Objective To analyze the epidemiological characteristics and disease burden of liver cancer in Guangdong Province in 2020, and to provide a scientific foundation for the development of regionalized prevention and control strategies for liver cancer. Methods According to the cancer registry data of Guangdong Province, the incidence, mortality and age-standardized rate by Chinese standard population in 2020 were calculated to analyze the epidemiological characteristics of liver cancer. The disability adjusted life years (DALYs), year of life loss (YLL), year of lived with disability (YLD), and cause-eliminated life expectancy were used to assess the disease burden of liver cancer. Results In 2020, the crude incidence rate and the age-standardized incidence rate of liver cancer in Guangdong Province were 27.79/100 000 and 20.84/100 000，respectively, and the crude mortality rate and the age-standardized mortality rate of liver cancer were 25.49/100,000 and 17.64/100 000, respectively. The total DALY and DALY rate of liver cancer in Guangdong Province were 515 311 person-years and 513.83/100 000, respectively. After eliminating the causes of death from liver cancer, the life expectancy in Guangdong Province increased from 84.60 years to 84.99 years. All indicators consistently demonstrated that the burden of liver cancer was higher in males than that in females, and the burden of liver cancer was higher in rural areas than that in urban areas. Conclusion Liver cancer in Guangdong Province exhibits a high incidence, mortality and disease burden level in 2020. There are obvious differences of gender, age and region in cancer burden. It is necessary to strengthen liver cancer screening and diagnosis and treatment in men, the elderly and those in rural areas to reduce the burden of liver cancer gradually in Guangdong Province.

Objective: To evaluate the implementation process of a school-family collaborative "online+offline" tobacco control intervention program in junior high school in Beijing and to explore the execution status, influencing factors and sustainability potential of the intervention, so as to provide evidence for optimizing youth tobacco control strategies. Methods: In November 2024, using the random number table method, four first year junior high school classes were selected from three schools each in Fengtai District, Tongzhou District, and Fangshan District of Beijing. One class served as the control group, while the other three classes were designated as intervention groups (one each for online intervention, offline intervention, and combined online offline intervention). The control group received only conventional education.The online intervention group was engaged in WeChat push interventions, including watching micro videos, viewing promotional materials, participating in online quizzes and mini games; the offline intervention group attended knowledge lectures, played peer games, and participated in offline knowledge competitions; the combined online offline intervention group integrated all the aforementioned online and offline intervention measures. The intervention period was from November 2024 to June 2025, spanning a total of 7 months. Based on the Practical, Robust Implementation and Sustainability Model(PRISM) framework, a qualitative research design was employed to conduct semi structured interviews with 48 participants (12 in each of the intervention groups and 12 organizational staff members) from the Centers for Disease Control and Prevention (CDC) in 3 districts and 3 sampled schools. The interview outlines were designed according to the intervention plan. Data was managed using Nvivo 12.0 software and analyzed following Colaizzi s seven step phenomenological analysis method. Theoretical saturation was assessed using a reserved subset of transcripts. Results: Four core themes were identified in the tobacco control intervention process. Overall fidelity of intervention implementation was largely consistent with the original plan, and students showed strong willingness and positive evaluations toward interactive formats such as knowledge contests and peer games, though occasional breakdowns in school-family communication and blurred boundaries between online and offline components were observed; the participants showed a polarized response in terms of satisfaction and participation, most students and parents recognized the significance of the activity, and some parents observed a reduction in smoking behavior; the implementation of internal tobacco control policies in the school was strict, and the atmosphere was favorable, but there was still room for improvement, such as the scarcity of community tobacco control activities and the difficulty in implementing smoke free units; implementation and sustainability infrastructure were preliminarily established, such as through homeroom teacher supervision and training student assistants to assisted in activities, while the sustainability support system required further refinement. Conclusion The school-family collaborative "online+offline" tobacco control intervention has demonstrated significant positive effects, but further optimization of activity design, enhancement of community reward mechanisms, and standardized training are required to improve the efficacy and sustainability of the intervention.

BACKGROUND:Epidemiological studies indicate that individuals with neurodegenerative diseases exhibit a comparatively lower risk of developing the majority of cancers.Although the precise mechanisms underlying this inverse correlation remain unclear,it is noteworthy that aberrant glucose metabolism,a pathological factor common to both conditions,may significantly contribute to this association.OBJECTIVE:To review the potential relationship between cancers and neurodegenerative diseases in glucose metabolism.METHODS:PubMed was searched for relevant literature using the search terms of"cancer,neurodegenerative diseases,Alzheimer's disease,Parkinson's disease,metabolic reprogramming,glucose metabolism,aerobic glycolysis,neuroprotection,aging,"and 136 articles were finally included for analysis.RESULTS AND CONCLUSION:Cancer and neurodegenerative diseases exhibit a profound pathological correlation at the level of glucose metabolism imbalance associated with aging.Cancer cells promote uncontrolled proliferation,invasion,and metastasis through the persistent activation of aerobic glycolysis,whereas neurodegenerative diseases are characterized by a reduction in aerobic glycolysis.Restoring aerobic glycolysis may confer neuroprotective effects and delay disease progression.The key nodes of glucose metabolism demonstrate a bidirectional regulatory pattern:metabolic regulators,which are significantly upregulated or aberrantly activated in cancer,are inhibited or functionally inactivated in neurodegenerative diseases.Mitochondria play a crucial role in mediating the aging process through the regulation of reactive oxygen species homeostasis and mitochondrial autophagy.They establish regulatory networks that connect cancer and neurodegenerative diseases,and maintaining their functional homeostasis is of paramount importance for disease prevention and treatment.

BACKGROUND:Epidemiological studies indicate that individuals with neurodegenerative diseases exhibit a comparatively lower risk of developing the majority of cancers.Although the precise mechanisms underlying this inverse correlation remain unclear,it is noteworthy that aberrant glucose metabolism,a pathological factor common to both conditions,may significantly contribute to this association.OBJECTIVE:To review the potential relationship between cancers and neurodegenerative diseases in glucose metabolism.METHODS:PubMed was searched for relevant literature using the search terms of"cancer,neurodegenerative diseases,Alzheimer's disease,Parkinson's disease,metabolic reprogramming,glucose metabolism,aerobic glycolysis,neuroprotection,aging,"and 136 articles were finally included for analysis.RESULTS AND CONCLUSION:Cancer and neurodegenerative diseases exhibit a profound pathological correlation at the level of glucose metabolism imbalance associated with aging.Cancer cells promote uncontrolled proliferation,invasion,and metastasis through the persistent activation of aerobic glycolysis,whereas neurodegenerative diseases are characterized by a reduction in aerobic glycolysis.Restoring aerobic glycolysis may confer neuroprotective effects and delay disease progression.The key nodes of glucose metabolism demonstrate a bidirectional regulatory pattern:metabolic regulators,which are significantly upregulated or aberrantly activated in cancer,are inhibited or functionally inactivated in neurodegenerative diseases.Mitochondria play a crucial role in mediating the aging process through the regulation of reactive oxygen species homeostasis and mitochondrial autophagy.They establish regulatory networks that connect cancer and neurodegenerative diseases,and maintaining their functional homeostasis is of paramount importance for disease prevention and treatment.

In recent years， the incidence of depression among adolescents has increased significantly， becoming a serious global public health problem. There is a close relationship between adolescent depression and lipid metabolism abnormalities. However， the mechanisms underlying this association have not been fully elucidated. This article provides a review of the mechanisms and interventions linking adolescent depression and lipid metabolism abnormalities， aiming to offer new ideas for interventions in adolescent depression. Using computerized searches of the China National Knowledge Infrastructure （CNKI）， Wanfang Data， PubMed， and Web of Science， studies on the mechanisms and interventions related to adolescent depression and lipid metabolism were collected， with the search period extending up to October 10， 2025. Two researchers independently conducted literature screening and extracted basic information from the included studies. A total of 12 original studies were included. The results indicate that adolescent depression is associated with certain lipid metabolites. Neurobiological factors， immune inflammation， oxidative stress， and gut microbiota imbalance play important roles in this association. In addition， lipid metabolites such as omega-3 polyunsaturated fatty acids （ω-3 PUFAs） have a significant ameliorating effect on adolescent depression. ［Funded by Shenzhen Fund for Guangdong Provincial High-level Clinical Key Specialties （number， SZGSP013）； Shenzhen Science and Technology Program （number， 20210617155253001）］

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.

Objective To investigate the clinical prognostic value of the prognostic nutritional index (PNI) in patients with renal cell carcinoma (RCC) undergoing partial nephrectomy.Methods A retrospective analysis was conducted on the clinical data of RCC patients who underwent partial nephrectomy at our hospital during 2020 and 2024.The optimal cutoff value of PNI was determined based on the maximum Youden index from the receiver operating characteristic (ROC) curve.The clinicopathological characteristics, overall survival (OS), and progression-free survival (PFS) between patients with different PNI levels were compared.Cox regression analysis was used to explore the independent prognostic factors for RCC patients. Results Based on the ROC curve, the optimal cutoff value corresponding to the maximum Youden index was calculated to be 46.675, at which point the sensitivity was 65.3% and the specificity was 62.2%.Of the 815 patients included in the study, 293 (36.0%) patients had a PNI＜46.675.Cox regression analysis confirmed that preoperative PNI, tumor diameter, ISUP classification, and ECOG score were independent risk factors for postoperative OS in patients with RCC who underwent PN (P＜0.05).In addition, preoperative PNI, tumor diameter, ISUP classification and ECOG score were independent risk factors for postoperative PFS (P＜0.05).After covariates in the multivariable Cox regression model were adjusted, patients in the low PNI group demonstrated significantly shorter overall survival (HR=2.392, P＜0.001) and significantly poorer progression-free survival (HR=2.076, P＜0.001) compared to those in the high PNI group.Conclusion Preoperative PNI has significant clinical value in evaluating the prognosis of RCC patients undergoing partial nephrectomy.An elevated preoperative PNI is associated with a better prognosis.

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: To investigate the epidemiological characteristics of lung cancer in cancer registration areas of Guangdong Province in 2020, so as to provide the evidence for improving prevention and control strategies of lung cancer. Methods: Data of incidence and mortality in 2020 from 30 cancer registries in Guangdong Province were collected from the Cancer Follow-up Registration System and the All-Cause Mortality Registration Reporting System of the Guangdong Provincial Center for Disease Control and Prevention. The crude incidence, crude mortality, and cumulative rate for 0 to 74 years were calculated. The Chinese population-standardized rate and world population-standardized rate were calculated using the age structure of the standard population from the Fifth National Population Census in 2000 and Segi's world standard population. The incidence and mortality characteristics of lung cancer in different genders, urban/rural areas and ages were described. Results: In 2020, there were 25 357 new cases of lung cancer in Guangdong Province. The crude incidence, Chinese population-standardized incidence, world population-standardized incidence, and cumulative incidence for 0 to 74 years were 60.40/100 000, 43.75/100 000, 43.26/100 000, and 5.30%, respectively. There were 14 366 lung cancer deaths. The crude mortality, Chinese population-standardized mortality, world population-standardized mortality, and cumulative mortality for 0 to 74 years were 38.82/100 000, 24.49/100 000, 24.36/100 000, and 2.88%, respectively. The crude incidence and crude mortality of lung cancer in males were higher than those in females (71.19/100 000 vs. 49.42/100 000, 52.94/100 000 vs. 24.36/100 000, both P<0.05). The crude incidence and crude mortality of lung cancer in urban areas were higher than those in rural areas (66.37/100 000 vs. 45.95/100 000, 40.68/100 000 vs. 35.07/100 000, both P<0.05). The crude incidence and crude mortality of lung cancer exhibited upward trends with increasing age (both P<0.05), peaking in the age of 80-<85 years (347.97/100 000 and 342.14/100 000). Conclusions Comparing to the national data, the incidence of lung cancer in registration areas of Guangdong Province remained relatively high, while mortality remained relatively low. Males, urban residents and the elderly constitute the key populations for lung cancer prevention and control. It is recommend to optimize the allocation of medical resources between urban and rural areas and strengthen lung cancer screening among high-risk groups.