A novel imidazoline derivative as corrosion inhibitor for P110 carbon steel in hydrochloric acid environment

novel imidazoline derivative,2-methyl-4-phenyl-1-tosyl-4,5-dihydro-1H-imidazole(IMI),was prepared and investigated as corrosion inhibitor for P110 carbon steel in 1.0 M HCl solution by weight loss measurements,potentiodynamic polarization and electrochemical impedance spectroscopy(EIS)tests.The inhibition efficiency increased with the rising concentration of IMI inhibitor.The test results and fitting data indicated that the IMI behaved as a mixed-type inhibitor and obeys the Langmuir adsorption isotherm.Scanning electron microscopy(SEM)was carried out to investigate the surface of carbon steel specimens,showing great protection from aggressive solution.Finally,inhibition mechanism of IMI on metal surface was further discussed.

Association of altered serum acylcarnitine levels in early pregnancy and risk of gestational diabetes mellitus

Gestational diabetes mellitus(GDM)is a high-prevalence disease and diagnosed in middle pregnancy.Acylcarnitines are a series of fatty acid esters of carnitine and play important roles in fatty acid and carbohydrate metabolism.However,the role of acylcarnitine on the development of GDM remains unclear.This case-control study involving 214 study participants(107 GDM cases and 107 matched controls)was conducted in a cohort,in China,from 2013 to 2015.The levels of carnitine and 36 acylcarnitines in serum samples collected at the early stage of pregnancy were determined by using ultra-high performance liquid chromatography coupled with tandem mass spectrometry.The associations of the levels of the 37 targeted compounds with GDM risk were investigated by using binary conditional logistic regression models.Alterations in acylcarnitine levels were observed 9–17 weeks before GDM diagnosis.The increases in levels of propionyl-carnitine,malonyl-carnitine,isovaleryl-carnitine,palmitoyl-carnitine and linoleoyl-carnitine were associated with GDM risk with odds ratios(ORs)per standard deviation(SD)increment greater than 1(p<0.05),after adjustment for potential confounding factors(pre-pregnancy body mass index and parity).On the contrary,the increases of decanoyl-carnitine,decenoyl-carnitine,tetradecenoyl-carnitine,tetradecandienoylcarnitine levels were associated with the reduced risk for GDM(ORs per SD<1,p<0.05).To our knowledge,the present study is the largest case-control study to investigate the association between early-pregnancy acylcarnitine levels in serum and GDM risk.The findings add to the evidence for the association between acylcarnitine levels and GDM risk.

Virology features of a family cluster of SARS-CoV-2 infections in Shanghai,China

The global spread of SARS-CoV-2 is currently continuing,and the World Health Organization has announced the risk assessment of the viruses as high.In this study,we analyzed virology features of SARS-CoV-2 causing a family cluster outbreak.Among the six family members,five have been laboratory-confirmed infection of SARS-CoV-2 viruses.A total of five SARS-CoV-2 viruses have been isolated from the nasopharyngeal swabs.The complete genome of the viruses exhibited 100%nucleotide identity with each other.Only two nucleotide differences have been observed between genomes of the isolated viruses and the HCoV/Wuhan/IVDC-HB-01/2019 strain.Therefore,SARS-CoV-2 has been confirmed as the causation of the family cluster infections.

Machine Learning for Investigation on Endocrine-Disrupting Chemicals with Gestational Age and Delivery Time in a Longitudinal Cohort

Endocrine-disrupting chemicals(EDCs)are widespread environmental chemicals that are often considered as risk factors with weak activity on the hormone-dependent process of pregnancy.However,the adverse effects of EDCs in the body of pregnant women were underestimated.The interaction between dynamic concentration of EDCs and endogenous hormones(EHs)on gestational age and delivery time remains unclear.To define a temporal interaction between the EDCs and EHs during pregnancy,comprehensive,unbiased,and quantitative analyses of 33 EDCs and 14 EHs were performed for a longitudinal cohort with 2317 pregnant women.We developed a machine learning model with the dynamic concentration information of EDCs and EHs to predict gestational age with high accuracy in the longitudinal cohort of pregnant women.The optimal combination of EHs and EDCs can identify when labor occurs(time to delivery within two and four weeks,AUROC of 0.82).Our results revealed that the bisphenols and phthalates are more potent than partial EHs for gestational age or delivery time.This study represents the use of machine learning methods for quantitative analysis of pregnancy-related EDCs and EHs for understanding the EDCs’mixture effect on pregnancy with potential clinical utilities.

Machine Learning for Investigation on Endocrine-Disrupting Chemicals with Gestational Age and Delivery Time in a Longitudinal Cohort

Endocrine-disrupting chemicals(EDCs)are widespread environmental chemicals that are often considered as risk factors with weak activity on the hormone-dependent process of pregnancy.However,the adverse effects of EDCs in the body of pregnant women were underestimated.The interaction between dynamic concentration of EDCs and endogenous hormones(EHs)on gestational age and delivery time remains unclear.To define a temporal interaction between the EDCs and EHs during pregnancy,comprehensive,unbiased,and quantitative analyses of 33 EDCs and 14 EHs were performed for a longitudinal cohort with 2317 pregnant women.We developed a machine learning model with the dynamic concentration information of EDCs and EHs to predict gestational age with high accuracy in the longitudinal cohort of pregnant women.The optimal combination of EHs and EDCs can identify when labor occurs(time to delivery within two and four weeks,AUROC of 0.82).Our results revealed that the bisphenols and phthalates are more potent than partial EHs for gestational age or delivery time.This study represents the use of machine learning methods for quantitative analysis of pregnancy-related EDCs and EHs for understanding the EDCs’mixture effect on pregnancy with potential clinical utilities.