BACKGROUND: Based on the China-VHD database, this study sought to develop and validate a Valvular Heart Disease- specific Age-adjusted Comorbidity Index (VHD-ACI) for predicting mortality risk in patients with VHD. METHODS & RESULTS: The China-VHD study was a nationwide, multi-centre multi-centre cohort study enrolling 13,917 patients with moderate or severe VHD across 46 medical centres in China between April-June 2018. After excluding cases with missing key variables, 11,459 patients were retained for final analysis. The primary endpoint was 2-year all-cause mortality, with 941 deaths (10.0%) observed during follow-up. The VHD-ACI was derived after identifying 13 independent mortality predictors: cardiomyopathy, myocardial infarction, chronic obstructive pulmonary disease, pulmonary artery hypertension, low body weight, anaemia, hypoalbuminaemia, renal insufficiency, moderate/severe hepatic dysfunction, heart failure, cancer, NYHA functional class and age. The index exhibited good discrimination (AUC, 0.79) and calibration (Brier score, 0.062) in the total cohort, outperforming both EuroSCORE II and ACCI (P < 0.001 for comparison). Internal validation through 100 bootstrap iterations yielded a C statistic of 0.694 (95% CI: 0.665-0.723) for 2-year mortality prediction. VHD-ACI scores, as a continuous variable (VHD-ACI score: adjusted HR (95% CI): 1.263 (1.245-1.282), P < 0.001) or categorized using thresholds determined by the Yoden index (VHD-ACI ≥ 9 vs. < 9, adjusted HR (95% CI): 6.216 (5.378-7.184), P < 0.001), were independently associated with mortality. The prognostic performance remained consistent across all VHD subtypes (aortic stenosis, aortic regurgitation, mitral stenosis, mitral regurgitation, tricuspid valve disease, mixed aortic/mitral valve disease and multiple VHD), and clinical subgroups stratified by therapeutic strategy, LVEF status (preserved vs. reduced), disease severity and etiology. CONCLUSION The VHD-ACI is a simple 13-comorbidity algorithm for the prediction of mortality in VHD patients and providing a simple and rapid tool for risk stratification.

Objective To summarize the epidemiological and clinical features of infectious diseases of the central nervous system(CNS)by a single-center analysis.Methods A retrospective analysis was conducted on the data of 1247 cases of CNS infectious diseases diagnosed and treated in the First Medical Center of PLA General Hospital from 2001 to 2020.Results The data for this group of CNS infectious diseases by disease type in descending order of number of cases were viruses 743(59.6％),Mycobacterium tuberculosis 249(20.0％),other bacteria 150(12.0％),fungi 68(5.5％),parasites 18(1.4％),Treponema pallidum 18(1.4％)and rickettsia 1(0.1％).The number of cases increased by 177 cases(33.1％)in the latter 10 years compared to the previous 10 years(P<0.05).No significant difference in seasonal distribution pattern of data between disease types(P>0.05).Male to female ratio is 1.87︰1,mostly under 60 years of age.Viruses are more likely to infect students,most often at university/college level and above,farmers are overrepresented among bacteria and Mycobacterium tuberculosis,and more infections of Treponema pallidum in workers.CNS infectious diseases are characterized by fever,headache and signs of meningeal irritation,with the adductor nerve being the more commonly involved cranial nerve.Matagenomic next-generation sequencing improves clinical diagnostic capabilities.The median hospital days for CNS infectious diseases are 18.00(11.00,27.00)and median hospital costs are ￥29,500(￥16,000,￥59,200).The mortality rate from CNS infectious diseases is 1.6％.Conclusions The incidence of CNS infectious diseases is increasing last ten years,with complex clinical presentation,severe symptoms and poor prognosis.Early and accurate diagnosis and standardized clinical treatment can significantly reduce the morbidity and mortality rate and ease the burden of disease.

ObjectiveAsthma is a common chronic inflammatory airway disease, and severe asthma poses a significant challenge in its diagnosis and management. Immune cells are involved in and altered by asthma pathogenesis, and several lipid metabolites can serve as diagnostic markers for the disease. In this study, we investigated the characterization of severe asthma at the metabolic and cellular level. MethodsDifferential metabolites in blood samples from severe asthma (41 cases) and controls (18 cases) were screened using multifactorial statistical analysis and independent samples t-tests; key pathways were identified by KEGG enrichment analysis, and biomarkers were characterized based on ROC curves; immune cell types and proportions in the blood were identified based on the results of cell-type annotations (5 severe and 3 control cases); and single-sample Gene Enrichment Analysis (ssGSEA) to investigate the characterization of differential metabolic pathways in single cells. ResultsCompared with controls, the abundance of 28 metabolites was increased and the abundance of 13 metabolites was decreased in the blood of patients with severe asthma (P<0.05); the differential metabolites were enriched in 4 pathways: sphingolipid metabolism, glycerophospholipid metabolism, nicotinate and nicotinamide metabolism, and histidine metabolism. Among them, 13 differential metabolites could be used as biomarkers for the diagnosis of severe asthma, including L-glutamic acid (AUC=0.809), nicotinamide (AUC=0.886), phytosphingosine (AUC=0.882), and sphinganine (AUC=0.893). In single-cell transcriptome analysis,5 key cell types were identified: CD4⁺ T cells, CD8⁺ T cells, NK cells, B cells, and monocytes. The number of NK cells was increased in patients with severe asthma, and severe asthma exhibited more frequent cellular communication, particularly dense communication between CD8⁺ T cells and other cell types. In healthy samples, monocytes were the primary cells engaging in dense communication. Single-sample gene enrichment analysis (ssGSEA) showed that 4 pathways enriched for differential metabolites had lower scores (P<0.01) in CD4⁺ T and CD8⁺ T cells in severe patients, and it was hypothesized that the expression of genes associated with these pathways was suppressed in these two types of cells. The suppressed genes included DGKA and NT5C3A, which are associated with immune processes. We observed that these genes play key roles in the regulation of T cell signaling, activation, differentiation, and immune responses. ConclusionL-glutamic acid, nicotinamide, phytosphingosine, and sphinganine can be used as biomarkers for the diagnosis of severe asthma; genes of the severe asthma-associated pathway were suppressed in CD4⁺ T cells and CD8⁺ T cells.

Objective The aim of this study was to assess the impact of bisphenol A (BPA) and its substitute, bisphenol F (BPF), on the colonic fecal community structure and function of mice.Methods We exposed 6-8-week-old male C57BL/6 mice to 5 mg/(kg·day) and 50 μg/(kg·day) of BPA or BPF for 14 days. Fecal samples from the colon were analyzed using 16S rRNA sequencing. Results Gut microbiome community richness and diversity, species composition, and function were significantly altered in mice exposed to BPA or BPF. This change was characterized by elevated levels of Ruminococcaceae UCG-010 and Oscillibacter and decreased levels of Prevotella 9 and Streptococcus. Additionally, pathways related to carbohydrate and amino acid metabolism showed substantial enrichment. Conclusion Mice exposed to different BP analogs exhibited distinct gut bacterial community richness, composition, and related metabolic pathways. Considering the essential role of gut bacteria in maintaining intestinal homeostasis, our study highlights the intestinal toxicity of BPs in vertebrates.

Objective To analyze the clinical characteristics of high energy metabolism in lung cancer patients and its correlation with body composition,nutritional status,and quality of life,and to develop a corresponding risk prediction model.Methods Retrospectively analyzed 132 primary lung cancer patients admitted to the First Hospital of Shanxi Medical University from January 2022 to May 2023,and categorized into high(n=94)and low energy metabolism group(n=38)based on their metabolic status.Differences in clinical data,body composition,Patient Generated Subjective Global Assessment(PG-SGA)scores,and European Organization for Research and treatment of Cancer(EORTC)Quality of Life Questionnaire-Core 30(QLQ-C30)scores were compared between the two groups.Logistic regression was used to identify the risk factors for high energy metabolism in lung cancer patients,and a risk prediction model was established accordingly;the Hosmer-Lemeshow test was used to assess the model fit,and the ROC curve was used to test the predictive efficacy of the model.Results Of the 132 patients with primary lung cancer,94(71.2%)exhibited high energy metabolism.Compared with low energy metabolism group,patients in high-energy metabolism group had a smoking index of 400 or higher,advanced disease staging of stage Ⅲ or Ⅳ,and higher levels of IL-6 level,low adiposity index,low skeletal muscle index,and malnutrition(P<0.05),and lower levels of total protein,albumin,hemoglobin level,and prognostic nutritional index(PNI)(P<0.05).There was no significant difference in age,gender,height,weight,BMI and disease type between the two groups(P>0.05).Logistic regression analysis showed that smoking index≥400,advanced disease stage,IL-6≥3.775 ng/L,and PNI<46.43 were independent risk factors for high energy metabolism in lung cancer patients.The AUC of the ROC curve for the established prediction model of high energy metabolism in lung cancer patients was 0.834(95%CI 0.763-0.904).Conclusion The high energy metabolic risk prediction model of lung cancer patients established in this study has good fit and prediction efficiency.

Objective:To investigate the risk factors of cardiovascular diseases(CVD)in patients with obstructive sleep apnea hypopnea syndrome(OSAHS).Methods:A total of 281 OSAHS patients admitted in Second People's Hospital of Huai'an City between May 2020 and March 2022 were selected and divided into CVD group(n=63)and no CVD group(n=218)according to presence of CVD.The OSAHS general data questionnaire designed by the re-search group was used to investigate the patients and univariate analysis was carried out,and the clinically significant single factors were included in the multivariate Logistic regression analysis.Influencing factors for CVD in OSAHS patients were analyzed.Results:Compared with no CVD group,patients in CVD group had significant higher age,body mass index(BMI),fasting plasma glucose(FPG)level,proportions of CVD family history,dyslipidemia and severe OSAHS(P＜0.05 or＜0.01).Multivariate Logistic regression analysis indicated that age,BMI,dyslipi-demia,FPG and severe OSAHS were independent risk factors for CVD in OSAHS patients(OR=2.213～2.482,P＜0.05 or＜0.01).Conclusion:Age,BMI,dyslipidemia,FPG and severe OSAHS are independent risk factors of CVD in OSAHS patients.Individualized prevention and control strategies can be formulated according to above fac-tors in clinic,which is of great significance for prevention of CVD occurrence.

Objective To understand the distribution and changes in antimicrobial resistance of clinically isolated Pseudomonas aeruginosa(P.aeruginosa)in the member hospitals of Hunan Provincial Antimicrobial Resistance Surveillance System from 2012 to 2021.Methods Antimicrobial susceptibility testing by disk diffusion or automa-ted instrument was performed on clinical isolates.Testing results were determined according to the standards of 2022 edition from American Clinical Laboratory Standards Institute(CLSI).Statistical analysis was performed by WHONET 5.6 software.Data were analyzed by trend test(Cochran-armitage)and Chi-square test with SPSS.Results A total of 176 441 strains of P.aeruginosa were surveilled by Hunan Provincial Antimicrobial Resistance Surveillance System from 2012 to 2021.99.4％of the strains were isolated from hospitalized patients,and about 70％of the strains were isolated from respiratory specimens.8.4％of P.aeruginosa were from children(0-17 years old),91.6％were from adults.Antimicrobial susceptibility testing results showed that P.aeruginosa was most sensitive to polymyxin B over 10 years,with a resis-tance rate of less than 6％.Resistance rates to piperacil-lin,piperacillin/tazobactam,ceftazidime,cefepime,aztreonam,imipenem,amikacin,gentamicin,tobramycin,cip-rofloxacin,levofloxacin,and polymyxin B all showed downward trends.A total of 29 920 carbapenem-resistant P.aeruginosa(CRPA)strains were detected.The average isolation rate of CRPA in this province was 18.0％over 10 years.CRPA detection rate from adult was 18.5％,higher than that from children(12.3％),and both showing downward trends.Conclusion The resistance rate of clinically isolated P.aeruginosa in Hunan Province to most commonly used antimicrobial agents is decreasing.

Objective: To observe the treatment response of a two-dose regimen of inotuzumab ozogamicin (inotuzumab), a monoclonal antibody targeting CD22, for patients with heavily treated relapsed/refractory B-cell acute lymphoblastic leukemia (R/R B-ALL), including those failed or relapsed after chimeric antigen receptor (CAR) -T-cell therapy. Methods: Pediatric and adult patients who received two doses of inotuzumab and who were evaluated after inotuzumab treatment were included. Antibody infusions were performed between March 2020 and September 2022. All patients expressed CD22 antigen as detected by flow cytometry (>80% leukemic cells displaying CD22) before treatment. For adults, the maximum dosage per administration was 1 mg (with a total of two administrations). For children, the maximum dosage per administration was 0.85 mg/m(2) (no more than 1 mg/dose; total of two administrations). The total dosage administered to each patient was less than the standard dosage of 1.8 mg/m(2). Results: Twenty-one patients with R/R B-ALL were included, including five children (<18 years old) and sixteen adults. Seventeen patients presented with 5.0% -99.0% leukemic blasts in the bone marrow/peripheral blood or with extramedullary disease, and four patients were minimal residual disease (MRD) -positive. Fourteen patients underwent both CD19 and CD22 CAR-T-cell therapy, four underwent CD19 CAR-T-cell therapy, and three underwent blinatumomab therapy. Eleven patients underwent allogeneic hematopoietic stem cell transplantation (allo-HSCT). After inotuzumab treatment, 14 of 21 patients (66.7% ) achieved a complete response (CR, one was MRD-positive CR), and all four MRD-positive patients turned MRD-negative. Four of six patients who failed recent CD22 CAR-T-cell therapy achieved a CR after subsequent inotuzumab treatment. Seven patients (33.3% ) demonstrated no response. Grade 1-3 hepatotoxicity occurred in five patients (23.8% ), one child with no response experienced hepatic veno-occlusive disease (HVOD) during salvage transplantation and recovered completely. Conclusion: For patients with heavily treated R/R B-ALL, including those who had undergone allo-HSCT and CD19/CD22 CAR-T-cell therapy, the two-dose regimen of inotuzumab resulted in a CR rate of 66.7%, and the frequency of hepatotoxicity and HVOD was low.
Adult ; Humans ; Child ; Adolescent ; Inotuzumab Ozogamicin ; Receptors, Chimeric Antigen ; Precursor Cell Lymphoblastic Leukemia-Lymphoma/therapy* ; Antibodies, Monoclonal ; Adaptor Proteins, Signal Transducing ; Antigens, CD19 ; Chemical and Drug Induced Liver Injury

The application of new-generation information technologies such as big data, the internet of things(IoT), and cloud computing in the traditional Chinese medicine(TCM)manufacturing industry is gradually deepening, driving the intelligent transformation and upgrading of the TCM industry. At the current stage, there are challenges in understanding the extraction process and its mechanisms in TCM. Online detection technology faces difficulties in making breakthroughs, and data throughout the entire production process is scattered, lacking valuable mining and utilization, which significantly hinders the intelligent upgrading of the TCM industry. Applying data-driven technologies in the process of TCM extraction can enhance the understanding of the extraction process, achieve precise control, and effectively improve the quality of TCM products. This article analyzed the technological bottlenecks in the production process of TCM extraction, summarized commonly used data-driven algorithms in the research and production control of extraction processes, and reviewed the progress in the application of data-driven technologies in the following five aspects: mechanism analysis of the extraction process, process development and optimization, online detection, process control, and production management. This article is expected to provide references for optimizing the extraction process and intelligent production of TCM.
Medicine, Chinese Traditional ; Drugs, Chinese Herbal ; Quality Control ; Big Data ; Algorithms

OBJECTIVE: To develop and validate a user-friendly risk score for older mitral regurgitation (MR) patients, referred to as the Elder-MR score. METHODS: The China Senile Valvular Heart Disease (China-DVD) Cohort Study functioned as the development cohort, while the China Valvular Heart Disease (China-VHD) Study was employed for external validation. We included patients aged 60 years and above receiving medical treatment for moderate or severe MR (2274 patients in the development cohort and 1929 patients in the validation cohort). Candidate predictors were chosen using Cox's proportional hazards model and stepwise selection with Akaike's information criterion. RESULTS: Eight predictors were identified: age ≥ 75 years, body mass index < 20 kg/m², NYHA class III/IV, secondary MR, anemia, estimated glomerular filtration rate < 60 mL/min per 1.73 m², albumin < 35 g/L, and left ventricular ejection fraction < 60%. The model displayed satisfactory performance in predicting one-year mortality in both the development cohort (C-statistic = 0.73, 95% CI: 0.69-0.77, Brier score = 0.06) and the validation cohort (C-statistic = 0.73, 95% CI: 0.68-0.78, Brier score = 0.06). The Elder-MR score ranges from 0 to 15 points. At a one-year follow-up, each point increase in the Elder-MR score represents a 1.27-fold risk of death (HR = 1.27, 95% CI: 1.21-1.34, P < 0.001) in the development cohort and a 1.24-fold risk of death (HR = 1.24, 95% CI: 1.17-1.30, P < 0.001) in the validation cohort. Compared to EuroSCORE II, the Elder-MR score demonstrated superior predictive accuracy for one-year mortality in the validation cohort (C-statistic = 0.71 vs. 0.70, net reclassification improvement = 0.320, P < 0.01; integrated discrimination improvement = 0.029, P < 0.01). CONCLUSIONS The Elder-MR score may serve as an effective risk stratification tool to assist clinical decision-making in older MR patients.