Objective To systematically analyze the disease burden, long-term trends, and age-sex distribution of major valvular heart disease (VHD) subtypes—rheumatic heart disease (RHD), non-rheumatic valvular disease (NRVD), and non-rheumatic calcific aortic valve disease (CAVD)—in global, Chinese, and US populations from 1990 to 2021, providing evidence for public health strategies and clinical resource allocation. Methods Based on publicly available data from the Global Burden of Disease (GBD) Study 2021, we extracted incidence, mortality, and disability-adjusted life years (DALYs) for VHD from 1990 to 2021. Age-standardized rates (ASRs) were calculated using the GBD 2021 global standard population, and the estimated annual percentage change (EAPC) with its 95% uncertainty interval (UI) was computed for the period. Data from the Agency for Healthcare Research and Quality (AHRQ), the European Society of Cardiology (ESC)/Eurostat surveys, and Chinese national registries were used for trend triangulation and contextual background. Results From 1990 to 2021, the ASR and disease burden of RHD significantly decreased globally and in China (EAPC for DALYs in China: −4.8%, 95%UI: −5.0% to −4.6%). In contrast, the burden of NRVD and CAVD steadily increased in aging populations like those in China and the US, with a higher burden observed in older adults and males. In 2021, the incidence of NRVD and CAVD peaked in individuals aged ≥65 years, with rates being significantly higher in men than in women. RHD burden was concentrated in low socio-demographic index (SDI) regions, whereas NRVD/CAVD burden was strongly associated with high-SDI regions. Conclusion The global VHD epidemiological landscape is transitioning from an RHD-dominant to an NRVD/CAVD-dominant pattern. China faces a dual challenge of a residual RHD burden and a rapidly growing burden of degenerative valvular diseases. Developing tailored screening, prevention, and treatment strategies for different disease subtypes and populations is crucial.

Objective To analyze the global pattern, long-term trends, and regional and population heterogeneity in the burden of 375 diseases and injuries and 88 risk factors from 1990 to 2023 using data from the Global Burden of Disease Study 2023 (GBD 2023). Methods Publicly available GBD 2023 estimates were extracted for incidence, deaths, years of life lost (YLLs), years lived with disability (YLDs), disability-adjusted life years (DALYs), and age-standardized rates at the global level and by socio-demographic index (SDI), GBD region, age group, and sex. Descriptive analysis, ranking comparison, stratified analysis, and trend analysis were performed, and 95% uncertainty interval was used to assess the stability of estimates. Results In 2023, non-communicable diseases were the leading contributor to global DALYs, accounting for 64.39% of DALYs across the three major disease and injury groups (communicable, maternal, neonatal, and nutritional diseases, non-communicable diseases, and injuries), and also had the highest age-standardized DALYs rate. From 1990 to 2023, age-standardized DALYs rates declined for three major disease and injury groups, with estimated annual percentage changes of −2.69%, −0.78%, and −1.47%, respectively. Cardiovascular diseases ranked first by both DALYs and deaths in 2023, whereas respiratory infections and tuberculosis ranked first by incidence. Non-communicable diseases were the leading cause of disease burden across all SDI strata. As the SDI level declined, the burden of communicable, maternal, neonatal, and nutritional diseases, as well as injuries, increased markedly. Behavioral and metabolic risks remained the main contributors to attributable deaths and DALYs. Conclusion From 1990 to 2023, the global burden of disease continues to shift toward non-communicable diseases; however, some regions still face a double burden of infectious, maternal and child, injury-related, and chronic diseases. Integrating deaths, DALYs, YLLs, YLDs, and risk-attributable burden helps identify priority areas for prevention and resource allocation across regions and populations.

This study established a high performance liquid chromatography(HPLC) fingerprint of Chuanxiong Rhizoma from different producing areas and screened its potential differential components for producing areas by chemometrics. Furthermore, the content of the above differential components in Chuanxiong Rhizoma from different producing areas was measured and compared. Then, the geoherbalism markers(geo-markers) that can be used to distinguish Dao-di and non-Dao-di Chuanxiong Rhizoma were excavated by chemometrics. In fingerprint studies, a total of 27 common peaks were determined, and the fingerprint similarity for 37 batches of Chuanxiong Rhizoma samples from different producing areas was above 0.968. The orthogonal partial least squares-discriminant analysis(OPLS-DA) was capable of distinguishing Chuanxiong Rhizoma from Sichuan and from three other provinces, as well as Dao-di Chuanxiong Rhizoma(from Dujiangyan) and non-Dao-di Chuanxiong Rhizoma(from other producing areas) in Sichuan province. Meanwhile, 14 potential differential components in Chuanxiong Rhizoma from different provinces and 16 potential differential components in Chuanxiong Rhizoma from different producing areas in Sichuan were screened by the variable importance in projection(VIP) analysis under OPLS-DA. The reference standards were used to identify 10 potential differential components in the common peaks, and subsequent content determination verified that the content of the above 10 potential differential components was different among different producing areas. Then, the OPLS-DA and VIP analysis were performed with the content of the 10 potential differential components as variables. The results showed that Z-ligustilide, chlorogenic acid, and the ratio of butylidenephthalide/senkyunolide A were the geo-markers that can distinguish Chuanxiong Rhizoma from Sichuan and Chuanxiong Rhizoma from Shaanxi, Hebei, and Jiangxi, while Z-ligustilide, n-butylphthalide, and the ratios of Z-ligustilide/senkyunolide A and butylidenephthalide/senkyunolide A were the geo-markers that can distinguish Dao-di Chuanxiong Rhizoma(from Dujiangyan) and non-Dao-di Chuanxiong Rhizoma(from other producing areas) in Sichuan province. This study elucidated the differences in material basis of Dao-di and non-Dao-di Chuanxiong Rhizoma based on fingerprinting and content determination combined with chemometrics, which provides a reference for the study of material basis of Dao-di traditional Chinese medicine.
Drugs, Chinese Herbal/chemistry* ; Rhizome/chemistry* ; Chromatography, High Pressure Liquid/methods* ; Chemometrics/methods* ; Quality Control

ObjectiveTransfer RNA-derived fragments (tRFs) are a recently characterized and rapidly expanding class of small non-coding RNAs, typically ranging from 13 to 50 nucleotides in length. They are derived from mature or precursor tRNA molecules through specific cleavage events and have been implicated in a wide range of cellular processes. Increasing evidence indicates that tRFs play important regulatory roles in gene expression, primarily by interacting with target messenger RNAs (mRNAs) to induce transcript degradation, in a manner partially analogous to microRNAs (miRNAs). However, despite their emerging biological relevance and potential roles in disease mechanisms, there remains a significant lack of computational tools capable of systematically predicting the interaction landscape between tRFs and their target mRNAs. Existing databases often rely on limited interaction features and lack the flexibility to accommodate novel or user-defined tRF sequences. The primary goal of this study was to develop a machine learning based prediction algorithm that enables high-throughput, accurate identification of tRF:mRNA binding events, thereby facilitating the functional analysis of tRF regulatory networks. MethodsWe began by assembling a manually curated dataset of 38 687 experimentally verified tRF:mRNA interaction pairs and extracting seven biologically informed features for each pair: (1) AU content of the binding site, (2) site pairing status, (3) binding region location, (4) number of binding sites per mRNA, (5) length of the longest consecutive complementary stretch, (6) total binding region length, and (7) seed sequence complementarity. Using this dataset and feature set, we trained 4 distinct machine learning classifiers—logistic regression, random forest, decision tree, and a multilayer perceptron (MLP)—to compare their ability to discriminate true interactions from non-interactions. Each model’s performance was evaluated using overall accuracy, receiver operating characteristic (ROC) curves, and the corresponding area under the ROC curve (AUC). The MLP consistently achieved the highest AUC among the four, and was therefore selected as the backbone of our prediction framework, which we named tRF Prospect. For biological validation, we retrieved 3 high-throughput RNA-seq datasets from the gene expression omnibus (GEO) in which individual tRFs were overexpressed: AS-tDR-007333 (GSE184690), tRF-3004b (GSE197091), and tRF-20-S998LO9D (GSE208381). Differential expression analysis of each dataset identified genes downregulated upon tRF overexpression, which we designated as putative targets. We then compared the predictions generated by tRF Prospect against those from three established tools—tRFTar, tRForest, and tRFTarget—by quantifying the number of predicted targets for each tRF and assessing concordance with the experimentally derived gene sets. ResultsThe proposed algorithm achieved high predictive accuracy, with an AUC of 0.934. Functional validation was conducted using transcriptome-wide RNA-seq datasets from cells overexpressing specific tRFs, confirming the model’s ability to accurately predict biologically relevant downregulation of mRNA targets. When benchmarked against established tools such as tRFTar, tRForest, and tRFTarget, tRF Prospect consistently demonstrated superior performance, both in terms of predictive precision and sensitivity, as well as in identifying a higher number of true-positive interactions. Moreover, unlike static databases that are limited to precomputed results, tRF Prospect supports real-time prediction for any user-defined tRF sequence, enhancing its applicability in exploratory and hypothesis-driven research. ConclusionThis study introduces tRF Prospect as a powerful and flexible computational tool for investigating tRF:mRNA interactions. By leveraging the predictive strength of deep learning and incorporating a broad spectrum of interaction-relevant features, it addresses key limitations of existing platforms. Specifically, tRF Prospect: (1) expands the range of detectable tRF and target types; (2) improves prediction accuracy through multilayer perceptron model; and (3) allows for dynamic, user-driven analysis beyond database constraints. Although the current version emphasizes miRNA-like repression mechanisms and faces challenges in accurately capturing 5'UTR-associated binding events, it nonetheless provides a critical foundation for future studies aiming to unravel the complex roles of tRFs in gene regulation, cellular function, and disease pathogenesis.

Objective To investigate the longitudinal relationship between sleep duration(SD)and positive youth development(PYD)among primary and secondary school students in Chengdu city using a cross-lagged model,and to provide scientific evidence for enhancing sleep management practices for students.Methods A total of 4061 students of grades 3 through 9 from the Chengdu Child Positive Development Cohort were included in this three-wave longitudinal study.There was a one-year interval between one survey and the following round of survey,and the time points for the baseline,12-month follow-up,and 24-month follow-up surveys were designated T0,T1,and T2.The PYD of the participants was assessed using the Chinese version of the Positive Youth Development Scale.The demographic data and the average daily SD over the past month were collected.Spearman correlation analysis was performed to examine the associations between SD and PYD,and a cross-lagged model was used to investigate the longitudinal relationship between them.Results The average daily SD for the 3 rounds of surveys conducted at T0,T1,and T2 was 9.00(8.04,10.00)hours,10.44(9.67,11.11)hours,and 10.39(9.83,11.00)hours,respectively,while the PYD scores were 5.30(4.73,5.71),5.27(4.73,5.73),and 5.39(4.89,5.77),respectively.Statistical significance was found in the differences of SD and PYD scores across the 3 rounds(P＜0.05).Spearman correlation analysis revealed synchronous correlations between SD and PYD at all three time points(r=0.10 at T0,r=0.18 at T1,and r=0.21 at T2,P＜0.05)and significant lagged correlations(e.g.,r=0.10 for T1-PYD and T2-SD,and likewise,significant correlation was found for the 3 other cross-lagged paths).The cross-lagged model demonstrated that PYD at T0 and T1 positively predicted SD at T1 and T2,respectively(β0-1=0.116[95％CI,0.083-0.150],β1-2=0.097(95％CI,0.067-0.127),P＜0.05),and that SD at T0 and T1 also positively predicted PYD at T1 and T2(β0-1=0.028[95％CI,0-0.056],β1-2=0.042[95％CI,0.010-0.074],P＜0.05).According to these findings,a bidirectional predictive relationship between SD and PYD across different time points was observed in primary and secondary school students.Furthermore,PYD demonstrated better performance for predicting SD than SD did for PYD.Subgroup analysis by sex confirmed the robustness of the predictive power of PYD for SD.Conclusion This study reveals a positive bidirectional predictive relationship between SD and PYD among primary and secondary school students,suggesting that higher levels of PYD may contribute to adequate sleep.These findings provide critical scientific evidence for schools and families to strengthen sleep management and promote the holistic development and well-being of adolescents.

Objective To evaluate the predictive value of dose-surface histogram(DSH)for radiation proctitis(RP)in prostate cancer(PCa)patients undergoing radiotherapy.Methods This prospective randomized controlled clinical trial included 380 PCa patients who underwent image-guided radiotherapy in the First Affiliated Hospital of Hebei Northern University from January 2018 to January 2023.Patients were randomly divided into observation group(n=200)and control group(n=180).The rectal dose distribution of patients in the two groups was evaluated by using DSH and dose-volume histogram(DVH),respectively.The receiver operating characteristic(ROC)curve was utilized to evaluate the predictive value of DSH for acute RP,with DVH serving as a reference.Results The difference was not statistically significant in clinical information such as age,KPS score,and body mass index(BMI)between the observation and control groups(P＞0.05),as well as in acute RP incidence(P＞0.05).There were significant differences in S40 and V40,S50 and V50,S60 and V60,S70 and V70,and S78 and V78 between the two groups(P＜0.05).S40,S50,V40,and V50 showed low efficacy(P＜0.001)in predicting acute RP at each level,with AUC≤0.700.S60 and V60 showed moderate efficacy(P＜0.001)in predicting acute RP at each level,with AUC 0.700-0.900.S70,S78,V70 and V78 showed high efficacy(P＜0.001)in predicting acute RP at each level,with AUC＞0.900.Conclusions The predictive value of DSH for rectal toxicity in patients with PCa is basically consistent with that of DVH.It is expected to become a novel and valuable tool for evaluating radiotherapy plans in the future.

Objective To explore the application value of musculoskeletal ultrasound(MSUS)in grading acute gastrocnemius muscle injuries.Methods A retrospective analysis was conducted on ultrasound images of 291 patients who presented with sudden calf pain and suspected acute gastrocnemius muscle injury in the General Hospital of Central Theater Command from March 2019 to July 2024.The images were independently reviewed and assessed by three ultrasound doctors with different qualifications to determine the presence and grade of gastrocnemius muscle injury.The consistency of grading results among three doctors was compared.The diagnostic results of three doctors were summarized.Then,the diagnostic results of 29 patients who underwent routine MRI scans were compared with those of MSUS,and the agreement between the two imaging modalities was assessed using the Kappa test.Results Among the 291 patients,171 cases(58.8%)were diagnosed with gastrocnemius muscle injury,including 55 cases(32.2%)with grade Ⅰ,109 cases(63.7%)with grade Ⅱ,and 7 cases(4.1%)with grade Ⅲ.There were 159 cases(93.0%)of unilateral medial head injury,10 cases(5.8%)of unilateral lateral head injury,and 2 cases(1.2%)of bilateral medial and lateral head injury.Compared with patients without gastrocnemius muscle injury,patients with gastrocnemius muscle injury were older(P<0.05),with no significant difference in gender and laterality(P>0.05).No significant differences in baseline characteristics were found among patients with different grades of injury(P>0.05).The three doctors diagnosed 173(59.5%),171(58.8%),and 171(58.8%)cases of injury,respectively,with an inter-class correlation coefficient(ICC)of 0.947(P<0.001).Among 29 patients who underwent MRI,the diagnostic agreement between MRI and ultrasound for grade Ⅰ,Ⅱ and Ⅲ injury was 8(27.6%),18(62.1%)and 3(10.3%)for MRI;and 9(31.0%),17(58.6%)and 3(10.3%)for MSUS,respectively,with a Kappa value of 0.808(P<0.001).Conclusions MSUS is effective for assessing the grade of acute gastrocnemius muscle injury,and shows high diagnostic consistency among doctors with different qualifications.It is recommended as the preferred method for diagnosing gastrocnemius muscle injury.

Abdominal aortic balloon occlusion is an emerging interventional treatment method,which has been used to control the risk of postpartum hemorrhage caused by pernicious placenta previa(PPP).Numerous studies have shown that abdominal aortic balloon occlusion can not only significantly reduce the difficulty and risk of surgery,shorten the time spent for surgery,but also further increase the success rate of surgery,which undoubtedly has a positive impact on both the delivery woman and neonate.This paper aims to make a brief introduction of the development history of abdominal aortic balloon occlusion and its technical principles,and to expound the timing of using abdominal aortic balloon occlusion and the clinical application of the balloon with different properties,focusing on the advantages of using abdominal aortic balloon in patients with PPP.It is expected that this paper can provide a reference for formulating clinical treatment strategies.

Objective To explore the value of shear wave elastography(SWE)combined with cervical length(CL)measurement in predicting spontaneous preterm birth(SPB)at 20 to 27 weeks of gestation.Methods A total of 161 pregnant women with antenatal care and examination in the hospi-tal from January 2020 to June 2022 were selected as research objects.All pregnant women underwent CL measurement and SWE detection by ultrasound during 20 to 27 weeks of gestation to obtain CL and SWE parameters.The occurrence of SPB was followed up,and the correlations of CL and SWE param-eters with the occurrence of SPB were analyzed.Results Among the 161 pregnant women,34 pa-tients experienced SPB.There were significant differences in the external os strain(EOS),elastic contrast index(ECI),internal os strain(IOS),and hardness ratio(HR)of SWE parameters between pregnant women with and without preterm birth(P＜0.05).Similarly,there was a significant differ-ence in CL between the two groups(P＜0.05).Spearman correlation analysis showed that ECI,IOS and EOS were positively correlated with the occurrence of preterm birth(P＜0.05),while HR and CL were negatively correlated with the occurrence of preterm birth(P＜0.05).The receiver operating characteristic(ROC)curve showed that the area under the curve(AUC)for CL in predicting preterm birth was 0.800,with a cut-off value of 2.710 cm.Among the SWE parameters,the AUCs for ECI,HR,IOS and EOS in predicting preterm birth were 0.651,0.642,0.705 and 0.653 respectively,with cut-off values of 5.140,53.650％,0.320 and 0.330 respectively.The ROC curve showed that the AUC for predicting preterm birth by SWE parameters combined with CL was 0.813,which was significantly higher than the AUC for prediction by the combination of the four SWE parameters(P＜0.05).The sensitivity and specificity at the maximum Youden index were 0.794 and 0.732 respectively.Conclusion CL,as well as ECI,HR,IOS and EOS among SWE parameters,have certain value in predicting SPB in pregnant women at 20 to 27 weeks of gestation,especially when the five indicators are combined for prediction.The critical values for prediction are 2.710 cm,5.140,53.650％,0.320 and 0.330 respectively.

Aconitum (Ranunculaceae) has a long-standing history in traditional Chinese medicine (TCM), where it has been widely used to treat conditions such as rheumatoid arthritis (RA), myocardial infarction, and heart failure. However, the potency of Aconitum alkaloids, the primary active components of Aconitum, also confers substantial toxicity. Therefore, assessing the efficacy and toxicity of these Aconitum alkaloids is crucial for ensuring clinical effectiveness and safety. Metabolomics, a quantitative method for analyzing low-molecular-weight metabolites involved in metabolic pathways, provides a comprehensive view of the metabolic state across multiple systems in vivo. This approach has become a vital investigative tool for facilitating the evaluation of their efficacy and toxicity, identifying potential sensitive biomarkers, and offering a promising avenue for elucidating the pharmacological and toxicological mechanisms underlying TCM. This review focuses on the applications of metabolomics in pharmacological and toxicological studies of Aconitum alkaloids in recent years and highlights the significant role of metabolomics in exploring compatibility detoxification and the mechanisms of TCM processing, aiming to identify more viable methods for characterizing toxic medicinal plants.
Aconitum/metabolism* ; Metabolomics/methods* ; Alkaloids/metabolism* ; Humans ; Animals ; Drugs, Chinese Herbal/pharmacology* ; Medicine, Chinese Traditional