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 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 design and test the reliability and validity of patient admission nursing assessment sheet of integrated Chinese and westem medicine ward.Methods On the basis of literature research,with traditional Chinese medicine"look,hear,ask and pulse-taking"as the basic framework,physical discrimination as the evaluation outcome,combined with the general nursing evaluation points of patients admitted to hospital,we designed the patient admission nursing assessment sheet of integrated Chinese and westem medicine ward,and then we adopted the Delphi method to conduct an inquiry on the draft of the admission sheet.In April 2022,3 clinical nurses were selected to evaluate 160 admitted patients from Integrated Traditional Chinese and Western Medicine Department of a tertiary A comprehensive hospital in Hubei Province,and Fleiss'Kappa coefficient and content validity coefficient were used to test the reliability and validity.Results The recovery rates of the 2 rounds of expert correspondence questionnaires were 95.45％and 100％.The authority coefficients were 0.931 and 0.957;the Kendall harmony coefficients were 0.101 and 0.106(P＜0.01);the importance scores of articles were 4.13～4.82;the coefficient of variation was 0.07～0.19 in 2nd round of expert correspondence.The final nursing assessment list for inpatients in the integrated Chinese and Western medicine ward included 3 parts:general demographic data assessment,four-Chinese medicine diagnosis assessment/physical discrimination,and nursing risk assessment,and there are 32 evaluation contents in the assessment sheet.The Fleiss'Kappa coefficient of this assessment sheet was 0.602(P＜0.001);the item content validity index was 0.857～1.000,and the overall content validity index was 0.920.Conclusion The admission nursing assessment sheet for inpatients in integrated Chinese and Westem medicine wards has good reliability and validity,and it is suitable for the integrated Chinese and Westem medicine wards.

AIM To evaluate the quality of Yanyangke Mixture.METHODS The HPLC fingerprints were established,after which cluster analysis,principal component analysis and partial least squares discriminant analysis were performed.The contents of liquiritin,rosmarinic acid,sheganoside,irisgenin,honokiol,monoammonium glycyrrhizinate,irisflorentin,isoliquiritin and magnolol were determined,the analysis was performed on a 35 ℃ thermostatic Agilent ZORBAX SB-C18 column(5 μm,250 mmx4.6 mm),with the mobile phase comprising of 0.1％phosphoric acid-acetonitrile flowing at 1 mL/min in a gradient elution manner,and multi-wavelength detection was adopted.RESULTS There were ten common peaks in the fingerprints for twelve batches of samples with the similarities of more than 0.9.Various batches of samples were clustered into three types,three principal components displayed the acumulative variance contribution rate of 87.448％,peaks 5、14(honokiol),3(liquiritin),11(monoammonium glycyrrhizinate)and 15(asarinin)were quality markers.Nine constituents showed good linear relationships within their own ranges(r＞0.999 0),whose average recoveries were 98.5％-103.6％with the RSDs of 0.92％-1.7％.CONCLUSION This stable and reliable method can provide a basis for the quality control of Yanyangke Mixture.

AIM To explore the ameliorative effects of Ziyin Mingmu Pills on mouse retinitis pigmentosa(RP)and the possible mechanism.METHODS The RP transgenic mice(rd10)were randomly divided into the model group,the Leding group(0.15 g/kg)and the low and high dose Ziyin Mingmu Pills groups(4.50,9.00 g/kg),in contrast to the C57BL/6 mice of the normal group,with 12 mice in each group.The mice had their retinal pathological changes detected by HE staining;their visual function detected by electroretinogram(ERG);their fundus conditions and retinal thickness detected by optical coherence tomography(OCT);their retinal blood perfusion detected by laser speckle blood flow technique;their mRNA expressions of Shh,Ptc,Smo,Gli1,N-myc and Cyclin mRNA detected by digital PCR;and their protein expressions of Shh,Ptc,Smo,Gli1,N-myc and Cyclin detected by immunofluorescence staining.RESULTS Compared with the normal group,the model group displayed pathological changes in the fundus and retina and decreased amplitudes of ERG a wave and b wave(P＜0.01);decreased retinal thickness(P＜0.01);decreased retinal blood perfusion(P＜0.01);and decreased retinal expressions of Shh,Ptc,Smo,Gli1,N-myc,Cyclin mRNA and protein(P＜0.01).Compared with the model group,the groups intervened with Ziyin Mingmu Pills or Leding shared improved pathological changes in the fundus and retina tissue,and increased retinal thickness(P＜0.01);increased retinal blood flow(P＜0.01);increased amplitudes of ERG a wave and b wave(P＜0.01);and increased retinal Shh,Ptc,Smo,Gli1,N-myc and Cyclin mRNA and protein expressions(P＜0.01).CONCLUSION Ziyin Mingmu Pills can improve the fundus pathological changes and visual function to delay RP in mice because of their efficacy in ameliorating retinal thickness and blood flow possibly by activating Shh signaling pathway.

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]

Vertebral artery hypoplasia(VAH)may lead changes of hemodynamic status in the posterior circulation,promote occurrence of intracranial atherosclerotic disease and formation of thrombi,hence result in hypoperfusion and infarction in posterior circulation cerebral tissue,which is potentially associated with posterior circulation cerebral vascular degeneration,vertebral artery dissection,cardiac disease and so on,also is a risk factor for posterior circulation infarction(PCI).The progresses of imaging researches revealing the association between VAH and PCI were reviewed in this article.

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.