Objective To investigate the causal relationship between metabolic syndrome and breast cancer by using a bidirectional two-sample Mendelian randomization (MR) approach. Methods Genome-wide association study (GWAS) summary statistics for metabolic syndrome and breast cancer were acquired from the Integrative Epidemiology Unit GWAS database and the GWAS Catalog, with populations encompassing the United States and East Asia. A bidirectional causal design was employed: a forward analysis with metabolic syndrome as the exposure and breast cancer as the outcome, followed by a reverse analysis wherein their roles were interchanged. The inverse-variance weighting (IVW) method was primarily used for effect estimation, supplemented by MR-Egger regression, the weighted median method, the simple mode method, and the weighted mode method. Instrument variable strength was screened using the F-statistic (F>10). Robustness of the results was assessed through heterogeneity tests, horizontal pleiotropy tests, forest plots, and leave-one-out sensitivity analyses. Results The IVW analysis indicated no significant causal relationship between metabolic syndrome and breast cancer (OR=1.00, 95%CI: 0.97-1.03), P>0.05). Sensitivity analyses yielded consistent results, suggesting the good robustness of the study findings. Conclusion This study found no evidence to support a causal relationship, either positive or negative, between metabolic syndrome and breast cancer.

ObjectiveDiscriminating atypical hepatocellular carcinoma (HCC) from other malignancies in liver nodules classified as Liver Imaging Reporting and Data System category M (LR-M) remains a significant diagnostic challenge on conventional ultrasound examination. The LR-M category, originally intended to capture non-HCC malignancies, paradoxically contains up to 63% of atypical HCCs that deviate from classic enhancement patterns, leading to potential misdiagnosis and suboptimal treatment planning. While deep learning has shown promise in HCC diagnosis, most existing models rely exclusively on single-modality ultrasound, overlooking the diagnostic benefits of integrating complementary information from multiple imaging sources. To address this gap, we propose a novel attention-weighted tri-modal ultrasound network (TUS-Net) that integrates contrast-enhanced ultrasound (CEUS), B-mode ultrasound (BUS), and time-intensity curves (TICs) to improve diagnostic accuracy for these clinically challenging lesions. MethodsOur framework incorporates a three-dimensional convolutional neural network (C3D) backbone to extract spatiotemporal features from CEUS videos, capturing dynamic vascular patterns critical for lesion characterization. To effectively fuse complementary modalities, we introduce a dual-channel feature fusion module (DCFFM) that adaptively combines features from CEUS and BUS through channel-wise attention mechanisms, allowing the model to dynamically weigh the contribution of each modality based on diagnostic relevance. Additionally, we propose a temporal intensity feature fusion module (TIFFM) that leverages quantitative hemodynamic information from TICs to guide the model’s attention toward diagnostically critical temporal phases, such as arterial wash-in and portal venous washout. The model is further enhanced by automated lesion localization using YOLOX and class activation mapping for interpretability, ensuring that predictions align with clinically meaningful imaging features. ResultsEvaluated on a tri-modal ultrasound dataset comprising 161 patients with pathologically confirmed LR-M nodules (131 atypical HCC and 30 non-HCC malignancies), our model achieved an accuracy of 86.83%, a sensitivity of 92.50%, a specificity of 75.50%, and an AUC of 89.32% in screening atypical HCC. Compared to single-modality baselines, TUS-Net demonstrated superior specificity, a clinically critical metric given the higher risk associated with misclassifying non-HCC malignancies. Ablation studies confirmed the contribution of each module, with the full model outperforming both standard C3D and 3D ResNet backbones integrated with attention mechanisms. A reader study involving junior and senior radiologists further validated the clinical utility of AI assistance, showing consistent improvements in specificity and inter-reader consistency, particularly for less experienced clinicians. ConclusionThese results surpass existing benchmark models and demonstrate the potential of our approach to enhance diagnostic precision in clinically specific cases. By intelligently fusing multi-modal ultrasound data with attention-guided mechanisms, TUS-Net offers a reliable and interpretable tool that holds promise for improving the non-invasive diagnosis of atypical HCC in challenging LR-M liver nodules.

ObjectiveDiscriminating atypical hepatocellular carcinoma (HCC) from other malignancies in liver nodules classified as Liver Imaging Reporting and Data System category M (LR-M) remains a significant diagnostic challenge on conventional ultrasound examination. The LR-M category, originally intended to capture non-HCC malignancies, paradoxically contains up to 63% of atypical HCCs that deviate from classic enhancement patterns, leading to potential misdiagnosis and suboptimal treatment planning. While deep learning has shown promise in HCC diagnosis, most existing models rely exclusively on single-modality ultrasound, overlooking the diagnostic benefits of integrating complementary information from multiple imaging sources. To address this gap, we propose a novel attention-weighted tri-modal ultrasound network (TUS-Net) that integrates contrast-enhanced ultrasound (CEUS), B-mode ultrasound (BUS), and time-intensity curves (TICs) to improve diagnostic accuracy for these clinically challenging lesions. MethodsOur framework incorporates a three-dimensional convolutional neural network (C3D) backbone to extract spatiotemporal features from CEUS videos, capturing dynamic vascular patterns critical for lesion characterization. To effectively fuse complementary modalities, we introduce a dual-channel feature fusion module (DCFFM) that adaptively combines features from CEUS and BUS through channel-wise attention mechanisms, allowing the model to dynamically weigh the contribution of each modality based on diagnostic relevance. Additionally, we propose a temporal intensity feature fusion module (TIFFM) that leverages quantitative hemodynamic information from TICs to guide the model’s attention toward diagnostically critical temporal phases, such as arterial wash-in and portal venous washout. The model is further enhanced by automated lesion localization using YOLOX and class activation mapping for interpretability, ensuring that predictions align with clinically meaningful imaging features. ResultsEvaluated on a tri-modal ultrasound dataset comprising 161 patients with pathologically confirmed LR-M nodules (131 atypical HCC and 30 non-HCC malignancies), our model achieved an accuracy of 86.83%, a sensitivity of 92.50%, a specificity of 75.50%, and an AUC of 89.32% in screening atypical HCC. Compared to single-modality baselines, TUS-Net demonstrated superior specificity, a clinically critical metric given the higher risk associated with misclassifying non-HCC malignancies. Ablation studies confirmed the contribution of each module, with the full model outperforming both standard C3D and 3D ResNet backbones integrated with attention mechanisms. A reader study involving junior and senior radiologists further validated the clinical utility of AI assistance, showing consistent improvements in specificity and inter-reader consistency, particularly for less experienced clinicians. ConclusionThese results surpass existing benchmark models and demonstrate the potential of our approach to enhance diagnostic precision in clinically specific cases. By intelligently fusing multi-modal ultrasound data with attention-guided mechanisms, TUS-Net offers a reliable and interpretable tool that holds promise for improving the non-invasive diagnosis of atypical HCC in challenging LR-M liver nodules.

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.

Aim To explore the mechanism of baicalin combined with heat stimulation in treating acute lym-phoblastic leukemia(ALL)based on network pharma-cology and in vitro experiments.Methods The CCK-8 assay was used to screen the suitable conditions for heat stimulation to interfere ALL cell lines Jurkat,CCRF-CEM,Hut-78 and a normal lymphocyte HMy2.CIR,and the effects of baicalin combined with heat stimulation on the proliferation of three ALL cell lines and a normal lymphocyte were tested.The key targets of baicalin combined with fever stimulation for the treatment of ALL were obtained based on network phar-macological analysis,and the potential mechanisms were predicted by gene ontology(GO)annotation and kyoto encyclopedia of genes and genomes(KEGG)en-richment.The expression levels of TNF-α,AKT1,TYMS and CASP3 mRNA in ALL cell lines Jurkat and CCRF-CEM were examined by RT-qPCR with baicalin alone and baicalin combined with heat stimulation.Results The optimal conditions for heat stimulation to intervene ALL cells were 41 ℃ for 24 h,and heat stimulation combined with baicalin synergistically inhibited the growth of ALL cell lines and effectively reduced the cy-totoxicity of baicalin.Based on the network pharmaco-logical analysis,55 intersecting targets of baicalin with ALL diseases and 77 intersecting targets of baicalin with fever were obtained.The results of GO annotation and KEGG enrichment suggested that baicalin com-bined with fever stimulation to intervene ALL might be associated with influencing intracellular reactive oxygen species metabolism,DNA transcription and apoptotic processes involved in cysteine enzymes.Apoptosis,TNF and IL-17 signaling pathways were the key pathways for baicalin combined with heat stimulation in treating ALL.Under heat stimulation at 41 ℃ using SDHA gene as housekeeping gene,in vitro experiments showed that baicalin significantly up-regulated the expression of TNF-α and CASP3,and down-regulated the expression of TYMS in ALL cells.Conclusions Based on net-work pharmacologic analyses and in vitro experiments,baicalin combined with heat stimulation can regulate TNF-α and CASP3 gene levels in ALL cells and de-stroy cellular structure to promote cell apoptosis,thus synergistically treating ALL.

Objective To investigate the distribution and antimicrobial resistance profiles of common pathogens isolated from cerebrospinal fluid(CSF)in CHINET program from 2015 to 2021.Methods The bacterial strains isolated from CSF were identified in accordance with clinical microbiology practice standards.Antimicrobial susceptibility test was conducted using Kirby-Bauer method and automated systems per the unified CHINET protocol.Results A total of 14 014 bacterial strains were isolated from CSF samples from 2015 to 2021,including the strains isolated from inpatients(95.3％)and from outpatient and emergency care patients(4.7％).Overall,19.6％of the isolates were from children and 80.4％were from adults.Gram-positive and Gram-negative bacteria accounted for 68.0％and 32.0％,respectively.Coagulase negative Staphylococcus accounted for 73.0％of the total Gram-positive bacterial isolates.The prevalence of MRSA was 38.2％in children and 45.6％in adults.The prevalence of MRCNS was 67.6％in adults and 69.5％in children.A small number of vancomycin-resistant Enterococcus faecium(2.2％)and linezolid-resistant Enterococcus faecalis(3.1％)were isolated from adult patients.The resistance rates of Escherichia coli and Klebsiella pneumoniae to ceftriaxone were 52.2％and 76.4％in children,70.5％and 63.5％in adults.The prevalence of carbapenem-resistant E.coli and K.pneumoniae(CRKP)was 1.3％and 47.7％in children,6.4％and 47.9％in adults.The prevalence of carbapenem-resistant Acinetobacter baumannii(CRAB)and Pseudomonas aeruginosa(CRPA)was 74.0％and 37.1％in children,81.7％and 39.9％in adults.Conclusions The data derived from antimicrobial resistance surveillance are crucial for clinicians to make evidence-based decisions regarding antibiotic therapy.Attention should be paid to the Gram-negative bacteria,especially CRKP and CRAB in central nervous system(CNS)infections.Ongoing antimicrobial resistance surveillance is helpful for optimizing antibiotic use in CNS infections.

Objective To investigate the antimicrobial resistance of clinical isolates from elderly patients(≥65 years)in major medical institutions across China.Methods Bacterial strains were isolated from elderly patients in 52 hospitals participating in the CHINET Antimicrobial Resistance Surveillance Program during the period from 2015 to 2021.Antimicrobial susceptibility test was carried out by disk diffusion method and automated systems according to the same CHINET protocol.The data were interpreted in accordance with the breakpoints recommended by the Clinical and Laboratory Standards Institute(CLSI)in 2021.Results A total of 514 715 nonduplicate clinical isolates were collected from elderly patients in 52 hospitals from January 1,2015 to December 31,2021.The number of isolates accounted for 34.3％of the total number of clinical isolates from all patients.Overall,21.8％of the 514 715 strains were gram-positive bacteria,and 78.2％were gram-negative bacteria.Majority(90.9％)of the strains were isolated from inpatients.About 42.9％of the strains were isolated from respiratory specimens,and 22.9％were isolated from urine.More than half(60.7％)of the strains were isolated from male patients,and 39.3％isolated from females.About 51.1％of the strains were isolated from patients aged 65-＜75 years.The prevalence of methicillin-resistant strains(MRSA)was 38.8％in 32 190 strains of Staphylococcus aureus.No vancomycin-or linezolid-resistant strains were found.The resistance rate of E.faecalis to most antibiotics was significantly lower than that of Enterococcus faecium,but a few vancomycin-resistant strains(0.2％,1.5％)and linezolid-resistant strains(3.4％,0.3％)were found in E.faecalis and E.faecium.The prevalence of penicillin-susceptible S.pneumoniae(PSSP),penicillin-intermediate S.pneumoniae(PISP),and penicillin-resistant S.pneumoniae(PRSP)was 94.3％,4.0％,and 1.7％in nonmeningitis S.pneumoniae isolates.The resistance rates of Klebsiella spp.(Klebsiella pneumoniae 93.2％)to imipenem and meropenem were 20.9％and 22.3％,respectively.Other Enterobacterales species were highly sensitive to carbapenem antibiotics.Only 1.7％-7.8％of other Enterobacterales strains were resistant to carbapenems.The resistance rates of Acinetobacter spp.(Acinetobacter baumannii 90.6％)to imipenem and meropenem were 68.4％and 70.6％respectively,while 28.5％and 24.3％of P.aeruginosa strains were resistant to imipenem and meropenem,respectively.Conclusions The number of clinical isolates from elderly patients is increasing year by year,especially in the 65-＜75 age group.Respiratory tract isolates were more prevalent in male elderly patients,and urinary tract isolates were more prevalent in female elderly patients.Klebsiella isolates were increasingly resistant to multiple antimicrobial agents,especially carbapenems.Antimicrobial resistance surveillance is helpful for accurate empirical antimicrobial therapy in elderly patients.

Objective To investigate the effect of cordyceps sinensis(CS)on the activation of fibroblasts through IL-6 trans-signaling pathway and its specific mechanism in the treatment of renal fibrosis.Methods Renal fibrosis mouse model was established by unilateral ischemia/reperfusion(UIR),and the mice were administered intragastrically CS,soluble glycoprotein 130 Fc(sgp130Fc)or Hyper-IL-6.Masson's trichrome staining was utilized to identify tubulointerstitial fibrosis.PAS staining was utilized to assess the extent of renal injury.Western blot was employed to analyze the expression levels of fibrosis markers[alpha-smooth muscle actin(α-SMA),fibronectin(FN)]and proteins associated with IL-6 trans-signaling pathway[phosphorylated signal transducer and activator of transcription 3(p-STAT3),soluble interleukin-6 receptor(sIL-6R)].The expression and localization of proteins were additionally detected by immunohistochemistry,immunofluorescence and qPCR.The effect of cordyceps sinensis extract cordycepin on IL-6 trans-signaling in fibroblasts was further investigated in vitro.Results The results from in vivo experiments showed that administration of CS during the chronic phase demonstrated a beneficial protective impact on inflammation and fibrosis in the affected kidney,and serum creatinine levels and collagen deposition were decreased.Western blot analysis revealed a decrease in the expression levels of α-SMA,FN,as well as IL-6 trans-signaling pathway protein p-STAT3,sIL-6R in the treatment group.Additionally,the mRNA expression levels of chemokines monocyte chemoattractant protein-1(MCP-1)and C-X-C motif chemokine ligand 12(CXCL12)were also decreased in the CS treatment group.Additionally,Hyper-IL-6 can partially counteract the therapeutic effects of CS.In vitro experiments further demonstrated that cordycepin inhibited the secretion of IL-6 from NRK-52E.Combined treatment of recombinant IL-6 and sIL-6R protein activated NRK-49F,leading to a significant increase in α-SMA,FN,and p-STAT3 expression levels.Cordycepin or sgp130Fc treatment significantly inhibited the proliferation of fibroblasts induced by IL-6 trans-signaling pathway.Conclusion CS can significantly reduce IL-6 secretion by renal tubular epithelial cells and inhibit the activation of IL-6 trans-signaling pathway in fibroblasts,thereby ameliorating renal interstitial fibrosis.

Objective To identify determinants of subtherapeutic voriconazole(VRCZ)concentrations in allogeneic hematopoietic stem cell transplantation(allo-HSCT)recipients and to develop/validate a nomogram-based risk prediction model.Methods This study retrospectively analyzed 310 VRCZ therapeutic drug monitoring(TDM)measurements from allo-HSCT recipients at 310 patients who under went allo-HSCT surgery at Hebei Yanda Ludaopei Hospital from October 2022 to October 2024 and received VRCZ for the prevention and treatment of invasive fungal infections before transplantion were selected as the study subjects.Cases were stratified into target-concentration group(0.5～5.0μg/ml)and subtherapeutic group(<0.5μg/ml).Through single factor and multiple factor Logistic regression analysis,indeipendent predictive factors forvecz plasma concentration non-compliance were screened,and a column chart prediction model(NPM)was constructed.The performance of the model was evaluateding area under the receiver operating characteristic curve(AUC),Hosmer-Lemeshow(H-L)goodness-of-fit test,and decision curve analysis(DCA).Results Among 310 VRCZ-TDM measurements,71.61%(222/310)achieved target concentrations.Multivariate analysis showed that CYP2C19 intermediate metabolite,daily dose of cyclosporine A(CSA),daily dose of VRCZ,creatinine(Cr)>97 μmol/L,albumin(Alb)and C-reactive protein(CRP)were independent influencing factors for VRCZ blood drug concentration non-compliance(Wald χ2=4.046～13.221,all P＜0.05).The nomogram demonstrated excellent discrimination,calibration(H-L goodness of fit test χ2=2.663,P=0.954),and clinical utility with net benefit across 0.05～0.96 risk thresholds.Conclusion The nomogram incorporating CYP2C19 gene phenotype,daily CSA dosing,daily VRCZ dosing,Cr levels,Alb and CRP provides a validated tool for optimizing VRCZ therapy in allo-HSCT recipients,enabling precision dosing strategies.

Objective To examine the changing prevalence and antimicrobial resistance profiles of Burkholderia cepacia in 52 hospitals across China from 2015 to 2021.Methods A total of 9 261 strains of B.cepacia were collected from 52 hospitals between January 1,2015 and December 31,2021.Antimicrobial susceptibility of the strains was tested using Kirby-Bauer method or automated antimicrobial susceptibility testing systems according to a unified protocol.The results were interpreted according to the breakpoints released in the Clinical & Laboratory Standards Institute(CLSI)guidelines(2023 edition).Results A total of 9 261 strains of B.cepacia were isolated from all age groups,especially elderly patients.The proportion was 11.1％(1 032 strains)in children,significantly lower than the proportion in adults.About half(46.5％,4 310/9 261)of the strains were isolated from patients at least 60 years old and 42.3％(3 919/9 261)of the strains were isolated from young adults.Most isolates(71.1％)were isolated from sputum and respiratory secretions,followed by urine(10.7％)and blood samples(8.1％).B.cepacia isolates were highly susceptible to the five antimicrobial agents recommended in the CLSI M100 document(33rd edition,2023).B.cepacia isolates showed relatively higher resistance rates to meropenem and levofloxacin.However,the resistance rates to ceftazidime,trimethoprim-sulfamethoxazole,and minocycline remained below 8.1％.The percentage of B.cepacia strains resistant to levofloxacin was the highest compared to other antibiotics in any of the three age groups(from 12.4％in the patients＜18 years old to 20.6％in the patients aged 60 years or older).Conclusions B.cepacia is one of the clinically important non-fermenting gram-negative bacteria.Accurate and timely reporting of antimicrobial susceptibility test results and ongoing antimicrobial resistance surveillance are helpful for rational prescription of antimicrobial agents and proper prevention and control of nosocomial infections.