Chronic urticaria (CU) is a persistent immune-mediated skin disease with an incompletely understood pathogenesis. As the largest micro-ecosystem in the human body, the gut microbiota participates in complex metabolic processes and produces a wide range of metabolites. The gut microbiota-metabolism axis plays a crucial role in the onset and progression of CU. Patients with CU commonly exhibit gut dysbiosis, characterized by a reduction in beneficial bacteria and an increase in opportunistic pathogens, accompanied by alterations in key metabolites. These changes may disrupt the intestinal barrier and modulate the function of immune cells such as mast cells and T cells, thereby triggering or aggravating distal cutaneous inflammation and contributing to CU pathophysiology. Certain bacterial taxa and metabolites hold promise as potential biomarkers for CU diagnosis, therapeutic response, and prognosis, while interventions targeting gut microbiota have demonstrated potential in ameliorating CU symptoms. Elucidating the characteristics and mechanistic roles of gut microbiota and metabolism in CU could provide a theoretical basis for developing novel individualized diagnostic and therapeutic strategies.
Humans ; Gastrointestinal Microbiome/physiology* ; Chronic Urticaria/etiology* ; Dysbiosis/complications* ; Mast Cells

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 establish a GC method for simultaneous determination of volatile index components of octyl acetate,benzylacetone,myristicin and dehydrocostus lactone in Tibetan medicine Bawei Chenxiang powders.Methods A GC method was used with octyl acetate,benzylacetone,myristicin and dehydrocostus lactone as indicator components,SH-WAX capillary column(30 m×0.25 mm,0.25 μm)and temperature programming were used,the carrier gas was nitrogen,the inlet temperature was 220 ℃,the detector temperature was 240 ℃,with split injection volume of 1 μL.Results The concentrations of octyl acetate,benzylacetone,myristicin and dehydrocostus lactone showed a good linear relationship with peak area(r＞0.999 5)in the range of 12.44-124.43 μg·mL-1,2.90-29.01 μg·mL-1,15.95-159.45 μg·mL-1,15.62-156.15 μg·mL-1,respectively;the average recovery rates were 100.40％(RSD=1.55％),97.80％(RSD=1.41％),99.50％(RSD=0.77％)and 99.50％(RSD=0.85％)(n=6).Conclusion The method has good specificity,precision,repeatability and accuracy,and can be used for the determination of volatile index components in Bawei Chenxiang powders.

Objective:To evaluate the efficacy and safety of Xiao′er Huangjin Zhike Granules in the treatment of cough caused by acute bronchitis in children, which is defined in TCM terms as a syndrome of phlegm-heat obstructing the lung.Methods:This was a block-randomized, double-blind, placebo-controlled, multicenter clinical trial.From January 2022 to September 2023, 359 children aged 3 to 7 years old diagnosed as acute bronchitis (lung-obstructing phlegm-heat syndrome) were enrolled from 21 participating hospitals and randomly assigned to the experimental group and placebo group in a 3︰1 ratio, and respectively treated with Xiao′er Huangjin Zhike Granules and its matching placebo.Cough resolution/general resolution rate after 7 days of treatment was used as the primary efficacy outcome for both groups.Results:(1)On the seventh day of treatment, the rate of cough disappearance/basically disappearance in the experimental group and placebo group were 73.95% and 57.61% retrospectively, which had statistically significance ( P=0.001).(2)After 7 days of treatment, the median duration of cough disappearance/basic disappearance were 5 days and 6 days in the two groups , with a statistically significant difference ( P=0.006).The area under the curve of cough symptom severity time was 7.20 ± 3.79 in the experimental group and 8.20±4.42 in the placebo group.The difference between the two groups was statistically significant ( P=0.039).(3) After 7 days of treatment, the difference between TCM syndrome score and baseline was -16.0 (-20.0, -15.0) points in the experimental group and -15.0 (-18.0, -12.0) points in the placebo group, with significant difference between the two groups ( P=0.004).In the experimental group, the clinical control rate, the markedly effective rate, the effective rate and the ineffective rate were 49.04%, 28.35%, 16.48% and 6.13% severally; and in the placebo group, the clinical control rate, the markedly effective rate, the effective rate and the ineffective rate were 38.04%, 26.09%, 29.35%, and 6.52% separately, which had statistically significant ( P=0.014).(4) There was no significant difference in the incidence of adverse events or adverse reactions during the trial between both groups.Moreover, while adverse reactions in the form of vomiting and diarrhea were occasionally reported, no serious drug-related adverse event or adverse reaction was reported.(5)The tested drug provided good treatment compliance, showing no statistically significant difference from the placebo in terms of compliance rate. Conclusions:Based on the above findings, it can be concluded that Xiao′er Huangjin Zhike Granules provides good safety, efficacy, and treatment compliance in the treatment of cough caused by acute bronchitis, and lung-obstructing phlegm-heat syndrome, in children.

Objective To observe the value of intratumoral and peritumoral radiomics based on diffusion weighted imaging(DWI)for predicting histological grade of breast cancer.Methods Preoperative DWI data of 700 patients with single breast cancer diagnosed by pathology were retrospectively analyzed.The patients were divided into training set(n= 560,including 381 of grade Ⅰ+Ⅱ and 179 of grade Ⅲ)and test set(n=140,including 95 of grade Ⅰ+Ⅱ and 45 of grade Ⅲ)at the ratio of 8∶2.Intratumoral ROI(ROIintra)was manually delineated on DWI,which was automatically expanded by 3 mm and 5 mm to decline peritumoral ROI(ROIperi,including ROI3 mm and ROI5 mm),then intratumoral-peritumoral ROI(ROIintra+3 mm,ROIintra+5 mm)were obtained.The optimal radiomics features were extracted and screened,and the radiomics model(RM)for predicting the histological grade of breast cancer were constructed.Receiver operating characteristic curves were drawn,and the areas under the curve(AUC)were calculated to evaluate the predictive efficacy of each model.Calibration curve method was used to evaluate the calibration degree,while decision curve analysis(DCA)was performed to explore the clinical practicability of each model.Results AUC of RMintra,RM+3 mm,RM+5mm,RMintra+3 mm and RMintra+5 mm was 0.750,0.724,0.749,0.833 and 0.807 in training set,while was 0.723,0.718,0.736,0.759 and 0.782 in test set,respectively.In training set,significant differences of AUC was found(all P＜0.01),while in test set,no significant difference of AUC was found among models(all P＞0.05).The calibrations of models were all high.DCA showed that taken 0.02-0.88 as the threshold,the clinical net benefit of RMintra+per were greater in training set,while taken 0.40-0.72 as the threshold,the clinical net benefit of RMintra+per was greater in test set.Conclusion Both DWI intratumoral and peritumoral radiomics could effectively predict histological grade of breast cancer.Combination of intratumoral and peritumoral radiomics was more effective.

Objective:To summarize the experience of brain death determination under veno-arterial extracorporeal membrane oxygenation (VA-ECMO) assisted circulatory support, especially apnea test (AT) precautions, and to provide references for brain death determination in this scenario.Methods:In 78 patients who had VA-ECMO at Organ Transplant Center, Zhengzhou People's Hospital from October 2019 to December 2023, 8 organ donors had brain death determination under VA-ECMO assisted circulatory support. Baseline data, clinical data, and VA-ECMO data during AT trial were collected from these 8 patients to summarize the process of brain death determination.Results:Six of the 8 donors met the criteria of brain death; 10 EEG, 12 evoked potentials and 15 ATs were performed. Complications in ATs, including hypotension, decreased oxygenation and arrhythmia, were alleviated after timely improved VA-ECMO flow and applied cardiotonic and pressor drugs.Conclusion:AT is key for successful brain death determination in organ donors under VA-ECMO assisted circulatory support; therefore, complications should be closely monitored and managed.

Purpose To investigate the predictive value of 18F-FDG PET/CT radiomics model for event-free survival(EFS)in extranodal natural killer/T-cell lymphoma(ENKTL).Materials and Methods A total of 90 patients with ENKTL who underwent 18F-FDG PET/CT examination before treatment in Henan Provincial People′s Hospital from January 2013 to January 2021 were retrospectively collected and randomly divided into training(63 cases)and validation groups(27 cases).Features were extracted from baseline PET and CT images.The least absolute shrinkage and selection operator algorithm combined with Cox survival analysis were used to select features and construct clinical model,radiomics model and clinical+radiomics composite model,the median risk score of the model was used as the cut-off value to divide the patients into high-risk group and low-risk group.C-index and receiver operating characteristic curve analysis were used to evaluate the predictive performance of the three models.The nomogram was constructed based on the optimal model and calibration curves were used to describe the consistency between the survival probability and the actual probability of the optimal model for predicting ENKTL patients,Kaplan-Meier analysis and log-rank test were used to evaluate the prognostic value of the optimal model.Results The composite model showed higher prognostic performance in the training(C-index 0.791,95%CI 0.702-0.879,area under the curve,AUC=0.882)and validation groups(C-index 0.770,95%CI 0.650-0.889,AUC=0.720)than that of the clinical and radiomics models alone.The calibration curves showed good consistency between the composite model in predicting the third year probability of EFS and the actual outcome,and the survival curves showed that the EFS of the high-risk group were significantly lower than that of the low-risk group.Conclusion Composite model based on radiomics and clinical parameters can provide more comprehensive prognostic information and improve diagnostic accuracy.The nomogram provides a non-invasive diagnostic tool for risk stratification of patients with ENKTL and facilitates individualized treatment.

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.

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.