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.

OBJECTIVE: This study aimed to explore the association between body mass index (BMI) and mortality based on the 10-year population-based multicenter prospective study. METHODS: A general population-based multicenter prospective study was conducted at four sites in rural China between 2013 and 2023. Multivariate Cox proportional hazards models and restricted cubic spline analyses were used to assess the association between BMI and mortality. Stratified analyses were performed based on the individual characteristics of the participants. RESULTS: Overall, 19,107 participants with a sum of 163,095 person-years were included and 1,910 participants died. The underweight (< 18.5 kg/m ²) presented an increase in all-cause mortality (adjusted hazards ratio [ aHR] = 2.00, 95% confidence interval [ CI]: 1.66-2.41), while overweight (≥ 24.0 to < 28.0 kg/m ²) and obesity (≥ 28.0 kg/m ²) presented a decrease with an aHR of 0.61 (95% CI: 0.52-0.73) and 0.51 (95% CI: 0.37-0.70), respectively. Overweight ( aHR = 0.76, 95% CI: 0.67-0.86) and mild obesity ( aHR = 0.72, 95% CI: 0.59-0.87) had a positive impact on mortality in people older than 60 years. All-cause mortality decreased rapidly until reaching a BMI of 25.7 kg/m ² ( aHR = 0.95, 95% CI: 0.92-0.98) and increased slightly above that value, indicating a U-shaped association. The beneficial impact of being overweight on mortality was robust in most subgroups and sensitivity analyses. CONCLUSION This study provides additional evidence that overweight and mild obesity may be inversely related to the risk of death in individuals older than 60 years. Therefore, it is essential to consider age differences when formulating health and weight management strategies.
Humans ; Body Mass Index ; China/epidemiology* ; Male ; Female ; Middle Aged ; Prospective Studies ; Rural Population/statistics & numerical data* ; Aged ; Follow-Up Studies ; Adult ; Mortality ; Cause of Death ; Obesity/mortality* ; Overweight/mortality*

Objective: To establish a machine learning-based precision blood donor recruitment model at the county level and assess its generalizability and applicability. Methods: A retrospective study was conducted using blood donation and SMS recruitment data from the Taicang Branch of the Suzhou Blood Center between 2019 and 2024. Multiple machine learning algorithms were employed, including extreme gradient boosting, support vector machine, k-nearest neighbor, logistic regression, decision tree, random forest, and multilayer perceptron. These were combined with techniques such as synthetic minority oversampling, undersampling, and cost-sensitive learning (using MFE and MSFE loss functions). Model parameters were optimized through grid search to identify the best-performing model. Results: In a prospective comparative study against conventional methods, the machine learning models increased the recruitment success rate among high-willingness donors by an average of 129.15%, and the recruitment efficiency per SMS improved by 125.02% compared with the traditional method. Under full-scale SMS sending, the recruitment rate per SMS increased by 42.61%, and SMS sending efficiency improved by 31.77%, significantly enhancing recruitment performance. Conclusion: This study represents the first application of a machine learning-based precision donor recruitment model at the county-level in China. The precise recruitment framework not only improves recruitment efficiency and reduces recruitment costs but also demonstrates strong scalability and generalizability. It provides a scientific and feasible intelligent pathway to ensure the safety and sustainability of the blood supply.

Objective:To prepare decellularized extracellular matrix (dECM)-gelatin methacryloyl (GelMA) composite hydrogels and to study their effects on hepatocyte proliferation.Methods:Hepatic dECM was prepared by elution, and GelMA hydrogel and 10%, 30% and 50% dECM-GelMA composite hydrogels were prepared by pepsin solubilization. The morphology of normal liver and dECM liver was observed by eyes and scanning electron microscopy using hematoxylin-eosin, Sirius red and periodate-Schiff staining, respectively. The internal structure of the dECM-GelMA composite hydrogels was observed by scanning electron microscopy, and the pore diameter was measured. Liver HL-7702 cells were co-cultured with GelMA hydrogel and 10%, 30% and 50% dECM-GelMA composite hydrogels, and the cell proliferation viability was determined by cell counting kit-8. The expression of proliferating cell nuclear antigen (PCNA), Wnt family protein 5a (Wnt5a), β-catenin, extracellular-regulated protein kinase 1/2 (ERK1/2) and phosphorylated ERK1/2 (p-ERK1/2) were detected by Western blotting. Comparisons were made using independent sample t-test or one-factor analysis of variance. Results:After decellularization, the hepatocyte morphology showed rounded depressions, and the extracellular matrix structure was intact. The GelMA hydrogel and 10%, 30% and 50% dECM-GelMA composite hydrogels showed inernally porous structures. The pore diameter increased from (3.06±1.35) μm in the GelMA hydrogel to (16.01±4.02) μm in the 50% dECM-GelMA composite hydrogel. On the 3rd, 5th and 7th day, the relative cell proliferation was higher in the 50% dECM-GelMA composite hydrogel group than that in the GelMA hydrogel group (1.89±0.04 vs 1.53±0.01, 9.36±0.04 vs 3.89±0.09, 7.15±0.27 vs 4.89±0.15, all P<0.05). The relative expression levels of PCNA, Wnt5a, β-catenin, and p-ERK1/2/ERK1/2 proteins in the 50% dECM-GelMA composite hydrogel group were higher than those in the GelMA hydrogel group (2.14±0.04 vs 1.00±0.03, 2.36±0.09 vs 1.00±0.08, 1.45±0.03 vs 1.00±0.04, 1.43±0.04 vs 1.00±0.01, all P<0.05). Conclusions:A dECM-GelMA composite hydrogel can be prepared, which may promote hepatocyte proliferation by upregulating the phosphorylation of ERK1/2 and activating Wnt/β-catenin signaling pathway.

Objective:To compare the morphological differences of psoas major muscles between patients with lumbar disc herniation (LDH) of lower limb pain and lumbocrural pain based on CT imaging data.Methods:Sixty patients with LDH admitted to Department of Neurosurgery, 900 th Hospital of PLA Joint Logistic Team from January 2012 to February 2023 were included. According to clinical symptoms, they were divided into lower limb pain group and lumbocrural pain group ( n=30). 3D CT images of the psoas major muscles in the 2 groups were reconstructed; the longest transverse axis perpendicular to the longitudinal axis of the psoas major muscle was chosen as the cross-sectional area, and the maximum psoas major muscle cross-sectional area was calculated; maximum psoas major muscle cross-sectional area index (PI max) was defined as ratio of maximum psoas major muscle cross-sectional area and L 5 vertebral cross-sectional area. PI max difference between lower limb pain group and lumbocrural pain group was compared; PI max difference among patients with different pain degrees (visual analog scale [VAS] scores) or pain courses was further compared in both lower limb pain group and lumbocrural pain group. Pearson correlation was used to analyze the correlations of PI max with pain degree and pain course in the 2 groups. Results:PI max in lower limb pain group was significantly larger than that in lumbocrural pain group (0.62±0.05 vs. 0.54±0.04, t=7.320, P<0.001). PI max in patients with severe pain from both lower limb pain group and lumbocrural pain group was significantly smaller than that in patients with moderate pain (0.61±0.05 vs. 0.65±0.04, t=2.422, P=0.022; 0.53±0.03 vs. 0.58±0.04, t=3.502, P=0.002). PI max in patients with short pain course from both lower limb pain group and lumbocrural pain group was significantly larger than that in patients with long pain course (0.64±0.05 vs. 0.59±0.04, t=2.570, P=0.016; 0.57±0.04 vs. 0.53±0.03, t=2.941, P=0.007). Pearson correlation showed that PI max was negatively correlated with pain degree and pain course in LDH patients from both groups ( P<0.05). Conclusion:Atrophy of psoas major muscles in LDH patients is aggravated with increased pain degree and pain course.

Alzheimer's disease (AD) is a progressive neurodegenerative disease associated with dysfunctions related to thinking, learning, and memory of the brain. AD has multiple pathological characteristics with complicated causes, constructing a suitable pathological model is crucial for the research of AD. Microfluidic chip technology integrates multiple functional units on a chip, which can realize microenvironmental control similar to the physiological environment. It is well applied in the construction of pathological model, early diagnosis as well as drug screening of AD. This paper focuses on the construction of AD microfluidic chips model from the perspective of cell type, culture formats and the chips structure as well as the research progress of microfluidic chips in AD application based on the pathological characteristics of AD, which will provide a reference for further elucidation of AD mechanism and drug development.

Humans ; Aged ; Lung Diseases ; Pneumonia/drug therapy* ; Adrenal Cortex Hormones/therapeutic use* ; Pulmonary Disease, Chronic Obstructive/drug therapy* ; Administration, Inhalation ; Community-Acquired Infections/drug therapy*

Biosensor analysis technology is a kind of technology with high specificity that can convert biological reactions into optical and electrical signals. In the development of drugs for Alzheimer's disease (AD), according to different disease hypotheses and targets, this technology plays an important role in confirming targets and screening active compounds. This paper briefly describes the pathogenesis of AD and the current situation of therapeutic drugs, introduces three biosensor analysis techniques commonly used in the discovery of AD drugs, such as surface plasmon resonance (SPR), biolayer interferometry (BLI) and fluorescence analysis technology, explains its basic principle and application progress, and summarizes their advantages and limitations respectively.

Objective To investigate the molecular mechanisms by which SOX7 regulates the SHP-2/Wnt/β-cate-nin/ROS pathway,affecting the proliferation,invasion,and migration of colorectal cancer cells.Methods Twenty nude mice with subcutaneously transplanted tumor models were randomly divided into four groups:SOX7 NC(n=5),SOX mimic(n=5),SOX7 NC+PHPS1(n=5),and SOX7 mimic+PHPS1(n=5)to observe tumor growth.Human colorectal cancer cell line SW480 cells were transfected via lipofection and divided into six groups:SOX7 NC,SOX7 mimic,SOX7 NC+H2 O2,SOX7 mimic+H2O2,SOX7 NC+PHPS1,and SOX7 mimic+PHPS1.The ex-pression of SHP-2/Wnt/β-catenin/ROS pathway-related proteins in SW480 cells of each group was detected by Western blot.The invasion and migration capabilities of SW480 cells were assessed through scratch and Transwell invasion assays,while cell proliferation was evaluated using CCK-8.Results In vivo experiments demonstrated that tumors in the SOX7 mimic group were significantly smaller than those in the SOX7 NC group(P＜0.01).Tumors treated with PHPS1 intervention exhibited a significant increase in volume.There was no statistical significance in the difference in tumor volume between the SOX7 mimic+PHPS1 group and the SOX7 NC+PHPS1 group.In vitro experiments revealed that SOX7 mimic inhibited the expression of Wnt,β-catenin,NOX2,NOX4,PI3K,P-PI3K,AKT,P-AKT proteins(P＜0.01),and promoted the expression of p-SHP-2 protein(P＜0.01).The addition of hydrogen peroxide and SHP-2 inhibitor reversed the effects of SOX7 on SW480 cells(P＜0.05),and significantly promoted the expression levels of Wnt,β-catenin,NOX2,NOX4,PI3K,P-PI3K,AKT,P-AKT proteins,with no sig-nificant difference,while significantly reducing the expression levels of SHP-2,p-SHP-2 proteins,with no significant difference.PHPS1 inhibited the expression of SHP-2,p-SHP-2 proteins(P＜0.05)and upregulated the expression of Wnt,β-catenin,NOX2,NOX4,PI3K,P-PI3K,AKT,P-AKT proteins(P＜0.05).Scratch,Transwell invasion and migration assays,and CCK-8 experiments indicated that SOX7 suppressed the migration,invasion,and proliferation of SW480 cells through oxidative stress and the SHP-2 pathway(P＜0.01),while H2O2 and PHPS1 intervention promoted the migration,invasion,and proliferation of SW480 cells(P＜0.05).Conclusion SOX7 can suppress the proliferation,invasion,and migration of colorectal cancer by targeting the SHP-2/Wnt/β-catenin/ROS pathway.