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.

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.

OBJECTIVE: To explore the efficacy and safety of Juan Bi Pill (JBP) in treatment of active rheumatoid arthritis (RA). METHODS: From February 2017 to May 2018, 115 participants from 4 centers were randomly divided into JBP group (57 cases) and placebo group (58 cases) in a 1:1 ratio using a random number table method. Participants received a dose of JBP (4 g, twice a day, orally) combined with methotrexate (MTX, 10 mg per week) or placebo (4 g, twice a day, orally) combined with MTX for 12 weeks. Participants were required with follow-up visits at 24 and 48 weeks, attending 7 assessment visits. Participants were undergo disease activity assessment 7 times (at baseline and 2, 4, 8, 12, 24, 48 weeks) and safety assessments 6 times (at baseline and 4, 8, 12, 24, 48 weeks). The primary endpoint was 28-joint Disease Activity Score (DAS28-ESR and DAS28-CRP). The secondary endpoints included American College of Rheumatology (ACR) criteria for 20% and 50% improvement (ACR20/50), Health Assessment Questionnaire Disability Index (HAQ-DI), clinical disease activity index (CDAI), visual analog scale (VAS), Short Form-36 (SF-36) score, Medial Outcomes Study (MOS) sleep scale score, serum erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), tender joint count, swollen joint count, and morning stiffness. The adverse reactions were observed during the treatment. RESULTS: After 12 weeks of treatment, DAS28-ESR and DAS28-CRP scores in both groups were lower than before treatment (both P<0.01), while the remission rate of DAS28-ESR and DAS28-CRP and low disease activity of JBP group were higher than those in the placebo group (both P<0.01). JBP demonstrated better efficacy on ACR20 and ACR50 compliance rate at 12 and 48 weeks comparing to placebo (all P<0.05). The CDAI and HAQ-DI score, pain VAS and global VAS change of RA patients and physicians, the serum ESR and CRP levels, and the number of tenderness and swelling joints were lower than before treatment at 4, 8, 12, 24, 48 weeks in both groups (P<0.05 or P<0.01), while the reduction of above indices in the JBP group was more obvious than those in the placebo group at 12 weeks (ESR and CRP, both P<0.05) or at 12 and 48 weeks (all P<0.01). There was no difference in adverse reactions between the 2 groups during treatment (P=0.75). CONCLUSION JBP combined with MTX could effectively reduce disease activity in patients with RA in active stage, reduce the symptoms of arthritis, and improve the quality of life, while ensuring safety, reliability, and fewer adverse effects. (Trial Registration: ClinicalTrials.gov, No. NCT02885597).
Humans ; Arthritis, Rheumatoid/drug therapy* ; Methotrexate/adverse effects* ; Female ; Double-Blind Method ; Male ; Middle Aged ; Treatment Outcome ; Drugs, Chinese Herbal/adverse effects* ; Drug Therapy, Combination ; Adult ; Antirheumatic Agents/adverse effects* ; Aged

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.

Non-functioning pituitary adenomas(NFPAs)are relatively common.Apart from hyperprolactinemia caused by pituitary compression,they typically lack overt hormonal hypersecretion and usually present with clinical symptoms due to mass effects.Previously considered a uniform entity,NFPAs are actually a highly heterogeneous group of tumors,including aggressive subtypes like silent corticotroph adenomas(SCA)and null cell adenomas.The 2022 WHO new classification of pituitary tumors employs transcription factors[e.g.,pituitary-specific transcription factor 1(PIT-1),T-box transcription factor 19(TBX19,also known as TPIT),steroidogenic factor 1(SF-1)]for detailed categorization,allowing precise subclassification of NFPAs into multiple subtypes derived from distinct cell lineages,including silent gonadotroph adenomas,SCA,and plurihormonal PIT-1-positive adenomas.This helps identify highly invasive subtypes with high recurrence risk,guiding clinical diagnosis and treatment,prognostic assessment,and individualized management.The new classification also provides a theoretical basis for targeted therapies of NFPAs(e.g.,somatostatin analogs and temozolomide).This review comprehensively discusses the latest pathological classification of NFPAs and its clinical implications,aiming to enhance understanding of this disease and offer valuable insights for precise diagnosis,treatment,and prognostic assessment.

[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.

Postoperative infection of internal fixation of closed fractures the lower limbs in adults represents a devastating complication, characterized by diagnostic challenges, prolonged treatment duration and high disability rates. Current management of these infections faces multiple challenges, such as difficulties in early accurate diagnosis, and various controversies about the treatment plan, leading to poor overall diagnosis and treatment results. To address these issues, based on evidence-based medicine and principles with emphasis on scientific rigor, clinical applicability and innovation, the Trauma Branch of the Chinese Medical Association, Orthopedic Branch of the Chinese Medical Doctor Association, Orthopedics Branch of the Chinese Medical Association, and Trauma Orthopedics and Polytrauma Group of the Resuscitation and Emergency Committee of the Chinese Medical Doctor Association have collaboratively organized a panel of relevant experts to develop the Guideline for diagnosis and treatment of infection after internal fixation of closed lower limb fractures in adults ( version 2025). The guideline proposed 10 recommendations, aiming to provide a foundation for standardized diagnosis and treatment of postoperative infection in adults with closed lower limb fractures.

BACKGROUND: Disease-modifying therapies have been approved for the treatment of relapsing multiple sclerosis (RMS). The present study aims to examine the safety of teriflunomide in Chinese patients with RMS. METHODS: This non-randomized, multi-center, 24-week, prospective study enrolled RMS patients with variant (c.421C>A) or wild type ABCG2 who received once-daily oral teriflunomide 14 mg. The primary endpoint was the relationship between ABCG2 polymorphisms and teriflunomide exposure over 24 weeks. Safety was assessed over the 24-week treatment with teriflunomide. RESULTS: Eighty-two patients were assigned to variant ( n  = 42) and wild type groups ( n  = 40), respectively. Geometric mean and geometric standard deviation (SD) of pre-dose concentration (variant, 54.9 [38.0] μg/mL; wild type, 49.1 [32.0] μg/mL) and area under plasma concentration-time curve over a dosing interval (AUC tau ) (variant, 1731.3 [769.0] μg∙h/mL; wild type, 1564.5 [1053.0] μg∙h/mL) values at steady state were approximately similar between the two groups. Safety profile was similar and well tolerated across variant and wild type groups in terms of rates of treatment emergent adverse events (TEAE), treatment-related TEAE, grade ≥3 TEAE, and serious adverse events (AEs). No new specific safety concerns or deaths were reported in the study. CONCLUSION: ABCG2 polymorphisms did not affect the steady-state exposure of teriflunomide, suggesting a similar efficacy and safety profile between variant and wild type RMS patients. REGISTRATION NCT04410965, https://clinicaltrials.gov .
Humans ; Crotonates/adverse effects* ; Toluidines/adverse effects* ; Nitriles ; Hydroxybutyrates ; Female ; Male ; Adult ; ATP Binding Cassette Transporter, Subfamily G, Member 2/genetics* ; Middle Aged ; Multiple Sclerosis, Relapsing-Remitting/genetics* ; Prospective Studies ; Young Adult ; Neoplasm Proteins/genetics* ; East Asian People