leading cause of premature death, we also compared smoking-attributable mortality between Hong Kong and the high-income countries. FINDINGS: From 1979-2016, Hong Kong accumulated a substantial survival advantage over high-income countries, with a difference of 1·86 years (95% CI 1·83-1·89) for males and 2·50 years (2·47-2·53) for females. As mortality from infectious diseases declined, the main contributors to Hong Kong's survival advantage were lower mortality from cardiovascular diseases for both males (TCAL difference 1·22 years, 95% CI 1·21-1·23) and females (1·19 years, 1·18-1·21), cancer for females (0·47 years, 0·45-0·48), and transport accidents for males (0·27 years, 0·27-0·28). Among high-income populations, Hong Kong recorded the lowest cardiovascular mortality and one of the lowest cancer mortalities in women. These findings were underpinned by the lowest absolute smoking-attributable mortality in high-income regions (39·7 per 100 000 in 2016, 95% CI 34·4-45·0). Reduced smoking-attributable mortality contributed to 50·5% (0·94 years, 0·93-0·95) of Hong Kong's survival advantage over males in high-income countries and 34·8% (0·87 years, 0·87-0·88) of it in females. INTERPRETATION: Hong Kong's leading longevity is the result of fewer diseases of poverty while suppressing the diseases of affluence. A unique combination of economic prosperity and low levels of smoking with development contributed to this achievement. As such, it offers a framework that could be replicated through deliberate policies in developing and developed populations globally. FUNDING: Early Career Scheme (RGC ECS Grant #27602415), Research Grants Council, University Grants Committee of Hong Kong.

Deep-Learning-Based Pre-Diagnosis Assessment Module for Retinal Photographs: A Multicenter Study.

Purpose: Artificial intelligence (AI) deep learning (DL) has been shown to have significant potential for eye disease detection and screening on retinal photographs in different clinical settings, particular in primary care. However, an automated pre-diagnosis image assessment is essential to streamline the application of the developed AI-DL algorithms. In this study, we developed and validated a DL-based pre-diagnosis assessment module for retinal photographs, targeting image quality (gradable vs. ungradable), field of view (macula-centered vs. optic-disc-centered), and laterality of the eye (right vs. left). Methods: A total of 21,348 retinal photographs from 1914 subjects from various clinical settings in Hong Kong, Singapore, and the United Kingdom were used for training, internal validation, and external testing for the DL module, developed by two DL-based algorithms (EfficientNet-B0 and MobileNet-V2). Results: For image-quality assessment, the pre-diagnosis module achieved area under the receiver operating characteristic curve (AUROC) values of 0.975, 0.999, and 0.987 in the internal validation dataset and the two external testing datasets, respectively. For field-of-view assessment, the module had an AUROC value of 1.000 in all of the datasets. For laterality-of-the-eye assessment, the module had AUROC values of 1.000, 0.999, and 0.985 in the internal validation dataset and the two external testing datasets, respectively. Conclusions: Our study showed that this three-in-one DL module for assessing image quality, field of view, and laterality of the eye of retinal photographs achieved excellent performance and generalizability across different centers and ethnicities. Translational Relevance: The proposed DL-based pre-diagnosis module realized accurate and automated assessments of image quality, field of view, and laterality of the eye of retinal photographs, which could be further integrated into AI-based models to improve operational flow for enhancing disease screening and diagnosis.

Artificial intelligence: A rapid case for advancement in the personalization of Gynaecology/Obstetric and Mental Health care.

To evaluate and holistically treat the mental health sequelae and potential psychiatric comorbidities associated with obstetric and gynaecological conditions, it is important to optimize patient care, ensure efficient use of limited resources and improve health-economic models. Artificial intelligence applications could assist in achieving the above. The World Health Organization and global healthcare systems have already recognized the use of artificial intelligence technologies to address 'system gaps' and automate some of the more cumbersome tasks to optimize clinical services and reduce health inequalities. Currently, both mental health and obstetric and gynaecological services independently use artificial intelligence applications. Thus, suitable solutions are shared between mental health and obstetric and gynaecological clinical practices, independent of one another. Although, to address complexities with some patients who may have often interchanging sequelae with mental health and obstetric and gynaecological illnesses, 'holistically' developed artificial intelligence applications could be useful. Therefore, we present a rapid review to understand the currently available artificial intelligence applications and research into multi-morbid conditions, including clinical trial-based validations. Most artificial intelligence applications are intrinsically data-driven tools, and their validation in healthcare can be challenging as they require large-scale clinical trials. Furthermore, most artificial intelligence applications use rate-limiting mock data sets, which restrict their applicability to a clinical population. Some researchers may fail to recognize the randomness in the data generating processes in clinical care from a statistical perspective with a potentially minimal representation of a population, limiting their applicability within a real-world setting. However, novel, innovative trial designs could pave the way to generate better data sets that are generalizable to the entire global population. A collaboration between artificial intelligence and statistical models could be developed and deployed with algorithmic and domain interpretability to achieve this. In addition, acquiring big data sets is vital to ensure these artificial intelligence applications provide the highest accuracy within a real-world setting, especially when used as part of a clinical diagnosis or treatment.

The influence of beam delivery uncertainty on dose uniformity and penumbra for pencil beam scanning in carbon-ion radiotherapy.

The dose uniformity and penumbra in the treatment field are important factors in radiotherapy, which affects the outcomes of radiotherapy. In this study, the integrated depth-dose-distributions (IDDDs) of 190 MeV/u and 260 MeV/u carbon beams in the active spot-scanning delivery system were measured and calculated by FLUKA Monte Carlo simulation based on the Heavy Ion Medical Machine (HIMM). Considering the dose distributions caused by secondary particles and scattering, we also used different types of pencil beam (PB) models to fit and compare the spatial distributions of PB. We superposed a bunch of PB to form a 20×20 cm2 treatment field with the double Gaussian and double Gaussian logistic beam models and calculated the influence of beam delivery error on the field flatness and penumbra, respectively. The simulated IDDDs showed good agreement with the measured values. The triple Gaussian and double Gaussian logistic beam models have good fitness to the simulated dose distributions. There are different influences on dose uniformity and penumbra resulting from beam uncertainties. These results would be helpful for understanding carbon ion therapy, and physical therapists are more familiar with beam characteristics for active scanning therapy, which provides a reference for commissioning and optimization of treatment plans in radiotherapy.

3D Printing Improve the Effectiveness of Fracture Teaching and Medical Learning: A Comprehensive Scientometric Assessment and Future Perspectives.

Fractures of complex body parts are often serious and difficult to handle, and they have high technical and training requirements. However, the realistic situation is that there are few opportunities for the junior residents, trainee doctors, and especially medical students to contact enough clinical practice and see such fracture patients. Fortunately, with the rapid development and continuous progress of 3D printing and related technologies, this situation has gradually gotten better and better. In this research, we confirmed that 3D printing technology could improve the effectiveness of fracture teaching and medical learning from multiple dimensions. We comprehensively screened and assessed 223 papers from the Web of Science (WoS) Core Collection on October 3, 2021, with "((3D) AND ((printing) OR (printed)) AND (fracture)) AND ((education) OR (training) OR (teaching))" as the retrieval strategy. Additionally, we used the VOSviewer software to analyze the keywords and countries and the organizations of the publications, then a series of scientometric and visualized analyses were made based on the retrieval results. Afterward, multiple databases were retrieved according to our selection criteria, we selected eight studies for the extensive literature analysis. The extracted data contained information of authors, problems solved, participants, methods, assessments, results, and benefits/limitations. These intuitive and in-depth analyses further confirmed and appraised the advantages of 3D printing in complex fracture models more objectively. In conclusion, 3D printing could improve the effectiveness and extension of fracture teaching, as well as medical learning, by providing the powerful interaction with 3D effect, wakening students learning interest, and allowing the junior residents, trainee doctors to have as realistic a virtual practice experience as possible. Through this research, it is expected that more researchers could be attracted to conduct more comprehensive and thorough studies on the application of 3D printing for training and educational propose, to promote the development of 3D technology-based medical education practice and further deepen the reform of medical education and improve the quality of fracture education and learning.

[Pollution Characteristics and Health Risk Assessment of Heavy Metals in Road Dust from Non-ferrous Smelting Parks].

In order to explore the pollution characteristics and health risks of heavy metals in dust from roads around non-ferrous smelting activities in different regions, dust samples from urban roads, non-ferrous smelting industry park roads, and tunnel roads were collected from the Mengzi area of Yunnan Province. The dust samples were suspended on Teflon filters by re-suspension equipment to obtain PM2.5 and PM10 samples. Eight heavy metals (Cr, Mn, Ni, Cu, Zn, As, Cd, and Pb) were analyzed by ICP-MS. The results showed that the average content of the total heavy metals in PM2.5 was higher than that in PM10. The average contents of Pb, Cd, As, and Zn were highest in all three types of road dust, and the difference in the total average content in different types of road dust was ranked as:tunnel roads > non-ferrous smelting industrial park roads > urban roads. The average contents of Pb and As in PM2.5 from the tunnel road dust were higher than other heavy metals, and reached up to 92338.3 mg·kg-1 and 12457.7 mg·kg-1, respectively. The average contents of Pb and Zn in PM2.5 from the industrial park road dust were the highest of all heavy metals (4381.7 mg·kg-1 and 4685.0 mg·kg-1, respectively). The average content of Zn and Pb in PM2.5 were the highest of all the heavy metals in the urban road dust (1952.6 mg·kg-1 and 1944.8 mg·kg-1, respectively). The average contents of Cu, Zn, As, Cd, and Pb in the different types of road dust were all higher than their background values in Yunnan Province. The results of the enrichment factor analysis and principal component analysis indicated that Cu, Zn, As, Cd, and Pb were obviously enriched in all three types of road dust, which were significantly affected by the non-ferrous smelting industry and traffic sources. In contrast, Cr, Mn, and Ni were not obviously enriched in the three types of road dust and were less affected by anthropogenic sources. The results of the health risk assessment testified that ingestion was the main exposure route, and that the children's non-carcinogenic risk was higher than that of adults. In PM2.5, As, Cd, and Pb in all types of road dust were associated with non-carcinogenic risks for adults and children. In PM10, As, Cd, and Pb in dust from the non-ferrous smelting industry park roads and tunnel roads had non-carcinogenic risks for humans. As in PM10 from the urban road dust was associated with a non-carcinogenic risk for children, whereas As in dust from the tunnel roads had carcinogenic risks.

The heterogeneous effect of socioeconomic driving factors on PM2.5 in China's 30 province-level administrative regions: Evidence from Bayesian hierarchical spatial quantile regression.

China has become one of the most serious PM2.5-dominated air pollution country. Despite a great deal of research has focused on analysing the influence of social and economic driving forces of PM2.5 pollution in China, most research in existence either applying mean regression or failing to consider the spatial autocorrelation. Motivated by this, this paper utilizes a Bayesian hierarchical spatial quantile regression method to explore the effect of socioeconomic activity on PM2.5 air pollution. By introducing spatial random effects into the model, the spatial autocorrelations of residuals are significantly reduced. The empirical study demonstrated that the PM2.5 concentration levels were strongly correlated with total population, urbanization rate, industrialization level and energy efficiency at all quantiles. For upper quantiles, the impact of urbanization rate on the haze is the greatest among all the predictors, then followed by the total population; while for lower quantiles, industrialization has the greatest impact on the PM2.5 concentration. The impacts of energy efficiency in the lower 15% and upper 15% quantiles are higher compared to any of the other quantiles.

Comparison of Walking Protocols and Gait Assessment Systems for Machine Learning-Based Classification of Parkinson's Disease.

Early diagnosis of Parkinson's diseases (PD) is challenging; applying machine learning (ML) models to gait characteristics may support the classification process. Comparing performance of ML models used in various studies can be problematic due to different walking protocols and gait assessment systems. The objective of this study was to compare the impact of walking protocols and gait assessment systems on the performance of a support vector machine (SVM) and random forest (RF) for classification of PD. 93 PD and 103 controls performed two walking protocols at their normal pace: (i) four times along a 10 m walkway (intermittent walk-IW), (ii) walking for 2 minutes on a 25 m oval circuit (continuous walk-CW). 14 gait characteristics were extracted from two different systems (an instrumented walkway-GAITRite; and an accelerometer attached at the lower back-Axivity). SVM and RF were trained on normalized data (accounting for step velocity, gender, age and BMI) and evaluated using 10-fold cross validation with area under the curve (AUC). Overall performance was higher for both systems during CW compared to IW. SVM performed better than RF. With SVM, during CW Axivity significantly outperformed GAITRite (AUC: 87.83 ± 7.81% vs. 80.49 ± 9.85%); during IW systems performed similarly. These findings suggest that choice of testing protocol and sensing system may have a direct impact on ML PD classification results and highlight the need for standardization for wide scale implementation.

Risk assessment of vertebral artery injury in anterior controllable antedisplacement and fusion (ACAF) surgery: a cadaveric and radiologic study.

PURPOSE: We have introduced a novel surgery technique named anterior controllable antedisplacement and fusion (ACAF) for the treatment of ossification of the posterior longitudinal ligament. As reported, the satisfactory postoperative outcome can be attributed to the larger decompression width. However, it may associate with high prevalence of vertebral artery injury (VAI) theoretically. Thus, assessment of the vulnerability of vertebral artery in ACAF is of great importance. METHODS: Computed tomographic scan data of 28 patients were retrospectively studied. Seven radiographic parameters were evaluated: uncinate process (UP) tips distance, transverse foramen (TF)-UP tips distance, TF-LWL (the ipsilateral limited wedging line) distance, the limited distance of lateral decompression, the maximum oblique angle of LWL, TF-LWG (the lateral wall of groove) distance, and width of groove. Eleven fresh cadaveric spines undergoing ACAF surgery were also studied. Two anatomic parameters were evaluated: width of groove and LWG-TF distance. RESULTS: The UP tips distance increased from C3 to C6 and tended to be larger in males. The UP tip-TF distance and LWL-TF distance were smallest at C4, but both were larger than 2 mm. Maximum oblique angle decreased from C3 to C6. Postoperatively, both radiographic and cadaveric measurements showed the width of groove was larger than UP tips distance, but LWG-TF distance was larger than 2 mm in all levels. CONCLUSION: UP can be used as anatomical landmarks to avoid VAI during ACAF surgery. Radiographic and cadaveric measurements verified the safety of ACAF surgery, even for those cases with wedging and lateral slotting.

Machine Learning Models for Genetic Risk Assessment of Infants with Non-syndromic Orofacial Cleft.

The isolated type of orofacial cleft, termed non-syndromic cleft lip with or without cleft palate (NSCL/P), is the second most common birth defect in China, with Asians having the highest incidence in the world. NSCL/P involves multiple genes and complex interactions between genetic and environmental factors, imposing difficulty for the genetic assessment of the unborn fetus carrying multiple NSCL/P-susceptible variants. Although genome-wide association studies (GWAS) have uncovered dozens of single nucleotide polymorphism (SNP) loci in different ethnic populations, the genetic diagnostic effectiveness of these SNPs requires further experimental validation in Chinese populations before a diagnostic panel or a predictive model covering multiple SNPs can be built. In this study, we collected blood samples from control and NSCL/P infants in Han and Uyghur Chinese populations to validate the diagnostic effectiveness of 43 candidate SNPs previously detected using GWAS. We then built predictive models with the validated SNPs using different machine learning algorithms and evaluated their prediction performance. Our results showed that logistic regression had the best performance for risk assessment according to the area under curve. Notably, defective variants in MTHFR and RBP4, two genes involved in folic acid and vitamin A biosynthesis, were found to have high contributions to NSCL/P incidence based on feature importance evaluation with logistic regression. This is consistent with the notion that folic acid and vitamin A are both essential nutritional supplements for pregnant women to reduce the risk of conceiving an NSCL/P baby. Moreover, we observed a lower predictive power in Uyghur than in Han cases, likely due to differences in genetic background between these two ethnic populations. Thus, our study highlights the urgency to generate the HapMap for Uyghur population and perform resequencing-based screening of Uyghur-specific NSCL/P markers.

A Comprehensive Functional Assessment of Carboxylesterase 1 Nonsynonymous Polymorphisms.

Carboxylesterase 1 (CES1) is the predominant human hepatic hydrolase responsible for the metabolism of many clinically important medications. CES1 expression and activity vary markedly among individuals; and genetic variation is a major contributing factor to CES1 interindividual variability. In this study, we comprehensively examined the functions of CES1 nonsynonymous single nucleotide polymorphisms (nsSNPs) and haplotypes using transfected cell lines and individual human liver tissues. The 20 candidate variants include CES1 nsSNPs with a minor allele frequency >0.5% in a given population or located in close proximity to the CES1 active site. Five nsSNPs, including L40Ter (rs151291296), G142E (rs121912777), G147C (rs146456965), Y170D (rs148947808), and R171C (rs201065375), were loss-of-function variants for metabolizing the CES1 substrates clopidogrel, enalapril, and sacubitril. In addition, A158V (rs202121317), R199H (rs2307243), E220G (rs200707504), and T290M (rs202001817) decreased CES1 activity to a lesser extent in a substrate-dependent manner. Several nsSNPs, includingL40Ter (rs151291296), G147C (rs146456965), Y170D (rs148947808), and R171C (rs201065375), significantly reduced CES1 protein and/or mRNA expression levels in the transfected cells. Functions of the common nonsynonymous haplotypes D203E-A269S and S75N-D203E-A269S were evaluated using cells stably expressing the haplotypes and a large set of the human liver. Neither CES1 expression nor activity was affected by the two haplotypes. In summary, this study revealed several functional nsSNPs with impaired activity on the metabolism of CES1 substrate drugs. Clinical investigations are warranted to determine whether these nsSNPs can serve as biomarkers for the prediction of therapeutic outcomes of drugs metabolized by CES1.

Prevalence and Occupational and Environmental Risk Factors of Self-Reported Asthma: Evidence from a Cross-Sectional Survey in Seven Chinese Cities.

OBJECTIVE: Asthma is one of the most common chronic diseases and associated with significant morbidity and mortality. However, few data on occupational and environmental risk factors of asthma are available, particularly in Asian adults. Based on a national cross-sectional survey, we assessed the prevalence and risk factors of asthma in Chinese adults. METHODS: A total of 9974 participants aged 15 years and over in seven Chinese cities were selected using a stratified four-stage random sampling. All participants were interviewed face-to-face in their homes using a standardized self-administered questionnaire. Multivariate logistic regression analyses were adopted to determine various risk factors for asthma. RESULTS: The prevalence of self-reported lifetime asthma was 2.46% among the entire adult population, 3.02% among males and 1.93% among females. The prevalence varied by age group, ethnicity, marital status, education, and floor space per person (p < 0.05). After adjusting for socio-demographic variables and smoking, we found independent occupational and environmental determinants of asthma, including a clearance-related job (OR = 2.28, 95%CI: 1.07-4.89), occupational exposure to industrial or occupational poisonous gas (OR = 4.21, 95%CI: 2.43-7.30), having large amounts of carpet in the workplace (OR = 2.61, 95%CI: 1.20-5.69) and using coal for cooking (OR = 2.65, 95%CI: 1.26-5.57). CONCLUSIONS: Asthma is a serious public health problem in China. Our study provides important updated information on the prevalence of asthma and its associated risk factors, which may help us better understand the epidemiology of asthma and prevent this disorder.

Prevalences and causes of vision impairment in elderly Chinese: a socioeconomic perspective of a comparative report nested in Jiangsu Eye Study.

AIM: To survey the prevalence and causes of visual impairment/blindness among elderly Chinese from different socioeconomic status in community-based design. METHODS: Cluster-sampling randomly selected residents from Binhu and Funing District, two areas representing different socioeconomic levels in China with Binhu in an advanced status and Funing in less-developed area. The participants subjected to ophthalmic examination. The presenting visual acuity (PVA) and best-corrected visual acuity (BCVA) were recorded. Visual impairment and blindness were defined according to World Health Organization criteria. The causes of visual impairment/blindness were identified by ophthalmic examination and/or questionnaire. The socioeconomic status included the per capita gross domestic product, numbers of hospital with ophthalmic service and the number of ophthalmologists per 1 million residents. RESULTS: We successfully included 12 867 participants form 2 areas in this study. The prevalence of PVA impairment (<20/63 to ≥20/400) in the better eye was 5.4% in Binhu and 23.6% in Funing, while the prevalence of blindness (<20/400) was 0.9% in Binhu and 2.3% in Funing. With BCVA, the prevalence of visual impairment was 2.4% in Binhu and 6.4% in Funing, while the prevalence of blindness was 0.8% in Binhu and 1.6% in Funing. The participants with older age and female gender had higher prevalence in visual impairment and blindness. The highest prevalences of vision impairment and blindness evaluated by BCVA at >80y age group reached 20.4% and 6.3% respectively. The prevalences of vision impairment and blindness evaluated by BCVA were 3.5% and 1.0% in male and 5.0% and 1.3% in female. The above differences were statistically significant (P<0.05). The predominant causes of visual impairment and blindness were cataract, retinal disorders and uncorrected refractive error in both areas. The socioeconomic status was associated with visual impairment and blindness. CONCLUSION: This community-based study build a sufficient sample size for an ophthalmic survey. Our data show the disparities on socioeconomic development and genders in visual impairment and blindness in China. Special emphasis of ophthalmic service should be placed on females and less-developed area.

Predicting Drug-Target Interactions via Within-Score and Between-Score.

Network inference and local classification models have been shown to be useful in predicting newly potential drug-target interactions (DTIs) for assisting in drug discovery or drug repositioning. The idea is to represent drugs, targets, and their interactions as a bipartite network or an adjacent matrix. However, existing methods have not yet addressed appropriately several issues, such as the powerless inference in the case of isolated subnetworks, the biased classifiers derived from insufficient positive samples, the need of training a number of local classifiers, and the unavailable relationship between known DTIs and unapproved drug-target pairs (DTPs). Designing more effective approaches to address those issues is always desirable. In this paper, after presenting better drug similarities and target similarities, we characterize each DTP as a feature vector of within-scores and between-scores so as to hold the following superiorities: (1) a uniform vector of all types of DTPs, (2) only one global classifier with less bias benefiting from adequate positive samples, and (3) more importantly, the visualized relationship between known DTIs and unapproved DTPs. The effectiveness of our approach is finally demonstrated via comparing with other popular methods under cross validation and predicting potential interactions for DTPs under the validation in existing databases.