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.

To investigate the role of the DPP4-nestin axis in liver fibrosis induced by alveolar cyst infection,a murine model was established using C57BL/6 mice via hepatic portal vein injection.Liver histopathological changes were assessed using HE staining,while immunohistochemistry and immunofluorescence were employed to evaluate the expression levels of nestin and DPP4 in infected mouse livers.In vitro,J S1 cell line was stimulated with recombinant DPP4 protein to es-tablish a cellular model,and qPCR,Western blot,and shRNA lentivirus interference techniques were utilized to examine the involvement of the DPP4-nestin axis in hepatic stellate cell activation.The findings demonstrated that compared to the Sham group,liver tissue structure disruption and collagen deposition were evident along with significantly increased expressions of nestin and DPP4(P＜0.050 0),which colocalized with nesin and α-SMA.Furthermore,stimulation with recombi-nant DPP4 protein significantly enhanced JS1 cell activation(P＜0.050 0)as well as upregulated nestin expression(P＜0.050 0)when compared to control group cells.Notably,shRNA lentivirus-mediated inhibition of nestin expression effectively suppressed the activating effects exerted by re-combinant DPP4 protein on JS1 cells(P＜0.050 0).Collectively,these results highlight the crucial regulatory role played by the DPP4-nestin axis in hepatic stellate cell activation triggered by alveo-lar infection;thus,targeting this axis may represent a novel therapeutic strategy for treating alveo-lar infection-induced liver fibrosis.

Objective To investigate the risk factors of hemodynamic instability after carotid artery stenting by meta-analysis.Methods Ten databases were searched:PubMed,ProQuest,ScienceDirect,Embase,Cochrane Library,Web of Science,China Knowledge Network,Wanfang Data,VIP Information Database,and China Biomedical Database.The search date was from inception until 2 February 2024,and meta-analysis was performed using Stata 16.0 statistical software.Results A total of 27 studies with 4199 subjects and 22 influencing factors were included.The studies showed a 37.4％(95％CI 30.3％-44.8％)incidence of haemodynamic instability after carotid stenting,Meta-analysis determined that age＞60 years(P＜0.001),hypertension(P＜0.001),calcified plaque(P＜0.001),stenosis＞70％(P=0.008),eccentric plaque(P=0.002),distance from the largest stenosis to the carotid bifurcation≤ 10 mm(P＜0.001),stenosis involvement of the balloon or bifurcation(P＜0.001),balloon post-dilation(P=0.003),open-loop stenting(P＜0.001),dilated balloon diameter≥5 mm(P=0.002),repeat balloon dilation(P=0.011)and balloon dilation pressure≥8 atm(P＜0.001)are risk factors for intraoperative and postoperative haemodynamic instability in patients undergoing carotid artery stenting surgery.Statin use was a protective factor(P＜0.001).Conclusions Medical staff working in the clinic should assess the patient's condition preoperatively,identify risk factors that may lead to haemodynamic instability,and avoid unnecessary intraoperative stimulation of patients who are already in a high-risk state.Reduce postoperative clinical complications in patients with carotid artery stenosis and improve patient recovery.

Objective To investigate the risk factors of hemodynamic instability after carotid artery stenting by meta-analysis.Methods Ten databases were searched:PubMed,ProQuest,ScienceDirect,Embase,Cochrane Library,Web of Science,China Knowledge Network,Wanfang Data,VIP Information Database,and China Biomedical Database.The search date was from inception until 2 February 2024,and meta-analysis was performed using Stata 16.0 statistical software.Results A total of 27 studies with 4199 subjects and 22 influencing factors were included.The studies showed a 37.4％(95％CI 30.3％-44.8％)incidence of haemodynamic instability after carotid stenting,Meta-analysis determined that age＞60 years(P＜0.001),hypertension(P＜0.001),calcified plaque(P＜0.001),stenosis＞70％(P=0.008),eccentric plaque(P=0.002),distance from the largest stenosis to the carotid bifurcation≤ 10 mm(P＜0.001),stenosis involvement of the balloon or bifurcation(P＜0.001),balloon post-dilation(P=0.003),open-loop stenting(P＜0.001),dilated balloon diameter≥5 mm(P=0.002),repeat balloon dilation(P=0.011)and balloon dilation pressure≥8 atm(P＜0.001)are risk factors for intraoperative and postoperative haemodynamic instability in patients undergoing carotid artery stenting surgery.Statin use was a protective factor(P＜0.001).Conclusions Medical staff working in the clinic should assess the patient's condition preoperatively,identify risk factors that may lead to haemodynamic instability,and avoid unnecessary intraoperative stimulation of patients who are already in a high-risk state.Reduce postoperative clinical complications in patients with carotid artery stenosis and improve patient recovery.

To investigate the role of the DPP4-nestin axis in liver fibrosis induced by alveolar cyst infection,a murine model was established using C57BL/6 mice via hepatic portal vein injection.Liver histopathological changes were assessed using HE staining,while immunohistochemistry and immunofluorescence were employed to evaluate the expression levels of nestin and DPP4 in infected mouse livers.In vitro,J S1 cell line was stimulated with recombinant DPP4 protein to es-tablish a cellular model,and qPCR,Western blot,and shRNA lentivirus interference techniques were utilized to examine the involvement of the DPP4-nestin axis in hepatic stellate cell activation.The findings demonstrated that compared to the Sham group,liver tissue structure disruption and collagen deposition were evident along with significantly increased expressions of nestin and DPP4(P＜0.050 0),which colocalized with nesin and α-SMA.Furthermore,stimulation with recombi-nant DPP4 protein significantly enhanced JS1 cell activation(P＜0.050 0)as well as upregulated nestin expression(P＜0.050 0)when compared to control group cells.Notably,shRNA lentivirus-mediated inhibition of nestin expression effectively suppressed the activating effects exerted by re-combinant DPP4 protein on JS1 cells(P＜0.050 0).Collectively,these results highlight the crucial regulatory role played by the DPP4-nestin axis in hepatic stellate cell activation triggered by alveo-lar infection;thus,targeting this axis may represent a novel therapeutic strategy for treating alveo-lar infection-induced liver fibrosis.

As artificial intelligence technology rapidly advances, its deployment within the medical sector presents substantial ethical challenges. Consequently, it becomes crucial to create a standardized, transparent, and secure framework for processing medical data. This includes setting the ethical boundaries for medical artificial intelligence and safeguarding both patient rights and data integrity. This consensus governs every facet of medical data handling through artificial intelligence, encompassing data gathering, processing, storage, transmission, utilization, and sharing. Its purpose is to ensure the management of medical data adheres to ethical standards and legal requirements, while safeguarding patient privacy and data security. Concurrently, the principles of compliance with the law, patient privacy respect, patient interest protection, and safety and reliability are underscored. Key issues such as informed consent, data usage, intellectual property protection, conflict of interest, and benefit sharing are examined in depth. The enactment of this expert consensus is intended to foster the profound integration and sustainable advancement of artificial intelligence within the medical domain, while simultaneously ensuring that artificial intelligence adheres strictly to the relevant ethical norms and legal frameworks during the processing of medical data.
Artificial Intelligence/legislation & jurisprudence* ; Humans ; Consensus ; Computer Security/standards* ; Confidentiality/ethics* ; Informed Consent/ethics*