In the 1950s and 1960s,Japan's implementation of policies prioritizing economic develop-ment caused a lack of effective supervision over the discharge of industrial wastewater and exhaust gases,which led to the occurrence of the"Four Major Pollution Diseases",including Minamata disease,causing serious social and public health problems.To more effectively address public nuisances and pro-vide compensation to victims,the Japanese government gradually established an environmental damage compensation system with administrative relief characteristics since the 1970s.Through long-term prac-tice and system optimization,this system has evolved into a mature institutional framework with a clear division of labor and efficient collaboration.This paper systematically reviews the development process of Japan's environmental damage compensation system and deeply analyzes its legal frame-work and supporting policies,aiming to provide useful references for the construction and improve-ment of China's environmental damage compensation system.Meanwhile,through the case analysis of Minamata disease,the paper explores the specific mechanisms and effects in the compensation practices,further revealing the system's operational characteristics and implications,and providing a reference ba-sis for the construction of China's environmental governance legal system.

BACKGROUND: Medical informatics accumulated vast amounts of data for clinical diagnosis and treatment. However, limited access to follow-up data and the difficulty in integrating data across diverse platforms continue to pose significant barriers to clinical research progress. In response, our research team has embarked on the development of a specialized clinical research database for cardiology, thereby establishing a comprehensive digital platform that facilitates both clinical decision-making and research endeavors. METHODS: The database incorporated actual clinical data from patients who received treatment at the Cardiovascular Medicine Department of Chinese PLA General Hospital from 2012 to 2021. It included comprehensive data on patients' basic information, medical history, non-invasive imaging studies, laboratory test results, as well as peri-procedural information related to interventional surgeries, extracted from the Hospital Information System. Additionally, an innovative artificial intelligence (AI)-powered interactive follow-up system had been developed, ensuring that nearly all myocardial infarction patients received at least one post-discharge follow-up, thereby achieving comprehensive data management throughout the entire care continuum for high-risk patients. RESULTS: This database integrates extensive cross-sectional and longitudinal patient data, with a focus on higher-risk acute coronary syndrome patients. It achieves the integration of structured and unstructured clinical data, while innovatively incorporating AI and automatic speech recognition technologies to enhance data integration and workflow efficiency. It creates a comprehensive patient view, thereby improving diagnostic and follow-up quality, and provides high-quality data to support clinical research. Despite limitations in unstructured data standardization and biological sample integrity, the database's development is accompanied by ongoing optimization efforts. CONCLUSION The cardiovascular specialty clinical database is a comprehensive digital archive integrating clinical treatment and research, which facilitates the digital and intelligent transformation of clinical diagnosis and treatment processes. It supports clinical decision-making and offers data support and potential research directions for the specialized management of cardiovascular diseases.

To investigate the effects and mechanisms of the water extract from Sorbus tianschanica(STE) on asthmatic airway inflammation, the mice were randomly divided into six groups, including a control group, a model group, a positive drug dexamethasone group(2 mg·kg~(-1)), a low-dose STE group(1 g·kg~(-1)), a medium-dose STE group(2 g·kg~(-1)), and a high-dose STE group(4 g·kg~(-1)). Except for the control group, all groups were subjected to ovalbumin induction to establish an asthma mouse model. The anti-inflammatory effects of STE were evaluated by examining pathological changes in lung tissue and measuring the levels of interleukin(IL)-4 and IL-5 in bronchoalveolar lavage fluid(BALF). Transcriptomic and proteomic methods were further employed to analyze differentially expressed genes and proteins, as well as their associated signaling pathways in lung tissue. Subsequently, the expression changes of key genes were verified by reverse transcription-quantitative polymerase chain reaction(RT-qPCR), and immunohistochemistry and Western blot methods were used to explore the regulatory mechanisms of STE in the pathogenesis of asthma in mice. Molecular docking was performed by using AutoDock Vina software to evaluate the binding affinity of the main active components in STE with the target proteins, including phosphatidylinositol-3-kinase catalytic subunit α(PIK3CA), Toll-like receptor 4(TLR4), protein kinase B1(Akt1), and matrix metallopeptidase 9(MMP9). The results showed significant inflammatory cell infiltration and fibrous tissue proliferation in the lung tissue of mice in the model group. However, these pathological changes were markedly reduced following STE intervention. Compared with those of the control group, the expression levels of IL-4 and IL-5 in the BALF of the model group were significantly increased but notably decreased following STE intervention. Transcriptomic and proteomic analyses identified key genes and proteins associated with allergic asthma, including tumor necrosis factor(TNF), IL-6, TLR4, PIK3CA, and MMP9. RT-qPCR validation revealed that high-dose STE intervention significantly downregulated the expressions of PIK3CA, IL-6, Akt1, MMP9, IL-13, nuclear factor-kappa B(NF-κB), TNF, CXC motif chemokine ligand 1(CXCL1), and TLR4 mRNAs and significantly upregulated the expression of signal transducer and activator of transcription 1(STAT1) mRNA. Western blot and immunohistochemical analyses confirmed that STE significantly downregulated the expressions of MMP9, TLR4, PIK3CA, and phosphorylated protein kinase B(p-Akt) in lung tissue of asthmatic mice. Moreover, molecular docking demonstrated that kaempferol-3,7-diglucoside, isoquercitrin, quercetin-3-gentiobioside, and hyperoside in STE exhibited stable binding affinities with PIK3CA, TLR4, Akt1, and MMP9, suggesting that the active components may exert anti-inflammatory effects by targeting and modulating asthma-related signaling pathways. In summary, STE exerts anti-asthmatic effects by inhibiting the expressions of PIK3CA, MMP9, p-Akt, and TLR4 and regulating the TLR4/PI3K/Akt/MMP9 signaling pathway.
Animals ; Asthma/metabolism* ; Toll-Like Receptor 4/metabolism* ; Signal Transduction/drug effects* ; Mice ; Phosphatidylinositol 3-Kinases/metabolism* ; Proto-Oncogene Proteins c-akt/metabolism* ; Matrix Metalloproteinase 9/metabolism* ; Mice, Inbred BALB C ; Drugs, Chinese Herbal/administration & dosage* ; Female ; Humans ; Lung/immunology* ; Male

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

OBJECTIVES: To establish a rapid and nondestructive identification method for human body fluid stains and non-biological stains using three-dimensional fluorescence spectroscopy. METHODS: The collected three-dimensional fluorescence spectrum data of human saliva, 3% blood, coffee and Fanta^® stains were processed with dimensionality reduction. After wavelet transform, spectral denoising and feature extraction, the classification formula was established. The Fisher discriminant was used for spectrum matching and recognition to establish the analysis method to distinguish stain types. RESULTS: According to the results of data training and comparison, all the recognition accuracies of Fanta^®, coffee, saliva and blood were more than 91.39%. Among them, saliva reached 100% recognition accuracy. CONCLUSIONS Three-dimensional fluorescence spectroscopy is a potential method for rapid and nondestructive identification of biological and non-biological stains.
Humans ; Forensic Medicine/methods* ; Coloring Agents/analysis* ; Coffee ; Spectrometry, Fluorescence ; Body Fluids/chemistry*

Objective: To investigate the diagnostic efficiency and clinical application value of an artificial intelligence-assisted diagnosis model based on a three-dimensional convolutional neural network (3D CNN) on echocardiographic videos of patients with hypertensive heart disease, chronic renal failure (CRF) and hypothyroidism with cardiac involvement. Methods: This study is a retrospective study. The patients with hypertensive heart disease, CRF and hypothyroidism with cardiac involvement, who admitted in Henan Provincial People's Hospital from April 2019 to October 2021, were enrolled. Patients were divided into hypertension group, CRF group, and hypothyroidism group. Additionally, a simple random sampling method was used to select control healthy individuals, who underwent physical examination at the same period. The echocardiographic video data of enrolled participants were analyzed. The video data in each group was divided into a training set and an independent testing set in a ratio of 5 to 1. The temporal and spatial characteristics of videos were extracted using an inflated 3D convolutional network (I3D). The artificial intelligence assisted diagnosis model was trained and tested. There was no case overlapped between the training and validation sets. A model was established according to cases or videos based on video data from 3 different views (single apical four chamber (A4C) view, single parasternal left ventricular long-axis (PLAX) view and all views). The statistical analysis of diagnostic performance was completed to calculate sensitivity, specificity and area under the ROC curve (AUC). The time required for the artificial intelligence and ultrasound physicians to process cases was compared. Results: A total of 730 subjects aged (41.9±12.7) years were enrolled, including 362 males (49.6%), and 17 703 videos were collected. There were 212 cases in the hypertensive group, 210 cases in the CRF group, 105 cases in the hypothyroidism group, and 203 cases in the normal control group. The diagnostic performance of the model predicted by cases based on single PLAX view and all views data was excellent: (1) in the hypertensive group, the sensitivity, specificity and AUC of models based on all views data were 97%, 89% and 0.93, respectively, while those of models based on a single PLAX view were 94%, 95%, and 0.94, respectively; (2) in the CRF group, the sensitivity, specificity and AUC of models based on all views data were 97%, 95% and 0.96, respectively, while those of models based on a single PLAX view were 97%, 89%, and 0.93, respectively; (3) in the hypothyroidism group, the sensitivity, specificity and AUC of models based on all views data were 64%, 100% and 0.82, respectively, while those of models based on a single PLAX view were 82%, 89%, and 0.86, respectively. The time required for the 3D CNN model to measure and analyze the echocardiographic videos of each subject was significantly shorter than that for the ultrasound physicians ((23.96±6.65)s vs. (958.25±266.17)s, P<0.001). Conclusions: The artificial intelligence assisted diagnosis model based on 3D CNN can extract the dynamic temporal and spatial characteristics of echocardiographic videos jointly, and quickly and efficiently identify hypertensive heart disease and cardiac changes caused by CRF and hypothyroidism.
Male ; Humans ; Artificial Intelligence ; Retrospective Studies ; Echocardiography/methods* ; Heart Diseases ; Hypertension ; Hypothyroidism

Introduction: During the COVID-19 pandemic, healthcare providers have been in great fear due to the high risk of contracting COVID-19 infection at any time. This study aimed to determine the mediating role of resilience on the relationship between fear of COVID-19 and burnout in primary care healthcare providers in Malaysia. Methods: This was an online cross-sectional study involving 1280 healthcare providers aged 18 years and older from 30 government primary care clinics in Malaysia. We used the COVID-19 Fear Scale, the Copenhagen Burnout Inventory Scale, and the Short Brief Resilience Scale to collect data from the respondents. Smart-PLS was used to perform mediation analysis. Results: The mean age of the respondents was 36 years old and mean duration of working experience was 11 years. The majority of the respondents were female (82.4%) and Malays (82.3%). The study population consisted of nurses (47.4%), doctors (26%), medical assistants (11.9), healthcare assistant (7.1%), medical laboratory technicians (6.4%) and drivers(1.3).The results show that fear of COVID-19 positively predicts burnout. According to the results, resilience mediates the relationship between fear of COVID-19 and all the three burnout domains, namely personal burnout (β=0.175,p<0.001), work-related burnout (β=0.175,p<0.001) and client-related burnout (β=0.172,p<0.001). Additionally, resilience reduces the impact of COVID-19 fear on the three domains of burnout. Conclusion: Our study has reported a mediating effect of resilience on the relationship between fear of COVID-19 and burnout.

Objective:To study correlation between high density lipoprotein-cholesterol(HDL-C)and red cell dis-tribution width(RDW)in patients with coronary heart disease(CHD).Methods:According to coronary stenotic severity,a total of 151 patients undergoing coronary CT angiography in our hospital were divided into CHD group(n=72)and non-CHD group(n=79).General clinical data were compared between two groups.Correlation be-tween HDL-C and RDW,influencing factors of CHD and diagnostic value of HDL-C and RDW for CHD were analyzed.Results:Compared with non-CHD group,there were significant rise in percentage of diabetes mellitus,age,serum triglyceride(TG)level and RDW,and significant reduction in serum HDL-C level in CHD group(P＜0.05 or＜0.01).Pearson correlation analysis indicated that serum HDL-C level was significant inversely correla-ted with RDW(r=-0.373,P=0.001),and there was a significant negative correlation between two indexes in CHD group and non-CHD group(r=-0.415,-0.243,P=0.001,0.031).Multivariate Logistic regression a-nalysis indicated that TG and RDW were independent risk factors for CHD(OR= 1.794,1.588,P=0.030,0.020),and HDL-C was its independent protective factor(OR=0.212,P=0.036).ROC curve indicated that AUC of combined detection of HDL-C and RDW diagnosing CHD was 0.674,its sensitivity was 58.33%and spe-cificity was 70.89%.Conclusion:RDW possesses certain diagnostic value for patients with coronary heart disease.

OBJECTIVE: To evaluate the associations of lipid indicators and mortality in Beijing Elderly Comprehensive Health Cohort Study. METHODS: A prospective cohort was conducted based on Beijing Elderly Comprehensive Health Cohort Study with 4499 community older adults. After the baseline survey, the last follow-up was March 31, 2021 with an average 8.13 years of follow-up. Cox proportional hazard model was used to estimate the hazard ratios (HR) with 95% CI for cardiovascular disease (CVD) death and all-cause death in associations with baseline lipid indicators. RESULTS: A total of 4499 participants were recruited, and the mean levels of uric acid, body mass index, systolic blood pressure, diastolic blood pressure, fasting plasma glucose, total cholesterol (TC), triglyceride, and low-density lipoprotein cholesterol (LDL-C) showed an upward trend with the increasing remnant cholesterol (RC) quarters (P_trend < 0.05), while the downward trend was found in high-density lipoprotein cholesterol (HDL-C). During the total 36,596 person-years follow-up, the CVD mortality and all-cause mortality during an average 8.13 years of follow-up was 3.87% (95% CI: 3.30%-4.43%) and 14.83% (95% CI: 13.79%-15.86%) with 174 CVD death participants and 667 all-cause death participants. After adjusting for confounders, the higher level of TC (HR = 0.854, 95% CI: 0.730-0.997), LDL-C (HR = 0.817, 95% CI: 0.680-0.982) and HDL-C (HR = 0.443, 95% CI: 0.271-0.724) were associated with lower risk of CVD death, and the higher level of HDL-C (HR = 0.637, 95% CI: 0.501-0.810) were associated with lower risk of all-cause death. The higher level of RC (HR = 1.276, 95% CI: 1.010-1.613) increase the risk of CVD death. Compared with the normal lipid group, TC ≥ 6.20 mmol/L group and LDL-C ≥ 4.10 mmol/L group were no longer associated with lower risk of CVD death, while RC ≥ 0.80 mmol/L group was still associated with higher risk of CVD death. In normal lipid group, the higher levels of TC, LDL-C and HDL-C were related with lower CVD death. CONCLUSIONS In community older adults, higher levels of TC and HDL-C were associated with lower CVD mortality in normal lipid reference range. Higher RC was associated with higher CVD mortality, which may be a better lipid indicator for estimating the CVD death risk in older adults.