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.

ObjectiveType I interferon (IFN-I) signaling is essential for antiviral innate immunity, yet its sustained or excessive activation contributes to the pathogenesis of several autoimmune diseases and interferonopathies, such as systemic lupus erythematosus and Aicardi-Goutières syndrome. Current strategies targeting this pathway, exemplified by JAK inhibitors, act mainly on downstream signal transduction and provide limited direct control over upstream IFN-I production, while also carrying the risk of broad immunosuppression. Phyllanthus emblica L. has long been used in traditional medicine for inflammatory disorders, but the bioactive constituent responsible for its regulation of IFN-I signaling and the underlying molecular mechanism have not been clearly defined. This study aimed to identify the active anti-inflammatory component of P. emblica and to characterize its mechanism of action on the IFN-I pathway in macrophages. MethodsActive components ofP. emblica and their candidate targets were screened by network pharmacology using the TCMSP and DrugBank databases (oral bioavailability≥30%, drug-likeness≥0.18) and intersected with inflammation-related genes retrieved from public databases. The predicted interaction between EGCG and IFN-I pathway proteins (TBK1, IRF3, STAT1) was evaluated by molecular docking, with BX795 and GSK8612 used as reference TBK1 inhibitors. Mechanistic experiments were performed in THP-1-derived macrophages and primary bone marrow-derived macrophages (BMDM). Upstream signaling was activated by transfection of the nucleic acid analogs poly(I∶C) and poly(dA∶dT) or by lipopolysaccharide (LPS) stimulation, whereas downstream signaling was activated by exogenous IFN-β. An siRNA-mediated TREX1 knockdown model was used to mimic endogenous nucleic acid-driven interferonopathy. Expression of IFN-β1 and interferon-stimulated genes (ISGs) was measured by RT-qPCR, protein phosphorylation by Western blot, and IFN-β secretion by ELISA. Cellular thermal shift assay (CETSA) and drug affinity responsive target stability (DARTS) were used to probe the interactionbetween EGCG and IRF3. ResultsNetwork pharmacology identified (-)-epigallocatechin-3-gallate (EGCG) as a candidate IFN-I-suppressive constituent of P. emblica, with predicted binding to TBK1, IRF3, and STAT1. Molecular docking yielded binding energies of -9.2, -7.2, and -8.2 kcal/mol for TBK1, IRF3, and STAT1, respectively, indicating an affinity for TBK1 comparable to that of the reference inhibitors BX795 (-5.7 kcal/mol) and GSK8612 (-6.4 kcal/mol). EGCG suppressed IFN-β1 and ISG mRNA expression under poly (I∶C), poly (dA∶dT), and LPS stimulation in both THP-1 macrophages and BMDM. At the protein level, EGCG reduced the phosphorylation of TBK1 and IRF3 without affecting the levels of the upstream sensors cGAS and RIG-I, and lowered IFN-β secretion in a concentration-dependent manner. CETSA and DARTS showed that EGCG did not enhance the thermal stability or protease resistance of IRF3, indicating that its effect on IRF3 is indirect. Following IFN-β stimulation, prolonged EGCG treatment reduced STAT1 phosphorylation in a time-dependent manner without an apparent change in IRF9, and partially attenuated ISG transcription; this effect was not monotonicly concentration-dependent, and CXCL10 showed the most consistent suppression. In TREX1-knockdown cells, the elevated mRNA levels of ISG15, ISG56, and CXCL10 were reduced by EGCG. ConclusionEGCG suppresses IFN-I responses by concurrently inhibiting TBK1-IRF3-dependent IFN‑β production and JAK-STAT1-mediated downstream transcription. These in vitro findings provide a mechanistic basis for the anti-inflammatory use of P. emblica in traditional medicine and identify EGCG as a candidate for further evaluation in interferon-driven autoimmune disease models.

We analyzed the epidemiological features and spatial distribution characteristics of human brucellosis in Jining city from 2014 to 2023,to provide a reference for further development of targeted prevention and control strategies and measures.Descrip-tive epidemiological methods were used to analyze the epidemiological characteristics of brucellosis cases in Jining from 2014 to 2023.The spatial regional correlation of brucellosis incidence in Jining and the clustering patterns of local areas were studied through spatial autocorrelation analysis with townships as the basic unit.A total of 3 520 cases of brucellosis were reported in Jining from 2014 to 2023,and the average annual incidence rate was 4.23/100 000,thus indicating a fluctuating trend overall.Reported cases peaked from March to August,and a sex ratio of 2.71 males to 1 female was observed.The 40-59 year age group had the most reported cases(50.39％).The incidence of brucellosis in Jining showed an imbalanced spatial distribution.Brucellosis incidence showed a spatially clustered distribution(Moran's I>0,P<0.05).Hotspots were distributed primarily in Sishui,Qufu,and Zoucheng.A total of one class Ⅰ clustering area and one class Ⅱ clustering area were detected in the spatial and temporal scans,and were located in Sishui,Qufu,and Liangshan county.After pathogenic AMOS-PCR typing analysis,64 Brucella isolates collected from Jinan City from 2022 to 2024 were all of the sheep strain,and sheep biovar 3 was predominant(70.31％).In 2014-2023,although Jining City experienced a high incidence of brucellosis,a downward trend was observed.Brucellosis showed a spatial clustering pattern concentrated in the northeastern region.Therefore,awareness and education must be strengthened among brucellosis practitioners in cluster areas,to en-hance case surveillance,improve the level of protection,and achieve early detection and treatment.

The active ingredient of WINREVAIR,sotatercept-csrk,is a recombinant activin receptor ⅡA-Fc(ActRⅡA-Fc)fusion protein that improves pro-proliferation(ActRⅡA/Smad2/3-mediated)and anti-proliferation(BMPRⅡ/Smad 1/5/8-mediated)signals,thereby regulating vascular proliferation.In March 2024,WINREVAIR was approved by the U.S.Food and Drug Administration for the treatment of pulmonary arterial hypertension(PAH)in adults.Clinical studies have shown that WINREVAIR can improve exercise capacity and reduce the incidence of all-cause death or clinical worsening of PAH by 84％.Common adverse drugreactions include headache,epistaxis,rash,etc.

Objective:To identify independent risk factors for early recurrence following curative resection of resectable pancreatic cancer and establish a nomogram prediction model.Methods:Clinical data from 405 patients with resectable pancreatic cancer treated at Renji Hospital, Shanghai Jiao Tong University School of Medicine from February 2010 to December 2020 were retrospectively reviewed. Patients were stratified into a training cohort (265 patients form February 2010 to December 2018) and a validation cohort (140 patients from January 2019 to December 2020) based on surgery dates. Optimal cutoff values for clinical variables were determined using X-tile software. Independent risk factors were identified through univariate and multivariate Cox proportional hazards regression analyses. Kaplan-Meier curves for recurrence-free survival (RFS) were generated across subgroups, and a nomogram was developed to predict early recurrence (within 1 year post-surgery). Time-dependent receiver operating characteristic (tROC) curves was drawn and area under the curve (AUC) metrics were utilized to evaluate predictive accuracy, while model reliability was assessed by calibration curves. Individualized risk scores derived from the nomogram were stratified into high- and low-risk groups using X-tile-derived cutoff values. Survival differences between groups were analyzed via log-rank tests. The clinical application value was judged by decision curve analysis (DCA) compared to TNM staging. Results:In the training cohort, 139 patients (52.45%) experienced early recurrence, with a median RFS of 11.1 months [interquartile range ( IQR): 6.0-26.0]. The validation cohort reported 70 early recurrences (50.00%) and a median RFS of 11.8 months ( IQR: 4.9-21.4). Univariate analysis revealed significant associations between early recurrence and tumor diameter, carcinoembryonic antigen (CEA), carbohydrate antigen 19-9 (CA19-9), carbohydrate antigen 125 (CA125), systemic immune-inflammation index (SⅡ), and prognostic nutritional index (PNI). Multivariate analysis identified tumor diameter ≥3.75 cm ( HR=1.718, 95% CI 1.223-2.412, P=0.002), CA19-9≥218 U/ml ( HR=1.567, 95% CI 1.107-2.220, P=0.011), CA125≥20.98 U/ml ( HR=2.501, 95% CI 1.768-3.539, P<0.001), SⅡ≥388.28 ( HR=1.708, 95% CI 1.096-2.662, P=0.018), and PNI<53.18 ( HR=0.596, 95% CI 0.404-0.879, P=0.009) as independent risk factors for early recurrence. The nomogram achieved AUC values of 0.771 and 0.708 in the training and validation cohorts, respectively. Calibration curves demonstrated strong agreement between predicted and observed survival probabilities. Kaplan-Meier analysis revealed significantly lower 1-year RFS rates in high-risk versus low-risk groups for both cohorts (training: HR=3.65, 95% CI 2.45-5.44, P<0.001; validation: HR=2.37, 95% CI 1.39-4.06, P=0.001). DCA indicated superior net benefit of the nomogram over TNM staging across threshold probabilities of 0.2-0.9. Conclusions:The proposed nomogram effectively integrates clinical and serological biomarkers to preoperatively assess early recurrence risk in resectable pancreatic cancer patients, offering enhanced precision for clinical decision-making.

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.

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:The purpose of this study is to explore the influencing factors of poor prognosis in henoch-schonlein purpura(HSP)children,and to construct a nomogram model and verify its validity through the analysis results of Logistic regression model.Methods:Collected the clinical data of 170 HSP children who were treated in Fuzhou Luoyuan County Hospital from January 2015 to May 2023 were retrospectively analyzed.They were divided into good prognosis group and poor prognosis group according to the prognosis after treatment.The clinical data and related biochemical indexes of different prognostic groups were compared.The factors of poor prognosis in HSP children were analyzed by Logistic regression model.A predictive model(nomogram)for poor prognosis in HSP children were constructed and evaluated its predictive performance.Prediction accuracy were evaluateed by receiver operating characteristic(ROC)curve.Results:170 HSP children,69 cases(40.59％)had poor prognosis and 101 cases(59.41％)had good prognosis.C-reactive protein(CRP),kidney injury at initial onset,platelet count(PLT),respiratory infection,recurrent rash,and immunoglobulin A(IgA)levels in poor prognosis group were higher than those in good prognosis group(P＜0.05).Multivariate logistic analysis showed that,recurrent rash,respiratory infection,elevated PLT,CRP and IgA levels were independent influencing factors of poor prognosis in HSP children(P＜0.05).The consistency index(C-index)of the column chart model established based on the results of multiple factor analysis was 0.798.The internal verification was carried out by Bootstrap self-sampling method,and the average absolute error of calibration curve was 0.017.ROC curve was drawn according to independent influencing factors and nomogram prediction probability,the area under the curve was 0.674(recurrent rash),0.649(respiratory infection),0.777(PLT),0.733(CRP),0.749(IgA)and 0.910(nomogram prediction probability)respectively.Conclusion:Recurrent rash,respiratory infection,PLT,CRP and IgA are independent factors affecting the prognosis of HSP children.The column chart prediction model constructed based on the above factors has certain predictive value for the poor prognosis in HSP children.