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.

Purpose: This study assessed the performance of the ultrasound-guided attenuation parameter (UGAP) in diagnosing and grading hepatic steatosis in patients with metabolic dysfunctionassociated steatotic liver disease (MASLD). Magnetic resonance imaging proton density fat fraction (MRI-PDFF) served as the reference standard. Methods: Patients with hepatic steatosis were enrolled in this prospective study and underwent UGAP measurements. MRI-PDFF values of ≥5%, ≥15%, and ≥25% were used as references for the diagnosis of steatosis grades ≥S1, ≥S2, and S3, respectively. Spearman correlation coefficients and area under the receiver operating characteristic curves (AUCs) were calculated. Results: Between July 2023 and June 2024, the study included 88 patients (median age, 40 years; interquartile range [IQR], 36 to 46 years), of whom 54.5% (48/88) were men and 45.5% (40/88) were women. Steatosis grades exhibited the following distribution: 22.7% (20/88) had S0, 50.0% (44/88) had S1, 21.6% (19/88) had S2, and 5.7% (5/88) had S3. The success rate for UGAP measurements was 100%. The median UGAP value was 0.74 dB/cm/MHz (IQR, 0.65 to 0.82 dB/ cm/MHz), and UGAP values were positively correlated with MRI-PDFF (r=0.77, P<0.001). The AUCs of UGAP for the diagnoses of ≥S1, ≥S2, and S3 steatosis were 0.91, 0.90, and 0.88, respectively. In the subgroup analysis, 98.4% (60/61) of patients had valid controlled attenuation parameter (CAP) values. UGAP measurements were positively correlated with CAP values (r=0.65, P<0.001). Conclusion Using MRI-PDFF as the reference standard, UGAP demonstrates good diagnostic performance in the detection and grading of hepatic steatosis in patients with MASLD.

OBJECTIVE To investigate the intervention effect and its potential mechanism of Yifei xuanfei jiangzhuo formula by inhibiting mitochondrial fission in a vascular dementia （VaD） model rats. METHODS VaD rat model was established by bilateral common carotid artery ligation. The experimental animals were randomly divided into sham operation group （SHAM）， model group （MOD），Yifei xuanfei jiangzhuo formula low-dose group （YFXF-L）， Yifei xuanfei jiangzhuo formula high-dose group （YFXF-H）， and Donepezil hydrochloride group （positive control）， with 9 animals in each group. After 30 days of intervention， the spatial learning memory ability was assessed by Morris water maze experiment； HE staining was used to observe histopathological changes in CA1 area of hippocampus； ELISA was used to detect the levels of serum inflammatory factors [interleukin-1β （IL-1β） and IL-4]； Western blot was used to detect the expressions of heat shock protein 90 （HSP90）/mixed lineage kinase domain-like protein （MLKL）/dynamin-related protein 1 （Drp1） pathway-related proteins， mitochondrial fusion proteins （MFN1， MFN2）， and adenosine triphosphate synthase 5A （ATP5A） in hippocampal tissues. The immunohistochemistry was used to detect the level of phosphorylated MLKL （p-MLKL）； real-time fluorescence quantitative PCR was adopted to detect mRNA expressions ofHSP90， MFN1， MFN2 and ATP5A. RESULTS Compared with SHAM group， the escape latency of rats in the MOD group was significantly prolonged， the number of crossing the platform was significantly reduced， and the hippocampal tissues showed typical neuronal damage characteristics， the positive expression level of p-MLKL and the serum level of IL-1β significantly increased， while the serum level of IL-4 significantly decreased， the protein and mRNA expression of HSP90， as well as the protein expressions of p-MLKL/MLKL and p-Drp1（Ser616）/Drp1 were all significantly increased in hippocampal tissue， the protein and mRNA expressions of MFN1， MFN2 and ATP5A， and protein expression of p-Drp1（Ser637）/Drp1 were all significantly decreased （P＜0.05）. After the intervention of Yifei xuanfei jiangzhuo formula， above indicators in each treatment group were all significantly reversed （P＜0.05）. CONCLUSIONS Yifei xuanfei jiangzhuo formula may alleviate neuronal damage and neuroinflammatory responses in VaD rats by regulating the HSP90/MLKL/Drp1 signaling pathway， inhibiting mitochondrial fission， thereby maintaining mitochondrial dynamic balance and improving mitochondrial function.

ObjectivePrimary liver cancer, predominantly hepatocellular carcinoma (HCC), is a significant global health issue, ranking as the sixth most diagnosed cancer and the third leading cause of cancer-related mortality. Accurate and early diagnosis of HCC is crucial for effective treatment, as HCC and non-HCC malignancies like intrahepatic cholangiocarcinoma (ICC) exhibit different prognoses and treatment responses. Traditional diagnostic methods, including liver biopsy and contrast-enhanced ultrasound (CEUS), face limitations in applicability and objectivity. The primary objective of this study was to develop an advanced, light-weighted classification network capable of distinguishing HCC from other non-HCC malignancies by leveraging the automatic analysis of brightness changes in CEUS images. The ultimate goal was to create a user-friendly and cost-efficient computer-aided diagnostic tool that could assist radiologists in making more accurate and efficient clinical decisions. MethodsThis retrospective study encompassed a total of 161 patients, comprising 131 diagnosed with HCC and 30 with non-HCC malignancies. To achieve accurate tumor detection, the YOLOX network was employed to identify the region of interest (ROI) on both B-mode ultrasound and CEUS images. A custom-developed algorithm was then utilized to extract brightness change curves from the tumor and adjacent liver parenchyma regions within the CEUS images. These curves provided critical data for the subsequent analysis and classification process. To analyze the extracted brightness change curves and classify the malignancies, we developed and compared several models. These included one-dimensional convolutional neural networks (1D-ResNet, 1D-ConvNeXt, and 1D-CNN), as well as traditional machine-learning methods such as support vector machine (SVM), ensemble learning (EL), k-nearest neighbor (KNN), and decision tree (DT). The diagnostic performance of each method in distinguishing HCC from non-HCC malignancies was rigorously evaluated using four key metrics: area under the receiver operating characteristic (AUC), accuracy (ACC), sensitivity (SE), and specificity (SP). ResultsThe evaluation of the machine-learning methods revealed AUC values of 0.70 for SVM, 0.56 for ensemble learning, 0.63 for KNN, and 0.72 for the decision tree. These results indicated moderate to fair performance in classifying the malignancies based on the brightness change curves. In contrast, the deep learning models demonstrated significantly higher AUCs, with 1D-ResNet achieving an AUC of 0.72, 1D-ConvNeXt reaching 0.82, and 1D-CNN obtaining the highest AUC of 0.84. Moreover, under the five-fold cross-validation scheme, the 1D-CNN model outperformed other models in both accuracy and specificity. Specifically, it achieved accuracy improvements of 3.8% to 10.0% and specificity enhancements of 6.6% to 43.3% over competing approaches. The superior performance of the 1D-CNN model highlighted its potential as a powerful tool for accurate classification. ConclusionThe 1D-CNN model proved to be the most effective in differentiating HCC from non-HCC malignancies, surpassing both traditional machine-learning methods and other deep learning models. This study successfully developed a user-friendly and cost-efficient computer-aided diagnostic solution that would significantly enhances radiologists’ diagnostic capabilities. By improving the accuracy and efficiency of clinical decision-making, this tool has the potential to positively impact patient care and outcomes. Future work may focus on further refining the model and exploring its integration with multimodal ultrasound data to maximize its accuracy and applicability.

Nuclear factor-erythroid 2-related factor 2 （Nrf2）/glutathione peroxidase 4 （GPX4） signaling pathway plays a key role in the occurrence and development of tumors， and is involved in tumor cell proliferation， apoptosis， ferroptosis， invasion， migration， and drug resistance. Based on the Nrf2/GPX4 signaling pathway， this paper summarizes the research progress of the anti- tumor effects of traditional Chinese medicine. It is found that flavonoids （ginkgetin， luteolin， etc.）， terpenoids （atractylenolide， cucurbitacin B， etc.）， saponins （polyphyllin Ⅰ， polyphyllin Ⅶ）， ester （brusatol） and other effective components， and traditional Chinese medicine extracts （total coumarins in Pileostegia tomentella and total flavonoids of Pterocarya hupehensis Skan）， traditional Chinese medicine compounds （Fushao diqin fang， Xiaoai jiedu fang， etc.） can promote ferroptosis in tumor cells by inhibiting Nrf2/GPX4 signaling pathway and the expressions of its upstream and downstream factor proteins， as well as by increasing Fe2+ levels and lipid peroxidation， thereby exerting an antitumor effect.

OBJECTIVE: To investigate the relationship between physical activity and genetic risk and their combined effects on the risk of developing chronic obstructive pulmonary disease. METHODS: This prospective cohort study included 318,085 biobank participants from the UK. Physical activity was assessed using the short form of the International Physical Activity Questionnaire. The participants were stratified into low-, intermediate-, and high-genetic-risk groups based on their polygenic risk scores. Multivariate Cox regression models and multiplicative interaction analyses were used. RESULTS: During a median follow-up period of 13 years, 9,209 participants were diagnosed with chronic obstructive pulmonary disease. For low genetic risk, compared to low physical activity, the hazard ratios ( HRs) for moderate and high physical activity were 0.853 (95% confidence interval [ CI]: 0.748-0.972) and 0.831 (95% CI: 0.727-0.950), respectively. For intermediate genetic risk, the HRs were 0.829 (95% CI: 0.758-0.905) and 0.835 (95% CI: 0.764-0.914), respectively. For participants with high genetic risk, the HRs were 0.809 (95% CI: 0.746-0.877) and 0.818 (95% CI: 0.754-0.888), respectively. A significant interaction was observed between genetic risk and physical activity. CONCLUSION Moderate or high levels of physical activity were associated with a lower risk of developing chronic obstructive pulmonary disease across all genetic risk groups, highlighting the need to tailor activity interventions for genetically susceptible individuals.
Humans ; Pulmonary Disease, Chronic Obstructive/epidemiology* ; Exercise ; Male ; Female ; Middle Aged ; Prospective Studies ; Aged ; Genetic Predisposition to Disease ; Risk Factors ; United Kingdom/epidemiology* ; Incidence ; Adult

Objective To investigate the causal relationship between gut microbiota and diffuse large B-cell lymphoma(DLBCL).Methods Employed a two-sample bidirectional Mendelian randomization approach,using summary statistics from genome-wide association studies(GWAS)to extract single nucleotide polymor-phisms(SNPs)associated with exposure and outcome as instrumental variables.Exposure instrumental varia-bles(P＜1×10-5)and outcome instrumental variables(P＜5×10-8)were selected based on the P-values of SNPs.Three different Mendelian randomization methods were used to analyze the relationship between gut microbiota and DLBCL,with sensitivity analyses including heterogeneity,pleiotropy,and leave-one-out tests.Results Bilophila(OR=2.043,95%CI:1.279-3.264),Coprobacter(OR=1.371,95%CI:1.035-1.816),Eubacterium eligens group(OR=1.996,95%CI:1.291-3.087)increased the risk of DLBCL.Alistipes(OR=0.588,95%CI:0.359-0.963),Eubacterium eligens group(OR=1.996,95%CI:1.291-3.087),Slackia(OR=0.688,95%CI:0.479-0.988)reduced the risk of DLBCL.Reverse Mendelian randomization a-nalysis failed to reveal any evidence of a causal relationship between DLBCL and the six gut microbiota.Con-clusion There is a causal association between gut microbiota and DLBCL.

Objective To investigate the occurrence status quo and influencing factors of low muscle mass(LMM)among young and middle-aged health examination population.Methods The young and middle-aged people undergoing the body composition analysis in this hospital from January to December 2023 were selected as the study subjects.The general data,body composition indices and biochemical indicators were col-lected.The body composition analysis was performed by the bioelectrical impedance analysis(BIA).LMM was diagnosed based on the skeletal muscle index.The univariate and multivariate logistic regression were used to analyze the influencing factors of LMN occurrence in the young and middle-aged health examination population.The receiver operating characteristic(ROC)curve and the area under the curve(AUC)were em-ployed to evaluate the predictive value of each indicator.Results A total of 2 351 people undergoing the phys-ical examination were included,aged 18-49 years old,366(15.57％)cases of LMM were detected out.The skeletal muscle index,sex,age,age group distribution,body mass index(BMI),body fat percentage(BFP),body fat percentage grade,visceral fat area(VFA),AST/ALT,Hb,serum creatinine,blood uric acid,HbA1c,fasting blood glucose,TC,LDL-C,HDL-C,TG and triglyceride-glucose index(TyG)had statistical differences between the LMM group and normal group(P＜0.05).Multivariate logistic regression revealed that the sex(OR=2.606,95％CI:1.755-3.870),BMI(OR=0.579,95％CI:0.538-0.623),BFP(OR=5.885,95％CI:4.176-8.292)and VFA(OR=0.955,95％CI:0.944-0.967)were the influencing factors for the LMM oc-currence in the young and middle-aged people undergoing the physical examination(P＜0.001).The ROC a-nalysis showed the AUC values of the sex,BMI,BFP and VFA for predicting LMM were 0.580,0.821,0.636 and 0.715 respectively,in which the predictive value of BMI was highest.Conclusion The population of fe-male,low BMI,high BFP and low VFA maybe the high-risk groups for LMM.The health management for the above-mentioned groups needs to be strengthened.

Objective To understand the clinical distribution characteristics and drug-resistance genotypes of carbapenem-resistant Pseudomonas aeruginosa(CRPA)in a tertiary hospital in Shanghai,so as to guide the rational use of antibiotics,reduce bacterial resistance and control nosocomial infections.Methods A total of 94 consecutive and non-redundant CRPA strains isolated from clinical specimens were collected in The First Affiliated Hospital of Naval Medical University between Jan.1,and Dec.31,2019.The distribution of CRPA across departments and sample sources were analyzed.Antimicrobial susceptibility testing against 13 antibiotics was determined using the VITEK 2 Compact automated microbiology analyzer and the Kirby-Bauer disk diffusion method.Polymerase chain reaction(PCR)was employed to detect genes encoding extended-spectrum β-lactamase resistance gene,carbapenemase resistance gene,and porin resistance gene.Homology analysis of selected isolates was performed using kSNP3,a k-mer-based software,for single nucleotide polymorphism(SNP)analysis.An evolutionary tree was constructed to assess genetic relationships.Additionally,multilocus sequence typing(MLST)was performed using the Pseudomonas aeruginosa typing scheme from the PubMLST database.Results Among the 94 CRPA isolates,high resistance rates were observed for carbapenems,including imipenem(92.6%,87 strains)and meropenem(87.2%,82 strains).In contrast,low resistance rates were detected for aminoglycosides,such as amikacin(10.6%,10 strains),gentamicin(20.2%,19 strains),and tobramycin(20.2%,19 strains).The top 3 departments in terms of isolate distribution were the Emergency Intensive Care Unit(9.6%,9 strains),Department of Cerebrovascular Surgery(8.5%,8 strains),and Department of Respiratory Medicine(8.5%,8 strains).PCR analysis of 94 CRPA strains detected outer membrane protein D2(OprD2)gene deletion in 47(50.0%)strains,13(13.8%)strains were positive for blaKPC,4(4.3%)strains for blaVIM,2(2.1%)strains for blaIMP,1(1.1%)strain for blaNDM,12(12.8%)strains for blaTEM,4(4.3%)strains for blaPER,and 2(2.1%)strains for blaGES,while blaOXA-48,blaBIC,blaSIM,blaVEB,and blaSHV were not detected.MLST identified 36 different sequence types(STs),with ST463 and ST274 being the most common,and 2 new ST(ST4023 and ST4024)were identified for the first time.Conclusion CRPA strains carry multiple resistance genes and exhibit concurrent resistance to several commonly used clinical antibiotics.The resistance is primarily associated with the presence of blaKPC,blaVIM and blaTEM genes and the deletion of OprD2 gene.Clinical monitoring of CRPA should be strengthened,and rational use of antimicrobial agents is essential to control its spread within the hospital.