Objective:To investigate whether the synonymous variation of the ATP-binding cassette transporter A3 (ABCA3) gene may increase the risk of respiratory distress syndrome (RDS) in Mongolian and Han newborns in Inner Mongolia.Methods:From January 2018 to June 2019, the children of Mongolian and Han nationality who were hospitalized in the Department of Neonatal Pediatrics, affiliated Hospital of Inner Mongolia Medical University and the control group were sequenced by ABCA3 exon gene to analyze whether there was synonymous mutation in ABCA3 gene.Results:A total of 101 children with RDS were enrolled, including 37 children with Mongolian and 64 with Han children. There were 113 patients in the control group, including 45 Mongolian children and 68 Han children. Children with Mongolian and Han nationality RDS and control group can detect multiple synonymous mutation sites, such as: F353F, P585P, A227A, V150V, L982L, A928A, S1372S, P1653P, E1618E, and A1027A, etc, among them, four synonymous variants of p.A227A, p.F353F, p.P585P and p.S1372S are common synonymous mutants. In both Mongolian and Han nationality, the frequency of ABCA3 gene synonymous mutation in RDS group was significantly higher than that in control group (Mongolian: χ2=9.402, P=0.002; Han: χ2=9.348, P=0.002 ). The mutation rates of F353F and P585P in Mongolian and Han children with RDS were higher than those in the control group, and the difference was statistically significant(Mongolian F353F: χ2=5.270, P=0.022; Han F353F: χ2=5.532, P=0.019.Mongolian P585P: χ2=4.711, P=0.030; Han P585P: χ2=4.480, P=0.034). Conclusions:The synonymous variation of ABCA3 gene may increase the risk of RDS in Mongolian and Han newborns in Inner Mongolia, and F353F and P585P may be one of the susceptible genes of RDS in Mongolian and Han newborns in Inner Mongolia.

Background Heavy metal emissions from mining and smelting areas are a global problem, and health risks associated with heavy metal contamination of soils are of great concern. The long-term mining of the largest realgar mine in Asia has caused severe arsenic and other metal pollution to the surrounding rivers and soils. Objective To understand the levels of metal contamination and health risks in agricultural soils of villages surrounding the largest realgar mine in Asia, and to lay a good foundation for further necessary pollution control actions and decisions. Methods A field survey was conducted to collect soil samples according to the Technical rules for monitoring of environmental quality of farmland soil (NY/T 395-2012), and then inductively coupled plasma mass spectrometry was used to determine the contents of 28 heavy metals [cadmium (Cd), arsenic (As), lead (Pb), mercury (Hg), chromium (Cr), nickel (Ni), copper (Cu), zinc (Zn), beryllium (Be), selenium (Se), cobalt (Co), antimony (Sb), molybdenum (Mo), vanadium (V), barium (Ba), thallium (Tl), boron (B) , bismuth (Bi), lithium (Li), manganese (Mn), strontium (Sr), calcium (Ca), rubidium (Rb), iron (Fe), magnesium (Mg), aluminum (Al), kalium (K), and titanium (Ti)]. Geoaccumulation index, single factor pollution index, and Nemerow comprehensive index were used to evaluate the degree and characteristics of single metal pollution and combined pollution in soil, respectively. A health risk assessment model was used to evaluate the risks of metals in soil to human health. Results The results of geoaccumulation index calculation revealed that 22 heavy metals were enriched in the soil, and the indexes of target heavy metals from high to low were Cd > Se > Pb >Hg > As > Co> Ni > Cu > Zn > Bi > Sb > Mo > Be> Cr > Ba >V > Li > Sr> Mn> Rb > Ca> Tl . The single factor pollution indexes of 17 heavy metals from high to low were Be > Cd > B > Mo > V > As > Ni > Cu > Pb > Zn > Co > Se > Tl > Ba > Cr > Hg > Sb. The Nemerow comprehensive index indicated all sampling points were graded as severe pollution. The mean of total non-carcinogenic health risk values and the mean of carcinogenic health risk values for the target heavy metals in the area were higher than the threshold (1) and the maximum acceptable risk (1.0×10^–4), respectively. The total carcinogenic health risks for adults and children reached 1.1×10^–3 and 1.67×10^–3, respectively. The mean non-carcinogenic health risk values of As, Co, Cr, and Pb pollution were greater than 1, and the maximum non-cancer risk value of Sb for children was greater than 1. The mean carcinogenic risk values of Ni, As, and Cu exceeded 1.0×10^–4 for both adults and children, and the maximum carcinogenic risk values of Be and Cr for children were more than 1.0×10^–4. Conclusion The farmland soil around the hugest realgar mine in Asia is contaminated by multiple metals. The study soil is seriously polluted by Cd, Se, Pb, As, Hg, Be, B, Mo, V, Ni, Cu, Pb, Zn, Co, and Ba. The pollution of Ni, As, Cu, Cr, and Be is considered as carcinogenic hazards to health, while the pollution of As, Co, Cr, Pb, and Sb poses non-carcinogenic health risks. Our study findings show that the soil is polluted by Co and Group 1 carcinogen Be, which could cause health risks; although Cr and Sb have not reached severe pollution levels, there are certain health risks and also need attention.

Objective To create risk predictive models of healthcare-seeking delay among imported malaria patients in Jiangsu Province based on machine learning algorithms, so as to provide insights into early identification of imported malaria cases in Jiangsu Province. Methods Case investigation, first symptoms and time of initial diagnosis of imported malaria patients in Jiangsu Province in 2019 were captured from Infectious Disease Report Information Management System and Parasitic Disease Prevention and Control Information Management System of Chinese Center for Disease Control and Prevention. The risk predictive models of healthcare-seeking delay among imported malaria patients were created with the back propagation (BP) neural network model, logistic regression model, random forest model and Bayesian model using thirteen factors as independent variables, including occupation, species of malaria parasite, main clinical manifestations, presence of complications, severity of disease, age, duration of residing abroad, frequency of malaria parasite infections abroad, incubation period, level of institution at initial diagnosis, country of origin, number of individuals travelling with patients and way to go abroad, and time of healthcare-seeking delay as a dependent variable. Logistic regression model was visualized using a nomogram, and the nomogram was evaluated using calibration curves. In addition, the efficiency of the four models for prediction of risk of healthcare-seeking delay among imported malaria patients was evaluated using the area under curve (AUC) of receiver operating characteristic curve (ROC). The importance of each characteristic was quantified and attributed by using SHAP to examine the positive and negative effects of the value of each characteristic on the predictive efficiency. Results A total of 244 imported malaria patients were enrolled, including 100 cases (40.98%) with the duration from onset of first symptoms to time of initial diagnosis that exceeded 24 hours. Logistic regression analysis identified a history of malaria parasite infection [odds ratio (OR) = 3.075, 95% confidential interval (CI): (1.597, 5.923)], long incubation period [OR = 1.010, 95% CI: (1.001, 1.018)] and seeking healthcare in provincial or municipal medical facilities [OR = 12.550, 95% CI: (1.158, 135.963)] as risk factors for delay in seeking healthcare among imported malaria cases. BP neural network modeling showed that duration of residing abroad, incubation period and age posed great impacts on delay in healthcare-seek among imported malaria patients. Random forest modeling showed that the top five factors with the greatest impact on healthcare-seeking delay included main clinical manifestations, the way to go abroad, incubation period, duration of residing abroad and age among imported malaria patients, and Bayesian modeling revealed that the top five factors affecting healthcare-seeking delay among imported malaria patients included level of institutions at initial diagnosis, age, country of origin, history of malaria parasite infection and individuals travelling with imported malaria patients. ROC curve analysis showed higher overall performance of the BP neural network model and the logistic regression model for prediction of the risk of healthcare-seeking delay among imported malaria patients (Z = 2.700 to 4.641, all P values < 0.01), with no statistically significant difference in the AUC among four models (Z = 1.209, P > 0.05). The sensitivity (71.00%) and Youden index (43.92%) of the logistic regression model was higher than those of the BP neural network (63.00% and 36.61%, respectively), and the specificity of the BP neural network model (73.61%) was higher than that of the logistic regression model (72.92%). Conclusions Imported malaria cases with long duration of residing abroad, a history of malaria parasite infection, long incubation period, advanced age and seeking healthcare in provincial or municipal medical institutions have a high likelihood of delay in healthcare-seeking in Jiangsu Province. The models created based on the logistic regression and BP neural network show a high efficiency for prediction of the risk of healthcare-seeking among imported malaria patients in Jiangsu Province, which may provide insights into health management of imported malaria patients.

Animals ; Carrier Proteins/metabolism* ; Histidine/metabolism* ; Humans ; Methylation ; Methyltransferases/metabolism* ; Neoplasm Proteins/metabolism* ; Neoplasms/metabolism* ; Protein Processing, Post-Translational