Objective To analyze the characteristics of viral hepatitis mortality and life loss among residents in Pudong New Area from 2003 to 2023, and to provide a basis for related prevention and control work. Methods Viral hepatitis mortality data were obtained from the Pudong New Area mortality monitoring system. The crude mortality rate (CMR), standardized mortality rate (SMR), potential years of life lost (PYLL), average years of life lost (AYLL), and standardized potential years of life lost (SPYLL) were calculated to analyze viral hepatitis deaths. The average annual change (AAPC) and annual percentage change (APC) of the mortality rate were calculated by Joinpoint regression analysis to analyze the trend of mortality. Results The CMR and SMR of viral hepatitis among residents in Pudong New Area from 2003 to 2023 were 3.89/100000 and 1.98/100000, respectively. Both CMR and SMR of viral hepatitis showed a decreasing trend over time (CMR:APC=-5.476, t=-13.581, P<0.001; SMR:APC=- 7.624, t= -21.253, P<0.001). The CMR for males was 4.75/100000 and the SMR for males was 2.65/100000; the CMR for females was 3.04/100000 and the SMR for females was 1.32/100000, with a higher mortality rate for males than for females（Z_CME=12.094，P<0.001; Z_SMR=-14.718，P<0.001). Deaths were concentrated in the age groups of 45-64 years old and 65 years old and above, accounting for 91.62% of the total deaths. The PYLL of deaths due to viral hepatitis among residents in Pudong New Area from 2003 to 2023 was 26912 person-years, with a PYLLR of 0.45% and an AYLL of 8.88 years per person. Conclusion The mortality rate of viral hepatitis among the residents of Pudong New Area in 2003-2023 shows a decreasing trend over time. The mortality rate of males is higher than that of females, and the deaths of middle-aged and elderly people account for a large proportion of the total deaths. Chronic hepatitis B is the main cause of death.

ObjectiveThe widespread adoption of portable fundus cameras for primary care and community screening is hindered by limitations in current autofocus(AF) technologies. Image-based methods relying on sharpness evaluation require iterative searches, resulting in slow convergence, while projection-based techniques are susceptible to optical artifacts and calibration errors. To address these challenges, this study introduces a novel AF system based on direct wavefront sensing, designed to deliver simultaneous high speed, high precision, and operational robustness within the compact form factor essential for portable ophthalmic devices. MethodsOur approach fundamentally reimagines the AF process by directly measuring the ocular wavefront aberration. We developed a custom portable fundus camera integrating a miniaturized Shack-Hartmann wavefront sensor (SHWS) into the optical path. An 850 nm laser diode projects a point source onto the retina via oblique illumination to minimize corneal reflections. Light scattered from this spot carries the eye’s refractive error through the imaging optics and is directed to the SHWS, positioned at a plane optically conjugate to the primary color CMOS imaging sensor. A microlens array within the SHWS samples the incident wavefront, generating a pattern of focal spots on a CCD. Real-time centroid analysis of these spots provides a map of local wavefront slopes. These measurements are processed through a singular value decomposition (SVD) algorithm to fit a Zernike polynomial basis set, enabling real-time reconstruction of the wavefront phase. The defocus component (S) is extracted from the second-order Zernike coefficients, providing a direct, quantitative measure of the refractive error in diopters. This value serves as a precise error signal in a closed-loop control system, which commands a voice-coil actuated focusing lens to its null position in a single, deterministic step, eliminating the need for iterative search algorithms. ResultsComprehensive evaluation demonstrated the system’s high performance. Testing on a calibrated model eye (OEMI-7) established a highly linear relationship between the computed defocus S and the focusing lens position across a ±20 Diopter (D) compensation range, achievable within a 5 mm mechanical travel. The system achieved a focusing precision of 0.08 D, corresponding to an 18-fold improvement over a conventional projection spot-size method tested under identical conditions. The total focus acquisition time, encompassing wavefront measurement, computation, and lens actuation, averaged under 0.5 s. Clinical validation with 25 human volunteers (50 eyes, refractive range -15 D to +10 D) confirmed practical efficacy. The wavefront-sensing AF succeeded in 92% of attempts with a mean time of 0.5 s, substantially outperforming a projection-based benchmark which achieved only a 32% success rate with an average time of 4.25 s. The system provided instantaneous directional guidance and maintained stability during minor ocular movements. Objective assessment of image quality, via amplitude contrast of retinal vasculature, showed consistent and significant enhancement following AF correction across the entire tested diopter range. ConclusionThis work successfully implements and validates a direct wavefront-sensing autofocus paradigm for portable fundus cameras. By directly quantifying and compensating for the optical defocus aberration, this method bypasses the fundamental limitations of image-processing and projection-based techniques, enabling rapid, precise, and deterministic diopter compensation. The developed system delivers an exceptional combination of a wide operational range (±20 D), high accuracy (0.08 D), fast convergence (0.5 s), and a compact physical footprint. This technology provides a practical and high-performance focusing solution capable of enhancing the reliability, throughput, and diagnostic utility of portable retinal imaging in large-scale screening applications. Future efforts will be directed towards system cost optimization and performance adaptation for diverse ocular conditions.

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.

Background The crude mortality rate of sudden death has shown a steady upward trend. Pudong New Area, Shanghai, is a highly urbanized and densely populated district that is substantially affected by the urban heat island effect and has experienced frequent extreme temperature events in recent years. However, evidence on the association between ambient temperatures and sudden death among local residents remains limited. Objective To examine the association between daily mean temperature and sudden death in Pudong New Area, and to provide evidence for reducing temperature-related risks of sudden death. Methods Data on sudden death, meteorological factors (daily average temperature, daily average relative humidity, and daily average air pressure), and air pollutants [inhalable particulate matter (PM₁₀), sulfur dioxide (SO₂), and nitrogen dioxide (NO₂)] in Pudong New Area from 2016 to 2021 were collected. A distributed lag non-linear model was used to examine the associations of daily mean temperature with total sudden death and sudden death due to cardiovascular and cerebrovascular diseases, including lagged and cumulative effects (lag0, 0-3, 0-7, 0-14, and 0-21 d). Stratified analyses were performed by sex, age, and education level to identify vulnerable populations. The temperature attributable burden of sudden death was assessed by calculating attributable number (AN) and attribute fraction (AF). Results A total of 19458 sudden deaths were reported in Pudong New Area from 2016 to 2021, of which 11082 (56.95%) were due to cardiovascular and cerebrovascular diseases. Both low (P₅, 4.6 ℃) and high (P₉₅, 30.2 ℃) temperatures were associated with increased risks of sudden death. The cumulative risks associated with low temperature peaked at lag 0-21 d for both total sudden death and sudden death due to cardiovascular and cerebrovascular diseases. In contrast, the cumulative risks associated with high temperature peaked at lag 0-7 d for total sudden death and at lag 0-3 d for sudden death due to cardiovascular and cerebrovascular diseases. Stratified analyses showed that females, individuals aged ≥65 years, and those with primary school education or below were more sensitive to low temperature; whereas females, individuals aged <65 years, and those with secondary school education or above were more sensitive to high temperature. Using 21.5 ℃ as the minimum mortality temperature, the AN of total sudden deaths attributable to non-optimal temperature was 3791 (95%CI: 1853, 5587), with an AF of 20.41% (95%CI: 9.47%, 28.94%); the AN attributable to low temperature was 3358 (95%CI: 1087, 5062), with an AF of 17.26% (95%CI: 5.89%, 26.01%), whereas the AN attributable to high temperature was 225 (95%CI: −274, 655), with an AF of 2.03% (95%CI: −2.53%, 5.78%). For sudden deaths due to cardiovascular and cerebrovascular diseases, the AN attributable to non-optimal temperature was 2721 (95%CI: 1747, 5575), with an AF of 24.55% (95%CI: 9.59%, 28.65%); the AN attributable to low temperature was 2496 (95%CI: 988, 3562), with an AF of 22.53% (95%CI: 8.97%, 32.41%), whereas the AN attributable to high temperature was 614 (95%CI: −15, 1176), with an AF of 3.15% (95%CI: −0.08%, 6.02%). Conclusions Both low (P₅, 4.6 ℃) and high (P₉₅, 30.2 ℃) temperatures are associated with increased risks of sudden death in Pudong New Area, Shanghai. High temperature shows a relative short-term effect, whereas low temperature shows a stronger cumulative effect. Females, people ≥65 years, and individuals with primary school educational attainment or below are more vulnerable to low temperature, while females, people <65 years, and those with secondary school educational attainment or above are more vulnerable to high temperature. The attributable burden of low temperature is greater than that of high temperature. Targeted health protection measures should be strengthened for vulnerable populations during extreme temperature events.

Tumor immune homeostasis is a dynamic equilibrium state in which the body removes abnormal mutated cells in time to prevent tumor development without damaging other normal cells under the surveillance of the immune system. It is an important concept to understand the process of tumor development. Programmed cell death （PCD） is a kind of regulable cell death including various forms such as apoptosis， autophagy， pyroptosis， necrosis， and ferroptosis. It is regarded as an important way for the body to remove abnormal or mutated cells. In recent years， modern research has found that PCD has a bi-directional regulatory effect on carcinogenesis and tumor development. In the early stage of tumor formation， PCD can control tumor development in time by playing a specific immune clearance role， while in the later tumorigenic stage， PCD can promote the growth and development of tumor cells by forming a tumor-specific microenvironment， resulting in carcinogenic effects. Therefore， PCD is regarded as an important way to maintain tumor immune homeostasis. Based on the idea of ''supporting the vital Qi and cultivating the root'' by professors Yu Guiqing and Piao Bingkui， the team proposed the theory of ''regulating Qi and resolving toxins'' and applied it to clinical tumor prevention and treatment. Based on the theory of ''regulating Qi and resolving toxins''， the research summarized the current progress of modern medical research on mechanisms related to PCD to explore the role of PCD in the regulation of tumor immune homeostasis. The article believed that the harmonious state of Qi movement was the basic condition for normal PCD to maintain tumor immune homeostasis， while the disorder of Qi movement and the evolution of tumor toxicity were the core processes of abnormal PCD and disorder of tumor immunity homeostasis， which led to the escape and development of tumor cells. Therefore， under the guidance of ''regulating Qi and removing toxins''， the idea of full-cycle prevention and treatment of tumors was proposed summarily. In the early stage of tumor formation， the method of ''regulating Qi movement and strengthening vital Qi'' was applied to reestablish tumor immune homeostasis and to promote the elimination of abnormal cells. In the late tumorigenic stage， the method of ''resolving toxins and dispelling evils'' was applied to reverse the specific microenvironment of tumors and inhibit the development of tumor cells， with a view to providing new theoretical support for the prevention and treatment of tumors through traditional Chinese medicine.

Tumor immune homeostasis is a dynamic equilibrium state in which the body removes abnormal mutated cells in time to prevent tumor development without damaging other normal cells under the surveillance of the immune system. It is an important concept to understand the process of tumor development. Programmed cell death （PCD） is a kind of regulable cell death including various forms such as apoptosis， autophagy， pyroptosis， necrosis， and ferroptosis. It is regarded as an important way for the body to remove abnormal or mutated cells. In recent years， modern research has found that PCD has a bi-directional regulatory effect on carcinogenesis and tumor development. In the early stage of tumor formation， PCD can control tumor development in time by playing a specific immune clearance role， while in the later tumorigenic stage， PCD can promote the growth and development of tumor cells by forming a tumor-specific microenvironment， resulting in carcinogenic effects. Therefore， PCD is regarded as an important way to maintain tumor immune homeostasis. Based on the idea of ''supporting the vital Qi and cultivating the root'' by professors Yu Guiqing and Piao Bingkui， the team proposed the theory of ''regulating Qi and resolving toxins'' and applied it to clinical tumor prevention and treatment. Based on the theory of ''regulating Qi and resolving toxins''， the research summarized the current progress of modern medical research on mechanisms related to PCD to explore the role of PCD in the regulation of tumor immune homeostasis. The article believed that the harmonious state of Qi movement was the basic condition for normal PCD to maintain tumor immune homeostasis， while the disorder of Qi movement and the evolution of tumor toxicity were the core processes of abnormal PCD and disorder of tumor immunity homeostasis， which led to the escape and development of tumor cells. Therefore， under the guidance of ''regulating Qi and removing toxins''， the idea of full-cycle prevention and treatment of tumors was proposed summarily. In the early stage of tumor formation， the method of ''regulating Qi movement and strengthening vital Qi'' was applied to reestablish tumor immune homeostasis and to promote the elimination of abnormal cells. In the late tumorigenic stage， the method of ''resolving toxins and dispelling evils'' was applied to reverse the specific microenvironment of tumors and inhibit the development of tumor cells， with a view to providing new theoretical support for the prevention and treatment of tumors through traditional Chinese medicine.

ObjectiveThe study explores the influence of voluntary wheel running on the behavioral abnormalities and the activation state of the hypothalamic-pituitary-adrenal (HPA) axis in autism spectrum disorder (ASD) rats through gut microbiota. MethodsSD female rats were selected and administered either400 mg/kg of valproic acid (VPA) solution or an equivalent volume of saline via intraperitoneal injection on day 12.5 of pregnancy. The resulting offspring were divided into 2 groups: the ASD model group (PASD, n=35) and the normal control group (PCON, n=16). Behavioral assessments, including the three-chamber social test, open field test, and Morris water maze, were conducted on postnatal day 23. After behavioral testing, 8 rats from each group (PCON, PASD) were randomly selected for serum analysis using enzyme-linked immunosorbent assay (ELISA) to measure corticotropin-releasing hormone (CRH), adrenocorticotropic hormone (ACTH), and corticosterone (CORT) concentration, to evaluate the functional state of the HPA axis in rats. On postnatal day 28, the remaining 8 rats in the PCON group were designated as the control group (CON, n=8), and the remaining 27 rats in the PASD group were randomly divided into 4 groups: ASD non-intervention group (ASD, n=6), ASD exercise group (ASDE, n=8), ASD fecal microbiota transplantation group (FMT, n=8), and ASD sham fecal microbiota transplantation group (sFMT, n=5). The rats in the ASD group and the CON group were kept under standard conditions, while the rats in the ASDE group performed 6 weeks of voluntary wheel running intervention starting on postnatal day 28. The rats in the FMT group were gavaged daily from postnatal day 42 with 1 ml/100 g fresh fecal suspension from ASDE rats which had undergone exercise for 2 weeks, 5 d per week, continuing for 4 weeks. The sFMT group received an equivalent volume of saline. After the interventions were completed, behavioral assessments and HPA axis markers were measured for all groups. ResultsBefore the intervention, the ASD model group exhibited significantly reduced social ability, social novelty preference, spontaneous activity, and exploratory interest, as well as impaired spatial learning, memory, and navigation abilities compared to the normal control group (P<0.05). Serum concentration of corticotropin-releasing hormone (CRH), adrenocorticotropic hormone (ACTH), and corticosterone (CORT) in the PASD group were significantly higher than those in the PCON group (P<0.05). Following 6 weeks of voluntary wheel running, the ASDE group showed significant improvements in social ability, social novelty preference, spontaneous activity, exploratory interest, spatial learning, memory, and navigation skills compared to the ASD group (P<0.05), with a significant decrease in serum CORT concentration (P<0.05), and a downward trend in CRH and ACTH concentration. After 4 weeks of fecal microbiota transplantation in the exercise group, the FMT group showed marked improvements in social ability, social novelty preference, spontaneous activity, exploratory interest, as well as spatial learning, memory, and navigation abilities compared to both the ASD and sFMT groups (P<0.05). In addition, serum ACTH and CORT concentration were significantly reduced (P<0.05), and CRH concentration also showed a decreasing trend. ConclusionExercise may improve ASD-related behaviors by suppressing the activation of the HPA axis, with the gut microbiota likely playing a crucial role in this process.

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.

[Purpose]To analyze the trends in pancreatic cancer mortality and disease burden among residents in Shanghai Pudong New Area from 2002 to 2022,and to investigate the effects of age,period,and birth cohort on mortality risk.[Methods]Data on pancreatic cancer deaths among residents of Pudong New Area from 2002 to 2022 were collected through the Shanghai Population Cause of Death Registration System.The crude mortality rate,age-standardized mortality rate by Chinese standard population(ASMRC),potential years of life lost(PYLL),potential years of life lost rate(PYLLR),and average years of life lost(AYLL)were calculated.Joinpoint regression was applied to calculate the average annual percentage change(AAPC)for analyzing the changing trend of the mortality rate of pancreatic cancer.The age-period-cohort model was applied with R 4.4.1 to analyze the age,period,and cohort effects on the mortality risk of pancreatic cancer.[Results]The crude mortality rate of pancreatic cancer among residents in Pudong New Area increased from 10.42/105 in 2002 to 18.73/105 in 2022,showing a significant upward trend(AAPC=2.90％,P＜0.001);the ASMRC was generally stable(AAPC=-0.05％,P=0.775).The crude mortality rate of males(17.09/105)was higher than that of females(13.75/105),and both showed an upward trend(AAPC=3.05％and 2.75％respectively,both P＜0.001).After the age of 40,the mortality rate of pancreatic cancer increased significantly with the growth of age in both sexes.The PYLL was 31 347 person-years,showing an upward trend(AAPC=1.83％,P＜0.001),and the AYLL was 3.59 years,showing a downward trend(AAPC=-2.45％,P＜0.001).The age effect showed that the mortality risk of pan-creatic cancer was increased with age;the period effect showed that the mortality risk decreased from 2002 to 2016 and then increased;the cohort effect showed that the mortality risk increased with the advancement of the birth cohort.[Conclusion]From 2002 to 2022,the crude mortality rate of pancreatic cancer in Pudong New Area showed an upward trend,and the mortality rate of males was higher than that of females.The mortality risk of pancreatic cancer increases with age,and the later the birth year of the residents,the higher the mortality risk.Early screening should be strengthened for men and the elderly,environmental and lifestyle risk factors should be paid attention to in combination with the characteristics of cohort effect,and the prevention and control strategy for the whole population should be optimized.