Objective To investigate the status and predictive factors of future acute exacerbation of asthma in adults. Methods Adult patients with asthma who were admitted to the hospital from March 2023 to July 2025 were selected. Based on the acute exacerbation status after follow-up, the patients were categorized into acute exacerbation group (n=107) and control group (n=245). The clinical data, pulmonary function [peak expiratory flow (PEF) and forced expiratory volume in the 1st second/forced vital capacity (FEV₁/FVC)] and health status [asthma control questionnaire (ACQ), Morisky medication adherence scale (MMAS-8), short form 36-item health survey (SF-36)] of the study subjects were collected. The correlation between the indicators and acute exacerbation was explored by logistic regression analysis, and the predictive efficiency was evaluated by ROC curve. Results The age, smoking history, asthma course, allergen exposure and ACQ in the acute exacerbation group were higher than those in the control group, while the average monthly family income, glucocorticoid treatment, PEF, FEV₁/FVC, MMAS-8 and SF-36 were lower (P<0.05). Multivariate logistic regression suggested that ACQ score, glucocorticoid treatment, MMAS-8 score, FEV₁/FVC, PEF and smoking history were the influencing factors of acute asthma exacerbation (P<0.05). ROC analysis revealed that the diagnostic efficiency of combined prediction was the highest (AUC=0.965), with sensitivity of 0.944 and specificity of 0.857 (P<0.05). Conclusion It is necessary to pay more attention to the medication compliance and asthma control level of patients, and integrate multi-dimensional information such as smoking history, pulmonary function and treatment conditions to accurately identify patients with high-risk acute exacerbation and implement targeted intervention measures.

Objective:To investigate the characteristics of the endometrial microbiota in patients with varying degrees of intrauterine adhesion (IUA).Methods:This single-center cross-sectional observational study enrolled 115 patients with IUA who were treated at the Hysteroscopic Center of Fuxing Hospital, Capital Medical University, from May 2022 to October 2023. After quality control and data preprocessing, 81 samples met the inclusion criteria for analysis. Patients were grouped according to an established IUA scoring and grading system into mild IUA ( n=38) and moderate-to-severe IUA ( n=43). Endometrial tissue was collected under sterile conditions. Bacterial genomic DNA was extracted, the 16S rRNA V3-V4 region was amplified, and sequencing was performed on an Illumina platform. Differences in endometrial microbiota diversity and composition were compared between the two groups. Results:Patients with varying degrees of IUA exhibited comparable species richness, evenness and diversity of endometrial microbiota. At the phylum level, the endometrial microbiota across all subjects was predominantly composed of Proteobacteria, Firmicutes, Cyanobacteriota, Bacteroidota, and Actinobacteriota, with Proteobacteria (32.29%) and Firmicutes (23.82%) showing the highest mean relative abundances. At the genus level, Ralstonia (16.67%), Lactobacillus (13.45%), and Streptococcus (7.07%) were the most abundant genera. Group comparisons showed that the abundance of Ralstonia was higher in the mild IUA group, whereas Lactobacillus, Vibrio and Pseudoalteromonas were more abundant in the moderate-to-severe IUA group; however, these differences did not reach statistical significance (all P>0.05). LEfSe analysis further indicated that Lactobacillus, Vibrio, Pseudoalteromonas, Aeromonas, Ureaplasma and Acetobacterium were relatively enriched in the moderate-to-severe IUA group, while Geobacillus, Stomatobaculum and Fusicatenibacter were more abundant in the mild IUA group. Conclusion:The composition of the endometrial microbiota differs among patients with varying IUA severity. IUA progression may be associated with alterations in the endometrial microbiota; however, causal relationships and underlying mechanisms require further investigation.

Objective:To evaluate the value of ultrasound radiomics combined with machine learning for early diagnosis of seronegative Hashimoto’s thyroiditis (SN-HT) .Methods:This retrospective study included 164 patients from Liaoning Provincial People’s Hospital , Lixin County People’s Hospital, Linghai Dalinghe Hospital, Fengcheng Phoenix Hospital, who underwent thyroidectomy for solitary nodules with normal thyroid function between Nov. 2016 and Jan. 2024. Postoperative pathology confirmed Hashimoto’s thyroiditis (HT) in some cases, who were further categorized into antibody-positive and antibody-negative groups based on serum antibody status. Patients without Hashimoto’s thyroiditis served as the control group. A total of 298 ultrasound images were analyzed. Radiomics features were extracted from hypoechoic non-nodular areas within 0.5 cm surrounding the tumor. Two senior pathologists and two senior ultrasound physicians independently assessed lymphocytic infiltration, eosinophilic changes of follicular epithelium, and the proportion of hypoechoic areas in pathology and ultrasound images, respectively. A machine learning model, CCH-NET, was developed using linear regression and t-distributed stochastic neighbor embedding (t-SNE) techniques. The dataset was divided into a training set (80%) and a validation set (20%) to compare the diagnostic accuracy of CCH-NET with that of senior ultrasound physicians. Results:In internal validation, CCH-NET achieved a diagnostic accuracy of 88.89% for both antibody-positive and antibody-negative groups, significantly higher than the 66.67% accuracy of senior ultrasound physicians ( P<0.01). In external validation, CCH-NET achieved 75.00% and 66.67% accuracy for the two groups, compared to 50.00% by senior ultrasound physicians. For the control group, both methods achieved 93.33% accuracy. The AUC of CCH-NET was 0.848, outperforming senior ultrasound physicians (0.681) ,demonstrating superior diagnostic performance. Conclusion:The radiomics-based CCH-NET model, using non-nodular hypoechoic areas as a specific indicator, can accurately identify early SN-HT in euthyroid patients. It significantly outperforms senior ultrasound physicians, improving diagnostic accuracy and reducing missed diagnoses.

OBJECTIVE To establish a extraction process of Changyan Heji Ⅱ(CYHJ-Ⅱ)based on UPLC-Q-TOF-MS/MS technology combined with response surface analysis,and to optimize the extraction process.METHODS The chemical components in CYHJ-Ⅱ were qualitatively analyzed by UPLC-Q-TOF-MS/MS technology,and the chemical components with good linear relation-ship in mass spectrometry response were selected as process investigation indicators;the extraction process parameters(water addition amount,extraction time and soaking time)were investigated by Box-Behnken design;the comprehensive score was obtained by princi-pal component analysis(PCA),and the optimal process was determined by the comprehensive score combined with response surface a-nalysis.RESULTS Through qualitative analysis,110 components were inferred and identified from CYHJ-Ⅱ,including 2 organic acids,82 flavonoids,13 terpenoids,and 13 alkaloids.Based on the results of qualitative analysis,48 index components with good lin-ear relationships were derived by UPLC-Q-TOF-MS/MS combined with Masshunter mass spectrometry data analysis software.PCA was performed and the comprehensive score was calculated.Response surface analysis was performed with the comprehensive score as an indicator.The optimal extraction process obtained by combining the response surface prediction results and actual production was:soaking for 45 min,8 times the amount of solvent,2 extractions,each time for 120 min.CONCLUSION This study provides a new idea for the investigation of the extraction process of traditional Chinese medicine compound prescriptions and expands a new method for the development of traditional Chinese medicine compound prescriptions.

Objective:To evaluate the impact of short-term exposure to daytime heatwaves, nighttime heatwaves, and compound heatwaves on the risk of road traffic mortality and calculate the attributable mortality burden.Methods:This study collected road traffic mortality data from the Disease Surveillance System in Guangdong, Hunan, Zhejiang, Yunnan, and Jilin Provinces from 2013 to 2018. A time-stratified case-crossover design was used in this study, with the death date for each case serving as the case day. Control days were selected from the same year, month, and day of the week as the case day. A conditional logistic regression model was employed to estimate the cumulative associations of short-term exposure to daytime heatwaves, nighttime heatwaves, and compound heatwaves on the risk of road traffic mortality (lag 0-1 day) and to calculate the attributable fractions (AF).Results:Compared to non-heatwave days, the excess risk ( ER) of road traffic mortality on daytime heatwave days, nighttime heatwave days, and compound heatwave days was 5.3% (95% CI: 0.5%-10.2%), 4.9% (95% CI: 0.5%-9.4%) and 7.5% (95% CI: 2.3%- 12.9%), with corresponding AF of 5.0% (95% CI: 0.5%-9.3%), 4.7% (95% CI: 0.5%-8.6%), and 6.9% (95% CI: 2.3%-11.4%), respectively. Stratified analysis showed that the risk of traffic mortalities caused by daytime heatwaves was higher in females ( ER=15.7%, 95% CI: 5.8%-26.5%) than in males ( ER=1.8%, 95% CI: -3.6%-7.4%). Elderly individuals over 64 years old ( ER=10.9%, 95% CI: 0.3%- 22.6%) had a higher risk of road traffic mortalities from compound heatwaves than those under 45 years old ( ER=2.6%, 95% CI: -5.4%-11.2%). The risk of road traffic injury mortality from motor vehicle accidents caused by compound heatwaves ( ER=16.6%, 95% CI:2.4%-32.7%) was higher than that from non-motor vehicle accidents ( ER=5.7%, 95% CI:0.1%-11.5%). Conclusions:Short-term exposure to daytime heatwaves, nighttime heatwaves, and compound heatwaves was associated with an increased risk of road traffic mortality, with the strongest association observed for compound heatwaves. The mortality burden attributable to compound heatwaves was higher than that for daytime and nighttime heatwaves. Heatwaves were more significantly associated with road traffic mortality risk among females, elderly individuals over 64 years old, and motor vehicle accidents.

Objective:To investigate the characteristics of the endometrial microbiota in patients with varying degrees of intrauterine adhesion (IUA).Methods:This single-center cross-sectional observational study enrolled 115 patients with IUA who were treated at the Hysteroscopic Center of Fuxing Hospital, Capital Medical University, from May 2022 to October 2023. After quality control and data preprocessing, 81 samples met the inclusion criteria for analysis. Patients were grouped according to an established IUA scoring and grading system into mild IUA ( n=38) and moderate-to-severe IUA ( n=43). Endometrial tissue was collected under sterile conditions. Bacterial genomic DNA was extracted, the 16S rRNA V3-V4 region was amplified, and sequencing was performed on an Illumina platform. Differences in endometrial microbiota diversity and composition were compared between the two groups. Results:Patients with varying degrees of IUA exhibited comparable species richness, evenness and diversity of endometrial microbiota. At the phylum level, the endometrial microbiota across all subjects was predominantly composed of Proteobacteria, Firmicutes, Cyanobacteriota, Bacteroidota, and Actinobacteriota, with Proteobacteria (32.29%) and Firmicutes (23.82%) showing the highest mean relative abundances. At the genus level, Ralstonia (16.67%), Lactobacillus (13.45%), and Streptococcus (7.07%) were the most abundant genera. Group comparisons showed that the abundance of Ralstonia was higher in the mild IUA group, whereas Lactobacillus, Vibrio and Pseudoalteromonas were more abundant in the moderate-to-severe IUA group; however, these differences did not reach statistical significance (all P>0.05). LEfSe analysis further indicated that Lactobacillus, Vibrio, Pseudoalteromonas, Aeromonas, Ureaplasma and Acetobacterium were relatively enriched in the moderate-to-severe IUA group, while Geobacillus, Stomatobaculum and Fusicatenibacter were more abundant in the mild IUA group. Conclusion:The composition of the endometrial microbiota differs among patients with varying IUA severity. IUA progression may be associated with alterations in the endometrial microbiota; however, causal relationships and underlying mechanisms require further investigation.

Objective:To evaluate the value of ultrasound radiomics combined with machine learning for early diagnosis of seronegative Hashimoto’s thyroiditis (SN-HT) .Methods:This retrospective study included 164 patients from Liaoning Provincial People’s Hospital , Lixin County People’s Hospital, Linghai Dalinghe Hospital, Fengcheng Phoenix Hospital, who underwent thyroidectomy for solitary nodules with normal thyroid function between Nov. 2016 and Jan. 2024. Postoperative pathology confirmed Hashimoto’s thyroiditis (HT) in some cases, who were further categorized into antibody-positive and antibody-negative groups based on serum antibody status. Patients without Hashimoto’s thyroiditis served as the control group. A total of 298 ultrasound images were analyzed. Radiomics features were extracted from hypoechoic non-nodular areas within 0.5 cm surrounding the tumor. Two senior pathologists and two senior ultrasound physicians independently assessed lymphocytic infiltration, eosinophilic changes of follicular epithelium, and the proportion of hypoechoic areas in pathology and ultrasound images, respectively. A machine learning model, CCH-NET, was developed using linear regression and t-distributed stochastic neighbor embedding (t-SNE) techniques. The dataset was divided into a training set (80%) and a validation set (20%) to compare the diagnostic accuracy of CCH-NET with that of senior ultrasound physicians. Results:In internal validation, CCH-NET achieved a diagnostic accuracy of 88.89% for both antibody-positive and antibody-negative groups, significantly higher than the 66.67% accuracy of senior ultrasound physicians ( P<0.01). In external validation, CCH-NET achieved 75.00% and 66.67% accuracy for the two groups, compared to 50.00% by senior ultrasound physicians. For the control group, both methods achieved 93.33% accuracy. The AUC of CCH-NET was 0.848, outperforming senior ultrasound physicians (0.681) ,demonstrating superior diagnostic performance. Conclusion:The radiomics-based CCH-NET model, using non-nodular hypoechoic areas as a specific indicator, can accurately identify early SN-HT in euthyroid patients. It significantly outperforms senior ultrasound physicians, improving diagnostic accuracy and reducing missed diagnoses.

OBJECTIVE To establish a extraction process of Changyan Heji Ⅱ(CYHJ-Ⅱ)based on UPLC-Q-TOF-MS/MS technology combined with response surface analysis,and to optimize the extraction process.METHODS The chemical components in CYHJ-Ⅱ were qualitatively analyzed by UPLC-Q-TOF-MS/MS technology,and the chemical components with good linear relation-ship in mass spectrometry response were selected as process investigation indicators;the extraction process parameters(water addition amount,extraction time and soaking time)were investigated by Box-Behnken design;the comprehensive score was obtained by princi-pal component analysis(PCA),and the optimal process was determined by the comprehensive score combined with response surface a-nalysis.RESULTS Through qualitative analysis,110 components were inferred and identified from CYHJ-Ⅱ,including 2 organic acids,82 flavonoids,13 terpenoids,and 13 alkaloids.Based on the results of qualitative analysis,48 index components with good lin-ear relationships were derived by UPLC-Q-TOF-MS/MS combined with Masshunter mass spectrometry data analysis software.PCA was performed and the comprehensive score was calculated.Response surface analysis was performed with the comprehensive score as an indicator.The optimal extraction process obtained by combining the response surface prediction results and actual production was:soaking for 45 min,8 times the amount of solvent,2 extractions,each time for 120 min.CONCLUSION This study provides a new idea for the investigation of the extraction process of traditional Chinese medicine compound prescriptions and expands a new method for the development of traditional Chinese medicine compound prescriptions.

Emergence agitation(EA)is a common complication after general anesthesia,especially in children and adolescents.Remifentanil,as a short-acting μ-receptor agonist,has become an important drug for the prevention and treatment of EA due to its rapid recovery and low risk of respiratory depression.This article reviews the mechanism of action and clinical research progress of remifentanil in the prevention and treatment of EA.Its mechanism of action involves the inhibition of pain signals mediated by traditional μ-receptor activation and potential new mechanism based on neural-endocrine-immune network,including regulation of microglial inflammatory pathways,and the modulation of cytokines and chemokines,etc.Clinical studies have shown that remifentanil can significantly shorten the recovery time,reduce the incidence of EA,and further optimize the analgesic effect and recovery quality by combining with other drugs(such as local anesthetics,sedatives,and opioid drugs).Future research should further explore the mechanism of action of remifentanil,optimize clinical treatment strategies,and conduct large-scale clinical trials to standardize the drug use plan,while paying attention to its long-term effects and the development of multimodal treatment plans to promote the further development of EA prevention and treatment plans.

Objective:To systematically evaluate the application of patient-reported outcome measures (PROMs) in adult lung transplant recipients, and to explore their clinical value in assessing quality of life following transplantation.Methods:This study was a systematic review. Relevant studies published between January 2014 and July 2024 were searched in the PubMed and OVID Medline databases using keywords such as "lung transplantation" "quality of life" "HRQoL" "health indice" "patient-reported outcome measure" "questionnaire" "profile" "scale" "score" and "survey". Only English-language articles were included. Eligible studies were those that applied PROMs to assess quality of life in adult lung transplant recipients and were approved by ethics committees. Reviews, case reports, abstracts, and studies involving transplant candidates or recipients of lung-liver or lung-kidney combined transplantation were excluded. Data extracted included basic study information, study design, participant characteristics, and PROM usage. Frequently used PROMs and lung transplant-specific PROMs were summarized, and results with clearly reported time points were analyzed.Results:A total of 63 studies were included, comprising 54 (85.7%) observational studies and 9 (14.3%) interventional studies. The majority of studies originated from the United States (18 studies, 28.6%). A total of 55 different PROMs were identified, including 30 generic and 25 disease-specific instruments. The five most frequently used PROMs were the Short Form 36 (SF-36; 30 studies, 47.6%), the EuroQol 5 Dimension (EQ-5D; 12 studies, 19.0%), the St. George's Respiratory Questionnaire (SGRQ; 11 studies, 17.5%), the Hospital Anxiety and Depression Scale (HADS; 7 studies, 11.1%), and the modified Medical Research Council dyspnea scale (mMRC; 5 studies, 7.9%). Lung transplant-specific PROMs included the Lung Transplant Quality of Life questionnaire (LT-QOL), the Lung Transplant Valued Life Activities (LT-VLA) scale, and the Pulmonary-Specific Quality of Life Scale (PQLS), which were applied in only 6 studies (9.5%). Across studies, lung transplantation was associated with significant improvements in recipients' quality of life, sustained over a follow-up period of 3 to 60 months.Conclusions:A wide range of PROMs have been employed to assess health-related quality of life in lung transplant recipients; however, transplant-specific PROMs remain relatively scarce. PROMs provide valuable insights for reflecting and dynamically monitoring long-term quality of life, supplementing evidence for clinical decision-making, and optimizing post-transplant care strategies.