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.

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.

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

OBJECTIVES: To investigate the effects of hyperoxia on the expression of N-methyl-D-aspartate receptor 1 (NMDAR1) and its synapse-associated molecules, including cannabinoid receptor 1 (CB1R), postsynaptic density 95 (PSD95), and synapsin (SYN), in the hippocampus of neonatal rats. METHODS: One-day-old Sprague-Dawley neonatal rats were randomly divided into a hyperoxia group and a control group (n=8 per group). The hyperoxia group was exposed to 80% ± 5% oxygen continuously, while the control group was exposed to room air, for 7 days. At 1, 3, and 7 days after hyperoxia exposure, hematoxylin and eosin (HE) staining was used to observe histopathological changes in the brain. The expression levels of NMDAR1, CB1R, PSD95, and SYN proteins and mRNAs in the hippocampus were detected by immunohistochemistry, Western blotting, and quantitative real-time PCR. RESULTS: After 7 days of hyperoxia exposure, the hyperoxia group showed decreased neuronal density and disordered arrangement in brain tissue. Compared with the control group, after 1 day of hyperoxia exposure, CB1R mRNA and both NMDAR1 and CB1R protein expression in the hyperoxia group were significantly downregulated, while SYN protein expression was significantly upregulated (P<0.05). After 3 days, mRNA expression of NMDAR1, CB1R, and SYN was significantly decreased (P<0.05); NMDAR1 and CB1R protein expression was significantly downregulated (P<0.05), while PSD95 and SYN protein expression was significantly upregulated (P<0.05). After 7 days of hyperoxia, the protein expression of NMDAR1 and CB1R was significantly upregulated (P<0.05). CONCLUSIONS Continuous hyperoxia exposure induces time-dependent changes in the expression levels of NMDAR1 and its synapse-associated molecules in the hippocampus of neonatal rats.
Animals ; Receptors, N-Methyl-D-Aspartate/genetics* ; Rats, Sprague-Dawley ; Hippocampus/pathology* ; Rats ; Animals, Newborn ; Receptor, Cannabinoid, CB1/genetics* ; Hyperoxia/metabolism* ; Disks Large Homolog 4 Protein/genetics* ; Synapsins/genetics* ; Synapses ; Male ; Female ; RNA, Messenger/analysis*

Hearing loss is the most prevalent disabling disease. Cochlear implantation（CI） serves as the primary intervention for severe to profound hearing loss. This consensus systematically explores the value of genetic diagnosis in the pre-operative assessment and efficacy prognosis for CI. Drawing upon domestic and international research and clinical experience, it proposes an evidence-based medicine three-tiered prognostic classification system（Favorable, Marginal, Poor）. The consensus focuses on common hereditary non-syndromic hearing loss（such as that caused by mutations in genes like GJB2, SLC26A4, OTOF, LOXHD1） and syndromic hereditary hearing loss（such as Jervell & Lange-Nielsen syndrome and Waardenburg syndrome）, which are closely associated with congenital hearing loss, analyzing the impact of their pathological mechanisms on CI outcomes. The consensus provides recommendations based on multiple round of expert discussion and voting. It emphasizes that genetic diagnosis can optimize patient selection, predict prognosis, guide post-operative rehabilitation, offer stratified management strategies for patients with different genotypes, and advance the application of precision medicine in the field of CI.
Humans ; Cochlear Implantation ; Prognosis ; Hearing Loss/surgery* ; Consensus ; Connexin 26 ; Mutation ; Sulfate Transporters ; Connexins/genetics*

Nipah virus (NiV) and related viruses form a distinct henipavirus genus within the Paramyxoviridae family. NiV continues to spillover into the humans causing deadly outbreaks with increasing human-bat interaction. NiV encodes the large protein (L) and phosphoprotein (P) to form the viral RNA polymerase machinery. Their sequences show limited homologies to those of non-henipavirus paramyxoviruses. We report two cryo-electron microscopy (cryo-EM) structures of the Nipah virus (NiV) polymerase L-P complex, expressed and purified in either its full-length or truncated form. The structures resolve the RNA-dependent RNA polymerase (RdRp) and polyribonucleotidyl transferase (PRNTase) domains of the L protein, as well as a tetrameric P protein bundle bound to the L-RdRp domain. L-protein C-terminal regions are unresolved, indicating flexibility. Two PRNTase domain zinc-binding sites, conserved in most Mononegavirales, are confirmed essential for NiV polymerase activity. The structures further reveal anchoring of the P protein bundle and P protein X domain (XD) linkers on L, via an interaction pattern distinct among Paramyxoviridae. These interactions facilitate binding of a P protein XD linker in the nucleotide entry channel and distinct positioning of other XD linkers. We show that the disruption of the L-P interactions reduces NiV polymerase activity. The reported structures should facilitate rational antiviral-drug discovery and provide a guide for the functional study of NiV polymerase.
Nipah Virus/chemistry* ; Cryoelectron Microscopy ; Viral Proteins/genetics* ; RNA-Dependent RNA Polymerase/genetics* ; Phosphoproteins/genetics* ; Humans ; Models, Molecular ; Protein Binding

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

Background Mitochondrial biogenesis is pivotal in coal workers' pneumoconiosis fibrosis, yet the role of the peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PGC-1α)-nuclear respiratory factor 1 (NRF1)-mitochondrial transcription factor A (TFAM) pathway inmitochondrial biogenesis remains elusive, warranting further investigation. Objective To elucidate the role of the PGC-1α-NRF1-TFAM pathway in mitochondrial biogenesis in a rat coal workers' pneumoconiosis model through in vivo and in vitro experiments. Methods (1)n vivo: twelve SPF male SD rats (200-220 g) were randomized into a control group and a coal dust group (n=6 per group). After acclimatization, the coal dust group received 1 mL 50 mg·mL⁻¹ coal dust suspension via intratracheal instillation; the controls received saline. Lung tissues were harvested after two months for histopathology [HE, Masson, and transmission electron microscopy (TEM) ], protein and mRNA analysis, and mitochondrial DNA (mtDNA) quantification by quantitative real-time polymerase chain reaction (qPCR). (2) In vitro: rat lung type II epithelial cells (RLE-6TN) cells were exposed to coal dust (50, 100, 200, and 400 mg·L⁻¹, 24 h). CCK-8 assay determined optimal doses. Ultrastructural changes were analyzed by TEM. Cells were transfected with OE-PGC-1α (PGC-1α overexpression) or shRNA-PGC-1α plasmids (PGC-1α knockdown), and the transfection efficiency was determined by reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR). The expression levels of alpah-smooth muscle actin (α-SMA), citrate synthase (CS), PGC-1α, NRF1, TFAM, and fibronectin (Fn) proteins and their corresponding mRNA were detected using Western blot and RT-qPCR, respectively. The relative content of mtDNA was determined by qPCR. Results In vivo: the control group lung samples exhibited soft, pink parenchyma, while the coal dust-exposed lungs showed blackened surfaces with soft texture. The histopathological evaluation revealed intact alveolar walls in the controls versus structural destruction, micro-nodules, and fibrotic areas in the coal dust group. After Masson staining, coal dust deposits were found surrounded by blue collagen fibers in the exposed lungs, but absent in the controls. The coal dust group displayed significant upregulation of fibrotic marker α-SMA and downregulation of mitochondrial biogenesis markers (CS, PGC-1α, NRF1, TFAM) and mtDNA compared to the controls (P<0.05). In vitro: coal dust exposure reduced cell density and induced morphological alterations. TEM revealed evenly distributed normal mitochondria in controls versus mitochondrial swelling, disrupted cristae, and reduced numbers in exposed cells. The mitochondrial biogenesis markers were elevated in the coal dust + OE-PGC-1α group compared to the coal dust + OE-NC group (P<0.05); in contrast, they were decreased in the coal dust + shRNA-PGC-1α group compared to the coal dust + shRNA-NC group (P<0.05). Compared to the control group, the expression levels of the fibrosis marker α-SMA mRNA and protein were increased in the coal dust group (P<0.05). Overexpression of PGC-1α reduced α-SMA expression, while downregulation of PGC-1α increased its expression (P<0.05). Conclusion Coal dust exposure induces mitochondrial dysfunction and pulmonary fibrosis in vivo and in vitro via the PGC-1α-NRF1-TFAM pathway dysregulation. Targeting this pathway may mitigate coal dust-induced fibrosis by restoring mitochondrial biogenesis.

Arsenic is a semi-metal,and lipid-soluble arsenic compounds are one of the widespread forms in the environment and food chain,but there is a lack of standards for lipid-soluble arsenic compounds,which is one of the bottlenecks in the current analytical detection and toxicological studies of organic arsenic.In this study,four saturated arsenic-containing hydrocarbons,AsHC 318,AsHC 332,AsHC 346,and AsHC 374(The number is relative molecular mass),were successfully synthesized in three steps by using dimethylarsinic acid,potassium iodide,sodium hydroxide,and four brominated alkanes(1-Bromotetradecane,1-bromopentadecane,1-bromohexadecane,and 1-bromooctadecane)as raw materials.The structures of these four saturated arsenic-containing hydrocarbons were characterized by proton nuclear magnetic resonance(1H NMR)spectroscopy,13C nuclear magnetic resonance(13C NMR)spectroscopy,and high-resolution mass spectrometry(HR-MS).The yields of the method were 8%-10%,and the synthesized compounds could be used in subsequent toxicity evaluation experiments to assess the toxic effects and mechanisms of action of arsenic-containing hydrocarbons.This study provided an effective method for synthesis of arsenic-containing hydrocarbons,enriching the synthesis methods of arsenic-containing hydrocarbons,and provided raw materials for the subsequent toxicological studies of arsenic-containing hydrocarbons.

A method was developed for determination of dilauryl thiodipropionate(DLTDP)in fried foods by coupling solid-phase extraction(SPE)pretreatment with reverse-phase liquid chromatography-tandem mass spectrometry(RPLC-MS/MS)detection.Samples were extracted with n-hexane as the solvent,purified using a neutral alumina SPE cartridge,and finally analyzed by RPLC-MS/MS.Quantitative analysis was performed using matrix-matched calibration curves combined with an external standard method under optimal experimental conditions.The results showed that DLTDP exhibited good linearity in the range of 2.0-50.0 μg/L,with a correlation coefficient(R2)≥0.999.The limit of detection(LOD)and the limit of quantification(LOQ)of the method were 0.15 mg/kg and 0.5 mg/kg,respectively.The mean recoveries at three fortification levels(0.5,1.0,and 200 mg/kg)in different samples ranged from 84.8%to 96.8%,with the relative standard deviations(RSDs)all less than 8.0%.The developed method was highly sensitive,accurate and reliable,and easy to operate,making it well suited for the routine quantitative analysis of DLTDP in fried foods.