Objective To investigate the expression and clinical significance of serum microRNA-141-3p(miR-141-3p)and Kelch like epichlorohydrin associated protein 1(KEAP1)in neonatal acute respiratory dis-tress syndrome(NARDS).Methods A total of 121 children with NARDS admitted to the hospital from Janu-ary 2022 to March 2024(NARDS group)and 65 healthy neonates during the same period(control group)were selected.According to the degree of disease,the children with NARDS were divided into the mild NARDS group(4≤oxygen index<8,48 cases),the moderate NARDS group(8≤oxygen index<16,46 ca-ses),the severe NARDS group(oxygen index≥16,27 cases),and the children with NARDS were divided into the death group(18 cases)and the survival group(103 cases)according to the 28-day prognosis.Serum miR-141-3p and KEAP1 levels were detected by fluorescence quantitative polymerase chain reaction and enzyme-linked immunosorbent assay.The correlation between serum miR-141-3p and KEAP1 levels in children with NARDS was analyzed by Pearson correlation coefficient.The correlation between serum miR-141-3p and KEAP1 levels and oxygen index in children with NARDS was analyzed by Spearman correlation coefficient.Receiver operating characteristic(ROC)curve was plotted to analyze the predictive efficacy of serum miR-141-3p and KEAP1 levels on the death in children with NARDS.Results Serum miR-141-3p level in the NARDS group was lower than that in the control group,and KEAP1 level in the NARDS group was higher than that in the control group(t=14.288,12.596,P<0.001).There was a binding site between miR-141-3p and KEAP1 at the 3'-untranslated region 131-138.Pearson correlation showed that serum miR-141-3p was negatively correlated with KEAP1 level in children with NARDS(r=-0.745,P<0.001).The levels of ser-um miR-141-3p increased sequentially in the severe NARDS group,moderate NARDS group,and mild NARDS group,while the level of KEAP1 decreased sequentially(F=185.469,113.126,P<0.001).Spearman correla-tion coefficient showed that oxygen index in children with NARDS was negatively correlated with serum miR-141-3p level(r=-0.815,P<0.001)and positively correlated with serum KEAP1 level(r=0.827,P<0.001).Serum miR-141-3p level in the dead group was lower than that in the survival group,and KEAP1 level was higher than that in the surviving group(t=4.213,4.495,P<0.001).The area under the curve of the combined prediction of serum miR-141-3p and KEAP1 levels for the death in children with NARDS was 0.878(95%CI:0.806-0.930),which was greater than 0.783(95%CI:0.699-0.853)and 0.786(95%CI:0.702-0.855)predicted by serum miR-141-3p and KEAP1 levels alone(Z=2.963,2.021,P<0.05).Conclu-sion The serum miR-141-3p level is decreased and the KEAP1 level is increased in children with NARDS,which is associated with worsening of the disease and poor prognosis.The combination of serum miR-141-3p and KEAP1 levels has high predictive efficacy for death in children with NARDS.

ObjectiveTo investigate the chemical hazards in the production line of lithium batteries, so as to provide a scientific basis for the management of occupational-health risk and to promote the healthy and sustainable development of the lithium battery industry. MethodsAn on-site survey on the process flow of the production of lithium battery was conducted in an enterprise. Volatile organic compounds (VOCs) in the occupational environment were collected by Summa canisters, carbonates and N-methyl pyrrolidone (NMP) were collected using activated carbon tubes, and airborne metals were collected using filter membranes. VOCs, carbonates and NMP were detected by gas chromatography-mass spectrometry (GC-MS), and airborne metal elements in the dust samples were analyzed by inductively coupled plasma mass spectrometry (ICP-MS). ResultsNon-targeted environmental monitoring results indicated that NMP was detected in the negative /positive electrode coating, assembly and drying filling workstations, dimethyl carbonate (DMC) was detected in the assembly, drying and electrolyte injection workstations, and ethyl methyl carbonate (EMC) was detected solely in the electrolyte injection workstation. Semi-quantitative analyses of VOCs identified 136 pollutants, including acrylonitrile and halohydrocarbons. Quantitative targeted environmental monitoring results revealed the highest geometric mean (GM) concentration of EMC (31.450 mg·m^-3) was found in the assembly and drying workstations, diethyl carbonate (DEC) was detected in all workstations. While vinylene carbonate (VC) and ethylene carbonate (EC) were detected only in electrolyte injection, assembly and drying workstations. NMP was detected in all positive electrode coating samples, with a GM concentration of 5.68 mg·m^-3 (concentration range: 4.0‒ 7.4 mg·m^-³). Lithium was exclusively detected in dust samples from the liquid injection workstation (GM: 0.014 μg·m^-³). ConclusionNMP, EMC, DEC, and other chemicals are identified at the key workstations such as the positive electrode coating, electrolyte injection, assembly and drying in the lithium production line. Furthermore, semi-quantitative VOCs analyses identified 136 pollutants, demonstrating a characteristic of multicomponent chemical exposure.

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] To analyse the distribution of antigen phenotypes in the Rh, MNS and Kidd blood group systems of Han and Tujia blood donors in Chongqing, and to provide data support for the establishment of an expanded blood group antigen phenotype database and the development of expanded blood group coordinated transfusion in blood donors. [Methods] The antigens of Rh, MNS and Kidd blood group systems in Han and Tujia blood donors in Chongqing were detected by test-tube method, and the Hardy-Weinborg anastomosis of the three blood group systems was calculated. Pearson's chi-square test and Fisher's exact probability method were used to compare the differences in phenotypic distribution frequencies among different regions and ethnic groups. [Results] Han and Tujia blood donors accounted for the highest proportion of CCee in the antigenic phenotype of the Rh blood group system, followed by CcEe, and then Ccee and ccEE. Tujia blood donors accounted for 52.02% of CCee, which was higher than that of Han blood donors (47.24%), while Han blood donors accounted for 32.20% of CcEe, which was higher than that of Tujia blood donors (28.94%). In the antigenic phenotype of the MNS blood group system, the blood donors of Han nationality and Tujia were MN>MM>NN,. The antigen phenotype distribution frequency of the Kidd blood group system was highest for Jk(a+b+) among both Han and Tujia blood donors, and the blood donors of Han nationality were Jk(a+b+)>Jk(a+b+), while those of Tujia were Jk(a-b+)>Jk(a+b-). The antigens of the three blood groups of Han and Tujia blood donors were consistent with the Hardy-Weinberg balance(P>0.05). There was no statistically significant difference in the frequency of antigen phenotypes of the three blood group systems between Han and Tujia blood donors(P>0.05). There were statistically significant differences in the phenotypic distribution frequency of Rh antigens between Chongqing and Xi'an, Zhejiang, Shantou, Foshan, Nanning and Yangzhou(P<0.05), but not with Guang'an and Shenzhen(P>0.05). There were statistically significant differences in the phenotypic distribution frequency of Rh antigens between Han, Tujia, Zang, Mongolian, Korean and Hani ethnic groups in Chongqing(P<0.05). There were statistically significant differences in the phenotypic distribution frequency of MNS antigens between Han blood donors in Chongqing and Urumqi, Hainan and Yuncheng, but not with Xi'an and Wenzhou. There was a statistically significant difference in the phenotypic distribution frequency of MNS antigen between Tujia blood donors in Chongqing and Urumqi and Hainan(P<0.05), but there was no significant difference in the phenotypic distribution frequency of MNS antigen between Tujia blood donors in Chongqing, Urumqi and Hainan(P>0.05). There was a statistically significant difference in the phenotypic distribution frequency of Kidd antigens between blood donors in Chongqing and Harbin(P<0.05), but not in Huizhou, Wenzhou and Yichang(P>0.05). [Conclusion] The population in Chongqing has multi-ethnic characteristics, and the antigenic phenotypes of Rh, MNS and Kidd blood group systems exhibit diversity and regional differences. Establishing an expanded blood bank can provide more options for precision blood transfusion.

Objective: To investigate the clinical characteristics, the types of unexpected antibodies, and their impacts on immunological risks among patients with different frequencies of cross-matching incompatibility, so as to propose corresponding solutions. Methods: Data of cross-matching incompatibility samples from 92 medical institutions during 2022 to 2024 were collected and divided into three groups based on the frequency of cross-matching. Statistical analysis was performed on disease types, distribution of hematologic diseases, alloantibody detection rates, and proportions of alloantibody types. Results: The 858 patients were divided into three groups based on the frequency of blood cross-matching incompatibility: ≥5 times (8.28%, 71/858), 2 to 4 times (28.21%, 242/858); 1 time (63.52%, 545/858). There was a clustered distribution of disease types in the ≥5 cross-matchings group, with 71.83% (51/71) of patients having tumors or hematologic and hematopoietic diseases. In contrast, the disease types in the 2 to 4 cross-matchings and 1 cross-matching groups were more diverse. An analysis of 249 patients with hematologic diseases found that multiple myeloma was the most common disease in all three groups, accounting for 31.43% (11/35), 35.37% (29/82), and 37.88% (50/132) respectively. In the ≥5 cross-matchings group, myelodysplastic syndrome (14.29%, 5/35) and thalassemia (14.29%, 5/35) were the second most common diseases. In contrast, in the 2 to 4 cross-matchings group and 1 cross-matching group, autoimmune hemolytic anemia was the second most common disease, with prevalence rates of 20.73% (17/82) and 24.24% (32/132), respectively. Alloantibodies were detected in 54.66% of the patients, with antibodies against Rh blood group being most frequent (>50%) in all three groups. The detection rates of alloantibodies/alloantibodies with coexisting autoantibodies decreased across groups: the ≥5 cross-matchings group (70.42%, 50/71) > the 2 to 4 cross-matchings group (54.96%, 133/242) > the 1 cross-matching group (52.48%, 286/545). Conclusion: The risk of alloantibody production increases in patients with multiple cross-matching incompatibilities, especially in those with tumors or hematologic diseases. For handling of cross-matching incompatibility cases, it is recommended to optimize the cross-matching process, implement individualized transfusion plans, and enhance the technical capabilities of clinical transfusion departments and blood group reference laboratories to ensure the safety and effectiveness of transfusions.

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 use multiple model integration to predict the potential distribution of suitable areas for Hedysari Radix in China and the main environmental factors affecting its distribution.Methods Based on 119 geographical distribution points obtained from species distribution databases,and incorporating 19 bioclimatic and topographic factors,a species distribution model was constructed using the Biomod2 software package 3.5-1 version ensemble modeling platform,integrating six algorithms including generalized linear model,gradient boosting machine,random forest,and others.Geographic information system spatial analysis methods were used to quantitatively assess the distribution characteristics of suitable habitats of Hedysari Radix under current climate conditions and under future climate scenarios,while identifying the primary environmental drivers of its distribution.Results The ensemble model showed significantly superior predictive performance.TSS value and AUC value were 0.924 and 0.992,respectively.Key environmental factors significantly influencing the distribution of Hedysari Radix included slope,aspect,daily average temperature difference,isothermity,seasonal variation coefficient of temperature,lowest temperature in the coldest month,annual precipitation,driest month precipitation,and seasonal variation coefficient of precipitation.Under current climate conditions,suitable habitats for Hedysari Radix were primarily concentrated in the regions of Longnan and Dingxi in Gansu Province,covering an area of approximately 26.17×104 km2.Under future climate scenarios,suitable habitats will gradually shift toward the northwest into lower temperature zones,with a significant reduction in area.Conclusion The habitat suitability model developed in this study provides a basis for the conservation and sustainable utilization of Hedysari Radix genetic resources,while also offering a methodological reference for ecological adaptability studies of medicinal plants.

Objective To use multiple model integration to predict the potential distribution of suitable areas for Hedysari Radix in China and the main environmental factors affecting its distribution.Methods Based on 119 geographical distribution points obtained from species distribution databases,and incorporating 19 bioclimatic and topographic factors,a species distribution model was constructed using the Biomod2 software package 3.5-1 version ensemble modeling platform,integrating six algorithms including generalized linear model,gradient boosting machine,random forest,and others.Geographic information system spatial analysis methods were used to quantitatively assess the distribution characteristics of suitable habitats of Hedysari Radix under current climate conditions and under future climate scenarios,while identifying the primary environmental drivers of its distribution.Results The ensemble model showed significantly superior predictive performance.TSS value and AUC value were 0.924 and 0.992,respectively.Key environmental factors significantly influencing the distribution of Hedysari Radix included slope,aspect,daily average temperature difference,isothermity,seasonal variation coefficient of temperature,lowest temperature in the coldest month,annual precipitation,driest month precipitation,and seasonal variation coefficient of precipitation.Under current climate conditions,suitable habitats for Hedysari Radix were primarily concentrated in the regions of Longnan and Dingxi in Gansu Province,covering an area of approximately 26.17×104 km2.Under future climate scenarios,suitable habitats will gradually shift toward the northwest into lower temperature zones,with a significant reduction in area.Conclusion The habitat suitability model developed in this study provides a basis for the conservation and sustainable utilization of Hedysari Radix genetic resources,while also offering a methodological reference for ecological adaptability studies of medicinal plants.

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.