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:This study investigated the role of solute carrier family 7 member 11(SLC7A11)in sul-fasalazine(SAS)-induced ferroptosis in acute myeloid leukemia(AML)cells,focusing on the inhibitory effect of nuclear factor E2-related factor 2(NRF2)nuclear translocation-mediated activation of SLC7A11 on ferroptosis and its underlying mechanisms.METHODS:SAS-induced proliferation in AML cell lines,Kasumi-1 and THP-1,was assessed using the MTS assay.Cell death inhibitors were employed to determine the mode of cell death.Lipid reactive oxygen species(ROS)levels were measured by flow cytometry;Fe2+,malonodialdehyde(MDA),glutathione(GSH)levels,and glutathione per-oxidase 4(GPX4)activity were assessed using micromethods.Quantitative PCR(qPCR)was performed to evaluate changes in SLC7A11 mRNA during SAS-induced ferroptosis,while Western blot measured SLC7A11 and GPX4 protein levels.Moreover,Western blot assessed NRF2 nuclear translocation post-SAS treatment.The NRF2 inhibitor ML385 was used to validate these effects.SLC7A11 mRNA and protein levels were then measured following combined SAS and ML385 treatment via qPCR and Western blot.Cell viability and ferroptosis-related indices were evaluated under the same treatment conditions.Furthermore,a shRNA vector targeting SLC7A11 was constructed to assess changes in cell viability and ferroptosis markers after SLC7A11 knockdown with SAS.GPX4 protein levels were examined following SLC7A11 knockdown.RESULTS:SAS significantly inhibited the proliferation of Kasumi-1 and THP-1 cells at 200 μmol/L and 300 μmol/L,respectively(P<0.05).Only ferroptosis inhibitors(Fer-1 and DFO)significantly reversed SAS-induced cy-totoxicity(P<0.01).SAS increased lipid ROS,Fe2+,and MDA levels(P<0.01),while reducing GSH and GPX4 activity(P<0.01).The mRNA and protein expressions of SLC7A11 increased during SAS-induced ferroptosis(P<0.01),where-as GPX4 protein decreased significantly(P<0.01).SAS significantly increased the nuclear-to-cytoplasmic NRF2 ratio(P<0.01),which decreased upon co-treatment with ML385(P<0.05).Following SAS and ML385 co-treatment,both SLC7A11 mRNA and protein levels were downregulated(P<0.01).This combination treatment further reduced AML cell viability(P<0.01),an effect reversed by Fer-1 and DFO(P<0.01).Compared with SAS alone,the combination of SAS and ML385 significantly increased lipid ROS,Fe2+,and MDA while reducing GSH levels and GPX4 activity(P<0.01).SLC7A11 knockdown was successfully achieved.Compared with the NC shRNA group,SLC7A11 knockdown cells showed significantly decreased viability after SAS treatment,which was reversed by Fer-1 and DFO(P<0.01).Lipid ROS,Fe2+,and MDA content were significantly increased(P<0.01),and GSH and GPX4 were substantially decreased(P<0.05).Moreover,GPX4 protein expression was considerably reduced after SLC7A11 knockdown(P<0.01).CONCLUSION:SAS induces ferroptosis in AML cells.It promotes the nuclear translocation of NRF2 protein,which activates SLC7A11 ex-pression.Inhibition of NRF2 or downregulation of SLC7A11 sensitizes AML cells to SAS-induced ferroptosis.

Objective To summarize the changing prevalence of carbapenem resistance in Enterobacterales based on the data of CHINET Antimicrobial Resistance Surveillance Program from 2015 to 2021 for improving antimicrobial treatment in clinical practice.Methods Antimicrobial susceptibility testing was performed using a commercial automated susceptibility testing system according to the unified CHINET protocol.The results were interpreted according to the breakpoints of the Clinical & Laboratory Standards Institute(CLSI)M100 31st ed in 2021.Results Over the seven-year period(2015-2021),the overall prevalence of carbapenem-resistant Enterobacterales(CRE)was 9.43％(62 342/661 235).The prevalence of CRE strains in Klebsiella pneumoniae,Citrobacter freundii,and Enterobacter cloacae was 22.38％,9.73％,and 8.47％,respectively.The prevalence of CRE strains in Escherichia coli was 1.99％.A few CRE strains were also identified in Salmonella and Shigella.The CRE strains were mainly isolated from respiratory specimens(44.23±2.80)％,followed by blood(20.88±3.40)％and urine(18.40±3.45)％.Intensive care units(ICUs)were the major source of the CRE strains(27.43±5.20)％.CRE strains were resistant to all the β-lactam antibiotics tested and most non-β-lactam antimicrobial agents.The CRE strains were relatively susceptible to tigecycline and polymyxins with low resistance rates.Conclusions The prevalence of CRE strains was increasing from 2015 to 2021.CRE strains were highly resistant to most of the antibacterial drugs used in clinical practice.Clinicians should prescribe antimicrobial agents rationally.Hospitals should strengthen antibiotic stewardship in key clinical settings such as ICUs,and take effective infection control measures to curb CRE outbreak and epidemic in hospitals.

Objective To characterize the changing species distribution and antibiotic resistance profiles of respiratory isolates in hospitals participating in the CHINET Antimicrobial Resistance Surveillance Program from 2015 to 2021.Methods Commercial automated antimicrobial susceptibility testing systems and disk diffusion method were used to test the susceptibility of respiratory bacterial isolates to antimicrobial agents following the standardized technical protocol established by the CHINET program.Results A total of 589 746 respiratory isolates were collected from 2015 to 2021.Overall,82.6％of the isolates were Gram-negative bacteria and 17.4％were Gram-positive bacteria.The bacterial isolates from outpatients and inpatients accounted for(6.0±0.9)％and(94.0±0.1)％,respectively.The top microorganisms were Klebsiella spp.,Acinetobacter spp.,Pseudomonas aeruginosa,Staphylococcus aureus,Haemophilus spp.,Stenotrophomonas maltophilia,Escherichia coli,and Streptococcus pneumoniae.Each microorganism was isolated from significantly more males than from females(P＜0.05).The overall prevalence of methicillin-resistant S.aureus(MRSA)was 39.9％.The prevalence of penicillin-resistant S.pneumoniae was 1.4％.The prevalence of extended-spectrum β-lactamase(ESBL)-producing E.coli and K.pneumoniae was 67.8％and 41.3％,respectively.The overall prevalence of carbapenem-resistant E.coli,K.pneumoniae,Enterobacter cloacae,Pseudomonas aeruginosa,and Acinetobacter baumannii was 3.7％,20.8％,9.4％,29.8％,and 73.3％,respectively.The prevalence of β-lactamase was 96.1％in Moraxella catarrhalis and 60.0％in Haemophilus influenzae.The H.influenzae isolates from children(＜18 years)showed significantly higher resistance rates to β-lactam antibiotics than the isolates from adults(P＜0.05).Conclusions Gram-negative bacteria are still predominant in respiratory isolates associated with serious antibiotic resistance.Antimicrobial resistance surveillance should be strengthened in clinical practice to support accurate etiological diagnosis and appropriate antimicrobial therapy based on antimicrobial susceptibility testing results.

Objective:To develop a modified University of Wisconsin preservation solution (UW solution) containing α 2-adrenergic receptor agonists (dexmedetomidine) and noble gases (argon) and investigate its protective effect on the isolated amputated skeletal muscle of rats. Methods:Sixty male SD rats were selected to establish a hindlimb cold preservation/perfusion model and were divided into blank control group, hypothermic storage group, UW solution perfusion group, and modified UW solution perfusion group using a random number table, with 15 rats in each group. Simultaneously, a cold preservation model of rat skeletal muscle myoblasts (L6 cells) was established and the rats were also divided into four groups in the same way. Animal models were prepared in different ways: In the blank control group, the hindlimbs received no special treatment; In the hypothermic storage group, the amputated hindlimbs were stored in a dry centrifuge tube at 4℃ for 18 hours; In the UW solution perfusion group, the amputated hindlimbs were perfused with UW solution and then stored in a centrifuge tube containing UW solution at 4℃ for 18 hours; In the modified UW solution perfusion group, the amputated hindlimbs were perfused with modified UW solution (containing 0.1 nmol/L dexmedetomidine and 50% volume fraction of argon) and then stored in a centrifuge tube containing the modified UW solution at 4℃ for 18 hours. Cell models were treated as follows: In the blank control group, L6 cells were cultured under standard conditions; In the hypothermic storage group and UW solution group, L6 cells were treated with conventional culture medium or UW solution, stored in argon-filled sealed bags at 4℃ for 8 hours, and then rewarmed and cultured for 6 hours; In the modified UW solution group, L6 cells were treated with the modified solution, stored in argon-filled sealed bags at 4℃ for 8 hours, and then rewarmed and cultured for 6 hours. After sample collection, skeletal muscle morphology, tissue edema and ultrastructure features were assessed by HE staining, wet-to-dry weight ratio, and transmission electron microscopy, respectively. Additionally, L6 cell morphology was examined by light microscopy. L6 cell viability was determined by cell counting kit-8 (CCK-8) assay (expressed as absorbance A value). Expression levels of glutathione peroxidase 4 (GPX4) protein in both skeletal muscle tissue and L6 cells were evaluated by immunofluorescence staining and Western blot, respectively.Results:After 18 hours of in vitro preservation of rat isolated amputated limbs, the following results were obtained: (1) HE staining results showed that the muscle fiber morphology of the modified UW solution perfusion group was close to that of the blank control group. Moreover, the area ratio of skeletal muscle cells in the modified UW solution perfusion group was significantly higher than that in the hypothermic storage group and UW solution perfusion group ( P<0.05). (2) The wet-dry weight ratio results showed that there was no statistically significant difference among the modified UW solution perfusion group, the blank control group and UW solution group ( P>0.05), with significantly lower ratios in all three groups than that in the hypothermic storage group ( P<0.05). (3) Transmission electron microscopy results revealed that the modified UW solution perfusion group showed no statistically significant differences in ultrastructural metrics, including myofiber diameter, sarcomere length, mitochondrial short-axis/long-axis ratio, and mitochondrial cristae count, compared with those in the blank control group ( P>0.05), and performed significantly better than both the hypothermic storage group and UW solution perfusion group ( P<0.05). (4) Morphological observation of L6 cells showed that the cellular morphology was regular in the modified UW solution perfusion group, close to that in the blank control group, while it was severely damaged in the hypothermic storage group. Moreover, the cells were reduced in number and partially damaged in the UW solution group. The sequence of cell viability expressed as absorbance A value was blank control group >modified UW solution perfusion group > UW solution perfusion group > hypothermic storage group, with statistically significant differences among the four groups ( P<0.05). (5) Immunofluorescence staining showed that there was no statistically significant difference in fluorescence intensity of GPX4 protein expression between the modified UW solution perfusion group and blank control group ( P>0.05), while the fluorescence intensity was higher in the modified UW solution perfusion group than that in the hypothermic storage group and UW solution perfusion group ( P<0.05). Western blot analysis showed that the relative expression level of GPX4 in the modified UW solution group was significantly lower than that in the blank control group ( P<0.05), but higher than that in the hypothermic storage group and UW solution perfusion group ( P<0.05). Conclusion:The modified UW solution can stabilize the expression level of GPX4 protein, thereby inhibiting ferroptosis and alleviating cold preservation injury in both rat amputated isolated limb skeletal muscle tissue and L6 cells.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

The study investigated the intrinsic changes in material basis of Angelicae Sinensis Radix during wine processing by headspace-gas chromatography-ion mobility spectrometry(HS-GC-IMS), headspace-solid phase microextraction-gas chromatography-mass spectrometry(HS-SPME-GC-MS), and ultra-high performance liquid chromatography-quadrupole-orbitrap mass spectrometry(UPLC-Q-Orbitrap-MS) combined with chemometrics. HS-GC-IMS fingerprints of Angelicae Sinensis Radix before and after wine processing were established to analyze the variation trends of volatile components and characterize volatile small-molecule substances before and after processing. Principal component analysis(PCA) and orthogonal partial least squares-discriminant analysis(OPLS-DA) were employed for differentiation and difference analysis. A total of 89 volatile components in Angelicae Sinensis Radix were identified by HS-GC-IMS, including 14 unsaturated hydrocarbons, 16 aldehydes, 13 ketones, 9 alcohols, 16 esters, 6 organic acids, and 15 other compounds. HS-SPME-GC-MS detected 118 volatile components, comprising 42 unsaturated hydrocarbons, 11 aromatic compounds, 30 alcohols, 8 alkanes, 6 organic acids, 4 ketones, 7 aldehydes, 5 esters, and 5 other volatile compounds. UPLC-Q-Orbitrap-MS identified 76 non-volatile compounds. PCA revealed distinct clusters of raw and wine-processed Angelicae Sinensis Radix samples across the three detection methods. Both PCA and OPLS-DA effectively discriminated between the two groups, and 145 compounds(VIP>1) were identified as critical markers for evaluating processing quality, including 4-methyl-3-penten-2-one, ethyl 2-methylpentanoate, and 2,4-dimethyl-1,3-dioxolane detected by HS-GC-IMS, angelic acid, β-pinene, and germacrene B detected by HS-SPME-GC-MS, and L-tryptophan, licoricone, and angenomalin detected by UPLC-Q-Orbitrap-MS. In conclusion, the integration of the three detection methods with chemometrics elucidates the differences in the chemical material basis between raw and wine-processed Angelicae Sinensis Radix, providing a scientific foundation for understanding the processing mechanisms and clinical applications of wine-processed Angelicae Sinensis Radix.
Wine/analysis* ; Gas Chromatography-Mass Spectrometry/methods* ; Chromatography, High Pressure Liquid/methods* ; Angelica sinensis/chemistry* ; Solid Phase Microextraction/methods* ; Drugs, Chinese Herbal/isolation & purification* ; Chemometrics ; Volatile Organic Compounds/chemistry* ; Principal Component Analysis ; Ion Mobility Spectrometry/methods*