The focus of green analytical chemistry (GAC) is to minimize the negative impacts of analytical procedures on human safety, human health, and the environment. Several factors, such as the reagents used, sample collection, sample processing, instruments, energy consumed, and the quantities of hazardous materials and waste generated during analytical procedures, need to be considered in the evaluation of the greenness of analytical assays. In this study, we propose a greenness evaluation metric for analytical methods (GEMAM). The new greenness metric is simple, flexible, and comprehensive. The evaluation criteria are based on both the 12 principles of GAC (SIGNIFICANCE) and the 10 factors of sample preparation, and the results are presented on a 0-10 scale. The GEMAM calculation process is easy to perform, and its results are easy to interpret. The output of GEMAM is a pictogram that can provide both qualitative and quantitative information based on color and number.

OBJECTIVE: To analyze the prevalence and burden of headache disorders in China and its provinces from 1990 to 2021. METHODS: Using data from the Global Burden of Disease Study (GBD) 2021, the number of prevalent cases, prevalence rate, disability-adjusted life years (DALYs), and age-standardized DALY rates were analyzed by sex, age group, and province for headache disorders and their subtypes (migraine and tension-type headache [TTH]) between 1990 and 2021. Percentage changes during this period were also estimated. RESULTS: In 2021, approximately 426 million individuals in China were affected by headache disorders, with an age-standardized prevalence rate of 27,582.61/100,000. The age-standardized DALY rate for all headache disorders was 487.15/100,000. Between 1990 and 2021, the number of prevalent cases increased by 37.78%, while the prevalence of all headache disorders, migraine, and TTH increased by 6.92%, 7.57%, and 7.86%, respectively. The highest prevalence was observed in the 30-34 age group (39,520.60/100,000). Migraine accounted for a larger proportion of DALYs attributable to headache disorders, whereas TTH has a greater impact on its prevalence. In 2021, the highest age-standardized DALY rates for headache disorders were observed in Heilongjiang (617.85/100,000) and Shanghai (542.86/100,000). CONCLUSION The prevalence of headache disorders is increasing in China. Effective health education, improve diagnosis and treatment are essential, particularly for middle-aged working populations and women of childbearing age.
Humans ; China/epidemiology* ; Female ; Male ; Adult ; Middle Aged ; Prevalence ; Young Adult ; Adolescent ; Aged ; Child ; Headache Disorders/epidemiology* ; Disability-Adjusted Life Years ; Child, Preschool ; Cost of Illness ; Infant ; Aged, 80 and over

OBJECTIVE: The objective of our study was to evaluate the vaccine effectiveness (VE) of the pentavalent rotavirus vaccine (RV5) among < 5-year-old children in three provinces of China during 2020-2024 via a propensity score-matched test-negative case-control study. METHODS: Electronic health records and immunization information systems were used to obtain data on acute gastroenteritis (AGE) cases tested for rotavirus (RV) infection. RV-positive cases were propensity score matched with RV-negative controls for age, visit month, and province. RESULTS: The study included 27,472 children with AGE aged 8 weeks to 4 years at the time of AGE diagnosis; 7.98% (2,192) were RV-positive. The VE (95% confidence interval, CI) of 1-2 and 3 doses of RV5 against any medically attended RV infection (inpatient or outpatient) was 57.6% (39.8%, 70.2%) and 67.2% (60.3%, 72.9%), respectively. Among children who received the 3rd dose before turning 5 months of age, 3-dose VE decreased from 70.4% (53.9%, 81.1%) (< 5 months since the 3rd dose) to 63.0% (49.1%, 73.0%) (≥ 1 year since the 3rd dose). The three-dose VE rate was 69.4% (41.3%, 84.0%) for RVGE hospitalization and 57.5% (38.9%, 70.5%) for outpatient-only medically attended RVGE. CONCLUSION Three-dose RV5 VE against rotavirus gastroenteritis (RVGE) in children aged < 5 years was higher than 1-2-dose VE. Three-dose VE decreased with time since the 3rd dose in children who received the 3rd dose before turning five months of age, but remained above 60% for at least one year. VE was higher for RVGE hospitalizations than for medically attended outpatient visits.
Humans ; Rotavirus Vaccines/immunology* ; China/epidemiology* ; Case-Control Studies ; Child, Preschool ; Infant ; Rotavirus Infections/epidemiology* ; Male ; Propensity Score ; Female ; Vaccine Efficacy ; Gastroenteritis/virology* ; Vaccines, Attenuated ; Rotavirus

OBJECTIVE: To evaluate the protective effects of gentiopicroside (GPS) against reactive oxygen species (ROS)-induced NOD-like receptor family, pyrin domain containing 3 (NLRP3) inflammasome activation in endothelial cells, aiming to reduce atherosclerosis. METHODS: Eight-week-old male ApoE-deficient mice were randomly divided into 2 groups (n=10 per group): the vehicle group and the GPS treatment group. Both groups were fed a high-fat diet for 16 weeks. GPS (40 mg/kg per day) was administered by oral gavage to the GPS group, while the vehicle group received an equivalent volume of the vehicle solution. At the end of the treatment, blood and aortic tissues were collected for assessments of atherosclerosis, lipid profiles, oxidative stress, and molecular expressions related to NLRP3 inflammasome activation, ROS production, and apoptosis. Additionally, in vitro experiments on human aortic endothelial cells treated with oxidized low-density lipoprotein (ox-LDL) were conducted to evaluate the effects of GPS on NLRP3 inflammasome activation, pyroptosis, apoptosis, and ROS production, specifically examining the role of the sirtuin 1 (SIRT1)/nuclear factor erythroid 2-related factor 2 (Nrf2) pathway. SIRT1 and Nrf2 inhibitors were used to confirm the pathway's role. RESULTS: GPS treatment significantly reduced atherosclerotic lesions in the en face aorta (P<0.01), as well as in the thoracic and abdominal aortic regions, and markedly decreased sinus lesions within the aortic root (P<0.05 or P<0.01). Additionally, GPS reduced oxidative stress markers and proinflammatory cytokines, including interleukin (IL)-1 β and IL-18, in lesion areas (P<0.05, P<0.01). In vitro, GPS inhibited ox-LDL-induced NLRP3 activation, as evidenced by reduced NLRP3 (P<0.01), apoptosis-associated speck-like protein containing a CARD, cleaved-caspase-1, and cleaved-gasdermin D expressions (all P<0.01). GPS also decreased ROS production, apoptosis, and pyroptosis, with the beneficial effects being significantly reversed by SIRT1 or Nrf2 inhibitors. CONCLUSION GPS exerts an antiatherogenic effect by inhibiting ROS-dependent NLRP3 inflammasome activation via the SIRT1/Nrf2 pathway.
NLR Family, Pyrin Domain-Containing 3 Protein/metabolism* ; Reactive Oxygen Species/metabolism* ; Iridoid Glucosides/therapeutic use* ; NF-E2-Related Factor 2/metabolism* ; Animals ; Atherosclerosis/metabolism* ; Inflammasomes/drug effects* ; Male ; Sirtuin 1/metabolism* ; Signal Transduction/drug effects* ; Humans ; Endothelial Cells/pathology* ; Mice ; Oxidative Stress/drug effects* ; Apoptosis/drug effects* ; Lipoproteins, LDL ; Mice, Inbred C57BL

The focus of green analytical chemistry(GAC)is to minimize the negative impacts of analytical pro-cedures on human safety,human health,and the environment.Several factors,such as the reagents used,sample collection,sample processing,instruments,energy consumed,and the quantities of hazardous materials and waste generated during analytical procedures,need to be considered in the evaluation of the greenness of analytical assays.In this study,we propose a greenness evaluation metric for analytical methods(GEMAM).The new greenness metric is simple,flexible,and comprehensive.The evaluation criteria are based on both the 12 principles of GAC(SIGNIFICANCE)and the 10 factors of sample prep-aration,and the results are presented on a 0-10 scale.The GEMAM calculation process is easy to perform,and its results are easy to interpret.The output of GEMAM is a pictogram that can provide both qualitative and quantitative information based on color and number.

Objective:To establish and validate a multimodal model based on radiomics and deep learning for predicting acute pancreatitis (AP) complicated with acute respiratory distress syndrome (ARDS).Methods:Patients diagnosed with AP from The First Affiliated Hospital of Soochow University, Donghai County People's Hospital and Jintan Affiliated Hospital of Jiangsu University between January 2017 and December 2023 were enrolled. Based on the diagnosis of ARDS within 1 week after admission, the patients were classified into the ARDS group and the non-ARDS group. Patients in the First Affiliated Hospital of Soochow University ( n=406) was used as the training set (non-ARDS group n=212 vs ARDS group n=194), while Donghai and Jintan hospitals served as the test set ( n=175; non-ARDS group n=104 vs ARDS group n=71). Clinical data, laboratory tests and the occurrence of systemic inflammatory response syndrome (SIRS) within 24 hours after admission were collected. Scoring systems such as bedside index for severity in acute pancreatitis (BISAP), Ranson score and modified CT severity index (MCTSI) were calculated. Radiomics features were extracted from three-dimensional CT images to develop a radiomics model based on XGBoost algorithm. At the same time, a deep learning model was constructed using deep convolutional networks to extract deep features. Finally, clinical features and the predictions from the aforementioned models were integrated to establish a multimodal model based on XGBoost algorithm. To enhance model visualization, variable importance ranking and local interpretable visualization were used. The receiver operating characteristic (ROC) curves of the three models and the three scores including BISAP, Ranson and MCTSI were plotted and the area under the curves (AUCs) were calculated to evaluate the prediction performance for ARDS in AP patients, as well as sensitivity and specificity. Results:In the multimodal model for predicting ARDS in AP patients, predictions of the deep learning model and the radiomics model were the most important variables, followed by SIRS, C-reactive protein, procalcitonin, albumin, glucose, creatinine, neutrophil, and Ca 2+. In the training set, the multimodal model achieved an AUC of 0.933 for predicting ARDS in AP patients, higher than the radiomics model (0.727), the deep learning model (0.877), MCTSI (0.870), Ranson (0.620) and BISAP (0.898). In the test set, the model's AUC was 0.916 for predicting ARDS in AP patients, higher than the radiomics model (0.660), the deep learning model (0.864), MCTSI (0.851), Ranson (0.609), and BISAP (0.860). Conclusions:Based on clinical structured data, radiomics and deep learning features, the multimodal model could predict the risk of ARDS in AP patients at an early stage, whose performance is better than the single-modal models and the traditional scoring systems.

Objective:To establish and validate a multimodal model based on radiomics and deep learning for predicting acute pancreatitis (AP) complicated with acute respiratory distress syndrome (ARDS).Methods:Patients diagnosed with AP from The First Affiliated Hospital of Soochow University, Donghai County People's Hospital and Jintan Affiliated Hospital of Jiangsu University between January 2017 and December 2023 were enrolled. Based on the diagnosis of ARDS within 1 week after admission, the patients were classified into the ARDS group and the non-ARDS group. Patients in the First Affiliated Hospital of Soochow University ( n=406) was used as the training set (non-ARDS group n=212 vs ARDS group n=194), while Donghai and Jintan hospitals served as the test set ( n=175; non-ARDS group n=104 vs ARDS group n=71). Clinical data, laboratory tests and the occurrence of systemic inflammatory response syndrome (SIRS) within 24 hours after admission were collected. Scoring systems such as bedside index for severity in acute pancreatitis (BISAP), Ranson score and modified CT severity index (MCTSI) were calculated. Radiomics features were extracted from three-dimensional CT images to develop a radiomics model based on XGBoost algorithm. At the same time, a deep learning model was constructed using deep convolutional networks to extract deep features. Finally, clinical features and the predictions from the aforementioned models were integrated to establish a multimodal model based on XGBoost algorithm. To enhance model visualization, variable importance ranking and local interpretable visualization were used. The receiver operating characteristic (ROC) curves of the three models and the three scores including BISAP, Ranson and MCTSI were plotted and the area under the curves (AUCs) were calculated to evaluate the prediction performance for ARDS in AP patients, as well as sensitivity and specificity. Results:In the multimodal model for predicting ARDS in AP patients, predictions of the deep learning model and the radiomics model were the most important variables, followed by SIRS, C-reactive protein, procalcitonin, albumin, glucose, creatinine, neutrophil, and Ca 2+. In the training set, the multimodal model achieved an AUC of 0.933 for predicting ARDS in AP patients, higher than the radiomics model (0.727), the deep learning model (0.877), MCTSI (0.870), Ranson (0.620) and BISAP (0.898). In the test set, the model's AUC was 0.916 for predicting ARDS in AP patients, higher than the radiomics model (0.660), the deep learning model (0.864), MCTSI (0.851), Ranson (0.609), and BISAP (0.860). Conclusions:Based on clinical structured data, radiomics and deep learning features, the multimodal model could predict the risk of ARDS in AP patients at an early stage, whose performance is better than the single-modal models and the traditional scoring systems.

Objective This study investigated the impact of occupational mercury(Hg)exposure on human gene transcription and expression,and its potential biological mechanisms. Methods Differentially expressed genes related to Hg exposure were identified and validated using gene expression microarray analysis and extended validation.Hg-exposed cell models and PTEN low-expression models were established in vitro using 293T cells.PTEN gene expression was assessed using qRT-PCR,and Western blotting was used to measure PTEN,AKT,and PI3K protein levels.IL-6 expression was determined by ELISA. Results Combined findings from gene expression microarray analysis,bioinformatics,and population expansion validation indicated significant downregulation of the PTEN gene in the high-concentration Hg exposure group.In the Hg-exposed cell model(25 and 10 μmol/L),a significant decrease in PTEN expression was observed,accompanied by a significant increase in PI3K,AKT,and IL-6 expression.Similarly,a low-expression cell model demonstrated that PTEN gene knockdown led to a significant decrease in PTEN protein expression and a substantial increase in PI3K,AKT,and IL-6 levels. Conclusion This is the first study to report that Hg exposure downregulates the PTEN gene,activates the PI3K/AKT regulatory pathway,and increases the expression of inflammatory factors,ultimately resulting in kidney inflammation.

The looming stimulus-evoked flight response to approaching predators is a defensive behavior in most animals. However, how looming stimuli are detected in the retina and transmitted to the brain remains unclear. Here, we report that a group of GABAergic retinal ganglion cells (RGCs) projecting to the superior colliculus (SC) transmit looming signals from the retina to the brain, mediating the looming-evoked flight behavior by releasing GABA. GAD2-Cre and vGAT-Cre transgenic mice were used in combination with Cre-activated anterograde or retrograde tracer viruses to map the inputs to specific GABAergic RGC circuits. Optogenetic technology was used to assess the function of SC-projecting GABAergic RGCs (scpgRGCs) in the SC. FDIO-DTA (Flp-dependent Double-Floxed Inverted Open reading frame-Diphtheria toxin) combined with the FLP (Florfenicol, Lincomycin & Prednisolone) approach was used to ablate or silence scpgRGCs. In the mouse retina, GABAergic RGCs project to different brain areas, including the SC. ScpgRGCs are monosynaptically connected to parvalbumin-positive SC neurons known to be required for the looming-evoked flight response. Optogenetic activation of scpgRGCs triggers GABA-mediated inhibition in SC neurons. Ablation or silencing of scpgRGCs compromises looming-evoked flight responses without affecting image-forming functions. Our study reveals that scpgRGCs control the looming-evoked flight response by regulating SC neurons via GABA, providing novel insight into the regulation of innate defensive behaviors.
Animals ; Superior Colliculi/physiology* ; Retinal Ganglion Cells/physiology* ; GABAergic Neurons/physiology* ; Mice, Transgenic ; Mice ; Optogenetics ; Visual Pathways/physiology* ; Mice, Inbred C57BL ; Photic Stimulation/methods* ; gamma-Aminobutyric Acid/metabolism* ; Male

Objective To understand the distribution and drug resistance profiles of common clinical isolates in the intensive care unit(ICU)of a hospital in Xi'an.Methods A retrospective analysis was conducted on the antimicrobial susceptibility test results of clinical bacterial isolates in ICU of the Second Affiliated Hospital of Xi'an Jiaotong University from January 1,2020 to December 31,2022.Results A total of 3 649 clinical isolates were isolated from the ICU,including 1 344(36.8％)strains of Gram-positive bacteria and 2 305(63.2％)strains of Gram-negative bacteria.The most common bacterial species were Klebsiella spp.(14.8％,540/3 649),Enterococcus spp.(14.3％,522/3 649),coagulase-negative Staphylococcus(12.3％,448/3 649),Acinetobacter spp.(12.0％,438/3 649),and Escherichia coli(11.6％,424/3 649).The prevalence of methicillin-resistant Staphylococcus aureus(MRS A),methicillin-resistant Staphylococcus epidermidis(MRSE),and methicillin-resistant coagulase-negative Staphylococcus(MRCNS)was 76.1％,82.4％,and 69.9％,respectively.MRSA,MRSE,and MRCNS strains showed significantly higher antimicrobial resistance rates than MSSA,MSSE,and MSCNS except for trimethoprim-sulfamethoxazole.No Staphylococcus strains were found resistant to vancomycin or linezolid.Enterococcus faecium demonstrated higher antimicrobial resistance rates than Enterococcus faecalis.No Enterococcus isolates were found resistant to vancomycin.Two strains of linezolid-resistant E.faecalis were identified.Klebsiella pneumoniae showed high resistance rates to imipenem and meropenem(38.4％and 40.2％,respectively).Less than 2.0％of the Escherichia coli strains were resistant to imipenem and meropenem,while more than 10.0％of the Enterobacter cloacae were resistant to imipenem and meropenem.About 27.1％and 19.6％of the Pseudomonas aeruginosa strains were resistant to imipenem and meropenem,respectively.Acinetobacter baumannii showed high resistance rates to imipenem and meropenem(86.0％and 86.7％,respectively).Conclusions K.pneumoniae and A.baumannii strains isolated from the intensive care unit showed high resistance rates to carbapenems.Other species of Enterobacterales are still susceptible to carbapenems at a low resistance rate.Linezolid-resistant strain was identified in Enterococcus spp.No cross resistance to vancomycin was found in Enterococcus isolates.Therefore,it is necessary to strengthen the surveillance of antimicrobial resistance and use antibiotics reasonably for controlling hospital infections.