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.

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.

Platelets are reprogrammed by cancer via a process called education, which favors cancer development. The transcriptional profile of tumor-educated platelets (TEPs) is skewed and therefore practicable for cancer detection. This intercontinental, hospital-based, diagnostic study included 761 treatment-naïve inpatients with histologically confirmed adnexal masses and 167 healthy controls from nine medical centers (China, n = 3; Netherlands, n = 5; Poland, n = 1) between September 2016 and May 2019. The main outcomes were the performance of TEPs and their combination with CA125 in two Chinese (VC1 and VC2) and the European (VC3) validation cohorts collectively and independently. Exploratory outcome was the value of TEPs in public pan-cancer platelet transcriptome datasets. The AUCs for TEPs in the combined validation cohort, VC1, VC2, and VC3 were 0.918 (95% CI 0.889-0.948), 0.923 (0.855-0.990), 0.918 (0.872-0.963), and 0.887 (0.813-0.960), respectively. Combination of TEPs and CA125 demonstrated an AUC of 0.922 (0.889-0.955) in the combined validation cohort; 0.955 (0.912-0.997) in VC1; 0.939 (0.901-0.977) in VC2; 0.917 (0.824-1.000) in VC3. For subgroup analysis, TEPs exhibited an AUC of 0.858, 0.859, and 0.920 to detect early-stage, borderline, non-epithelial diseases and 0.899 to discriminate ovarian cancer from endometriosis. TEPs had robustness, compatibility, and universality for preoperative diagnosis of ovarian cancer since it withstood validations in populations of different ethnicities, heterogeneous histological subtypes, and early-stage ovarian cancer. However, these observations warrant prospective validations in a larger population before clinical utilities.
Humans ; Female ; Blood Platelets/pathology* ; Biomarkers, Tumor/genetics* ; Ovarian Neoplasms/pathology* ; China

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.

Hepatotoxicity induced by bioactive constituents in traditional Chinese medicines or herbs,such as bavachin(BV)in Fructus Psoraleae,has a prolonged latency to overt drug-induced liver injury in the clinic.Several studies have described BV-induced liver damage and underlying toxicity mechanisms,but little attention has been paid to the deciphering of organisms or cellular responses to BV at no-observed-adverse-effect level,and the underlying molecular mechanisms and specific indicators are also lacking during the asymptomatic phase,making it much harder for early recognition of hepatotoxicity.Here,we treated mice with BV for 7 days and did not detect any abnormalities in biochemical tests,but found subtle steatosis in BV-treated hepatocytes.We then profiled the gene expression of hepatocytes and non-parenchymal cells at single-cell resolution and discovered three types of hepatocyte subsets in the BV-treated liver.Among these,the hepa3 subtype suffered from a vast alteration in lipid metabolism,which was characterized by enhanced expression of apolipoproteins,carboxylesterases,and stearoyl-CoA desaturase 1(Scd1).In particular,increased Scd1 promoted monounsaturated fatty acids(MUFAs)syn-thesis and was considered to be related to BV-induced steatosis and polyunsaturated fatty acids(PUFAs)generation,which participates in the initiation of ferroptosis.Additionally,we demonstrated that mul-tiple intrinsic transcription factors,including Srebf1 and Hnf4a,and extrinsic signals from niche cells may regulate the above-mentioned molecular events in BV-treated hepatocytes.Collectively,our study deciphered the features of hepatocytes in response to BV insult,decoded the underlying molecular mechanisms,and suggested that Scd1 could be a hub molecule for the prediction of hepatotoxicity at an early stage.

Abstract: Objective　To investigate whether the complement system of COVID-19 is affected by vaccination, and also to explore the relationship between complement and length of stay in hospital, with a view to providing input for clinical diagnosis and the management of COVID-19 patients. Methods　The patients admitted from November 1st to November 30th, 2021 in the Eighth Affiliated Hospital of Guangzhou Medical University were selected as subjects. According to the time of vaccination, the patients were divided into two groups as vaccinated within half a year and over half a year. Then C3, C4, IgG, IgM, IgA, COVID-19 IgG and IgM, neutral granulocyte and lymphocyte count were detected and all patients' hospitalized days were recorded. With their hospitalization days of 14 d as a threshold, the patients were divided into two groups, and the above indicators were compared. Results　C3 concentration was (0.86±0.157) g/L in patients with vaccination within six months, which is significantly lower than (0.96±0.172) g/L in those over six months (P<0.05), but as for C4, IgG, IgM, IgA, COVID-19 IgG and IgM, neutral granulocyte and lymphocyte counts, there were no significant difference between two groups; as for the patients within 14 days of hospitalization, C3 and C4 concentrations were lower than those for more than 14 days, but the COVID-19 antibody IgG and IgM, lymphocyte counts were higher (all P<0.05). Conclusion　Vaccination within half a year can make the body's immune function and complement system to identify viruses earlier, more beneficial to remove viruses, but this effect is weakened after half a year; when admission, C3, C4, COVID-19 IgG level and lymphocyte counts can be used to predict hospitalization time in COVID-19 patients.

Objective Coronavirus disease 2019 (COVID-19) and tuberculosis (TB) are major public health and social issues worldwide. The long-term follow-up of COVID-19 with pulmonary TB (PTB) survivors after discharge is unclear. This study aimed to comprehensively describe clinical outcomes, including sequela and recurrence at 3, 12, and 24 months after discharge, among COVID-19 with PTB survivors. Methods From January 22, 2020 to May 6, 2022, with a follow-up by August 26, 2022, a prospective, multicenter follow-up study was conducted on COVID-19 with PTB survivors after discharge in 13hospitals from four provinces in China. Clinical outcomes, including sequela, recurrence of COVID-19, and PTB survivors, were collected via telephone and face-to-face interviews at 3, 12, and 24 months after discharge. Results Thirty-two COVID-19 with PTB survivors were included. The median age was 52 (45, 59) years, and 23 (71.9％) were men. Among them, nearly two-thirds (62.5％) of the survivors were moderate, three (9.4％) were severe, and more than half (59.4％) had at least one comorbidity (PTB excluded). The proportion of COVID-19 survivors with at least one sequela symptom decreased from 40.6％ at 3 months to 15.8％ at 24 months, with anxiety having a higher proportion over a follow-up. Cough and amnesia recovered at the 12-month follow-up, while anxiety, fatigue, and trouble sleeping remained after 24 months. Additionally, one (3.1％) case presented two recurrences of PTB and no re-positive COVID-19 during the follow-up period. Conclusion The proportion of long symptoms in COVID-19 with PTB survivors decreased over time, while nearly one in six still experience persistent symptoms with a higher proportion of anxiety. The recurrence of PTB and the psychological support of COVID-19 with PTB after discharge require more attention.

The risk of plague epidemics and relapse of various types of plague foci persists in Inner Mongolia Autonomous Region. For Marmota sibirica plague foci, the animal plague has not been found but antibody has been detected positive. Nowadays, Marmota sibirica has been increasing in population and distribution in China. In bordering countries Mongolia and Russia, the animal plague has been continuously prevalent. For Spermophilus dauricus plague foci, the animal plague has been taken place now and then. Compared to the above foci, the animal plague is most prevalent in Meriones unguiculatus plague foci and frequently spread to humans. Due to higher strain virulence and historical disaster in Marmota sibirica plague foci and Spermophilus dauricus plague foci, plague prevention and control should be strengthened on these foci. In addition to routine surveillance, epidemic dynamics need to be further monitored in these two foci, in order to prevent their relapse and spread to humans.
Animals ; China/epidemiology* ; Epidemics ; Humans ; Plague/prevention & control* ; Prevalence ; Sciuridae ; Yersinia pestis