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.

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.

This paper provided a comprehensive review of the concept, current situation and influencing factors of survivors returning to work after hematopoietic stem cell transplantation, both in domestic and international contexts. The research analyzed and elaborates on four aspects of individual factors, disease-related factors, occupational factors, and social support, in order to provide references for constructing an intervention program for the return to work of hematopoietic stem cell transplantation survivors based on the cultural background in China.

BACKGROUND:Inflammation,oxidative stress and bacterial infection are the main causes of delayed wound healing in diabetes.In recent years,various inorganic nanomaterials have been widely used in the treatment of skin wound healing due to their antibacterial activities,but their effects on anti-oxidation and anti-inflammation are limited. OBJECTIVE:To investigate the effect of Prussian blue nanoparticles on the wound repair of diabetes in terms of antioxidant,anti-inflammatory and photothermal antibacterial activities. METHODS:Prussian blue nanoparticles were prepared and characterized.(1)In vitro:The biocompatibility of Prussian blue nanoparticles with different concentrations was detected by MTT assay.The cytoprotective effect of Prussian blue nanoparticles and the intracellular reactive oxidative species level were examined under the condition of hydrogen peroxide.The ability of Prussian blue nanoparticles to decompose hydrogen peroxide and superoxide anion radicals was tested;the effect of Prussian blue nanoparticles on lipopolysaccharide-induced macrophage inflammation was investigated.The photothermal antibacterial activity of Prussian blue nanoparticles was detected by the plate colony counting method.(2)In vivo:ICR mice were intraperitoneally injected with streptozotocin to establish a diabetes mouse model.After the model was successfully established,a 6 mm wound was created on the back with a hole punch.There were the control group(no treatment),the Prussian blue group and the Prussian blue with light group.The wound healing and histomorphological changes were observed. RESULTS AND CONCLUSION:(1)In vitro:Prussian blue nanoparticles in 25-200 μg/mL were non-toxic to cells.Prussian blue nanoparticles had the extremely strong antioxidant capacity and mitigated the intracellular reactive oxidative species at a high oxidative stress environment,resulting in a pronounced cytoprotective effect.The Prussian blue nanoparticles not only exhibited hydrogen peroxide degradation activity but also showed strong superoxide scavenging ability.Prussian blue nanoparticles also displayed significant anti-inflammatory activity and extremely strong antibacterial ability after light irradiation.(2)In vivo:After 14 days,the wound sizes of the Prussian blue group and Prussian blue with light group were significantly reduced,and the healing speed of Prussian blue with light group was the fastest.Hematoxylin-eosin and Masson staining showed a lot of granulation tissue formation and collagen deposition in the Prussian blue group and the Prussian blue with light group,of which the Prussian blue with light group was the most.Immunofluorescence staining displayed that,compared with the control group,the expressions of α-SMA and CD31 were increased significantly in Prussian blue group and Prussian blue with light group(P<0.05),but F4/80 expression was decreased significantly in Prussian blue group and Prussian blue with light group(P<0.05),indicating more obvious improvement in the Prussian blue with light group.(3)These results showed that Prussian blue nanoparticles could promote the skin wound healing of the diabetes mouse model by exerting anti-inflammatory,antioxidant and antibacterial effects.

To summarize the nursing experience of a patient with gastric cancer who developed cytokine release syndrome after using immune checkpoint inhibitors.Key points of nursing care:development of nursing assessment decisions with a holistic view to guide safe nursing care;taking into account the contradiction between bleeding and thrombosis and providing good care for upper gastrointestinal bleeding;implementing a nursing strategy focusing on cleaning and anti-infection for IV oral mucositis;implementing risk management for severe pulmonary lesions;providing good hormone medication care and discharge follow-up management.The patient was successfully discharged on 52nd day with a 3-month follow-up in good condition.

Objective To study the effect of curcumin on wound healing in diabetic mice.Methods The effect of curcumin on fibroblast activity was examined by the MTT assay,and the ROS detection kit was used to detect the effect of curcumin on the hydrogen peroxide-induced scavenging effect of reactive oxygen species(ROS)in fibroblasts.Q-PCR was used to detect the effects of curcumin on the mRNA expression of inflammatory factors CD86,CD206,IL-6 and ARG1 in lipopolysaccharide-induced RAW 264.7macrophage.The wound model of diabetes was established by intraperitoneal injection of streptozotocin.Hematoxylin-eosin(HE)and Masson staining were used to evaluate wound healing and histomorphological changes,and immunofluorescence staining was used to determine skin tissue α-smooth muscle actin,CD86 and CD206 expression.Results Curcumin had no significant effect on fibroblast activity at concentrations less than 20 mol·L-1;curcumin scavenged hydrogen peroxide-induced intracellular ROS in fibroblasts;curcumin decreased the mRNA expression of CD86 and IL-6 while increasing CD206 and ARG1 in lipopolysaccharide-induced RAW 264.7 macrophages.After in vivo administration,compared with the control group,wound healing was significantly faster in the curcumin(15,30 mg·mL-1)group after 7 d and 14 d of wound perforation(P＜0.01).Hematoxylin-eosin(HE)and Masson staining results confirmed a significant increase in granulation tissue and a significant increase in collagen deposition in the curcumin(15,30 mg·mL-1)group.Immunofluorescence assay showed significantly higher expression of CD206(P＜0.01)and significantly reduced expression of CD86(P＜0.01)in the skin wounds of curcumin(15,30 mg·mL-1)for 14 d.In addition,the expression of α-SMA in the wound of the high-dose curcumin(30 mg·mL-1)group was significantly higher than that of the low-dose curcumin group(P＜0.01).Conclusion Curcumin accelerates diabetic wound healing by promoting granulation tissue proliferation and collagen deposition in refractory diabetic wounds in mice through its anti-inflammatory and antioxidant effects.

Green analytical chemistry(GAC)focuses on mitigating the adverse effects of analytical activities on human safety,human health,and environment.In addition to the 12 principles of GAC,proper GAC tools should be developed and employed to assess the greenness of different analytical assays.The 15 widely used GAC metrics,i.e.,national environmental methods index(NEMI),advanced NEMI,assessment of green profile(AGP),chloroform-oriented toxicity estimation scale(ChlorTox Scale),Analytical Eco-Scale,Green Certificate Modified Eco-Scale,analytical method greenness score(AMGS),green analytical pro-cedure index(GAPI),ComplexGAPI,red-green-blue(RGB)additive color model,RGB 12 algorithm,analytical greenness calculator(AGREE),AGREE preparation(AGREEprep),HEXAGON,and blue appli-cability grade index(BAGI),are selected as the typical tools.This article comprehensively presents and elucidates the principles,characteristics,merits,and demerits of 15 widely used GAC tools.This review is helpful for researchers to use the current GAC metrics to assess the environmental sustainability of analytical assays.

Psychiatric comorbidity is common in symptom-based diagnoses like autism spectrum disorder (ASD), attention/deficit hyper-activity disorder (ADHD), and obsessive-compulsive disorder (OCD). However, these co-occurring symptoms mediated by shared and/or distinct neural mechanisms are difficult to profile at the individual level. Capitalizing on unsupervised machine learning with a hierarchical Bayesian framework, we derived latent disease factors from resting-state functional connectivity data in a hybrid cohort of ASD and ADHD and delineated individual associations with dimensional symptoms based on canonical correlation analysis. Models based on the same factors generalized to previously unseen individuals in a subclinical cohort and one local OCD database with a subset of patients undergoing neurosurgical intervention. Four factors, identified as variably co-expressed in each patient, were significantly correlated with distinct symptom domains (r = -0.26-0.53, P < 0.05): behavioral regulation (Factor-1), communication (Factor-2), anxiety (Factor-3), adaptive behaviors (Factor-4). Moreover, we demonstrated Factor-1 expressed in patients with OCD and Factor-3 expressed in participants with anxiety, at the degree to which factor expression was significantly predictive of individual symptom scores (r = 0.18-0.5, P < 0.01). Importantly, peri-intervention changes in Factor-1 of OCD were associated with variable treatment outcomes (r = 0.39, P < 0.05). Our results indicate that these data-derived latent disease factors quantify individual factor expression to inform dimensional symptom and treatment outcomes across cohorts, which may promote quantitative psychiatric diagnosis and personalized intervention.
Humans ; Male ; Female ; Attention Deficit Disorder with Hyperactivity ; Obsessive-Compulsive Disorder ; Adult ; Autism Spectrum Disorder ; Bayes Theorem ; Adolescent ; Young Adult ; Magnetic Resonance Imaging ; Middle Aged ; Child ; Brain/metabolism* ; Cohort Studies ; Comorbidity

Green analytical chemistry (GAC) focuses on mitigating the adverse effects of analytical activities on human safety, human health, and environment. In addition to the 12 principles of GAC, proper GAC tools should be developed and employed to assess the greenness of different analytical assays. The 15 widely used GAC metrics, i.e., national environmental methods index (NEMI), advanced NEMI, assessment of green profile (AGP), chloroform-oriented toxicity estimation scale (ChlorTox Scale), Analytical Eco-Scale, Green Certificate Modified Eco-Scale, analytical method greenness score (AMGS), green analytical procedure index (GAPI), ComplexGAPI, red-green-blue (RGB) additive color model, RGB 12 algorithm, analytical greenness calculator (AGREE), AGREE preparation (AGREEprep), HEXAGON, and blue applicability grade index (BAGI), are selected as the typical tools. This article comprehensively presents and elucidates the principles, characteristics, merits, and demerits of 15 widely used GAC tools. This review is helpful for researchers to use the current GAC metrics to assess the environmental sustainability of analytical assays.

Objective: To investigate the prognostic utility of radiomics features extracted from 18 F-fluorodeoxyglucose (FDG) PET/CT combined with clinical factors and metabolic parameters in predicting progression-free survival (PFS) and overall survival (OS) in individuals diagnosed with extranodal nasal-type NK/T cell lymphoma (ENKTCL). Materials and Methods: A total of 126 adults with ENKTCL who underwent 18 F-FDG PET/CT examination before treatment were retrospectively included and randomly divided into training (n = 88) and validation cohorts (n = 38) at a ratio of 7:3.Least absolute shrinkage and selection operation Cox regression analysis was used to select the best radiomics features and calculate each patient’s radiomics scores (RadPFS and RadOS). Kaplan–Meier curve and Log-rank test were used to compare survival between patient groups risk-stratified by the radiomics scores. Various models to predict PFS and OS were constructed, including clinical, metabolic, clinical + metabolic, and clinical + metabolic + radiomics models. The discriminative ability of each model was evaluated using Harrell’s C index. The performance of each model in predicting PFS and OS for 1-, 3-, and 5-years was evaluated using the time-dependent receiver operating characteristic (ROC) curve. Results: Kaplan–Meier curve analysis demonstrated that the radiomics scores effectively identified high- and low-risk patients (all P < 0.05). Multivariable Cox analysis showed that the Ann Arbor stage, maximum standardized uptake value (SUVmax), and RadPFS were independent risk factors associated with PFS. Further, β2-microglobulin, Eastern Cooperative Oncology Group performance status score, SUVmax, and RadOS were independent risk factors for OS. The clinical + metabolic + radiomics model exhibited the greatest discriminative ability for both PFS (Harrell’s C-index: 0.805 in the validation cohort) and OS (Harrell’s C-index: 0.833 in the validation cohort). The time-dependent ROC analysis indicated that the clinical + metabolic + radiomics model had the best predictive performance. Conclusion The PET/CT-based clinical + metabolic + radiomics model can enhance prognostication among patients with ENKTCL and may be a non-invasive and efficient risk stratification tool for clinical practice.