[This corrects the article DOI: 10.1016/j.apsb.2022.05.019.].

OBJECTIVE To establish a content determination method for multiple components in Sophora flavescens from different origins and to evaluate its quality by combining with chemometrics. METHODS Thirteen batches （No. K1-K13） of S. flavescens from different origins were selected as test samples. A high-performance liquid chromatography-tandem triple quadrupole mass spectrometry （HPLC-MS/MS） method was established to determine the contents of 12 components， including matrine， oxymatrine， betaine， cytisine， N-methylcytisine， sophoridine， genistein， sophoricoside， sophorone， formononetin， sophorolone Ⅰ and norkurarinone in S. flavescens. Chromatographic separation was performed on a Shim-pack GIST-HP C18 column with a mobile phase consisting of methanol （A） and water containing 0.1% formic acid （B）， using gradient elution at a flow rate of 0.25 mL/min， column temperature of 35 ℃， and an injection volume of 3 μL. Mass spectrometry was conducted using an electrospray ionization source with positive and negative ion scanning. Data were collected in segments using the multiple reaction monitoring mode. Technique for order preference by similarity to ideal solution （TOPSIS） and grey relational analysis （GRA）methods were employed to compare and comprehensively evaluate the 13 batches of S. flavescens from different origins. RESULTS The methodological validation for the content determination met the relevant regulatory requirements. The contents of the 12 components were 490.66-1 231.00， 11 088.10- 18 021.50， 7.91-25.38， 903.97-1 713.64， 336.08-1 485.54，1 065.33-2 075.50， 27.52-71.80， 109.36-517.83， 6 034.55-10 632.73， 21.26-145.35， 814.84-1 911.32， 1 040.87-3 446.37 μg/g）， respectively. TOPSIS results showed that the top 7 samples in Euclidean distance ranking were K6， K12， K11， K3， K5， K10， K13. The GRA results showed that the top 7 samples in the relative correlation ranking were K12， K11， K10， K6， K13， K5， K3. CONCLUSIONS The established HPLC-MS/MS method is rapid， accurate， highly sensitive， stable and reliable. Combined with chemometrics methods， it can be used for the quality control and evaluation of S. flavescens. The comprehensive quality of samples K3， K5， K6（ from Hebei）， K10（ from Sichuan）， K11-K13（ from Shanxi）， etc. is relatively superior.

Cardiovascular disease (CVD), chronic kidney disease (CKD), and metabolic disorders are the 3 major chronic diseases threatening human health, which are closely related and often coexist, significantly increasing the difficulty of disease management. In response, the American Heart Association (AHA) proposed a novel disease concept of “cardiovascular-kidney-metabolic (CKM) syndrome” in October 2023, which has triggered widespread concern about the co-treatment of heart and kidney diseases and the prevention and treatment of metabolic disorders around the world. This review posits that effectively managing CKM syndrome requires a new and multidimensional paradigm for diagnosis and risk prediction that integrates biological insights, advanced technology and social determinants of health (SDoH). We argue that the core pathological driver is a “metabolic toxic environment”, fueled by adipose tissue dysfunction and characterized by a vicious cycle of systemic inflammation and oxidative stress, which forms a common pathway to multi-organ injury. The at-risk population is defined not only by biological characteristics but also significantly impacted by adverse SDoH, which can elevate the risk of advanced CKM by a factor of 1.18 to 3.50, underscoring the critical need for equity in screening and care strategies. This review systematically charts the progression of diagnostic technologies. In diagnostics, we highlight a crucial shift from single-marker assessments to comprehensive multi-marker panels. The synergistic application of traditional biomarkers like NT-proBNP (reflecting cardiac stress) and UACR (indicating kidney damage) with emerging indicators such as systemic immune-inflammation index (SII) and Klotho protein facilitates a holistic evaluation of multi-organ health. Furthermore, this paper explores the pivotal role of non-invasive monitoring technologies in detecting subclinical disease. Techniques like multi-wavelength photoplethysmography (PPG) and impedance cardiography (ICG) provide a real-time window into microcirculatory and hemodynamic status, enabling the identification of early, often asymptomatic, functional abnormalities that precede overt organ failure. In imaging, progress is marked by a move towards precise, quantitative evaluation, exemplified by artificial intelligence-powered quantitative computed tomography (AI-QCT). By integrating AI-QCT with clinical risk factors, the predictive accuracy for cardiovascular events within 6 months significantly improves, with the area under the curve (AUC) increasing from 0.637 to 0.688, demonstrating its potential for reclassifying risk in CKM stage 3. In the domain of risk prediction, we trace the evolution from traditional statistical tools to next-generation models. The new PREVENT equation represents a major advancement by incorporating key kidney function markers (eGFR, UACR), which can enhance the detection rate of CKD in primary care by 20%-30%. However, we contend that the future lies in dynamic, machine learning-based models. Algorithms such as XGBoost have achieved an AUC of 0.82 for predicting 365-day cardiovascular events, while deep learning models like KFDeep have demonstrated exceptional performance in predicting kidney failure risk with an AUC of 0.946. Unlike static calculators, these AI-driven tools can process complex, multimodal data and continuously update risk profiles, paving the way for truly personalized and proactive medicine. In conclusion, this review advocates for a paradigm shift toward a holistic and technologically advanced framework for CKM management. Future efforts must focus on the deep integration of multimodal data, the development of novel AI-driven biomarkers, the implementation of refined SDoH-informed interventions, and the promotion of interdisciplinary collaboration to construct an efficient, equitable, and effective system for CKM screening and intervention.

AIM To study the chemical constituents from Dictamni Cortex.METHODS The 70％ethanol extract from Dictamni Cortex was isolated and purified by HP-20 macroporous resin,silica gel,MCI,ODS and preparative HPLC,then the structures of obtained compounds were identified by physicochemical properties and spectral data.RESULTS Thirty-three compounds were isolated and identified as rutin(1),apigenin(2),catechin(3),hesperetin(4),leonuriside A(5),androsin(6),2-methoxy-4-acetylphenol-O-α-rhamnopyranosyl-(1"-6')-β-glucopyranoside(7),vanillic acid(8),gallic acid(9),4-hydroxybenzoic acid(10),benzoic acid(11),involcranoside B(12),benzyl β-D-glucopyranoside(13),bphenylethyl-rutinoside(14),1-bromonaphthalene(15),cimifugin(16),9(S),12(S),13(S)-trihydroxyoctadeca-10(E),15(Z)-dienoic acid(17),methyl-9,12,13-trihydroxyoctadeca-10,15-dienoate(18),7,8-dihydroxy-9,12(Z,Z)-octadecadienoic acid(19),vernolic acid(20),9,10(erythro)-dihydroxy-11 E-octadecadienoic acid methyl ester(21),(7Z,9E,13Z)-11-hydroxyhexadeca-7,9,13-trienoic acid(22),(7Z,10Z,14E,16Z,19Z)-13-hydroxydocosa-7,10,14,16,19-pentaenoic acid(23),(9E)-8,11,12-trihydroxyoctadecenoic acid methyl ester(24),n-hexanol-O-rutinoside(25),hexyl β-sophoroside(26),3-pentyl 6'-(3-hydroxy-3-methylglutaryl)-β-D-glucopyranoside(27),3-methylbut-3-enyl-6-O-β-D-glucopyranosyl-β-D-glucopyranoside(28),3-methyl-but-2-en-1-yl β-D-glucopyranoside(29),3-methylbutan-1-ol-β-D-glucopyranoside(30),pregnenolone(31),2-butoxytetrahydrofuran(32),psydrin(33).CONCLUSION Compounds 2-4,8-13,15-16,25-28 and 32-33 are isolated from Rutaceae family for the first time.

AIM To evaluate the quality consistency of Tongmai Granules,Tongmai Tablets,Tongmai Capsules and Tongmai Oral Liquid.METHODS The HPLC fingerprints were established,after which the contents of danshensu,protocatechuic aldehyde,3'-hydroxy puerarin,puerarin,puerarin apioside,daidzin,ferulic acid,salvianolic acid B and salvianolic acid A were determined,and cluster analysis and principal component analysis were adopted in the quality analysis from the perspective of daily intake.RESULTS There were 21 common peaks in the fingerprints for 39 batches of samples with the similarities of 0.765-0.997.Various batches of samples were clustered into 5 categories,2 principal components demonstrated the accumulative variance contribution rate of 83.53％ .The daily intakes of various constituents in different dosage forms exhibited obvious differences,especially for that of salvianolic acid B,which were low in tablets and capsules,and their heterogeneities existed among the same dosage forms.CONCLUSION This simple and accurate method can provide a reference for the quality evaluation of Tongmai preparations from different manufacturers.

Objective To investigate the feasibility and accuracy of an ultrasound-based transfer learning artificial intelligence model in predicting the malignancy probability of partially cystic thyroid nodules(PCTN).Methods A retrospective analysis was conducted on 246 patients with PCTN who had definitive pathological results and were admitted to Weihai Municipal Hospital,Cheeloo College of Medicine,Shandong University from January 2021 to December 2023.Patients were randomly divided into training and test cohorts at a ratio of 7:3.Ultrasonic image features of PCTN were evaluated,and independent risk factors were identified using multivariate logistic regression analysis,with the area under the curve(AUC)subsequently calculated.Additionally,five different pre-trained models-Inception_v3,EfficientNet,VGG19,ResNet50,and DenseNet121-were selected for transfer learning after data preprocessing using the PyTorch framework in Python.The AUC values of these models were calculated and compared.Results Solid portion greater than 50%,eccentric acute angle,ill-defined margin,spiculated or microlobulated margin,rim calcification,and microcalcification exhibited statistically significant differences(P<0.05)in distinguishing between benign and malignant PCTN.The AUC value derived from these independent risk factors was 0.843.Furthermore,among the five transfer learning models evaluated,the ResNet50 model demonstrated the highest diagnostic efficiency,achieving an AUC value of 0.903 2.Conclusion The ultrasound-based transfer learning artificial intelligence model demonstrated superior performance compared to traditional ultrasound image evaluation methods,enabling accurate prediction of the nature of PCTN and thereby reducing unnecessary ultrasound-guided fine needle biopsies.

The incidence rate of kidney diseases in China has always remained high.At present,the clinical treat-ment mainly focuses on symptomatic treatment to delay the progression of the disease,and there is a lack of eco-nomical and effective treatment methods.MicroRNA plays an important regulatory role in the occurrence and devel-opment of diseases.This study aims to explore the role and regulatory mechanism of miR-142a-3p in adriamycin(ADR)-induced renal tubular epithelial cell(TCMK-1)injury,with a focus on its potential as a therapeutic target for ADR nephropathy.First,cell viability was assessed using the CCK-8 kit,and a mouse renal tubular epithelial cell model induced by ADR was established.Subsequently,alterations in miR-142a-3p and its target gene ATG16L1 mRNA levels were quantified using RT-qPCR.Western blotting was used to detect the protein levels of autophagy marker proteins and pyroptosis marker proteins.Monodansylcadaverin(MDC)staining was performed and the autophagy of cells was detected by flow cytometry.The results showed that the relative expression of miR-142a-3p in TCMK-1 cells induced by ADR was increased and the relative expression of its target gene ATG16L1 was decreased(P＜0.0001).Western blotting results showed that the levels of p62(P＜0.001)and pyroptosis-related proteins(P＜0.001)were increased,while the protein levels of autophagy-related proteins were decreased(P＜0.05).The flow cytometry results showed that there was no difference in the mean fluorescence intensity of autoph-agosomes between the ADR group and the autophagosome inhibitor group(3-MA group)(P＞0.05),indicating that after ADR induction,cell autophagy was inhibited and pyroptosis was enhanced.When the expression of miR-142a-3p was inhibited by transfecting miR-142a-3p inhibitor,the relative expression level of the target gene ATG16L1 was restored(P＜0.001).Western blotting showed that the protein level of p62(P＜0.01)and pyropto-sis-related proteins(P＜0.01)were decreased,and the protein level of autophagy-related proteins was restored(P＜0.001).Flow cytometry results further indicated that cell autophagy was restored(P＜0.0001).In conclusion,ADR targets A TG1 6L1 through miR-142a-3p to reduce the autophagy level of TCMK-1,and simultaneously activates GSDMD-mediated pyroptosis.

This study was aimed at providing basic data for the control and prevention of Yersinia enterocolitica(Ye)infections.Ye isolates from stool samples collected from patients with diarrhea in a Beijing district between January 2019 and June 2024 were studied.Basic patient information and stool samples were collected,and quantitative polymerase chain reaction(qPCR)was applied to enriched cultures.Further analyses included virulence gene detection,whole-genome sequencing,and drug resistance detection.The detection rate of Ye was 0.76%(11/1 439),according to culture methods,thus yielding 12 Ye strains from distinct patients:11 isolated during the study period and 1 from 2017.The 12 Ye positive patients were 6-41 years of age,and their clinical presentations predominantly featured watery stools(66.67%,8/12)and loose stools(33.33%,4/12).The frequencies of nausea,vomiting,and fever were 41.67%(5/12),41.67%(5/12),and 8.33%(1/12),respectively.The drug resistance rates of Ye to TET,AMP,and NAL were 50.00%(6/12),33.33%(4/12),and 25.00%(3/12),respectively.One Ye strain exhibited multidrug resistance to ETP,MEM,TET,CIP,NAL,and AMP.According to qPCR detection of five common virulence genes,two Ye strains were identified as ystA+/ystB-type(ystA+/ystB-/ail+/yadA+/virF+),whereas ten strains were identified as ystA-/ystB+type(ystA-/ystB+/ail-/yadA-/virF-).VFDB database analysis based on genome sequences indicated that 12 Ye strains carried an average of 11 key virulence genes associated with adhesion,invasion,protease activity,and flagellar movement,and predicted 106 virulence genes and 12 virulence gene profiles.Only the two ystA+/ystB-Ye strains contained elements related to the TTSS and ABC transporter function.Detection of ystA-/ystB+Ye in stool isolation and culture of diarrhea cases might potentially have been missed in some cases,thus highlighting the importance of fluorescence PCR screening of fecal growth solutions to enhance isolation efficiency.Moreover,our findings revealed the genetic diversity of Ye isolated from diarrhea cases,thereby indicating the presence of multiple types of virulence genes within this pathogen.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.