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.

The column chromatography and semi-preparative liquid phase chromatography with several chromatographic packing materials, including macroporous adsorbent resin, silica gel, ODS, and Sephadex LH-20, were used for the separation and purification of n-butanol-soluble portion of ethanol extract of Leonurus japonicus Houtt. The structures of isolates were identified by HR-MS, IR, NMR, and acid hydrolysis reaction. Six phenylethanol glycosides were obtained from L. japonicus and identified as leonoside G (1), leonoside E (2), leonoside B (3), leonoside F (4), cistanoside G (5), and salidroside (6). Compound 1 is a new phenylethanol glycoside.

Objective: To investigate the use of e-cigarette and analyze its influencing factors among high school students in Wuhan City, so as to provide insights into developing control measures for adolescents. Methods: From September to November 2019, high school students in Wuhan City were selected by the multi-stage stratified cluster random sampling method, and basic information, e-cigarette use and second-hand smoke exposure were collected through questionnaire surveys. Proportions of e-cigarette current use and attempt to use were analyzed, and factors affecting the current use of e-cigarettes were identified using a multivariable logistic regression model. Results: Totally 5 700 questionnaires were allocated, and 5 602 valid questionnaires were recovered, with an effective rate of 98.28%. The respondents included 2 925 males (52.21%) and 2 677 females (47.79%); 4 033 high school students (71.99%) and 1 569 vocational high school students (28.01%). The proportion of attempt to use e-cigarettes was 9.23%, and the proportion of current e-cigarette use was 2.03%. Multivariable logistic regression analysis identified male (OR=2.112, 95%CI: 1.285-3.471), vocational high school (OR=1.967, 95%CI: 1.214-3.186), private high school (OR=9.684, 95%CI: 5.648-16.605), family second-hand smoke exposure (OR=3.064, 95%CI: 1.741-5.392), second-hand smoke exposure in public places (OR=4.402, 95%CI: 1.687-11.484) and having close friends who were smokers (OR=6.432, 95%CI: 3.219-12.852) as factors affecting current e-cigarette use among high school students. Conclusions The proportion of current e-cigarette use among high school students in Wuhan City was 2.03%. Male, vocational high school, private high school, second-hand smoke exposure and having close friends who were smokers may be promoting factors for e-cigarette use.

Objective Data mining technology was used to mine the medication rules of the prescriptions used in the treatment of pediatric atopic dermatitis by Chinese medical master XUAN Guo-Wei.Methods The medical records of effective cases of pediatric atopic dermatitis treated by Professor XUAN Guo-Wei at outpatient clinic were collected,and then the medical data were statistically analyzed using frequency statistics,association rule analysis and cluster analysis.Results A total of 242 prescriptions were included,involving 101 Chinese medicinals.There were 23 commonly-used herbs,and the 16 high-frequency herbs(frequency＞100 times)were Glycyrrhizae Radix et Rhizoma,Saposhnikoviae Radix,Glehniae Radix,Perillae Folium,Ophiopogonis Radix,Cynanchi Paniculati Radix et Rhizoma,Microctis Folium,Dictamni Cortex,Scrophulariae Radix,Coicis Semen,Cicadae Periostracum,Lilii Bulbus,Rehmanniae Radix,Kochiae Fructus,Sclerotium Poriae Pararadicis,and Euryales Semen.The analysis of the medicinal properties showed that most of the herbs were sweet and cold,and mainly had the meridian tropism of the spleen,stomach and liver meridians.The association rule analysis yielded 24 commonly-used drug combinations and 20 association rules.Cluster analysis yielded 2 core drug combinations.Conclusion For the treatment of pediatric atopic dermatitis,Professor XUAN Guo-Wei focuses on the clearing,supplementing and harmonizing therapies,and the medication principle of"supporting the healthy-qi to eliminate the pathogen,and balancing the yin and yang"is applied throughout the treatment.