Objective To explore the number of peripheral blood regulatory T cells (Treg) in patients with chronic kidney disease (CKD) and its correlation with vascular calcification. Methods This was a single-center, cross-sectional, and observational study. Non-dialysis patients with CKD treated at Zhongshan Hospital, Fudan University from March 2021 to March 2022 were enrolled. Abdominal aortic calcification (AAC) was assessed using lateral abdominal X-ray. Number of Treg and cytokine levels were measured by flow cytometry. Logistic regression analysis was performed to evaluate the related factors for AAC in CKD patients. Results A total of 83 patients were included, aged 17–86 years, with 57 males (68.7%). The distribution of CKD stages was as follows: stage G1 in 7 patients (8.4%), stage G2 in 17 patients (20.5%), stage G3 in 21 patients (25.3%), stage G4 in 19 patients (22.9%), and stage G5 in 19 patients (22.9%). No AAC was observed in patients with stages G1 and G2, while the prevalence of AAC in patients with stages G3, G4, and G5 was 23.8%, 21.1%, and 26.3%, respectively. Compared with stage G1 patients, those with stages G3–5 showed decreased number of peripheral blood Treg and elevated levels of interleukin (IL)-6 and IL-17F (P＜0.05). The area under the receiver operating characteristic curve for number of peripheral blood Treg in predicting AAC in CKD patients was 0.766 (95%CI 0.652–0.879, P＝0.002). Logistic regression analysis showed that decreased number of Treg was related factor for AAC in CKD patients (OR＝0.957, 95%CI 0.922–0.992, P＝0.018). Conclusion As CKD progresses, number of peripheral blood Treg significantly decreases, which is correlated with AAC in CKD patients.

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.

Subclinical atherosclerosis underlies most cardiovascular diseases, manifesting before clinical symptoms and representing a key focus for early prevention strategies. Recent advancements highlight the importance of early detection and management of subclinical atherosclerosis. This review underscores that traditional risk factor levels considered safe, such as low-density lipoprotein cholesterol (LDL-C) and glycated haemoglobin (HbA1c), may still permit the development of atherosclerosis, suggesting a need for stricter thresholds. Early-life interventions are crucial, leveraging the brain's neuroplasticity to establish lifelong healthy habits. Preventive strategies should include more aggressive management of LDL-C and HbA1c from youth and persist into old age, supported by public health policies that promote healthy environments. Emphasising early education on cardiovascular health can fundamentally shift the trajectory of cardiovascular disease prevention and optimise long-term health outcomes.
Humans ; Atherosclerosis/diagnosis* ; Risk Factors ; Cardiovascular Diseases/prevention & control* ; Cholesterol, LDL/blood* ; Glycated Hemoglobin ; Cardiology/trends* ; Heart Disease Risk Factors

Objective To observe the effect of tuina on glutamate content and synaptic ultrastructure in spinal dorsal horn of rats with chronic sciatic nerve compression injury;To explore the potential mechanism of tuina regulation of the SNAP25/VGLUT2 pathway in alleviating lumbar disc herniation.Methods A chronic sciatic nerve compression injury model was used to simulate neuropathic pain in lumbar disc herniation.24 SD rats were randomly divided into blank group,model group and tuina group,with 8 rats in each group.From the 4th day after modeling,the tuina group was intervened with the tuina method for 10 minutes once a day for 14 consecutive days.The paw withdrawal threshold(PWT)and paw withdrawal latency(PWL)of rats in each group on the day before modeling,and the 4th,10th,14th and 17th days after modeling were detected.The spinal cord tissue of the modeling side was taken,synaptic ultrastructure of spinal dorsal horn neurons was observed using transmission electron microscopy,immunofluorescence staining was used to detect the expression of NR2A in the spinal dorsal horn,Western blot was used to detect the expression of SNAP25 protein in the spinal dorsal horn,immunohistochemistry was used to detect the expression of VGLUT2 in the spinal dorsal horn,ELISA was used to detect the content of glutamate in the spinal dorsal horn.Results Compared with the blank group,PWT and PWL of the model group were significantly reduced on the 4th,10th,14th and 17th days after modeling(P<0.001),with accumulation of vesicles in the presynaptic membrane of the dorsal horn of the spinal cord,increase in the area of the postsynaptic dense zone,and enlargement of the synaptic cleft,while the protein expressions of NR2A,SNAP25 and VGLUT2 in the spinal dorsal horn increased(P<0.05,P<0.001),and the content of glutamate increased(P<0.001).Compared with the model group,PWT and PWL of the tuina group rats significantly increased on the 10th,14th and 17th days after modeling(P<0.001),synaptic vesicles were evenly distributed,the area of the postsynaptic dense zone decreased,and the synaptic cleft decreased,while the protein expressions of NR2A,SNAP25 and VGLUT2 in the spinal dorsal horn decreased(P<0.05,P<0.001),and the content of glutamate decreased(P<0.01).Conclusion Tuina may regulate the content of glutamate through the SNAP25/VGLUT2 pathway in the spinal dorsal horn,improve the synaptic ultrastructure of neurons,and have an analgesic effect on lumbar disc herniation.

Objective To explore the changes of durability properties of reusable surgical gowns when used in dry and wet conditions.Methods Reusable surgical gowns made of single-layer polyester fiber or 3-layer composite material were selected as test samples,and a Martindale abrasion and pilling tester was used as the basic test platform and modified to form fixtures suitable for the wet state environment.The reusable surgical gowns underwent abrasion experiments in wet and dry conditions to observe the changes in their fiber structure,and were subjected to water penetration resistance and swelling strength tests.Results Visually the reusable surgical gowns had few changes of the microscopic textile fiber structure in dry and wet conditions,and the gowns made of single-layer polyster fiber gained advantages over the outer layers of those of 3-layer composite material in abrasion resistance with the same friction cycles.In dry and wet conditions,the hydrostatic pressure values of the gowns of single-layer polyster fiber gradually decreased with the increase of the degree of abrasion,which were always lower than those of the gowns of 3-layer composite material;the swelling strength of the gowns of single-layer polyster fiber was always greater than that of the gowns of 3-layer composite material,which decreased with the deterioration of the wear more significantly than that of the gowns of 3-layer composite material.Conclusion The reusable surgical gowns made of single-layer polyester fiber or 3-layer composite material have few differences in durability and protective properties at the early stages of ablation in dry and wet conditions.The durability of the gowns decreases as the degree of wear increases,while the trend of the decrease is slowing down until the fabric breaks down and completely loses its barrier effect.[Chinese Medical Equipment Journal,2025,46(6):28-33]

OBJECTIVE To assess the current status of hospital-acquired infections and their economic burden in or-thopedic patients based on diagnosis-related groups(DRG).METHOD Based on the National Health Insurance dis-ease diagnosis-related groups,32 413 orthopedic patients from a tertiary care hospital in Beijing in 2021 were grouped,hospital-acquired infections were retrospectively analyzed,and the direct and indirect economic burdens of different DRG groups were assess using indictors such as hospitalization time and cost,bed turnover loss,and labor time loss.RESULTS A total of 32 413 patients were included,the incidence of hospital-acquired infection was 0.47％(153/32 413),the site of infection was predominantly the surgical site(57.99％),and hospital-acquired infections in the hematologic system had a greater impact on cost-consumption indices and time-consumption indi-ces.The infection cases were concentrated in 19.58％of the DRGs groups.The IF23 group(lower limb bone sur-gery with complications and comorbidities)had the highest direct economic burden(24 010 yuan/case)due to hos-pital-acquired infections,and the increase in the cost of consumables and medication was the main factor causing the direct economic burden.At both the hospital level and family-society level,the top three DRG groups in terms of indirect economic burden due to hospital-acquired infections were IB15,IB13 and IF23.CONCLUSION Hospital-acquired infections in orthopedic patients have a tendency to be concentrated,quantitatively assessment of their e-conomic burden based on DRGs not only illustrates the importance of hospital-acquired infection prevention and control,but also accurately identifies the disease groups that require focused management,providing an evidence-based basis for precise prevention and control of hospital-acquired infections.

Objective To investigate the risk factors for acute respiratory failure in immunocompromised patients with Pneumocystis jirovecii pneumonia(PJP).Methods Clinical data of 123 immunocompromised patients complicated with PJP hospitalized at Mengchao Hepatobiliary Hospital of Fujian Medical University from January 2021 to December 2023 were retrospectively collected and analyzed.SPSS 22.0 statistical software package was used to perform multivariate binary logistic regression analysis to identify risk factors for acute respiratory failure in PJP patients.Results Among the 123 PJP patients,77 were HIV-positive,and 46 were HIV-negative.HIV-negative PJP patients were more likely to have comorbidities such as hypertension(P＜0.001),diabetes mellitus(P＜0.001),coronary heart disease(P=0.034),chronic kidney disease(P＜0.001),chronic liver disease(P=0.019),chronic lung disease(P=0.011),and malignant tumor(P＜0.001).They were also more prone to respiratory failure(P＜0.001)and ICU admission(P＜0.001).The HIV-positive patients had significantly lower CD4+T lymphocyte counts and albumin levels(P＜0.001).Forty patients developed acute respiratory failure,and six patients died.Multivariate analysis showed that high neutrophil-to-lymphocyte ratio(NLR)(P=0.031),non-HIV infection(P=0.002),and concomitant infections with other pathogens(P＜0.001)were independent risk factors for incidence of respiratory failure.ROC curve analysis revealed that the area under the curve(AUC)was 0.686(0.584,0.789)for non-HIV infection,0.731(0.637,0.826)for concomitant infections with other pathogens,0.648(0.546,0.750)for NLR.The predicted probability was 0.845(0.778,0.912).Conclusions Non-HIV infection,high NLR,and concomitant infections with other pathogens are independent risk factors for incidence of respiratory failure in PJP patients.The panel combining these factors provides a higher predictive value for respiratory failure.Timely assessment of patient condition and early treatment are vital for better outcomes.

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.