Chronic kidney disease(CKD)is a chronic disease characterized by renal structural damage and dysfunction.At present,there is still a lack of effective therapeutic drugs and prevention and treatment methods for CKD in clinical practice.More and more studies have shown that necroptosis,as a new type of programmed cell death,plays a vital role in the onset and progression of CKD.Targeting key molecules in the necroptosis pathway,such as RIPK1,RIPK3 and MLKL,the development of small molecule inhibitors has become an emerging strategy for the treatment of CKD,and has shown significant potential to pro-tect the kidneys and alleviate renal fibrosis in a variety of in vitro and in vivo models.Therefore,this article summarizes the re-search progress of the mechanism of necroptosis in recent years,and focuses on the potential role of necroptosis in the pathogene-sis of CKD and the therapeutic potential of targeting this path-way,providing a new perspective and research direction for the prevention and treatment of CKD in the future.

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.

Chronic kidney disease(CKD)is a chronic disease characterized by renal structural damage and dysfunction.At present,there is still a lack of effective therapeutic drugs and prevention and treatment methods for CKD in clinical practice.More and more studies have shown that necroptosis,as a new type of programmed cell death,plays a vital role in the onset and progression of CKD.Targeting key molecules in the necroptosis pathway,such as RIPK1,RIPK3 and MLKL,the development of small molecule inhibitors has become an emerging strategy for the treatment of CKD,and has shown significant potential to pro-tect the kidneys and alleviate renal fibrosis in a variety of in vitro and in vivo models.Therefore,this article summarizes the re-search progress of the mechanism of necroptosis in recent years,and focuses on the potential role of necroptosis in the pathogene-sis of CKD and the therapeutic potential of targeting this path-way,providing a new perspective and research direction for the prevention and treatment of CKD in the future.

Background::Hepatitis B poses a heavy burden for children in China, however, the national studies on the distributional characteristics and health care costs of children with severe hepatitis B is still lacking. This study aimed to analyze the disease characteristics, health economic effects, and medical cost for children with severe hepatitis B in China.Methods::Based on patient information in the Hospital Quality Monitoring System, cases with severe hepatitis B were divided into four groups according to age, and the etiology and symptoms of each group were quantified. The cost of hospitalization was calculated for cases with different disease processes, and severity of disease. The spatial aggregation of cases and the relationship with health economic factors were analyzed by Moran’s I analysis. Results::The total number of children discharged with hepatitis B from January 2016 to April 2022 was 1603, with an average age of 10.5 years. Liver failure cases accounted for 43.48% (697/1603) of total cases and cirrhosis cases accounted for 11.23% (180/1603). According to the grouping of disease progression, there were 1292 cases without associated complications, and the median hospitalization cost was $818.12. According to the spatial analysis, the aggregation of cases was statistically significant at the prefectural and provincial levels in 2019, 2020, and 2021 (all P <0.05). The number of severe cases was negatively correlated with gross domestic product (Moran’s I <0) and percentage of urban population (Moran’s I <0), and positively correlated with the number of pediatric beds per million population (Moran’s I >0). Conclusion::The number of severe hepatitis B cases is low in areas with high gross domestic product levels and high urban population ratios, and health care costs have been declining over the years.

Objective:To explore the predictive efficacy of the HFA-ICOS score for cancer therapy-related cardiac dysfunction (CTRCD) in Chinese patients with breast cancer and lymphoma.Methods:This study was a single-center retrospective cohort study which included patients with breast cancer and lymphoma who were treated with anthracyclines from February 2018 to February 2025 at the First Affiliated Hospital of Dalian Medical University. Patients were evaluated at baseline with cardiac biomarkers and echocardiography, including left ventricular ejection fraction and global longitudinal strain of the left ventricle. After anthracycline therapy, they were followed up at 1, 3, 6, and 12 months. Data involved biomarkers and echocardiography were collected to determine whether CTRCD had occurred. The patients were categorized into low-risk, intermediate-risk, high-risk, and very-high-risk groups using the HFA-ICOS scoring model. The cumulative probability of CTRCD under different HFA-ICOS risk stratification was analyzed using Kaplan-Meier survival curves. The effect of HFA-ICOS risk stratification on CTRCD was assessed using an univariate Cox proportional hazards regression model. The predictive efficacy of the HFA-ICOS model and its utility in clinical decision-making were assessed with receiver operating characteristic (ROC) curves, calibration curves, and decision curves at each time point.Results:A total of 286 patients, aged 55 (44, 61) years, were enrolled, of whom 33 (11.5%) cases were male. And 113 (39.5%) patients developed CTRCD during a median follow-up time of 111 (70, 210) days. HFA-ICOS risk stratification showed that 228 (79.7%) were low-risk, 49 (17.1%) were intermediate-risk, and a total of 9 (3.1%) were high-risk and very high-risk. The difference in the occurrence of CTRCD over time between patients with different HFA-ICOS risk stratification was statistically significant ( Plog-rank<0.001). Cox proportional regression hazards analysis showed an increased risk of CTRCD development in intermediate-risk ( HR=1.95, 95% CI 1.22-3.00, P=0.006) and high-risk and very high-risk patients ( HR=4.12, 95% CI 1.66-8.54, P=0.004) compared with low-risk patients. The ROC curves showed that the area under the curve of the HFA-ICOS model predicting CTRCD was 0.532, 0.597, 0.600 and 0.577 at 1, 3, 6 and 12 months, respectively. The calibration curves indicated Brier scores of 0.041 (95% CI 0.013-0.067), 0.144 (95% CI 0.115-0.173), 0.232 (95% CI 0.215-0.249) and 0.236 (95% CI 0.220-0.251) at 1, 3, 6 and 12 months, correspondingly. The clinical decision curve suggested that clinical intervention may have a net benefit when the risk threshold is between 0.15 and 0.18 at 1 month, between 0.10 and 0.50 at 3 months, and between 0.30 and 0.70 at 6 and 12 months. Conclusion:The HFA-ICOS score could predict the occurrence of CTRCD in patients with breast cancer and lymphoma treated with anthracycline drugs, although its predictive efficacy is limited, and the prediction model requires further validation in a larger population.

Aim To explore the effect of astragalosideⅣ(AS-Ⅳ)on lipopolysaccharide(LPS)-induced po-larization and migration of RAW 264.7 macrophages and the underlying mechanism.Methods 1 mg·L-1 LPS was used to construct cell migration model.Scratch assay was utilized to determine cell migration rate.Immunofluorescence staining was utilized to de-tect the expression and location of F4/80,iNOS and Arg-1.CCK-8 assay was used to determine the viabili-ty of RAW 264.7 cells.Griess assay was used to measure NO content.Molecular docking was used to analyze the interaction between AS-Ⅳ and the core tar-gets such as cGAS and STING protein.Western blot was employed to detect the expression of iNOS,Arg-1,cGAS,STING,NF-κB p65 and p-NF-κB p65 protein.Results AS-Ⅳ significantly inhibited the migration and M1 polarization of RAW 264.7 cells induced by LPS.Moreover,AS-Ⅳ could interact with cGAS and STING protein,especially cGAS.Further Western blot assay showed that AS-Ⅳ significantly downregulated the expression of iNOS,cGAS,STING and p-NF-κB p65 protein.Conclusions AS-Ⅳ could promote mac-rophage M1 to M2 polarization,thereby inhibited mac-rophage migration through restraining the cGAS/STING/NF-κB signaling pathway,which provides a new therapeutic target for AS-Ⅳ to improve the early inflammatory response of AS.

Objective To establish a rat model of acclimatization to motion sickness(MS)induced by rotational stimulation.Methods To determine the stimulation conditions of MS,SD rats were divided into a static control group(SCG)and a single rotation stimulation group(SRG)before being subjected to the motion sickness index(MSI)measurement,open-field experiment and Morris water maze experiment after rotational stimulation to verify the feasibility of MS being induced in rats.Morris water maze experiments were performed to find out whether rotational stimulation could be used to induce MS in rats.During experiments on acclimatization,the SD rats were divided into the control group(Ctrl),one day of rotational stimulation group(Day1),three days of continuous rotational stimulation group(Day3),and seven days of continuous rotational stimulation group(Day7)before the changes in the MSI and behavior of these rats were recorded so as to explore the relationship between continuous stimulation and MS acclimatization in rats.Results After rotational stimulation,the rats showed a significant increase in the number of fecal pellets(P<0.0001)and in the MSI(P<0.0001)compared with the SCG.In the open field experiment,the rats showed a significant decrease in the spontaneous activity time(AT)(P<0.0001),total spontaneous activity distance(TD)(P<0.001)and distance moved by the center point per second(DMCPS)(P<0.001).The time taken to climb onto the platform(latency to find the platform,LP)(P<0.0001)and the total distance to the platform(distance to the platform,DP)(P<0.001)were significantly increased during the Morris water maze experiment.Acclimatization experiments revealed a significant increase in MSI and in the number of fecal pellets in the Day1 and Day3 groups of rotational stimulation compared to the Ctrl group(P<0.0001).AT(P<0.01),TD(P<0.05)and DMCPS(P<0.01)were significantly decreased,while LP and DP were significantly increased(P<0.0001),but there was no statistically significant difference in indices compared with the Day7 group(P>0.05).Conclusion Sinusoidal stimulation can induce MS in rats,and twice-a-day,continuous rotational stimulation for seven days can lead to acclimatization.The rat MS model can be assessed via behavioral experiments.

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.

Artificial intelligence (AI) researchers and cheminformatics specialists strive to identify effective drug precursors while optimizing costs and accelerating development processes. Digital molecular representation plays a crucial role in achieving this objective by making molecules machine-readable, thereby enhancing the accuracy of molecular prediction tasks and facilitating evidence-based decision making. This study presents a comprehensive review of small molecular representations and AI-driven drug discovery downstream tasks utilizing these representations. The research methodology begins with the compilation of small molecule databases, followed by an analysis of fundamental molecular representations and the models that learn these representations from initial forms, capturing patterns and salient features across extensive chemical spaces. The study then examines various drug discovery downstream tasks, including drug-target interaction (DTI) prediction, drug-target affinity (DTA) prediction, drug property (DP) prediction, and drug generation, all based on learned representations. The analysis concludes by highlighting challenges and opportunities associated with machine learning (ML) methods for molecular representation and improving downstream task performance. Additionally, the representation of small molecules and AI-based downstream tasks demonstrates significant potential in identifying traditional Chinese medicine (TCM) medicinal substances and facilitating TCM target discovery.
Artificial Intelligence ; Drug Discovery/methods* ; Humans ; Machine Learning ; Medicine, Chinese Traditional ; Small Molecule Libraries/chemistry*

[This corrects the article DOI: 10.1016/j.apsb.2022.05.019.].