Socio-Economic Predictors and Distribution of Tuberculosis Incidence in Beijing, China: A StudyUsing a Combination of Spatial Statistics and GIS Technology

Evidence shows that multiple factors, such as socio-economic status and access to health care facilities, affect tuberculosis (TB) incidence. However, there is limited literature available with respect to the correlation between socio-economic/health facility factors and tuberculosis incidence. This study aimed to explore the relationship between TB incidence and socio-economic/health service predictors in the study settings. A retrospective spatial regression analysis was carried out based on new sputum smear-positive pulmonary TB cases in Beijing districts. Global Moran’s I analysis was adopted to detect the spatial dependency followed by spatial regression models (spatial lag model, and spatial error model) along with the ordinary least square model were applied to examine the correlation between TB incidence and predictors. A high incidence of TB was seen in densely populated districts in Beijing, e.g., Haidian, Mentougou, and Xicheng. After comparing the R2, log-likelihood, and Akaike information criterion (AIC) values among three models, the spatial error model (R2 = 0.413; Log Likelihood = −591; AIC = 1199.76) identified the best model fit for the spatial regression model. The study showed that the number of beds in health institutes (p \u3c 0.001) and per capita gross domestic product (GDP) (p = 0.025) had a positive effect on TB incidence, whereas population density (p \u3c 0.001) and migrated population (p \u3c 0.001) had an adverse impact on TB incidence in the study settings. High TB incidence districts were detected in urban and densely populated districts in Beijing. Our findings suggested that socio-economic predictors influence TB incidence. These findings may help to guide TB control programs and promote targeted intervention

The impact of artificial intelligence technology on the management of front-line employees in manufacturing enterprises: The proposal of a theoretical model

Artificial intelligence technology has greatly changed production process equipment and technological systems. The changes in production technology systems and environments will inevitably affect people's psychological behavior, thereby affecting employee performance. This article found that artificial intelligence technology has the effects of interpersonal isolation, skill deprivation, innovation opportunity deprivation, and sense of achievement deprivation on frontline employees in manufacturing enterprises, leading to a sense of powerlessness and fatigue among frontline employees, resulting in a decrease in employee satisfaction, an increase in turnover rate, and an increase in insecurity. To address the impact of artificial intelligence technology on frontline employees, traditional human resource management models should be changed, employee care should be strengthened, team activities should be emphasized, life situations should be created, and diverse skills should be developed

The Association between Environmental Factors and Scarlet Fever Incidence in Beijing Region: Using GIS and Spatial Regression Models

(1) Background: Evidence regarding scarlet fever and its relationship with meteorological, including air pollution factors, is not very available. This study aimed to examine the relationship between ambient air pollutants and meteorological factors with scarlet fever occurrence in Beijing, China. (2) Methods: A retrospective ecological study was carried out to distinguish the epidemic characteristics of scarlet fever incidence in Beijing districts from 2013 to 2014. Daily incidence and corresponding air pollutant and meteorological data were used to develop the model. Global Moran’s I statistic and Anselin’s local Moran’s I (LISA) were applied to detect the spatial autocorrelation (spatial dependency) and clusters of scarlet fever incidence. The spatial lag model (SLM) and spatial error model (SEM) including ordinary least squares (OLS) models were then applied to probe the association between scarlet fever incidence and meteorological including air pollution factors. (3) Results: Among the 5491 cases, more than half (62%) were male, and more than one-third (37.8%) were female, with the annual average incidence rate 14.64 per 100,000 population. Spatial autocorrelation analysis exhibited the existence of spatial dependence; therefore, we applied spatial regression models. After comparing the values of R-square, log-likelihood and the Akaike information criterion (AIC) among the three models, the OLS model (R2 = 0.0741, log likelihood = −1819.69, AIC = 3665.38), SLM (R2 = 0.0786, log likelihood = −1819.04, AIC = 3665.08) and SEM (R2 = 0.0743, log likelihood = −1819.67, AIC = 3665.36), identified that the spatial lag model (SLM) was best for model fit for the regression model. There was a positive significant association between nitrogen oxide (p = 0.027), rainfall (p = 0.036) and sunshine hour (p = 0.048), while the relative humidity (p = 0.034) had an adverse association with scarlet fever incidence in SLM. (4) Conclusions: Our findings indicated that meteorological, as well as air pollutant factors may increase the incidence of scarlet fever; these findings may help to guide scarlet fever control programs and targeting the intervention

Using polysaccharides for the enhancement of functionality of foods: A review

peer-reviewedBackground: Flavor, taste and functional ingredients are important ingredients of food, but they are easily lost or react during heating and are not stable. Carbohydrate-carbohydrate interactions (CCIs) and carbohydrate-protein interactions (CPIs) are involved in a variety of regulatory biological processes in nature, including cell differentiation, proliferation, adhesion, inflammation and immune responses. Polysaccharides have high molecular weights and many intramolecular hydrogen bonds, can be easily modified chemically and biochemically to enhance bioadhesive and biostability of tissues. Therefore, polysaccharides are the foundation for building complex and stable biosystems that are non-toxic with highydrophilicity and easily biodegradable. Scope and approach: In this review, we summarize the principles and applications of polysaccharide delivery systems in a variety of foods. Key findings and conclusions: This review focuses on the self-assembly of carbohydrates with complex structures and discusses the latest advances in self-assembly systems. The host-guest complexes formed by polyvalent sugar conjugates have the potential to provide, control or target delivery or release systems. They can also extend the shelf life of food and prevent oxidation and isomerization during food storage. Moreover, very few studies have outlined a comprehensive overview of the use of various types of food polysaccharide matrixes for the assembly and protection of food ingredients, which is a very important area for further study

Correlation and combining ability analysis of physiological traits and some agronomic traits in maize

Combining ability information on the physiological traits in maize (Zea mays L) and the relationship between physi¬ological traits and biomass, grain yield (GY) and yield components (YC) can help maize breeders design experi¬ments for improving inbred lines and/or developing hybrids with improved GY or YC (GYYC). A six-parent diallel experiment (Griffing method 3) was conducted for combining ability and correlation analyses. The objectives of this study were to 1) study the correlation between physiological traits and biomass at seedling stage; 2) study which physiological traits at seedling stage have significant correlation with biomasses at both seedling and later growth stages and GYYCs; 3) evaluate combining ability of the physiological traits that are significantly correlated with either GY or one of the YCs. Results showed plant heights at 20 day, 40 day, and leaf area were highly corre¬lated with both dry weights of shoots and roots. All chlorophyll-related organelles were significantly correlated with only dry weights of shoots. However, dry matter at seedling stage seemed not to be related to dry matter in later growth stages. Five physiological traits (stomatal conductance, transpiration rate, net photosynthetic rate, two quantum yield related traits) at seedling stage were identified to greatly impact dry matter at later growth stages. Results also showed that 13 out of 35 physiological traits studied were significantly correlated with GYYCs. Differ¬ent germplasms for improving GYYCs could be used based on both correlation between the 13 traits and GYYCs and combining ability effects of each line for the 13 selected traits

Association of blood pressure with development of metabolic syndrome components: a five-year retrospective cohort study in Beijing

Background: Raised blood pressure (BP) is associated with the incidence of metabolic syndrome (MetS). It is unknown if subjects with different BP levels may develop certain components of MetS over time. We investigated the incidence of MetS relative to different levels of BP over a 5-year period in a Chinese population in Tongren Hospital, Beijing. Methods: During the period of 2006–2011, we recruited 2,781 participants with no MetS, or self-reported type 2 diabetes, dyslipidemia, hypertension, or cardiovascular disease at baseline. Association rule was used to identify the transitions of MetS components over time. Results: The incidence of MetS at follow-up was 9.74% for men and 3.21% for women in the group with optimal BP; 10.29% and 7.22%, respectively, in the group with normal BP; 10.49% and 10.84%, respectively, in the group with high-normal BP; and 14.48% and 23.21%, respectively in the group with high BP. The most common transition was from healthy to healthy in the groups with optimal or normal BP (17.9–49.3%), whereas in the high-normal BP group, 16.9-22.1% of subjects with raised BP returned to healthy status or stayed unchanged, while 13.8-21.4% of people with high BP tended to develop raised fasting glucose levels. Conclusions: The incidence of MetS increased in parallel with the increase in BP. People with optimal and normal BP levels were less susceptible to developing MetS over time, whereas abnormal BP seemed to be a pre-existing phase of MetS. High-normal BP was a crucial status for MetS prevention

Concentrations and gas-particle partitioning of PCDD/Fs in the urban air of Dalian, China

PCDD/Fs in the urban air of Dalian, China were monitored with high-volume active sampler from November 2009 to October 2010. The concentration of Cl4-8DD/Fs ranged from 3065 to 49538 fg m(-3), with an average of 10249 fg m(-3). The international toxic equivalents (I-TEQ) value of that was 61.8-1182 fg m(-3), with an average of 235 fg m(-3), which was comparable to those in the other urban locations around the world. It was found that the Cl4-8DD/Fs appeared to be present mainly in the particle phase during winter, spring and autumn, while during summer which were dominantly in gas phase. The ratio of Cl4-8DD/Fs present in particle phase increased with the increasing level of chlorination. The concentrations of PCDFs and PCDDs decreased with the increase of chlorinated level, while the concentrations of 2,3,7,8-PCDDs congeners increased with the increase of chlorination level. The homolog profiles of the concentrations of PCDFs presented were higher than those of the PCDDs, which indicated the PCDD/Fs pollution source of the air in Dalian was characteristic for thermal source pollution. The correlation analysis of meteorological parameters with the concentrations of Cl4-8CDD/Fs was conducted using SPSS packages, and it was found that the ambient temperature and atmospheric pressure were important factors influence the concentration of PCDD/Fs in the air. The respiratory risk and intake dioxins of the residents around the sampling sites were studied in the paper. It was found that Junge-Pankow model was much more accurate in predicting the gas-particle partitioning behavior of PCDD/Fs homologues during winter, while the Harner-Bidleman model shows better agreement with the measured data during winter and summer

Association between γ-glutamyl transferase and metabolic syndrome: A cross-sectional study of an adult population in Beijing

The relationship between liver enzymes and clustered components of metabolic syndrome (MetS) is explored and the predictive power of γ-glutamyl transferase (GGT) for the diagnosis of MetS in an adult population in Beijing is investigated. A total of 10,553 adults aged 20-65 years who underwent health examinations at Beijing Tongren Hospital in 2012 were enrolled in the study. Multivariate logistic regression analysis is conducted to determine the associations between the levels of various liver enzymes and clustered components of MetS. A receiver operating characteristic analysis is used to determine the optimal cut-off value of GGT for the diagnosis of MetS. A high level of GGT is found to be positively associated with clustered components of MetS in both men and women after adjusting for age, body mass index (BMI), history of alcoholic fatty liver, and the presence of taking anti-hypertensive, anti-dyslipidemic, and anti-diabetic drugs. Among all components of MetS, GGT is more predictive of triglyceride, and BMI. The area-under-the-curve values of GGT for discriminating MetS from normal metabolic status in men and women are 0.73 and 0.80, respectively. The optimal cut-off value of GGT for men is 31.50 U/L, demonstrating a sensitivity of 74.00% and specificity of 62.00%. For women, it is 19.50 U/L (sensitivity 76.00% and specificity 70.00%). GGT is therefore recommended as a useful diagnostic marker for MetS, because the test is inexpensive, highly sensitive, and frequently encountered in clinical practice

Preparation, characterization and targeting of micronized 10-hydroxycamptothecin-loaded folate-conjugated human serum albumin nanoparticles to cancer cells

Qingyong Li, Chen Liu, Xiuhua Zhao, Yuangang Zu, Ying Wang, Baoyou Zhang, Dongmei Zhao, Qi Zhao, Lin Su, Yang Gao, Baihe SunKey Laboratory of Forest Plant Ecology, Ministry of Education, Northeast Forestry University, Harbin, Heilongjiang, People's Republic of ChinaBackground: The purpose of this study was to develop a method for targeted delivery of 10-hydroxycamptothecin (HCPT)-loaded nanoparticles (NPs) to cancer cells.Methods: We first used a supercritical antisolvent process to prepare micronized HCPT (nHCPT), and then folate-conjugated human serum albumin (HSA) nHCPT-loaded NPs (FA-HSA-nHCPT-NPs) were prepared using a NP-coated method combined with a desolvation technique. The amount of folate conjugation was 16 µg · mg-1 HSA.Results: The particle size of the spherical nHCPT microparticles obtained was 118.5 ± 6.6 nm. The particle size and zeta potential of the FA-HSA-nHCPT-NPs were 233.9 ± 1.2 nm and -25.23 ± 2.98 mV, respectively. The FA-HSA-nHCPT-NPs exhibited a smooth surface and a distinct spherical shape, and the results of differential scanning calorimetry and X-ray diffraction indicated that the FA-HSA-nHCPT-NPs presented in a nanostructured amorphous state. The FA-HSA-nHCPT-NPs showed sustained-release characteristics for 120 hours in vitro, with a drug-loading content of 7.3% and an encapsulating efficiency of 79.1%.Conclusion: The FA-NPs were effective delivery systems for uptake by SGC7901 cells compared with folate-free NPs. These results suggest that a NP-coated method combined with a desolvation technique is effective for preparing NPs with drugs having poor solubility in water and most organic solvents, using albumin as the wall material. FA-HSA-NPs are a stable delivery system and have the potential for targeted delivery of anticancer drugs.Keywords: nanoparticle-coated, desolvation technique, 10-hydroxycamptothecin, human serum albumin, folate, targeted delivery&nbsp

Prediction of the 20-year incidence of diabetes in older Chinese: Application of the competing risk method in a longitudinal study

The competing risk method has become more acceptable for time-to-event data analysis because of its advantage over the standard Cox model in accounting for competing events in the risk set. This study aimed to construct a prediction model for diabetes using a subdistribution hazards model