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 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.

Objective To explore the role and possible molecular mechanism of Isocitrate dehydrogenase 1(IDH1)gene in proliferation and migration of intrahepatic cholangiocarcinoma(iCCA)cell HuCCT1.Methods HuCCT1 cells with IDH1 gene knockout(HuCCT1IDH1-/-)were constructed by CRISPR/Cas9 gene editing technology.To investigate the capacities of proliferation,migration and invasion of HuCCT1WT(HuCCT1 cells with wild-type IDH1 gene)and HuCCT1IDH1-/-cells,assays of CCK-8,clone formation,scratch and transwell were performed.Western blotting was used to detect the expression levels of epithelial-mesenchymal transition(EMT)associated proteins E-cadherin,N-cadherin,Vimentin,MMP-9,Wnt3a and β-catenin in two groups of cells.The transcriptome sequencing data of HuCCT1WT and HuCCT1IDH1-/-cells were analyzed by bioinformatics methods,Western blotting was used to verify the expression of signaling pathway-related proteins.Results Compared with HuCCT1WT cells,HuCCT1IDH1-/-cells showed the number of proliferation and clone formation significantly reduced(P<0.05),the proportion of cells blocked in G2/M phase was significantly increased(P<0.01),the rate of scratch healing was significantly decreased(P<0.01),and the number of migrated cells(P<0.001)and invaded cells(P<0.05)was significantly reduced.qRT-PCR assay showed that the expression levels of IDH1,Vimentin,MMP-9 and genes related to the regulation of G2/M cycle proliferation,Cyclin A2,Cyclin B1 and CDK1 mRNA were down-regulated in HuCCT1IDH1-/-cells(P<0.05),and the expression of CDH1 mRNA encoding E-cadherin was up-regulated(P<0.01);Western blotting assay showed that the expression level of E-cadherin in HuCCT1IDH1-/-cells was significantly increased(P<0.05),and the expression level of N-cadherin,Vimentin and MMP-9 protein was significantly decreased(P<0.05)than that in HuCCT1WT cells.Data of transcriptome sequencing revealed 1476 differentially expressed genes(DEGs)between two groups of HuCCT1 cells.Go enrichment analysis showed the DEGs were significantly enriched in cell biological processes associated with inflammatory response,cell signaling and cell metabolism.Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway analysis suggested that the DEGs may be involved in some signaling pathways such as Wnt,MAPK,Rap1,Hippo and TNF,which are closely related to the regulation of proliferation and invasion of tumor cells.Western blotting verification results showed that compared with HuCCT1WT cells,the relative expression of Wnt3a and β-catenin proteins of HuCCT1IDH1-/-cells was significantly decreased(P<0.05).Conclusions IDH1 gene may participate in the control of biological functions of HuCCT1 cells,including cell proliferation,migration,invasion and epithelial mesenchymal transition.The mechanism may be related to the activation of the Wnt/β-catenin signaling pathway.

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:To explore the expression of KAP1 (KRAB-associated protein 1, KAP1) in Malignant pleural mesothelioma (MPM) based on the cancer genome atlas (TCGA) and clinical trials. And elucidate the correlation between the expression of KAP1 and the clinical pathological parameters of patients with MPM and its prognosis.Methods:In April 2022, Based on the second generation KAP1mRNA sequencing data and clinicopathological data of MPM patients downloaded from TCGA database, the correlation between KAP1mRNA expression and clinical parameters was analyzed, and the correlation between KAP1 protein expression and clinicopathological parameters and its prognostic value were analyzed based on Chuxiong data set cohort clinical samples. The expression of KAP1 mRNA in MPM samples and matched normal tumor adjacent tissues was detected by qRT-PCR, and the expression of KAP1 protein in MPM and normal pleural tissues was detected by immunohistochemistry and Westernblotting. To construct a Kaplan-Meier model to explore the effect of KAP1 expression on the prognosis of MPM patients, and to analyze the prognostic factors of MPM patients by Cox regression.Results:qRT-PCR and Western blotting detection showed that the expression levels of KAP1 gene in four different MPM cells (NCI-H28, NCI-H2052, NCI-H2452, and MTSO-211H) were significantly higher than those in normal pleural mesothelial cells Met-5A. qRT-PCR, Western blotting and IHC results demonstrated that the mRNA and protein expression levels of KAP1 in MPM tissues was significantly higher than that in matching normal mesothelial tissues, and the expression level of KAP1 protein was correlated with TP 53 protein expression levels and serum CEA levels ( P<0.05) . The mRNA expression level was significantly correlated with the prognosis, The overall survival time of mesothelioma patients with high KAP1mRNA expression was significantly shorter (HR=3.7, Logrank P<0.001) . Tumor type, age and the mRNA expression were related to the prognosis of MPM patients ( P<0.05) . Multivariate analysis showed that tumor type and KAP1 mRNA expression level were independent prognostic factors of MPM patients ( P<0.05) . Conclusion:In this study, TCGA database and Chuxiong cohort experiment samples were used to collect the relevant information of KAP1 expression in malignant melanoma tissues. It was confirmed that KAP1 is highly expressed in MPM tissues. The mRNA expression level and pathological type are correlated with the prognosis of patients.

Following the principles of evidence-based medicine,in accordance with the structure and drafting rules of standardized documents,based on literature research,according to the characteristics of chronic cough in children and issues that need to form a consensus,the TCM Guidelines for Diagnosis and Treatment of Chronic Cough in Children was formulated based on the Delphi method,expert discussion meetings,and public solicitation of opinions.The guideline includes scope of application,terms and definitions,eti-ology and diagnosis,auxiliary examination,treatment,prevention and care.The aim is to clarify the optimal treatment plan of Chinese medicine in the diagnosis and treatment of this disease,and to provide guidance for improving the clinical diagnosis and treatment of chronic cough in children with Chinese medicine.

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 design a medical alliance nursing collaborative management system based on block chain to provide technical support for nursing collaborative management.Methods A medical alliance system was designed based on the five-layer technical architecture of consortium blockchain,which used Linux system for establishing a Fabric development environment and network configuration,smart contract and Java language software development kit for realizing user interface operation,Bootstrap and Jquery technologies for constructing the front-end Web interface,Spring Boot for constructing the back-end interface.There were five functional modules for consortium blockchain member management,business management,data storage and sharing,patient medical record management and platform supervision involved in the system,which were developed with the technologies of encryption algorithm,hash operation,smart contract,consensus mechanism and etc.Results The system developed contributed to improving the mechanism for quality nursing resource sinking and continuous nursing care,reconfiguring the point incentive mechanism,promoting the collaborative development of nursing symbiotic network and enabling multi-node interactions to form a synergistic synergy.Conclusion The system developed conforms to the development trend of integrated care service of medical alliance,enhances the core competitiveness of nursing care and provides references for formulating blockchain solutions applicable to medical alliance scenarios.[Chinese Medical Equipment Journal,2024,45(3):47-55]

Objective:To explore the plasma metabolomic characteristics of children with transfusion-dependent thalassemia(TDT),and reveal the changes of metabolic pattern in children with TDT.Methods:23 children with TDT who received regular blood transfusion in Ganzhou Women and Children's Health Care Hospital in 2021 were selected,and 11 healthy children who underwent physical examination during the same period were selected as the control group.The routine indexes between children with TDT and the control group were compared,and then the metabolic composition of plasma samples from children with TDT and the control group was detected by liquid chromatography-mass spectrometry.An OPLS-DA model was established to perform differential analysis on the detected metabolites,and the differential metabolic pathways between the two groups were analyzed based on the differential metabolites.Results:The results of routine testing showed that the indexes of ferritin,bilirubin,total bile acid,glucose and triglycerides in children with TDT were significantly higher than those in healthy controls,while hemoglobin and total cholesterol were significantly lower(all P＜0.05).However there was no significant difference in lactate dehydrogenase between the two groups(P＞0.05).Compared with the control group,190 differential metabolites(VIP＞1)were identified in TDT children.Among them,168 compounds such as arginine,proline and glycocholic acid were significantly increased,while the other 22 compounds such as myristic acid,eleostearic acid,palmitic acid and linoleic acid were significantly decreased.The metabolic pathway analysis showed that the metabolic impact of TDT on children mainly focused on the upregulation of amino acid metabolism and downregulation of lipid metabolism.Conclusion:The amino acid and lipid metabolism in children with TDT were significantly changed compared with the healthy control group.This finding is helpful to optimize the treatment choice for children with TDT,and provides a new idea for clinical treatment.

Objective:To explore the expression of KAP1 (KRAB-associated protein 1, KAP1) in Malignant pleural mesothelioma (MPM) based on the cancer genome atlas (TCGA) and clinical trials. And elucidate the correlation between the expression of KAP1 and the clinical pathological parameters of patients with MPM and its prognosis.Methods:In April 2022, Based on the second generation KAP1mRNA sequencing data and clinicopathological data of MPM patients downloaded from TCGA database, the correlation between KAP1mRNA expression and clinical parameters was analyzed, and the correlation between KAP1 protein expression and clinicopathological parameters and its prognostic value were analyzed based on Chuxiong data set cohort clinical samples. The expression of KAP1 mRNA in MPM samples and matched normal tumor adjacent tissues was detected by qRT-PCR, and the expression of KAP1 protein in MPM and normal pleural tissues was detected by immunohistochemistry and Westernblotting. To construct a Kaplan-Meier model to explore the effect of KAP1 expression on the prognosis of MPM patients, and to analyze the prognostic factors of MPM patients by Cox regression.Results:qRT-PCR and Western blotting detection showed that the expression levels of KAP1 gene in four different MPM cells (NCI-H28, NCI-H2052, NCI-H2452, and MTSO-211H) were significantly higher than those in normal pleural mesothelial cells Met-5A. qRT-PCR, Western blotting and IHC results demonstrated that the mRNA and protein expression levels of KAP1 in MPM tissues was significantly higher than that in matching normal mesothelial tissues, and the expression level of KAP1 protein was correlated with TP 53 protein expression levels and serum CEA levels ( P<0.05) . The mRNA expression level was significantly correlated with the prognosis, The overall survival time of mesothelioma patients with high KAP1mRNA expression was significantly shorter (HR=3.7, Logrank P<0.001) . Tumor type, age and the mRNA expression were related to the prognosis of MPM patients ( P<0.05) . Multivariate analysis showed that tumor type and KAP1 mRNA expression level were independent prognostic factors of MPM patients ( P<0.05) . Conclusion:In this study, TCGA database and Chuxiong cohort experiment samples were used to collect the relevant information of KAP1 expression in malignant melanoma tissues. It was confirmed that KAP1 is highly expressed in MPM tissues. The mRNA expression level and pathological type are correlated with the prognosis of patients.