BACKGROUND: Ischemic heart disease (IHD) represents the most significant disease burden among all cardiovascular diseases (CVDs). The increasing prevalence of metabolic risks in the 21^st century has a profound impact on the disease burden associated with IHD. We analyzed the global, regional, and national burdens of IHD attributable to metabolic risks from 1990 to 2021. METHODS: The data were taken from Global Burden of Disease (GBD) study 2021. Deaths, disability-adjusted life years (DALYs), the average annual percent change (AAPC), age-standardized death rates per 100,000 persons (ASDR) and age-standardized rate per 100,000 persons (ASR) of DALYs ranging from 1990 to 2021, were extracted and stratified according to region, nationality, socio-demographic index (SDI), sex, and age. Additionally, the global future trends were predicted using Nordpred prediction model. RESULTS: Compared to 1990, in 2021, the number of death and DALYs from metabolic risk-attributed IHD increased globally by 67.35% and 59.91%, respectively; whereas ASDR and ASR of DALYs showed a decreasing trend and the most severe impact was observed in male and elderly populations. In addition, the burden of disease showed an inverted V-shaped relationship with SDI from 1990 to 2021. AAPC showed a significant increase in developing countries and a decrease in developed countries. We also analyzed the effects of different risk factors including metabolic risk factors on IHD in different SDI regions and genders. The prediction of future disease burden showed that the number of death and DALYs will keep rising, while ASDR and ASR of DALYs will maintain a certain downward trend. CONCLUSIONS The results of this study highlighted the need for screening and intervention for metabolic risk factors in specific regions and populations, this should call for increased collaboration between developing and developed countries to reduce the burden of disease and improve the prognosis of patients with IHD.

Metabolic dysfunction-associated steatohepatitis (MASH), a severe type of metabolic dysfunction-associated steatotic liver disease (MASLD), is a leading etiology of end-stage liver disease worldwide, posing significant health and economic burdens. microRNA-320 (miR-320), a ubiquitously expressed and evolutionarily conserved miRNA, has been reported to regulate lipid metabolism; however, whether and how miR-320 affects MASH development remains unclear. By performing miR-320 in situ hybridization with RNAscope, we observed a notable downregulation of miR-320 in hepatocytes during MASH, correlating with disease severity. Most importantly, miR-320 downregulation in hepatocytes exacerbated MASH progression as demonstrated that hepatocyte-specific miR-320 deficient mice were more susceptible to high-fat, high-fructose, high-cholesterol diet (HFHC) or choline-deficient, amino acid-defined, high-fat diet (CDAHFD)-induced MASH compared with control littermates. Conversely, restoration of miR-320 in hepatocytes ameliorated MASH-related steatosis and fibrosis by injection of adeno-associated virus 8 (AAV8) carrying miR-320 in different types of diet-induced MASH models. Mechanistic studies revealed that miR-320 specifically regulated fibroblast growth factor 1 (FGF1) production in hepatocytes by inhibiting regulator factor X1 (RFX1) expression. Notably, knockdown of Rfx1 in hepatocytes mitigated MASH by enhancing FGF1-mediated AMPK activation. Our findings underscore the therapeutic potential of hepatic miR-320 supplementation in MASH treatment by inhibiting RFX1-mediated FGF1 suppression.

Objective To determine the knee joint cartilage thickness using different methods and explore the feasibility of mathematical statistical models of dataset for the prediction of cartilage thickness.Methods A total of 304 patients diagnosed as knee osteoarthritis(OA)combined with varus deformity and undergoing unilateral total knee arthroplasty at the Second Affiliated Hospital of Army Medical University from March 2023 to March 2024 were selected for the study.All patients had complete preoperative and postoperative clinical data.The healthy cartilage at four anatomical sites of patients,including the distal femur lateral condyle,lateral tibial plateau,posterior medial femoral condyle,and posterior lateral femoral condyle were selected,and the knee joint cartilage thickness was determined based on preoperative MRI analysis,robotic navigation system tracing,tissue section of surgical specimen and digital vernier caliper.The baseline indicators of demographics,disease and imaging ffor patients were collected to construct a dataset,and four models of linear regression analysis,principal component analysis,Least Absolute Shrinkage and Selection Operator(LASSO)regression analysis,and K-nearest neighbors(KNN)analysis were established for predicting the accuracy,determination coefficient(R2)and root mean square error(RMSE),and the regression equation for predicting cartilage thickness was established.Results The knee joint cartilage thicknesses determined by preoperative MRI analysis,robotic navigation system tracing,tissue section of surgical specimen had no statistically significant difference with that by digital vernier caliper(P>0.05).The predictive efficiencies of models of linear regression analysis,principal component analysis,and LASSO regression analysis for the knee joint cartilage thickness all failed to meet the expectations(R2<0.3,RMSE>0.03).The predictive effect of KNN model on the cartilage thickness of the distal femur lateral condyle and lateral tibial plateau was not ideal(R2=0.23,RMSE=0.29),while it had potential predictive value(accuracy=0.21,accuracy=0.15).Conclusion The prediction model of knee joint cartilage thickness based on individual parameters has certain scientificity,and the feasibility of KNN model is relatively high.However,due to insufficient sample size and unclear individual parameter weight,the efficiencies of the four established prediction models are not ideal,which fails to provide definite prediction equations.Therefore,the construction scheme of the prediction model still needs to be further optimized.

Objective To determine the knee joint cartilage thickness using different methods and explore the feasibility of mathematical statistical models of dataset for the prediction of cartilage thickness.Methods A total of 304 patients diagnosed as knee osteoarthritis(OA)combined with varus deformity and undergoing unilateral total knee arthroplasty at the Second Affiliated Hospital of Army Medical University from March 2023 to March 2024 were selected for the study.All patients had complete preoperative and postoperative clinical data.The healthy cartilage at four anatomical sites of patients,including the distal femur lateral condyle,lateral tibial plateau,posterior medial femoral condyle,and posterior lateral femoral condyle were selected,and the knee joint cartilage thickness was determined based on preoperative MRI analysis,robotic navigation system tracing,tissue section of surgical specimen and digital vernier caliper.The baseline indicators of demographics,disease and imaging ffor patients were collected to construct a dataset,and four models of linear regression analysis,principal component analysis,Least Absolute Shrinkage and Selection Operator(LASSO)regression analysis,and K-nearest neighbors(KNN)analysis were established for predicting the accuracy,determination coefficient(R2)and root mean square error(RMSE),and the regression equation for predicting cartilage thickness was established.Results The knee joint cartilage thicknesses determined by preoperative MRI analysis,robotic navigation system tracing,tissue section of surgical specimen had no statistically significant difference with that by digital vernier caliper(P>0.05).The predictive efficiencies of models of linear regression analysis,principal component analysis,and LASSO regression analysis for the knee joint cartilage thickness all failed to meet the expectations(R2<0.3,RMSE>0.03).The predictive effect of KNN model on the cartilage thickness of the distal femur lateral condyle and lateral tibial plateau was not ideal(R2=0.23,RMSE=0.29),while it had potential predictive value(accuracy=0.21,accuracy=0.15).Conclusion The prediction model of knee joint cartilage thickness based on individual parameters has certain scientificity,and the feasibility of KNN model is relatively high.However,due to insufficient sample size and unclear individual parameter weight,the efficiencies of the four established prediction models are not ideal,which fails to provide definite prediction equations.Therefore,the construction scheme of the prediction model still needs to be further optimized.

Due to the limitations of current anti-HBV therapies, the HBV core (HBc or HBcAg) protein assembly modulators (CpAMs) are believed to be potential anti-HBV agents. Therefore, discovering safe and efficient CpAMs is of great value. In this study, we established a HiBiT-based high-throughput screening system targeting HBc and screened novel CpAMs from an in-house marine chemicals library. A novel lead compound 8a, a derivative of the marine natural product naamidine J, has been successfully screened for potential anti-HBV activity. Bioactivity-driven synthesis was then conducted, and the structure‒activity relationship was analyzed, resulting in the discovery of the most effective compound 11a (IC₅₀ = 0.24 μmol/L). Furthermore, 11a was found to significantly inhibit HBV replication in multiple cell models and exhibit a synergistic effect with tenofovir disoproxil fumarate (TDF) and IFNa2 in vitro for anti-HBV activity. Treatment with 11a in a hydrodynamic-injection mouse model demonstrated significant anti-HBV activity without apparent hepatotoxicity. These findings suggest that the naamidine J derivative 11a could be used as the HBV core protein assembly modulator to develop safe and effective anti-HBV therapies.

As artificial intelligence technology rapidly advances, its deployment within the medical sector presents substantial ethical challenges. Consequently, it becomes crucial to create a standardized, transparent, and secure framework for processing medical data. This includes setting the ethical boundaries for medical artificial intelligence and safeguarding both patient rights and data integrity. This consensus governs every facet of medical data handling through artificial intelligence, encompassing data gathering, processing, storage, transmission, utilization, and sharing. Its purpose is to ensure the management of medical data adheres to ethical standards and legal requirements, while safeguarding patient privacy and data security. Concurrently, the principles of compliance with the law, patient privacy respect, patient interest protection, and safety and reliability are underscored. Key issues such as informed consent, data usage, intellectual property protection, conflict of interest, and benefit sharing are examined in depth. The enactment of this expert consensus is intended to foster the profound integration and sustainable advancement of artificial intelligence within the medical domain, while simultaneously ensuring that artificial intelligence adheres strictly to the relevant ethical norms and legal frameworks during the processing of medical data.
Artificial Intelligence/legislation & jurisprudence* ; Humans ; Consensus ; Computer Security/standards* ; Confidentiality/ethics* ; Informed Consent/ethics*

Female ; Glomerulonephritis, IGA ; metabolism ; pathology ; Glomerulonephritis, Membranous ; metabolism ; pathology ; Glomerulosclerosis, Focal Segmental ; metabolism ; pathology ; Humans ; Kidney ; metabolism ; pathology ; Male ; Renal Insufficiency, Chronic ; metabolism ; pathology

The present study was conducted to explore the effect of light intensity on growth, bioactivity compounds accumulation and anti-oxidative activity of Sedum sarmentosum. The growth, yield, contents of total flavonoids, total phenolic, quercetin, kaempferol and isorhamnetin, and antioxidant activities were assessed in S. sarmentosum under five light intensities, namely 100% full sunlight (G1), 77% full sunlight (G2), 60% full sunlight (G3), 38% full sunlight (G4), and 16% full sunlight (G5). The results showed that light intensity significantly affected the growth and the chemical compounds accumulation. With the decrease of light intensity, the maximum branch length and the average internode distance increased. G2 treatment greatly promoted the numbers of leaf layers and branches, and G3 treatment remarkably improved the yield. The highest total flavonoids and phenolic contents were obtained in G3 treatment. Meanwhile, the highest quercetin and isorhamnetin contents were obtained in G1 treatment. The difference of kaempferol content was not significant. In addition, based on DPPH, FTC and FRAP methods, the antioxidant activities of the aqueous extracts under G1 treatment were superior to the others. The results indicated that more than 60% full sunlight was the optimum light intensity condition to achieve high yield and quality of S. sarmentosum.
Antioxidants ; Flavonoids ; Phenols ; Plant Extracts ; Sedum

Using four Uncariae Cum Uncis materials including Uncaria sinensis (HGT), U. hirsutea (MGT), Jianhe U. rhynchophylla (JHGT) and U. rhynchophylla(GT) as the research objects, the correlations between medicinal materials' yield and photosynthetic ecophysiology-factors in the plant exuberant growth period were studied. Results showed that the Uncaria plants net photosynthetic rate (Pn) changed by unimodal curve. There was not "midday depression" phenomenon. There was a different relationship among the photosynthetic ecophysiology-factors and between photosynthetic ecophysiology-factors and medicinal materials' yield. Pn,Tl,Gs had a significant correlation with medicinal materials' yield（M）and were the most important factors of growth.