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.

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.

Porcine arthritis,one of the common chronic diseases in large-scale pig farms,can signifi-cantly reduce the production performance of meat pigs.In this study,a strain of Haemophilus pa-rasuis(HPS)was isolated from the joint fluid of a lame pig.The HPS was analyzed in terms of se-rotypes,virulence genes,and resistance genes.Additionally,it was treated with sensitive antibiotics to provide a theoretical basis for the comprehensive prevention and treatment of arthritis in meat pigs in future production settings.A strain of HPS type 14 was isolated from the joint fluid of dis-eased pigs.The HPS isolate demonstrated sensitivity to β-lactams and tetracyclines,while florfeni-col and polymyxin effectively inhibited its growth at low concentrations.However,the bacteria ex-hibited resistance to sulfonamides and ciprofloxacin.The treatment of affected pigs with clinical ar-thritis using doxycycline and enrofloxacin injections proved effective.Compared to the infected group,in which the sick pigs experienced difficulty flexing their carpal and tarsal joints and exhibi-ted significant lameness,the pigs in the treatment group showed marked improvement.Their joints were only slightly swollen,and the clinical symptoms of arthropathy were alleviated.

Objective:To analyze the baseline characteristics and prognostic factors of Pneumocystis carinii pneumonia (PCP) in patients with liver diseases (LD), and to develop the nomogram prognosis model. Methods:The patients with liver diseases complicated with PCP (LD-PCP) who admitted to Beijing Youan Hospital, Capital Medical University from January 1st, 2021 to October 31st, 2024 were enrolled in this study. This study was a retrospective case-control study. Data were collected, including baseline characteristics at admission and clinical outcomes. Multivariate logistic regression analysis was employed to identify the independent risk factors of poor prognosis in LD-PCP patients. Subsequently, a nomogram was developed to construct the prognostic model. The performance of this model was evaluated using the receiver operater characteristic (ROC) curve and calibration curve.Results:A total of 61 patients with LD-PCP were enrolled, including 21 in the death group and 40 in the survival group. Multivariate logistic regression analysis revealed that co-infection with Aspergillus and elevated lactate dehydrogenase (LDH) levels were independent risk factors for mortality in patients with LD-PCP (odds ratio ( OR)=12.802, 95% confidence interval ( CI) 1.334 to 122.845, P=0.027; OR=1.009, 95% CI 1.002 to 1.016, P=0.014). The predictive value of prognostic model based on the nomogram was better evidenced by the ROC curve, which yielded an area under the curve (AUC) of 0.835 (95% CI 0.725 to 0.944), with the sensitivity of 71.4% and the specificity of 87.5%. The predictive accuracy of the model was further validated by the calibration curve, and the Brier value was 0.151, the model fitting line had a high degree of agreement with the standard reference line ( P=0.953). Conclusions:Co-infection with Aspergillus and LDH levels are independent risk factors for the poor prognosis of patients with LD-PCP. The prognosis model based on the nomogram has better predictive value and clinical practicality, which could identify high-risk patients in the early stage, and provide theoretical guidance on the patient management.

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.

Spine fracture and dislocation are common traumatic spinal conditions that often require surgical intervention due to compromised spinal stability. Surgical approaches include anterior, posterior, and combined anterior-posterior spinal procedures. According to the specific surgical requirements, patients may be placed in the prone position or repositioned between prone and supine positions during surgery. Intraoperative repositioning has become an essential step in patient positioning. However, during repositioning, patients with spinal fracture and dislocation are at increased risk for complications such as hemodynamic instability, nerve injury, and pressure injuries to the skin and soft tissue. Notably, due to the instability of the spinal cord, even minor manipulations can further exacerbate the damage, potentially leading to severe outcomes like paraplegia. Although the current clinical guidelines provide instructive recommendations for standard position, there remains no specific protocols for intraoperative repositioning in patients with spine fracture and dislocation. With a concern for the lack of clinical studies on positioning techniques, risk prevention, and operational norms for special patients, no applicable guidelines or standards are available. A consensus was required to provide clinical reference, meet the requirements of surgical treatment, and minimize the safety risks of patients caused by improper placement of positions. Professional Committee of Operating Room Nursing of Shaanxi Nursing Association organized experts in nursing management and operating room nursing from major hospitals across China to formulate Expert consensus on intraoperative repositioning for patients with spinal fracture and dislocation ( version 2025). The consensus provides 11 recommendations covering pre-repositioning preparation, intraoperative maneuvers, and post-repositioning observation, aiming to provide references for clinical standardization of the intraoperative repositioning process and protection of patients′ safety.

Objective:To investigates the efficacy and safety of allogeneic hematopoietic stem cell transplantation(allo-HSCT)in treating older patients(≥60 years old)with hematologic malignancies.Methods:We conducted a retrospective study involving 67 patients aged 60 years and above, diagnosed with malignant hematological diseases, who received allo-HSCT at the Clinical Research Centrer for Haematologic Diseases of the First Affiliated Hospital of Soochow University between June 2015 and March 2023.We collected pre-transplant data, including the patients' age, gender, pre-transplantation disease risk stratification, disease status, and the haematopoietic cell transplantation comorbidity index(HCT-CI). We retrospectively analyzed clinical data regarding treatment-related toxicity, infections, acute and chronic graft-versus-host disease(a/cGVHD), as well as recurrent and non-recurrent deaths, to estimate the overall survival(OS)rate and event-free survival (EFS)rate.Results:Sixty-seven patients were included in the study, comprising 55 males(82.1%)and 12 females(17.9%), with a median age of 63(61, 65) years .The cohort consisted of 42 cases of acute myeloid leukaemia, 22 cases of myelodysplastic syndromes, and 3 cases of acute lymphoblastic leukaemia.The Kaplan-Meier analysis showed that the 1-year OS and EFS rates were 62.9% and 59.2%, respectively, while the 2-year OS and EFS rates were 55.3% and 51.8%, respectively.The cumulative incidence of 1-year non-relapse mortality and relapse was 25.4% and 21.2%, respectively.A total of 13 patients developed grade Ⅱ-Ⅳ aGVHD, with a 1-year cumulative incidence of 22.0%, and 7 patients developed cGVHD requiring treatment.When stratified by age group, the OS rate was higher in patients aged 60~64 years compared to those aged ≥65 years; however, this difference was not statistically significant(Log-rank χ2=0.99, P=0.317). In contrast, when stratified by disease load, the OS rate was significantly higher in the complete remission(CR)group than in the non-CR group, with a statistically significant difference(Log-rank χ2=15.04, P<0.001). When stratified by donor type, the OS rate was higher in the human leukocyte antigens (HLA) allogeneic group compared to the haploinsufficiency group; however, the difference was not statistically significant(Log-rank χ2=2.71, P=0.100). Twenty-seven patients died at an average of 125 days (range 3-1 054 days) after HSCT.The causes of death included leukemia recurrence in 9 cases (33.3%), infection in 8 cases (29.6%), GVHD in 5 cases (18.5%), poor implantation in 3 cases (11.1%), multi-organ failure in 1 case (3.7%), and cerebrovascular accident in 1 case (3.7%). The results of multifactorial analysis indicated that a pre-transplant tumor load greater than 5% was an independent risk factor for OS after transplantation ( HR=4.59, 95% CI: 2.01-10.42, P<0.001)as well as for disease recurrence ( OR=13.11, 95% CI: 1.96-87.87, P=0.008). Additionally, the occurrence of infection was identified as an independent risk factor for non-recurrent death after transplantation( OR=3.95, 95% CI: 1.13 to 13.71, P=0.031). Conclusions:For patients aged 60 years or older with hematologic malignancies, HSCT can serve as a viable treatment option, particularly for those with refractory recurrence and high cytogenetic risk, as it has the potential to significantly enhance prognosis and increase both EFS and OS rates.