This study aimed to characterize and identify the non-volatile components in aqueous and ethanolic extracts of the stems and leaves of Rhododendron tomentosum by using sensitive and efficient ultra-performance liquid chromatography-quadrupole-time of flight mass spectrometry(UHPLC-Q-TOF-MS) combined with a self-built information database. By comparing with reference compounds, analyzing fragment ion information, searching relevant literature, and using a self-built information database, 118 compounds were identified from the aqueous and ethanolic extracts of R. tomentosum, including 35 flavonoid glycosides, 15 phenolic glycosides, 12 flavonoids, 7 phenolic acids, 7 phenylethanol glycosides, 6 tannins, 6 phospholipids, 5 coumarins, 5 monoterpene glycosides, 6 triterpenes, 3 fatty acids, and 11 other types of compounds. Among them, 102 compounds were reported in R. tomentosum for the first time, and 36 compounds were identified by comparing them with reference compounds. The chemical components in the ethanolic and aqueous extracts of R. tomentosum leaves and stems showed slight differences, with 84 common chemical components accounting for 71.2% of the total 118 compounds. This study systematically characterized and identified the non-volatile chemical components in the ethanolic and aqueous extracts of R. tomentosum for the first time. The findings provide a reference for active ingredient research, quality control, and product development of R. tomentosum.
Rhododendron/chemistry* ; Chromatography, High Pressure Liquid/methods* ; Drugs, Chinese Herbal/chemistry* ; Mass Spectrometry/methods* ; Plant Leaves/chemistry*

Traditional Chinese medicine(TCM) decoction pieces are a core carrier for the inheritance and innovation of TCM, and their quality and safety are critical to public health and the sustainable development of the industry. Conventional quality control models, while having established a well-developed system through long-term practice, still face challenges such as relatively long inspection cycles, insufficient objectivity in characterizing complex traits, and urgent needs for improving the efficiency of integrating multidimensional quality information when confronted with the dual demands of large-scale production and precision quality control. With the rapid development of artificial intelligence, machine learning can deeply analyze multidimensional data of the morphology, spectroscopy, and chemical fingerprints of decoction pieces by constructing high-dimensional feature space analysis models, significantly improving the standardization level and decision-making efficiency of quality evaluation. This article reviews the research progress in the application of machine learning in the processing, production, and rapid quality evaluation of TCM decoction pieces. It further analyzes current challenges in technological implementation and proposes potential solutions, offering theoretical and technical references to advance the digital and intelligent transformation of the industry.
Machine Learning ; Drugs, Chinese Herbal/standards* ; Quality Control ; Medicine, Chinese Traditional/standards* ; Humans

Acute kidney injury (AKI) has high morbidity and mortality, but effective clinical drugs and management are lacking. Previous studies have suggested that macrophages play a crucial role in the inflammatory response to AKI and may serve as potential therapeutic targets. Emerging evidence has highlighted the importance of forkhead box protein O1 (FoxO1) in mediating macrophage activation and polarization in various diseases, but the specific mechanisms by which FoxO1 regulates macrophages during AKI remain unclear. The present study aimed to investigate the role of FoxO1 in macrophages in the pathogenesis of AKI. We observed a significant upregulation of FoxO1 in kidney macrophages following ischemia-reperfusion (I/R) injury. Additionally, our findings demonstrated that the administration of FoxO1 inhibitor AS1842856-encapsulated liposome (AS-Lipo), mainly acting on macrophages, effectively mitigated renal injury induced by I/R injury in mice. By generating myeloid-specific FoxO1-knockout mice, we further observed that the deficiency of FoxO1 in myeloid cells protected against I/R injury-induced AKI. Furthermore, our study provided evidence of FoxO1's pivotal role in macrophage chemotaxis, inflammation, and migration. Moreover, the impact of FoxO1 on the regulation of macrophage migration was mediated through RhoA guanine nucleotide exchange factor 1 (ARHGEF1), indicating that ARHGEF1 may serve as a potential intermediary between FoxO1 and the activity of the RhoA pathway. Consequently, our findings propose that FoxO1 plays a crucial role as a mediator and biomarker in the context of AKI. Targeting macrophage FoxO1 pharmacologically could potentially offer a promising therapeutic approach for AKI.

ObjectiveTo explore the impact of Gegen Qinliantang（GQT） on the fecal short-chain fatty acids（SCFAs） metabolism in antibiotic-associated diarrhea（AAD） through targeted metabolomics. MethodA total of 240 SD rats were randomly divided into six groups（n=40， half male and half female）， including blank group， model group， bifidobiogen group（0.15 g·kg^-1）， and GQT high-， medium-， and low-dose groups（10.08， 5.04， 2.52 g·kg^-1）， except for the blank group， clindamycin（250 mg·kg^-1） was given to all groups by gavage for modeling every day for 7 d. After successful modeling， each administered group was gavaged with the corresponding dose of the drug， and the blank and model groups were gavaged with an equal volume of normal saline solution， 1 time/d， for 14 d. At 0， 3， 7， 14 d after the drug intervention， eight rats were randomly selected from each group， respectively. Gas chromatography-time-of-flight mass spectrometry（GC-TOF-MS） was used to perform targeted metabolomic analysis of SCFAs in the feces of rats， and partial least squares-discriminant analysis（PLS-DA） was applied to compare the differences in metabolic profiles between groups at different treatment times， and to compare the changes in the contents of SCFAs in rat feces between groups. ResultPLS-DA results showed that the blank group could be clearly distinguishable from the model group， with GQT exhibiting a closer proximity to the blank group after 7 d of treatment. After further analyzing the composition of SCFAs， it was found that the proportion of acetic acid increased and the proportions of butyric acid， valeric acid， hexanoic acid and isovaleric acid decreased in the model group compared with the blank group. After the treatment with GQT， the proportions of butyric acid， isobutyric acid， valeric acid， and isovaleric acid increased， and the proportions of acetic acid， propionic acid and caproic acid decreased. Subsequent differential analysis revealed that GQT could significantly improve the content of butyric acid， and had a certain retrogressive effect on the contents of valeric acid and hexanoic acid. ConclusionThe medium dose group of GQT can improve the contents of SCFAs in AAD feces after 7 days of treatment， which may be related to the improvement of the composition ratio of SCFAs and the contents of butyric acid， valeric acid and caproic acid.

Objective: The aim of the present study was to develop the beverage addiction scale for college students,so as to provide an effective tool for assessing college students beverage addiction. Methods: In November 2022, a cluster sample of 8 792 college students from three colleges in Yunnan and Jiangxi were surveyed by Beverage Addication Scale for College Students. After a through literature review, 12 items were proposed, including withdrawal symptoms, health effects and addiction symptoms, with 4 items each. The ttest and correlation analysis were used to filter the items, and exploratory factor analysis and confirmatory factor analysis were used to evaluate the structural validity of the questionnaire. In May 2023, 5 279 college students from the above three universities were surveyed again to evaluate the reliability and validity of the scale and the positive demarcation value of symptoms. Results: The scale fitted into a tool for measuring symptoms of beverage dependence in a college student population. The scale was composed of 11 items in accordance with withdrawal symptoms, health effects and addiction symptoms, with a cumulative contribution rate of variance was 74.51%. Cronbach α coefficients for the overall scale and three dimensions were 0.94, 0.88, 0.90 and 0.92, respectively. The correlation coefficient between each item and the total score ranged from 0.56 to 0.79, and the correlation coefficient with the dimension ranged from 0.71 to 0.92. The confirmatory factor analysis model was well fitted, and the RMSEA, CFI, TLI and SRMR value were 0.06, 0.95, 0.93 and 0.04, respectively. The application of the scale showed that scores on the scale and each dimension ≥P85 were positive for symptoms. Conclusion The reliability and validity of Beverage Addiction Scale for College Students is good, which can be used to evaluate the beverage addiction symptom of college students.

Objective To explore the mediating role of sleep quality on the impact of occupational stress on depressive symptom among secondary industry occupational population. Methods A total of 895 secondary industry workers in Wuxi City were selected as the study subjects using the stratified random sampling method. The Core Occupational Stress Scale (COSS), Patient Health Questionnaire-9 items (PHQ-9) and Self-Filled Sleep Questionnaire (SSQ) were used to investigate their occupational stress, depressive symptom and sleep quality. Depressive symptom, occupational stress and sleep quality were respectively regarded as dependent variable, independent variable and mediating variable. Process program was used to construct a mediation model, and Bootstrap method was used to test its mediating effect. Results The median score and 25th, 75th percentile for occupational stress, depressive symptom and sleep quality was 43.0 (43.0, 48.0), 6.0 (3.0, 8.0) and 3.0 (1.0, 3.0) points. The detection rates of occupational stress, depressive symptom, sleep disorder were 28.7%, 63.0% and 38.8%, respectively. Spearman correlation analysis result showed that occupational stress was positively correlated with both depressive symptom and sleep quality (Spearman correlation coefficients of 0.46 and 0.49, all P<0.01). Bootstrap test result showed that occupational stress had positive effect on both depressive symptom and sleep quality (partial regression coefficients of 0.25 and 0.09, all P<0.01). Sleep quality partially mediated the impact of occupational stress on depressive symptom with the mediation effect value of 0.05 (95% confidence interval of 0.03-0.06), accounting for 20.0% of the total effect. Conclusion Both depression symptom and sleep quality are positively correlated with occupational stress in secondary industry occupational population. Sleep quality of this population partially mediate the impact of occupational stress on depressive symptom.

Background::Bladder cancer, characterized by a high potential of tumor recurrence, has high lifelong monitoring and treatment costs. To date, tumor cells with intrinsic softness have been identified to function as cancer stem cells in several cancer types. Nonetheless, the existence of soft tumor cells in bladder tumors remains elusive. Thus, our study aimed to develop a microbarrier microfluidic chip to efficiently isolate deformable tumor cells from distinct types of bladder cancer cells.Methods::The stiffness of bladder cancer cells was determined by atomic force microscopy (AFM). The modified microfluidic chip was utilized to separate soft cells, and the 3D Matrigel culture system was to maintain the softness of tumor cells. Expression patterns of integrin β8 (ITGB8), protein kinase B (AKT), and mammalian target of rapamycin (mTOR) were determined by Western blotting. Double immunostaining was conducted to examine the interaction between F-actin and tripartite motif containing 59 (TRIM59). The stem-cell-like characteristics of soft cells were explored by colony formation assay and in vivo studies upon xenografted tumor models. Results::Using our newly designed microfluidic approach, we identified a small fraction of soft tumor cells in bladder cancer cells. More importantly, the existence of soft tumor cells was confirmed in clinical human bladder cancer specimens, in which the number of soft tumor cells was associated with tumor relapse. Furthermore, we demonstrated that the biomechanical stimuli arising from 3D Matrigel activated the F-actin/ITGB8/TRIM59/AKT/mTOR/glycolysis pathways to enhance the softness and tumorigenic capacity of tumor cells. Simultaneously, we detected a remarkable up-regulation in ITGB8, TRIM59, and phospho-AKT in clinical bladder recurrent tumors compared with their non-recurrent counterparts.Conclusions::The ITGB8/TRIM59/AKT/mTOR/glycolysis axis plays a crucial role in modulating tumor softness and stemness. Meanwhile, the soft tumor cells become more sensitive to chemotherapy after stiffening, that offers new insights for hampering tumor progression and recurrence.

Animals ; Macaca mulatta ; Optic Disk ; Glaucoma ; Craniocerebral Trauma ; Intraocular Pressure ; Low Tension Glaucoma

Background::Pulmonary embolism (PE) is a severe and acute cardiovascular syndrome with high mortality among patients with autoimmune inflammatory rheumatic diseases (AIIRDs). Accurate prediction and timely intervention play a pivotal role in enhancing survival rates. However, there is a notable scarcity of practical early prediction and risk assessment systems of PE in patients with AIIRD.Methods::In the training cohort, 60 AIIRD with PE cases and 180 age-, gender-, and disease-matched AIIRD non-PE cases were identified from 7254 AIIRD cases in Tongji Hospital from 2014 to 2022. Univariable logistic regression (LR) and least absolute shrinkage and selection operator (LASSO) were used to select the clinical features for further training with machine learning (ML) methods, including random forest (RF), support vector machines (SVM), neural network (NN), logistic regression (LR), gradient boosted decision tree (GBDT), classification and regression trees (CART), and C5.0 models. The performances of these models were subsequently validated using a multicenter validation cohort.Results::In the training cohort, 24 and 13 clinical features were selected by univariable LR and LASSO strategies, respectively. The five ML models (RF, SVM, NN, LR, and GBDT) showed promising performances, with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.962-1.000 in the training cohort and 0.969-0.999 in the validation cohort. CART and C5.0 models achieved AUCs of 0.850 and 0.932, respectively, in the training cohort. Using D-dimer as a pre-screening index, the refined C5.0 model achieved an AUC exceeding 0.948 in the training cohort and an AUC above 0.925 in the validation cohort. These results markedly outperformed the use of D-dimer levels alone.Conclusion::ML-based models are proven to be precise for predicting the onset of PE in patients with AIIRD exhibiting clinical suspicion of PE.Trial Registration::Chictr.org.cn: ChiCTR2200059599.