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 reliability and safety of continuous monitoring of vital signs in patients using wireless wearable monitoring devices after video-assisted thoracoscopic surgery (VATS) for lung cancer. Methods The patients undergoing VATS for lung cancer in West China Hospital, Sichuan University from May to August 2023 were prospectively enrolled. Both wireless wearable and traditional wired devices were used to monitor the vital signs of patients after surgery. Spearman correlation analysis, paired sample t test and ratio Bland-Altman method were used to test the correlation, difference and consistency of monitoring data measured by the two devices. The effective monitoring rate of the wireless wearable device within 12 hours was calculated to test the reliability of its continuous monitoring. Results A total of 20 patients were enrolled, including 15 females and 5 males with an average age of 46.20±11.52 years. Data collected by the two monitoring devices were significantly correlated (P<0.001). Respiratory rate and blood oxygen saturation data collected by the two devices showed no statistical difference (P>0.05), while heart rate measured by wireless wearable device was slightly lower (=−0.307±1.073, P<0.001), and the blood pressure (=1.259±5.354, P<0.001) and body temperature(=0.115±0.231, P<0.001) were slightly higher. The mean ratios of heart rate, respiratory rate, blood oxygen saturation, blood pressure and body temperature collected by the two devices were 0.996, 1.004, 1.000, 1.014, and 1.003, respectively. The 95% limits of agreement (LoA) and 95% confidence interval of 95%LoA of each indicator were within the clinically acceptable limit. The effective monitoring rate of each vital signs within 12 hours was above 98%. Conclusion The wireless wearable device has a high accuracy and reliability for continuous monitoring vital signs of patients after VATS for lung cancer, which provides a security guarantee for subsequent large-scale clinical application and further research.

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.

Objective This study investigated the impact of occupational mercury(Hg)exposure on human gene transcription and expression,and its potential biological mechanisms. Methods Differentially expressed genes related to Hg exposure were identified and validated using gene expression microarray analysis and extended validation.Hg-exposed cell models and PTEN low-expression models were established in vitro using 293T cells.PTEN gene expression was assessed using qRT-PCR,and Western blotting was used to measure PTEN,AKT,and PI3K protein levels.IL-6 expression was determined by ELISA. Results Combined findings from gene expression microarray analysis,bioinformatics,and population expansion validation indicated significant downregulation of the PTEN gene in the high-concentration Hg exposure group.In the Hg-exposed cell model(25 and 10 μmol/L),a significant decrease in PTEN expression was observed,accompanied by a significant increase in PI3K,AKT,and IL-6 expression.Similarly,a low-expression cell model demonstrated that PTEN gene knockdown led to a significant decrease in PTEN protein expression and a substantial increase in PI3K,AKT,and IL-6 levels. Conclusion This is the first study to report that Hg exposure downregulates the PTEN gene,activates the PI3K/AKT regulatory pathway,and increases the expression of inflammatory factors,ultimately resulting in kidney inflammation.

Objective:To investigate the expression and mechanism of microRNA-98-5p(miR-98-5p)and DNA methyltransferase 3A(DNMT3A)in patients with ulcerative colitis(UC)and animal model of ulcerative colitis.Methods:One-hundred UC patients in our hospital from January 2021 to December 2022 were collected as the observation group,another 100 healthy subjects were collected as the control group,serum miR-98-5p,DNMT3A mRNA and protein ex-pression were detected,the relationship between miR-98-5p,DNMT3A mRNA and clinical pathological characteristics of UC was analyzed;the UC rat model was established and randomly grouped into a blank group(CT group),a model group(UC group),a miR-98-5p control group(antagomiR-NC group),and a miR-98-5p inhibitor group(antagomiR-98-5p group),ELISA was applied to measure the levels of serum interleukin-1(IL-1),IL-6,and tumor necrosis factor(TNF),HE staining was applied to observe the histopathology changes of the colon,RT-qPCR and Western blot were applied to detect miR-98-5p,DNMT3A mRNA and protein expression levels in colon tissue.Results:Compared with the control group,the serum miR-98-5p expression of UC patients was increased,DNMT3A mRNA and DNMT3A were decreased,the expression of miR-98-5p and DNMT3A mRNA were associated with disease grading,mucopurulent bloody stools,mucosal acute and chronic inflammation,and atypical hyperplasia(P＜0.05);compared with the CT group,the colon mucosal layer of rats in the UC group showed defects,and obvious infil-tration of inflammatory cells,serum IL-1,IL-6,TNF and colon miR-98-5p expression were in-creased,the DNMT3A mRNA and protein expression were decreased(P＜0.05);compared with the antagomiR-NC group,the mucosal layer defect and inflammatory cell infiltration in the an-tagomiR-98-5p group were reduced,and the structure was clearer,serum IL-1,IL-6,TNF,and colon miR-98-5p expression were decreased,DNMT3A mRNA and protein expression were in-creased(P＜0.05);miR-98-5p had a targeted binding site with DNMT3A.Conclusion:Serum miR-98-5p expression of UC patients and the colon tissue of model rats was increased,while DNMT3A expression was decreased.Down-regulating miR-98-5p expression can promote the expression of DNMT3A and improve UC symptoms.

ObjectiveTo explore whether miR-375 regulates the malignant characteristics of osteosarcoma (OS) by influencing the expression of MMP13. MethodsPlasmid DNAs and miRNAs were transfected into OS cells and HEK293 cells using Lipofectamine 3000 reagent. Real-time quantitative polymerase chain reaction was performed to measure the expression of miR-375 and MMP13 in OS patients and OS cells. Western blot was performed to analyze the MMP13 protein in the patients with OS and OS cells. The targeting relationship between miR-375 and MMP13 was analyzed by luciferase assay. Migration and invasion were analysed by heal wound and transwell assays, respectively. ResultsmiR-375 expression in OS tissues was lower than that in normal tissues. The expression of MMP13 was upregulated in OS tissues. MMP13 expression was negatively correlated withmiR-375 expression in patients with OS. Migration and invasion were significantly inhibited in OS cells with the miR-375 mimic compared with OS cells with the miRNA control. MMP13 partially reversed the inhibition of migration and invasion induced by miR-375 in the OS cells. ConclusionmiR-375 attenuates migration and invasion by downregulating the expression of MMP13 in OS cells.

Objective To investigate factors influencing the occurrence of early haematoma expansion(HE)in patients with spontaneous intracerebral hemorrhage(sICH),to develop a predictive model and evaluate its predictive efficacy.Methods A retrospective cohort of 238 patients with sICH,admitted to General Hospital of Western Theater Command between January 2017 and December 2022,was analyzed.Patients were categorized into two groups based on the criteria of HE exceeding 33%in relative volume or 6 ml in absolute volume:HE group(n=62)and non-haematoma expansion(NHE)group(n=176).Clinical characteristics,laboratory findings,Non-contrast Computed Tomography(NCCT)imaging,and Glasgow Coma Scale(GCS)scores were compared between the two groups.Multifactorial logistic regression analysis was employed to identify risk factors for HE and to model the probability of its occurrence.The R language rms package was utilized to construct a nomogram model for predicting HE in sICH patients,Additionally,the related clinical,NCCT,and GCS models were constructed.The predictive efficacy of each model for HE in sICH patients was evaluated using area under Receive Operative Characteristic(ROC)curve(AUC),and the clinical application value of each model was assessed using accuracy,sensitivity,specificity,and Jordon's index.The Delong test was applied to analyze differences in the predictive values of the models.Results Significant differences in satellite sign,vortex sign,and history of anticoagulant treatment were observed between two groups(P<0.05).Multifactorial logistic regression analysis revealed independent risk factors for HE in sICH patients,including the first CT examination time,homogeneity,history of anticoagulant medication,volume,maximal diameter,hypodensity sign,island sign,satellite sign,and vortex sign(P<0.05).The AUCs for the constructed clinical model,NCCT model,GCS model and nomogram model in predicting the occurrence of HE in sICH patients were 0.672,0.706,0.518 and 0.754,respectively.The nomogram model demonstrated higher accuracy,sensitivity,Jordon's index and AUC compared with those in the clinical and NCTT models.Conclusions The first CT examination time,homogeneity,history of anticoagulant treatment,volume,maximum diameter,hypodensity sign,island sign,satellite sign,and vortex sign are independent predictors of early HE in sICH patients.The nomogram model,constructed with the above parameters,demonstrated high predictive efficacy for HE and holds potential for clinical application.

Objective:To compare the short-term effect and adverse reaction of venetoclax(VEN)combined with azacitidine(AZA)versus"7+3"regimen in newly diagnosed elder patients with acute myeloid leukemia(AML).Methods:From January 2021 to January 2022,the clinical data of seventy-nine newly diagnosed elder patients with AML at the Second Hospital of Shanxi Medical University and the Shanxi Bethune Hospital were retrospectively analyzed,including VEN+AZA group(41 cases)and"7+3"group(38 cases).The propensity score matching(PSM)method was used to balance confounding factors,then response,overall survival(OS),progression-free survival(PFS)and adverse reactions between the two groups were compared.Results:The ORR of VEN+AZA group and"7+3"group was 68％and 84％,respectively,and the CRc was 64％and 72％,respectively,the differents were not statistically significant(P＞0.05).In the VEN+AZA group,there were 5 non-remission(NR)patients,4 with chromosome 7 abnormality(7q-/-7),and 1 with ETV6 gene mutation.Median followed-up time between the two groups was 8 months and 12 months,respectively,and the 6-months OS was 84％vs 92％(P=0.389),while 6-months PFS was 84％vs 92％(P=0.258).The main hematological adverse reactions in two groups were stage Ⅲ-Ⅳmyelosuppression,and the incidence rate was not statistically different(P＞0.05).The median time of neutrophil recovery in two groups was 27(11-70)d,25(14-61)d(P=0.161),and platelet recovery was 27(11-75)d,25(16-50)d(P=0.270),respectively.The infection rate of VEN+AZA group was lower than that of"7+3"group(56％vs 88％,P=0.012).The rate of lung infections of two groups was 36％and 64％,respectively,the difference was statistically significant(P=0.048).Conclusion:The short-term effect of VEN+AZA group and"7+3"regimens in eldrly AML patients are similar,but the VEN+AZA regimen had a lower incidence of infection.The presence of chromosome 7 abnormality(7q-/-7)may be a poor prognostic factor for elderly AML patients treated with VEN+AZA.

Objective:To investigate the reasons for the weak expression of RHCE gene in a patient whose mimicking anti-Ce combined with anti-Jkb caused cross-matching non-combination.Methods:ABO,Rh,and Kidd blood group antigens were identified by test tube method and capillary centrifugation.Antibody screening and antibody specificity identification were performed using saline,polybrene and antiglobulin in tri-media association with multispectral cells.RHCE gene sequencing and haploid analysis were performed by multiplex PCR technique and RHCE protein modeling was performed using Swiss-Model.Results:The serum of the patient contained anti-Ce mimicking autoantibodies along with anti-Jkb antibodies.c.48G＞C,c.150C＞T,c.178C＞A,c.201A＞G,c.203A＞G,and c.307C＞T mutations were detected in the RHCE triple-molecule sequencing.A 109 bp insertion sequence was found in intron 2,with fragment loss from intron 5-8.The Rh-group genotype was DCe/DCe,and phenotype was CCDee.Conclusion:Genotyping techniques can assist in deducing the molecular mechanisms of some weakly expressed RhC,c,E,and e in patients'sera to aid in the identification of difficult antibodies and thus ensure the safety of patients'blood transfusion.

Objective To investigate the risk factors for bronchopulmonary dysplasia(BPD)in twin preterm infants with a gestational age of<34 weeks,and to provide a basis for early identification of BPD in twin preterm infants in clinical practice.Methods A retrospective analysis was performed for the twin preterm infants with a gestational age of<34 weeks who were admitted to 22 hospitals nationwide from January 2018 to December 2020.According to their conditions,they were divided into group A(both twins had BPD),group B(only one twin had BPD),and group C(neither twin had BPD).The risk factors for BPD in twin preterm infants were analyzed.Further analysis was conducted on group B to investigate the postnatal risk factors for BPD within twins.Results A total of 904 pairs of twins with a gestational age of<34 weeks were included in this study.The multivariate logistic regression analysis showed that compared with group C,birth weight discordance of>25%between the twins was an independent risk factor for BPD in one of the twins(OR=3.370,95%CI:1.500-7.568,P<0.05),and high gestational age at birth was a protective factor against BPD(P<0.05).The conditional logistic regression analysis of group B showed that small-for-gestational-age(SGA)birth was an independent risk factor for BPD in individual twins(OR=5.017,95%CI:1.040-24.190,P<0.05).Conclusions The development of BPD in twin preterm infants is associated with gestational age,birth weight discordance between the twins,and SGA birth.