Diabetic kidney disease （DKD） is a kidney disease caused by hyperglycemia，which is one of the most common microvascular complications of diabetes. Due to the high incidence of diabetes，the incidence of DKD has also increased year by year，and DKD has become a global public health problem. The pathogenesis of DKD is related to mechanisms such as oxidative stress，inflammation，renal fibrosis，and decreased mitophagy activity，which are developed under a variety of complex mechanisms. In traditional Chinese medicine，it is believed that the incidence of DKD is closely related to damp heat. Therefore，it is necessary to grasp the treatment method of clearing heat and removing dampness in clinical medication. Huangkui Capsules （HKC） have the effect of clearing damp heat，detoxifying， and detumescence. Because of its unique curative effect on DKD，HKC is often used in the treatment of DKD. HKC plays a role in the treatment of DKD with a variety of pharmacokinetic and pharmacodynamic processes. In many laboratory studies，it has been found that the specific mechanisms of HKC in the treatment of DKD include increasing mitophagy，reducing mitochondrial damage，reducing renal fibrosis，controlling inflammatory response，and inhibiting oxidative stress，which can achieve the purpose of reducing renal damage and promoting renal function. Some clinical studies have also verified that the application of HKC alone can exert renal protective function through anti-inflammatory，anti-oxidative stress，anti-renal fibrosis effects，as well as reduction of urinary protein. Since DKD is not a single injury of renal function，it is often accompanied by problems in blood pressure，blood lipids，blood circulation，body immunity， and other aspects. Therefore，the combination of HKC with other drugs can often achieve more comprehensive results，improve the advantages of various drugs，and improve the therapeutic effect. The combination of drugs such as antihypertensive，lipid-lowering， vascular circulation improvement，immunity inhibition，and anti-oxidative stress with HKC has achieved good results. In addition，HKC is often used in combination with other Chinese patent medicines in clinics. The application of HKC in the treatment of DKD has made some progress，but there are still many places worthy of further study，and the research on the mechanism of HKC is not comprehensive enough. The research on its long-term effect and safety in clinical application is relatively lacking，and the drug variety is relatively single when combined with certain drugs. These problems deserve further attention. Finally，it is necessary to pay attention to the promotion and application of HKC in clinical practice so that HKC can be better applied in clinical practice and better solve practical problems for patients.

Diabetic kidney disease （DKD） is a kidney disease caused by hyperglycemia，which is one of the most common microvascular complications of diabetes. Due to the high incidence of diabetes，the incidence of DKD has also increased year by year，and DKD has become a global public health problem. The pathogenesis of DKD is related to mechanisms such as oxidative stress，inflammation，renal fibrosis，and decreased mitophagy activity，which are developed under a variety of complex mechanisms. In traditional Chinese medicine，it is believed that the incidence of DKD is closely related to damp heat. Therefore，it is necessary to grasp the treatment method of clearing heat and removing dampness in clinical medication. Huangkui Capsules （HKC） have the effect of clearing damp heat，detoxifying， and detumescence. Because of its unique curative effect on DKD，HKC is often used in the treatment of DKD. HKC plays a role in the treatment of DKD with a variety of pharmacokinetic and pharmacodynamic processes. In many laboratory studies，it has been found that the specific mechanisms of HKC in the treatment of DKD include increasing mitophagy，reducing mitochondrial damage，reducing renal fibrosis，controlling inflammatory response，and inhibiting oxidative stress，which can achieve the purpose of reducing renal damage and promoting renal function. Some clinical studies have also verified that the application of HKC alone can exert renal protective function through anti-inflammatory，anti-oxidative stress，anti-renal fibrosis effects，as well as reduction of urinary protein. Since DKD is not a single injury of renal function，it is often accompanied by problems in blood pressure，blood lipids，blood circulation，body immunity， and other aspects. Therefore，the combination of HKC with other drugs can often achieve more comprehensive results，improve the advantages of various drugs，and improve the therapeutic effect. The combination of drugs such as antihypertensive，lipid-lowering， vascular circulation improvement，immunity inhibition，and anti-oxidative stress with HKC has achieved good results. In addition，HKC is often used in combination with other Chinese patent medicines in clinics. The application of HKC in the treatment of DKD has made some progress，but there are still many places worthy of further study，and the research on the mechanism of HKC is not comprehensive enough. The research on its long-term effect and safety in clinical application is relatively lacking，and the drug variety is relatively single when combined with certain drugs. These problems deserve further attention. Finally，it is necessary to pay attention to the promotion and application of HKC in clinical practice so that HKC can be better applied in clinical practice and better solve practical problems for patients.

Objective To construct large medical model named by "Huaxi HongYi"and explore its application effectiveness in assisting medical record generation. Methods By the way of a full-chain medical large model construction paradigm of "data annotation - model training - scenario incubation", through strategies such as multimodal data fusion, domain adaptation training, and localization of hardware adaptation, "Huaxi HongYi" with 72 billion parameters was constructed. Combined with technologies such as speech recognition, knowledge graphs, and reinforcement learning, an application system for assisting in the generation of medical records was developed. Results Taking the assisted generation of discharge records as an example, in the pilot department, after using the application system, the average completion times of writing a medical records shortened (21 min vs. 5 min) with efficiency increased by 3.2 time, the accuracy rate of the model output reached 92.4%. Conclusion It is feasible for medical institutions to build independently controllable medical large models and incubate various applications based on these models, providing a reference pathway for artificial intelligence development in similar institutions.

Objective To quantitatively assess the association of short-term exposure to polycyclic aromatic hydrocarbons (PAHs) in ambient fine particulate matter (PM2.5) with residents mortality. Methods A time-stratified case-crossover study was conducted from 2020 to 2022 among 10606 non-accidental residents by using the Guangzhou Cause of Death Surveillance System in Conghua District, Guangzhou. Exposure levels of PAHs in PM_2.5 and meteorological data during the study period were obtained from the Center for Disease Control and Prevention in Conghua District and the China Meteorological Administration Land Data Assimilation System (CLDAS-V2.0), respectively. Conditional Poisson regression model was used to estimate the exposure-response association between PAHs and the mortality risk. Results Fluoranthene, chrysene, benzo[k]fluoranthene, benzo[a]pyrene, and indeno[1,2,3-cd]pyrene were significantly associated with an increased risk of mortality. For every one interquartile range increase in exposure levels, the non-accidental mortality risks increased by 8.33% (95% CI: 1.80%, 15.27%), 4.67% (95% CI: 1.86%, 7.57%), 6.07% (95% CI: 2.08%, 10.21%), 4.62% (95% CI: 1.85%, 7.47%), and 4.70% (95% CI: 0.53%, 9.03%), respectively. The estimated non accidental deaths attributable to exposure to fluoranthene, chrysene, benzo[k]fluorine, benzo[a]pyrene and indine[1,2,3-cd]pyrene were 5.91%, 6.08%, 6.51%, 6.46%, and 4.21%, respectively. Conclusions Short-term exposure to PAHs in PM_2.5, including fluoranthene, chrysene, benzo[k]fluoranthene, benzo[a]pyrene and indine[1,2,3-cd]pyrene, was significantly associated with an increased risk of mortality among residents.

Alcoholic liver disease(ALD), a major cause of chronic liver disease worldwide, poses a serious threat to human health. Despite the availability of various drugs for treating ALD, their efficacy is often uncertain, necessitating the search for new therapeutic approaches. Traditional Chinese medicine polysaccharides have garnered increasing attention in recent years due to their versatility, high efficiency, and low side effects, and they have demonstrated significant potential in preventing and treating ALD. Emerging studies have suggested that these polysaccharides exert their therapeutic effects through multiple mechanisms, including the inhibition of oxidative stress and the regulation of lipid metabolism, gut microbiota, and programmed cell death. This review summarizes the recent research progress in the pharmacological effects and regulatory mechanisms of traditional Chinese medicine polysaccharides in treating ALD, aiming to provide a scientific basis and theoretical support for their application in the prevention and treatment of ALD.
Humans ; Liver Diseases, Alcoholic/metabolism* ; Polysaccharides/administration & dosage* ; Drugs, Chinese Herbal/administration & dosage* ; Animals ; Oxidative Stress/drug effects* ; Medicine, Chinese Traditional ; Gastrointestinal Microbiome/drug effects* ; Lipid Metabolism/drug effects*

Tailored lipid nanoparticles (LNPs)-mediated small interfering RNA (siRNA) nanomedicines show promise in treating liver disease, such as acute liver injury (ALI) and non-alcoholic steatohepatitis (NASH). However, constructing LNPs that address biosafety concerns, ensure efficient delivery, and target specific hepatic subcellular fractions has been challenging. To evade above obstacles, we develop three novel self-degradable "gemini-like" ionizable lipids (SS-MA, SS-DC, SS-MH) by incorporating disulfide bonds and modifying the length of ester bond and tertiary amino head. Our findings reveal that the disulfide-bond-bridged LNPs exhibit reduction-responsive drug release, improving both biosafety and siRNA delivery efficiency. Furthermore, the distance of ester bond and tertiary amino head significantly influences the LNPs' pK _a, thereby affecting endosomal escape, hemolytic efficiency, absorption capacity of ApoE, uptake efficiency of hepatocytes and liver accumulation. We also develop the modified-mannose LNPs (M-LNP) to target liver macrophages specifically. The optimized M-MH_LNP@TNFα exhibits potential in preventing ALI by decreasing tumor necrosis factor α (TNFα) levels in the macrophages, while MH_LNP@DGAT2 could treat NASH by selectively degrading diacylglycerol O-acyltransferase 2 (DGAT2) in the hepatocytes. Our findings provide new insights into developing novel highly effective and low-toxic "gemini-like" ionizable lipids for constructing LNPs, potentially achieving more effective treatment for hepatic diseases.

Background Exposure to air pollutants increases the risk of diseases in multiple systems, including respiratory and cardiovascular systems, yet its association with neurological diseases remains unclear. Objective To quantitatively evaluate the association between short-term exposure to air pollutants and outpatient and emergency visits for neurological diseases, identify potential susceptible populations, and quantify associated disease burden. Methods Daily 24-hour average concentrations of fine particulate matter (PM_2.5), inhalable particulate matter (PM₁₀), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), and carbon monoxide (CO), daily maximum 8-hour average concentration of ozone (O₃), daily meteorological data (24-hour average temperature, 24-hour average relative humidity), and data on daily outpatient and emergency department visits for neurological diseases from two hospitals in Conghua District, Guangzhou, China, were collected from 2015 to 2022. A time-stratified case-crossover design was adopted, and a conditional Poisson regression model was constructed to analyze the association between air pollution exposure and neurological disease visits. Two-pollutant models and sensitivity analysis were used to validate model stability. Stratified analyses by season (cold season: from November to March; warm season: from April to October), sex (male, female), and age (≤45 years, 46–60 years, ≥61 years) were performed to identify vulnerable group. Additionally, the number and proportion of neurological disease visits attributable to short-term air pollutant exposure were calculated. Results A total of 72 673 outpatient and emergency department visits for neurological diseases were included during the study period. Most of the patients were middle-aged and elderly individuals (69.89% were over 45 years old) and females (60.25%). The results of single-pollutant models showed that for each interquartile range (IQR) increase in exposure to PM_2.5, PM₁₀, SO₂, NO₂, CO, and O₃, the risk of outpatient and emergency department visits for neurological diseases increased by 7.54% (95%CI: 4.69%, 10.46%), 6.66% (95%CI: 3.92%, 9.46%), 16.72% (95%CI: 10.58%, 23.19%), 8.12% (95%CI: 4.82%, 11.53%), 5.60% (95%CI: 2.34%, 8.97%), and 6.11% (95%CI: 2.91%, 9.40%), respectively. The results of the two-pollutant model showed that the association between PM_2.5 and SO₂ exposure and outpatient and emergency department visits for neurological diseases were relatively stable. The stratified analyses showed that the effect of SO₂ was stronger in the cold season. It was estimated that 8.32% (95%CI: 5.55%, 10.96%) and 6.65% (95%CI: 4.27%, 8.96%) of the outpatient and emergency department visits were attributable to short-term exposure to SO₂ and PM_2.5, respectively. Conclusion Exposure to PM_2.5 and SO₂ is associated with increased risks of outpatient and emergency visits for neurological diseases. SO₂ shows stronger effects during the cold season, and exposure to air pollution contributes to up to 8.32% of neurological disease visits.

Objective To construct risk prediction models of death or readmission in patients with acute heart failure(AHF)during the vulnerable phase based on machine learning algorithms and screen the optimal model.Methods A total of 651 AHF patients with admitted to Department of Cardiology of the Second Affiliated Hospital of Army Medical University from October 2019 to July 2021 were included.The clinical data consisting of admission vital signs,comorbidities and laboratory results were collected from electronic medical records.The composite endpoint was defined as all-cause death or readmission for worsening heart failure within 3 months after discharge.The patients were divided into a training set(521 patients)and a test set(130 patients)in a ratio of 8:2 through the simple random sampling.Six machine learning models were developed,including logistic regression(LR),random forest(RF),decision tree(DT),light gradient boosting machine(LGBM),extreme gradient boosting(XGBoost)and neural networks(NN).Receiver operating characteristic(ROC)curve and decision curve analysis(DCA)were used to evaluate the predictive performance and clinical benefit of the models.Shapley additive explanation(SHAP)was used to explain and evaluate the effect of different clinical characteristics on the models.Results A total of 651 AHF patients were included,of whom 203 patients(31.2%)died or were readmitted during the vulnerable phase.ROC curve analysis showed that the AUC values of the LR,RF,DT,LGBM,XGBoost and NN model were 0.707,0.756,0.616,0.677,0.768 and 0.681,respectively.The XGBoost model had the highest AUC value.DCA showed that the XGBoost model exhibited greater clinical net benefit compared with other models,with the best predictive performance.SHAP algorithm analysis showed that the clinical features that had the greatest impact on the output of the model were serum uric acid,D-dimer,mean arterial pressure,B-type natriuretic peptide,left atrial diameter,body mass index,and New York Heart Association(NYHA)classification.Conclusion The XGBoost model has the best predictive performance in predicting the risk of death or readmission of AHF patients during the vulnerable phase.

ObjectiveTo construct a quantitative prediction model of three indicators（moisture absorption rate， film thickness and coating weight gain） during the coating process of Vitamin C Yinqiao tablets（VCYT） by near-infrared spectroscopy（NIRS）， and to realize the endpoint judgment. MethodReal-time NIRS data of 4 batches of VCYT during the coating process were collected by diffuse reflection method. The coating method employed was the rolling coating method， and the samples were obtained at the spray stage from the coater's sampling port every 10 minutes， and 57 batches of samples（about 1 800 tablets） were collected at various coating times， the tablets were embedded in molten paraffin， cut longitudinally， and observed by stereomicroscope. The film thickness， with a target value of 38 μm， was then measured using Motic Images Advanced 3.2 software. Furthermore， the mositure absorption rate of samples， aiming for a target value of 3%， was determined in accordance with guiding principles for drug hygroscopicity testing in the 2020 edition of Chinese Pharmacopoeia， and 3 samples were randomly selected from each batch（10 tablets per batch）， and the coating weight gain was calculated（target value of 4%）. Partial least squares regression（PLSR） was used to construct a quantitative model of the 3 coating indicators， and the predicted values of the coating indicators were smoothed using the moving average method and used to determine the coating endpoints. ResultThe prediction determination coefficients（R_p²） for moisture absorption rate， film thickness and coating weight gain were 0.933 4， 0.932 6 and 0.965 9， the root mean square errors of prediction（RMSEP） were 0.163 5%， 1.870 9 μm and 0.240 3%， the relative percent deviations（RPD） were 3.711 0， 2.760 7 and 5.415 8， respectively. The results of the external validation set demonstrated that the real-time predicted values obtained by the models exhibited the same trend as the measured values， and the coating endpoint could be accurately predicted（with a prediction error of less than 7.32 min and a relative error of less than 5.63%）. ConclusionThe established NIRS model exhibits excellent predictive performance and can be used for quality control of VCYT during the coating process.