Objective To investigate the 30-day mortality risk factors in elderly patients with heart failure with reduced ejection fraction (HFrEF) after isolated coronary artery bypass grafting (CABG) and to construct a nomogram for predicting mortality risk. Methods A retrospective analysis of elderly (≥70 years) HFrEF patients undergoing isolated CABG at Tianjin Chest Hospital from 2010 to 2024 was performed. Simple random sampling in R software was used to divide the dataset into training and validation sets in a 7 : 3 ratio. The training set was further divided into survivors and non-survivors. Univariate logistic regression was performed to identify differences between groups, followed by multivariate logistic regression to select independent risk factors for death and to establish a death-risk nomogram, which underwent internal validation. The predictive value of the nomogram was assessed by plotting receiver operating characteristic (ROC) curves, calibration curves, and decision-curve analyses for both the training and validation sets. Results A total of 656 patients were included. The training set consisted of 458 patients (survivors 418, deaths 40); the validation set consisted of 198 patients (survivors 180, deaths 18). In the training set, univariate analysis showed significant differences between survivors and deaths for creatinine (Cr) level, brain natriuretic peptide (BNP), maximum Cr, intra-aortic balloon pump (IABP) use, assisted ventilation, reintubation, hyperlactatemia, low cardiac output syndrome, and renal failure (P<0.05). After multivariable logistic regression, five independent risk factors were identified: IABP use (OR=3.391, 95%CI 1.065-11.044, P=0.038), reintubation (OR=15.991, 95%CI 4.269-67.394, P<0.001), hyperlactatemia (OR=8.171, 95%CI 2.057-46.089, P=0.007), Cr (OR=4.330, 95%CI 0.997-6.022, P=0.024), and BNP (OR=1.603, 95%CI 1.000-2.000, P=0.010). Accordingly, a nomogram predicting mortality risk was constructed. The ROC and calibration analyses indicated good predictive value: area under the curve (AUC) in the training set was 0.898 (95%CI 0.831-0.966) and in the validation set was 0.912 (95%CI 0.805-1.000). Calibration and decision-curve analyses showed good agreement and clinical utility. Conclusion The nomogram incorporating IABP use, reintubation, hyperlactatemia, creatinine, and BNP provides good predictive value for 30-day mortality after CABG in elderly patients with HFrEF and demonstrates potential clinical utility.

Aim To study the correlation between es-trogen metabolism function of intestinal flora and liver fibrosis disease phenotype and differential intestinal bacteria by fecal microbiota transplantation(FMT).Methods C57BL/6J male mice were divided into normal group(Control-M),liver fibrosis Model group(Model),FMT-1 group(normal mice fecal microbiota transplantation from liver fibrosis mice),and FMT-2 group(liver fibrosis mice fecal microbiota transplanta-tion from female mice).The model group was induced by high fat and high glucose combined with low dose of CCl4 for 16 weeks.In the FMT group,the bacteria were destroyed by mixed antibacterial solution and then the corresponding fecal microbiota solution was given.The model group was established in the FMT-2 group and the model group at the same time.Liver function(ALT,AST)was detected by biochemical methods;liver inflammation(IL-1α,IL-6)was detected by ELISA;liver pathology was detected by HE and Mas-son methods;the expressions of α-SMA,collagen Ⅰ,estrogen receptor ERα,ERβ and GPER were detected by Western blot;estrogen metabolic enzymes β-glucu-ronidase and β-glucosidase in intestinal flora were de-tected by double antibody sandwich assay;gut microbi-ota was detected by 16S rDNA method;the correlation between estrogen metabolic enzymes,estrogen receptors and disease phenotypes and disease-related differential bacteria was analyzed by Pearson correlation analysis.Results Liver function,inflammation and fibrosis in-dices were significantly higher in the model group than those in the control-M group and significantly lower in the FMT-2 group than in the model group;estrogen metabolic enzymes of the intestinal flora significantly increased in the model group compared to the control-M group and significantly decreased in the FMT-2 group compared to the model group;the model group showed a significant increase in ERβ and GPER and a significant decrease in ERα compared to the control-M group,while the FMT-2 group showed a significant de-crease in ERβ and GPER and a significant increase in ERα compared to the model group;the FMT-2 group increased the enterobacterial abundance and diversity reduced by modelling;estrogen metabolic enzymes,es-trogen receptor ERβ and GPER were all positively cor-related with the disease phenotype,while the opposite was true for ERα;estrogen metabolic enzymes were positively correlated with Allobaculum,Ruminococcus and Alistipes,and negatively correlated with Akkerman-sia,Lactobacillus and Prevotella.Conclusions Fecal microbiota transplantation in female mice can alleviate liver fibrosis in male mice,which is related to the im-provement of estrogen metabolism of intestinal flora.

Objective:To establish and validate a multimodal model based on radiomics and deep learning for predicting acute pancreatitis (AP) complicated with acute respiratory distress syndrome (ARDS).Methods:Patients diagnosed with AP from The First Affiliated Hospital of Soochow University, Donghai County People's Hospital and Jintan Affiliated Hospital of Jiangsu University between January 2017 and December 2023 were enrolled. Based on the diagnosis of ARDS within 1 week after admission, the patients were classified into the ARDS group and the non-ARDS group. Patients in the First Affiliated Hospital of Soochow University ( n=406) was used as the training set (non-ARDS group n=212 vs ARDS group n=194), while Donghai and Jintan hospitals served as the test set ( n=175; non-ARDS group n=104 vs ARDS group n=71). Clinical data, laboratory tests and the occurrence of systemic inflammatory response syndrome (SIRS) within 24 hours after admission were collected. Scoring systems such as bedside index for severity in acute pancreatitis (BISAP), Ranson score and modified CT severity index (MCTSI) were calculated. Radiomics features were extracted from three-dimensional CT images to develop a radiomics model based on XGBoost algorithm. At the same time, a deep learning model was constructed using deep convolutional networks to extract deep features. Finally, clinical features and the predictions from the aforementioned models were integrated to establish a multimodal model based on XGBoost algorithm. To enhance model visualization, variable importance ranking and local interpretable visualization were used. The receiver operating characteristic (ROC) curves of the three models and the three scores including BISAP, Ranson and MCTSI were plotted and the area under the curves (AUCs) were calculated to evaluate the prediction performance for ARDS in AP patients, as well as sensitivity and specificity. Results:In the multimodal model for predicting ARDS in AP patients, predictions of the deep learning model and the radiomics model were the most important variables, followed by SIRS, C-reactive protein, procalcitonin, albumin, glucose, creatinine, neutrophil, and Ca 2+. In the training set, the multimodal model achieved an AUC of 0.933 for predicting ARDS in AP patients, higher than the radiomics model (0.727), the deep learning model (0.877), MCTSI (0.870), Ranson (0.620) and BISAP (0.898). In the test set, the model's AUC was 0.916 for predicting ARDS in AP patients, higher than the radiomics model (0.660), the deep learning model (0.864), MCTSI (0.851), Ranson (0.609), and BISAP (0.860). Conclusions:Based on clinical structured data, radiomics and deep learning features, the multimodal model could predict the risk of ARDS in AP patients at an early stage, whose performance is better than the single-modal models and the traditional scoring systems.

OBJECTIVE: To investigate the association between long-term glycemic control and cerebral infarction risk in patients with diabetes through a large-scale cohort study. METHODS: This prospective, community-based cohort study included 12,054 patients with diabetes. From 2006 to 2012, 38,272 fasting blood glucose (FBG) measurements were obtained from these participants. FBG trajectory patterns were generated using latent mixture modelling. Cox proportional hazards models were applied to assess the subsequent risk of cerebral infarction associated with different FBG trajectory patterns. RESULTS: At baseline, the mean age of the participants was 55.2 years. Four distinct FBG trajectories were identified based on FBG concentrations and their changes over the 6-year follow-up period. After a median follow-up of 6.9 years, 786 cerebral infarction events were recorded. Different trajectory patterns were associated with significantly varied outcome risks (Log-Rank P < 0.001). Compared with the low-stability group, Hazard Ratio ( HR) adjusted for potential confounders were 1.37 for the moderate-increasing group, 1.23 for the elevated-decreasing group, and 2.08 for the elevated-stable group. CONCLUSION Sustained high FBG levels were found to play a critical role in the development of ischemic stroke among patients with diabetes. Controlling FBG levels may reduce the risk of cerebral infarction.
Humans ; Cerebral Infarction/blood* ; Middle Aged ; Male ; Female ; Blood Glucose/analysis* ; Fasting/blood* ; Aged ; Prospective Studies ; Risk Factors ; Diabetes Mellitus/blood* ; Adult ; Proportional Hazards Models

High mobility group box protein 1(HMGB1)is one of the most widely expressed protein member in the HMGs family,which is well known for its involvement in the body inflammatory response.Previous researches have found that it plays a significant role in cell migration,immune identification and neuroprotection.Spinal cord injury is a disease that causes severe damage to the nervous system,and neural circuits are disrupted after a spinal cord injury,which leads to many conditions including ischemia and hypoxia,inflammatory responses,demyelinating lesions,and glial scar formation that are detrimental to nerve regeneration and repair,making it one of the most difficult diseases to treat in the modern spinal surgery field.HMGB1 is upregulated after spinal cord injury,thereby regulating neuroinflam-matory responses,and participating in the neuronal apoptosis,promoting neuronal regeneration,and inducing neural stem cell differentiation and migration,which plays an important role in the process of neural function recovery.This paper summarizes the structure and function of HMGB1,as well as its role in spinal cord injury,in order to provide direction for founding therapeutic target for neurological function recovery after spinal cord injury.

This study was aimed at understanding the prevalence and drug resistance status of Salmonella of waterfowl ori-gin in the Guangdong region in the past decade,to guide prevention and control efforts.The drug-sensitive paper slide method was used to conduct drug susceptibility testing on 314 waterfowl-originating Salmonella strains isolated from 238 waterfowl farms in the Guangdong region from 2013 to 2023.The isolated Salmonella strains were most resistant to penicillin,amoxicil-lin,cefradine,and cefazolin in the β-lactam group;sulphadoxine dimethylpyrimidine in the sulphonamide group;and tetracy-cline in the tetracycline group.The resistance rates ranged from 73.57％to 89.49％.The highest sensitivity was observed to amikacin,gentamicin,and kanamycin in the aminoglycoside group,and norfloxacin in the quinolone group,with susceptibility rates all exceeding 50％.The 280 strains of Salmonella showed multi-drug resistance to six classes of antimicrobial drugs and high resistance(as much as 60.83％)to five drug classes.Correlation analysis revealed the highest correlations for florfenicol with gentamicin,and for amoxicillin with penicillin(r=0.650 for both),followed by gentamicin with kanamycin(r=0.620).Salmonella resistance in waterfowl in Guangdong Province was generally severe and showed a complex pattern of drug resist-ance.Detection of waterfowl pathogens should be strengthened to prevent the spread of drug-resistant bacteria and support ra-tional use of antibiotics.This work provides a reference for Salmonella prevention and control in waterfowl farms.

Extended reality(XR)technology includes virtual reality(VR),augmented reality(AR)and mixed reality(MR)Combining virtual environments with physical world,the extended reality(XR)technology has great potential in rehabilitation of patients with stroke.This article reviews the intervention effects of XR technology on the functions of limb,swallowing,speech and cognition and psychological outcomes in patients with stroke.Based on this review,issues in application of XR are identified and targeted solutions are proposed,thereby offering a guidance for application of XR technology in stroke rehabilitation in China.

Germplasm degeneration occurs during the long-term cultivation of root-and rhizome-derived traditional Chinese medicine(RR-TCM), which seriously restricts the high-quality development of their industry. Therefore, it is urgent to solve the problem of germplasm degeneration through variety breeding. In this paper, based on previously published research articles, monographs, and news reports, the research progresses on the number and origins, breeding methods, and selection of new varieties of RR-TCM listed in the Chinese Pharmacopoeia(Edition 2020) were summarized and analyzed. The results show that there are 169 kinds of RR-TCM listed in the Chinese Pharmacopoeia(Edition 2020), originated from 223 origins with three breeding methods(i.e., seed propagation, vegetative reproduction, and tissue culture), and there are 215 species derived from seed propagation, 177 species derived from vegetative reproduction, and 164 species derived from tissue culture. To date, there are 62 origins breeding new varieties through conventional breeding, cross breeding, mutation breeding, ploidy breeding, or modern biotechnology breeding methods, including 57 origins breeding 145 new varieties through conventional breeding, 10 origins breeding 43 new varieties through mutation breeding, and seven origins breeding 12 new varieties through cross breeding method. They are used mainly to improve yield, disease resistance, and active ingredient content, but only a few new varieties have been widely used. This review will provide useful references in variety breeding, quality breeding, and standardized planting of RR-TCM.
Plant Breeding/methods* ; Plant Roots/growth & development* ; Rhizome/growth & development* ; Drugs, Chinese Herbal ; Plants, Medicinal/classification* ; Medicine, Chinese Traditional

Following the core outcome set standards for development(COS-STAD), this study aims to construct core outcome set(COS) for clinical research on traditional Chinese medicine(TCM) treatment of simple obesity. Firstly, a comprehensive review was conducted on the randomized controlled trial(RCT) and systematic review(SR) about TCM treatment of simple obesity that were published in Chinese and English databases to collect reported outcomes. Additional outcomes were obtained through semi-structured interviews with patients and open-ended questionnaire surveys for clinicians. All the collected outcomes were then merged and organized as an initial outcome pool, and then a preliminary list of outcomes was formed after discussion by the working group. Subsequently, two rounds of Delphi surveys were conducted with clinicians, methodology experts, and patients to score the importance of outcomes in the list. Finally, a consensus meeting was held to establish the COS for clinical research on TCM treatment of simple obesity. A total of 221 RCTs and 12 SRs were included, and after integration of supplementary outcomes, an initial outcome pool of 141 outcomes were formed. Following discussions in the steering advisory group meeting, a preliminary list of 33 outcomes was finalized, encompassing 9 domains. Through two rounds of Delphi surveys and a consensus meeting, the final COS for clinical research on TCM treatment of simple obesity was determined to include 8 outcomes: TCM symptom scores, body mass index(BMI), waist-hip ratio, waist circumference, visceral fat index, body fat rate, quality of life, and safety, which were classified into 4 domains: TCM-related outcomes, anthropometric measurements, quality of life, and safety. This study has preliminarily established a COS for clinical research on TCM treatment of simple obesity. It helps reduce the heterogeneity in the selection and reporting of outcomes in similar clinical studies, thereby improving the comparability of research results and the feasibility of meta-analysis and providing higher-level evidence support for clinical practice.
Humans ; Obesity/therapy* ; Medicine, Chinese Traditional ; Randomized Controlled Trials as Topic ; Treatment Outcome ; Drugs, Chinese Herbal/therapeutic use*

The study investigated the intrinsic changes in material basis of Angelicae Sinensis Radix during wine processing by headspace-gas chromatography-ion mobility spectrometry(HS-GC-IMS), headspace-solid phase microextraction-gas chromatography-mass spectrometry(HS-SPME-GC-MS), and ultra-high performance liquid chromatography-quadrupole-orbitrap mass spectrometry(UPLC-Q-Orbitrap-MS) combined with chemometrics. HS-GC-IMS fingerprints of Angelicae Sinensis Radix before and after wine processing were established to analyze the variation trends of volatile components and characterize volatile small-molecule substances before and after processing. Principal component analysis(PCA) and orthogonal partial least squares-discriminant analysis(OPLS-DA) were employed for differentiation and difference analysis. A total of 89 volatile components in Angelicae Sinensis Radix were identified by HS-GC-IMS, including 14 unsaturated hydrocarbons, 16 aldehydes, 13 ketones, 9 alcohols, 16 esters, 6 organic acids, and 15 other compounds. HS-SPME-GC-MS detected 118 volatile components, comprising 42 unsaturated hydrocarbons, 11 aromatic compounds, 30 alcohols, 8 alkanes, 6 organic acids, 4 ketones, 7 aldehydes, 5 esters, and 5 other volatile compounds. UPLC-Q-Orbitrap-MS identified 76 non-volatile compounds. PCA revealed distinct clusters of raw and wine-processed Angelicae Sinensis Radix samples across the three detection methods. Both PCA and OPLS-DA effectively discriminated between the two groups, and 145 compounds(VIP>1) were identified as critical markers for evaluating processing quality, including 4-methyl-3-penten-2-one, ethyl 2-methylpentanoate, and 2,4-dimethyl-1,3-dioxolane detected by HS-GC-IMS, angelic acid, β-pinene, and germacrene B detected by HS-SPME-GC-MS, and L-tryptophan, licoricone, and angenomalin detected by UPLC-Q-Orbitrap-MS. In conclusion, the integration of the three detection methods with chemometrics elucidates the differences in the chemical material basis between raw and wine-processed Angelicae Sinensis Radix, providing a scientific foundation for understanding the processing mechanisms and clinical applications of wine-processed Angelicae Sinensis Radix.
Wine/analysis* ; Gas Chromatography-Mass Spectrometry/methods* ; Chromatography, High Pressure Liquid/methods* ; Angelica sinensis/chemistry* ; Solid Phase Microextraction/methods* ; Drugs, Chinese Herbal/isolation & purification* ; Chemometrics ; Volatile Organic Compounds/chemistry* ; Principal Component Analysis ; Ion Mobility Spectrometry/methods*