Objective To establish a gas chromatography-mass spectrometry (GC-MS) method for the simultaneous determination of four carbonate compounds (CCs), including ethyl methyl carbonate (EMC), diethyl carbonate (DEC), vinylene carbonate (VC), and ethylene carbonate (EC) in workplace air. Methods Vapor-phase EMC, DEC, VC, and EC in workplace air were collected using activated carbon tubes. After desorption with dichloromethane, the samples were analyzed by GC-MS. Qualitative identification was performed based on retention times and characteristic ions, while quantitative analysis was conducted using peak areas of selected characteristic ions. Results The quantitative determination ranges for the four CCs were from 0.57×10⁻³ to 200.00 mg/L, with correlation coefficients ≥0.999 45. The detection limit ranged from 0.17 to 1.60 μg/L, and the lower limit of quantification ranged from 0.57 to 5.33 μg/L. The minimum detection concentration and minimum quantitation concentration were 0.11-1.07 and 0.38-3.55 μg/m³, respectively. Mean spiked recoveries ranged from 85.70% to 111.65%. The intra- and inter-batch relative standard deviations were 0.11%-2.04% and 1.27%-5.18%, respectively. Mean desorption efficiencies of the method ranged from 74.70% to 118.20%. EMC, DEC, and EC samples were stable for up to five days at 4 °C, while VC samples were stable for up to three days at 4 °C. Conclusion The GC-MS method is suitable for the simultaneous determination of the four CCs including EMC, DEC, VC, and EC in workplace air.

Objective This study aimed to investigate the antimicrobial resistance profiles of bacterial strains isolated from pediatric intensive care units(PICU)in China for better antimicrobial therapy.Methods Clinical isolates were collected from 17 institutions,including tertiary care children's hospitals and pediatric department of tertiary general hospitals in China from January 1,2020 to December 31,2022.Antimicrobial susceptibility testing was carried out according to a unified protocol using Kirby-Bauer method or automated systems.Results were interpreted according to the breakpoints released by the Clinical and Laboratory Standards Institute(CLSI)in 2020.Results A total of 10 688 isolates were collected,including gram-positive organisms(39.2％)and gram-negative organisms(60.8％).The top three organisms were S.aureus(13.6％,1 453/10 688),A.baumannii(10.0％,1 067/10 688),and coagulase-negative Staphylococcus(9.9％,1 058/10 688).Multi-drug resistant organisms(MDROs)were very common in children.The prevalence of methicillin-resistant Staphylococcus aureus(MRSA),carbapenem-resistant Enterobacterales(CRE),carbapenem-resistant E.coli,carbapenem-resistant K.pneumoniae(CRKP),carbapenem-resistant A.baumannii(CRAB),and carbapenem-resistant P.aeruginosa(CRPA)was 41.1％,19.4％,8.8％,30.9％,67.4％,and 28.8％,respectively.Overall,more than 50％of Enterobacteriales isolates were resistant to cephalosporins,while nearly 25％of Enterobacteriales isolates were resistant to carbapenems.MDROs were highly resistant to commonly used antibiotics.More than 80％of CRE and CRAB strains were resistant to all beta-lactam antibiotics.CRE and CRAB showed low resistance rates to tigecycline and polymyxin.CRPA showed lower resistance rates to piperacillin,beta-lactamase inhibitor combinations than the resistance rates to third and fourth generation cephalosporins.All of the Staphylococcus and Enterococcus isolates were susceptible to vancomycin and tigecycline.None of PRSP strains isolated from meningitis and nonmeningitis samples were resistant to rifampicin,vancomycin,or linezolid.The prevalence of β-lactamase-negative ampicillin-resistant(BLNAR)strains was 43.3％in Haemophilus influenzae.Conclusions MDROs were prevalent in PICU.It is necessary to establish an effective multidisciplinary team(MDT)to control the antimicrobial resistance.

Objective:To explore the potential categories of transformation shock levels among newly employed nurses and their influencing factors, in order to provide references for implementing targeted interventions.Methods:In March and April 2024, a convenience sampling method was used to select 208 nurses undergoing standardized training at Beijing Tiantan Hospital, Capital Medical University, as the study subjects. The Transformation Shock Evaluation Scale, the Chinese Registered Nurse Core Competency Scale, and the Work Confidence and Ability Scale were used for data collection. Latent profile analysis was employed to identify the potential categories of transformation shock levels among newly employed nurses, and multinomial Logistic regression was used to analyze the influencing factors for different categories.Results:A total of 208 questionnaires were distributed, with 195 valid ones returned, yielding an effective response rate of 93.75%. The overall transformation shock score of the 195 newly employed nurses was (2.10±0.84), the career core competency score was (179.30±35.32), and the work confidence and ability score was (80.01±10.64). The transformation shock levels of the newly employed nurses were divided into four categories: high transformation shock-negative coping group (22.56%, 44/195), moderate transformation shock-psychological barrier group (29.23%, 57/195), lower transformation shock-high communication effectiveness group (36.41%, 71/195), and low transformation shock-high coping ability group (11.79%, 23/195). Logistic regression analysis showed that education level, clinical nursing ability, leadership, communication, critical thinking and research ability were influencing factors for the different categories of transformation shock levels among newly employed nurses ( P<0.05) . Conclusions:The transformation shock levels among newly employed nurses exhibit distinct categorical characteristics. Nursing education and management personnel should provide targeted training based on the influencing factors of different categories to reduce the level of transformation shock.

Objective:To explore the potential categories of transformation shock levels among newly employed nurses and their influencing factors, in order to provide references for implementing targeted interventions.Methods:In March and April 2024, a convenience sampling method was used to select 208 nurses undergoing standardized training at Beijing Tiantan Hospital, Capital Medical University, as the study subjects. The Transformation Shock Evaluation Scale, the Chinese Registered Nurse Core Competency Scale, and the Work Confidence and Ability Scale were used for data collection. Latent profile analysis was employed to identify the potential categories of transformation shock levels among newly employed nurses, and multinomial Logistic regression was used to analyze the influencing factors for different categories.Results:A total of 208 questionnaires were distributed, with 195 valid ones returned, yielding an effective response rate of 93.75%. The overall transformation shock score of the 195 newly employed nurses was (2.10±0.84), the career core competency score was (179.30±35.32), and the work confidence and ability score was (80.01±10.64). The transformation shock levels of the newly employed nurses were divided into four categories: high transformation shock-negative coping group (22.56%, 44/195), moderate transformation shock-psychological barrier group (29.23%, 57/195), lower transformation shock-high communication effectiveness group (36.41%, 71/195), and low transformation shock-high coping ability group (11.79%, 23/195). Logistic regression analysis showed that education level, clinical nursing ability, leadership, communication, critical thinking and research ability were influencing factors for the different categories of transformation shock levels among newly employed nurses ( P<0.05) . Conclusions:The transformation shock levels among newly employed nurses exhibit distinct categorical characteristics. Nursing education and management personnel should provide targeted training based on the influencing factors of different categories to reduce the level of transformation shock.

Artificial intelligence technology has been applied in occupational hazards monitoring, occupational health risks prediction and occupational disease diagnosis, occupational hazard risk assessment, laboratory information management systems, and health and safety management etc. Artificial intelligence will significantly improve the efficiency of occupational health management through the application of deep data integration, predictive analysis, personalized health advice, automated compliance monitoring, and augmented reality technology. However, issues such as data privacy, technological compatibility, regulatory updates, ethical considerations, and user trust still need to be addressed. Interdisciplinary cooperation and technological innovation are crucial to achieving this goal.

Objective This study aimed to investigate the antimicrobial resistance profiles of bacterial strains isolated from pediatric intensive care units(PICU)in China for better antimicrobial therapy.Methods Clinical isolates were collected from 17 institutions,including tertiary care children's hospitals and pediatric department of tertiary general hospitals in China from January 1,2020 to December 31,2022.Antimicrobial susceptibility testing was carried out according to a unified protocol using Kirby-Bauer method or automated systems.Results were interpreted according to the breakpoints released by the Clinical and Laboratory Standards Institute(CLSI)in 2020.Results A total of 10 688 isolates were collected,including gram-positive organisms(39.2％)and gram-negative organisms(60.8％).The top three organisms were S.aureus(13.6％,1 453/10 688),A.baumannii(10.0％,1 067/10 688),and coagulase-negative Staphylococcus(9.9％,1 058/10 688).Multi-drug resistant organisms(MDROs)were very common in children.The prevalence of methicillin-resistant Staphylococcus aureus(MRSA),carbapenem-resistant Enterobacterales(CRE),carbapenem-resistant E.coli,carbapenem-resistant K.pneumoniae(CRKP),carbapenem-resistant A.baumannii(CRAB),and carbapenem-resistant P.aeruginosa(CRPA)was 41.1％,19.4％,8.8％,30.9％,67.4％,and 28.8％,respectively.Overall,more than 50％of Enterobacteriales isolates were resistant to cephalosporins,while nearly 25％of Enterobacteriales isolates were resistant to carbapenems.MDROs were highly resistant to commonly used antibiotics.More than 80％of CRE and CRAB strains were resistant to all beta-lactam antibiotics.CRE and CRAB showed low resistance rates to tigecycline and polymyxin.CRPA showed lower resistance rates to piperacillin,beta-lactamase inhibitor combinations than the resistance rates to third and fourth generation cephalosporins.All of the Staphylococcus and Enterococcus isolates were susceptible to vancomycin and tigecycline.None of PRSP strains isolated from meningitis and nonmeningitis samples were resistant to rifampicin,vancomycin,or linezolid.The prevalence of β-lactamase-negative ampicillin-resistant(BLNAR)strains was 43.3％in Haemophilus influenzae.Conclusions MDROs were prevalent in PICU.It is necessary to establish an effective multidisciplinary team(MDT)to control the antimicrobial resistance.

Artificial intelligence technology has been applied in occupational hazards monitoring, occupational health risks prediction and occupational disease diagnosis, occupational hazard risk assessment, laboratory information management systems, and health and safety management etc. Artificial intelligence will significantly improve the efficiency of occupational health management through the application of deep data integration, predictive analysis, personalized health advice, automated compliance monitoring, and augmented reality technology. However, issues such as data privacy, technological compatibility, regulatory updates, ethical considerations, and user trust still need to be addressed. Interdisciplinary cooperation and technological innovation are crucial to achieving this goal.

This article reported a prenatally diagnosed case of complete ring chromosome 15. A 38-year-old woman who conceived by in vitro fertilization and frozen embryo transfer underwent amniocentesis for prenatal diagnosis at 18 +5 weeks of gestation due to advanced maternal age. The result of G-banding karyotyping was mos 46,XX,r(15)[88]/45,X,-15[11]/46,XX,r(15;15)[1]. No numerical abnormalities of chromosomes or definite pathogenic copy number variations (CNVs) were detected by chromosomal microarray analysis. Amniocentesis was performed again at 31 +6 weeks of gestation. The result of genome copy number variation sequencing indicated no pathogenic CNV and fluorescence in situ hybridization on cultured amniocytes revealed nuc ish(15q)×1[15]/(15q)×3[5]/(15q)×2[80]. Based on all the prenatal diagnosis results, it was suggested that the fetus carried a complete ring chromosome 15. As the peripheral blood chromosomes of the couple were normal and no obvious abnormalities were detected by the prenatal ultrasound either in our hospital or another hospital, the pregnant woman decided to continue the pregnancy after genetic counseling and delivered a baby girl at 41 weeks of gestation. The girl showed no physical abnormalities during a seven-month follow-up.

Objective:To explore the effect of standardized training and intelligent assessment system for nurses.Methods:In August 2020 and August 2021, convenience sampling was used to select new nurses who received standardized training at Beijing Tiantan Hospital affiliated with Capital Medical University as participants. Forty-two new nurses in August 2020 were designated as control group, and 42 new nurses in August 2021 were designated as intervention group and assessed through an intelligent assessment system. The assessment costs of two groups of nurses were compared.Results:Intervention group had shorter pre-exam preparation time, pre-exam waiting time, score summary time, and consumables costs compared to control group, and the differences were statistically significant ( P<0.05) . Conclusions:The construction of an intelligent assessment system improves the scientificity, effectiveness, and fairness of standardized training for nurses. Continuous improvement and perfection are still needed in the future.

This study aimed to identify the related substances of lorazepam tablets by liquid chromatography mass spectrometry (LC-MS). To separate the related substances of lorazepam tablets, gradient elution was performed using acetonitrile and 0.1% acetic acid -20 mmol/L of ammonium acetate as mobile phase on Inert Sustain C18 (250 mm × 4.6 mm, 5 μm).The accurate mass and elemental composition of the parent ions and their product ions of related substances were determined by electrospray-ionization quadrupole time-of-flight high resolution mass spectrometry (ESI-Q-TOF/MS).The structures of the related substances were identified by spectral analysis. Under the established analytical condition, lorazepam and its related substances were adequately separated, and 22 major related substances with content greater than 0.1% were detected and identified by hyphenated techniques in lorazepam tablets and their stressed samples.Among them, 5 were the impurities listed in the USP or EP, and the others were unknown related substances identified for the first time in this paper.The LC-MS technique can effectively separate and identify the related substances of lorazepam tablets, which provides some reference for quality control.