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 role of pulmonary surfactant(PS)combined with budesonide in improving oxygenation and clinical outcomes of neonatal acute respiratory distress syndrome(ARDS).Methods The present study is a historically controlled trial.Infants with ARDS requiring mechanical ventilation and PS replacement therapy were collected from the neonatal unit of Southwest Medical University.Those from January 2022 to November 2022 were set as intervention group(PS+ budesonid,n=35),treated with intratracheal instillation of a mixed suspension of budesonide(0.25 mg/kg)and PS(200 mg/kg),and continuous budesonide nebulization(0.25 mg/kg,twice per day)until withdrawal,then compared with a historical cohort,who just received intratracheal instillation of PS(200 mg/kg)(January 2020-December 2021,PS group,n=35).Baseline data such as gender,mode of delivery,1 min and 5 min Apgar score,birth weight,gestational age,time of onset,and cause of onset were recorded in both groups.The oxygenation and clinical outcomes of infants were compared between the two groups,including:(1)Arterial blood gas analysis indicators,such as partial pressure of oxygen(PaO2)and oxygenation index(OI)before treatment and at 6,12 and 24 hours of treatment;(2)Clinical observation and evaluation indicators,such as the time to withdrawal,duration of oxygen supplementation,length of stay,improvement of the radiological images of the lungs at 72 h of treatment,and repeated PS use;(3)Blood chemistry indicators,such as white blood cell(WBC),neutrocyte(NEU),procalcitonin(PCT)before treatment and at 3 and 7 days of treatment;and(4)Observation indicators of complications,weight growth,and mortality outcomes,such as the incidences of intracranial hemorrhage,gastrointestinal hemorrhage,neonatal necrotizing enterocolitis(NEC),and hyperglycemia,weight growth,and fatality rate.Results The differences in baseline data between the two groups were not statistically different(P＞0.05).The levels of PaO2 of the two groups were increased after treatment for different time periods,while the levels of OI were decreased(P＜0.001),and the levels of above indexes changed more significantly in PS+budesonide group than those in PS group(P＜0.05).The time to withdrawal,duration of oxygen supplementation,and length of stay in PS+budesonide group were shorter than those in PS group;the radiological images of the lungs showed that the pulmonary inflammation absorption was significantly better in PS+ budesonide group than that in PS group,while no significant difference between the two groups of infants with repeated PS use.The NEU was significantly higher in PS+budesonide group than in PS group at 3 d and 7 d of treatment(P＜0.001);and at 3 days of treatment,the PCT levels were significantly lower in PS+budesonide group than that in PS group(P＜0.05).The incidences of intracranial hemorrhage,gastrointestinal hemorrhage,NEC,hyperglycemia,weight growth,and fatality rate were not significantly different between the two groups(P＞0.05).Conclusion The use of budesonide in addition to surfactant may improve the oxygenation of neonates with ARDS,improve the inflammatory infiltrates in lungs,shorten the duration of mechanical ventilation and oxygen supplementation,and without short-term complications associated with budesonide use.

Objective·To analyze the clinicopathologic characteristics,gene mutation profile,and prognostic factors of thyroid diffuse large B-cell lymphoma(DLBCL).Methods·From November 2003 to December 2021,a total of 66 patients with thyroid DLBCL[23 cases(34.8%)with primary thyroid DLBCL,and 43 cases(65.2%)with secondary thyroid DLBCL]admitted to Ruijin Hospital,Shanghai Jiao Tong University School of Medicine were retrospectively analyzed for their clinicopathological data,survival and prognostic factors.Gene mutation profiles were evaluated by targeted sequencing(55 lymphoma-related genes)in 40 patients.Results·Compared to primary thyroid DLBCL,secondary thyroid DLBCL had advanced ratio of Ann Arbor stage Ⅲ?Ⅳ(P=0.000),elevated serum lactate dehydrogenase(LDH)(P=0.043),number of affected extranodal involvement≥2(P=0.000),non-germinal center B cell(non-GCB)(P=0.030),BCL-2/MYC double expression(DE)(P=0.026),and international prognostic index(IPI)3?5-scores(P=0.000).The proportion of patients who underwent thyroid surgery(P=0.012)was lower than that of patients with primary thyroid DLBCL.The complete remission(CR)rate in primary thyroid DLBCL patients was higher than that in secondary thyroid DLBCL patients(P=0.039).Fifty-five patients(83%)received rituximab combined with cyclophosphamide,doxorubicin,vincristine,and prednisone(R-CHOP)-based first-line regimen.The estimated 5-year progression free survival(PFS)rate of primary thyroid DLBCL patients was 95.0%,higher than the 49.7%of the secondary patients(P=0.010).Univariate analysis showed that Ann Arbor Ⅲ?Ⅳ(HR=4.411,95%CI 1.373?14.170),elevated LDH(HR=5.500,95%CI 1.519?19.911),non-GCB(HR= 5.291,95%CI 1.667?16.788),and DE(HR=6.178,95%CI 1.813?21.058)were adverse prognostic factors of PFS in patients with thyroid DLBCL.Ann Arbor Ⅲ?Ⅳ(HR=7.088,95%CI 0.827?60.717),elevated LDH(HR=6.982,95%CI 0.809?60.266),and DE(HR=18.079,95%CI 1.837?177.923)were adverse prognostic factors of overall survival(OS).Multivariate analysis showed that Ann Arbor Ⅲ?Ⅳ(HR=4.693,95%CI 1.218?18.081)and elevated LDH(HR=5.058,95%CI 1.166?21.941)were independent adverse prognostic factors of PFS in patients with thyroid DLBCL.Targeted sequencing data showed mutation frequency>20%in TET2(n=14,35%),KMT2D(n=13,32%),TP53(n=11,28%),GNA13(n=10,25%),KMT2C(n=9,22%),and TP53 were adverse prognostic factors of PFS in patients with thyroid DLBCL(P=0.000).Conclusion·Patients with primary thyroid DLBCL have better PFS and OS than those with secondary thyroid DLBCL.Ann Arbor Ⅲ?Ⅳ,elevated LDH,non-GCB,and DE(MYC and BCL2)are adverse prognostic factors in thyroid DLBCL.TET2,KMT2D,TP53,GNA13,and KMT2C are commonly highly mutated genes in thyroid DLBCL,and the prognosis of patients with TP53 mutations is poor.

AIM:To investigate the role of Ywhab in the growth of mouse B-cell lymphoma,and to explore the potential underlying mechanisms.METHODS:The correlation between Ywhab and human diffuse large B-cell lymphoma(DLBCL)was investigated by bioinformatics analysis.Infection with retroviral vector was performed to establish stable mouse B-cell lymphoma 38B9 cell line with overexpression of Ywhab gene,which was verified by RT-qPCR and Western blot.The impact of Ywhab overexpression on 38B9 cell growth both in vitro and in vivo was detected by cell counting,CCK-8 assay,and subcutaneous tumor loading experiments.The expression of apoptosis-related proteins was detected by RT-qPCR and Western blot.Co-immunoprecipitation combined with mass spectrometry(CoIP-MS)was employed to search for proteins specifically binding to Ywhab gene product 14-3-3β,which was confirmed by Western blot and molecu-lar docking analysis.RESULTS:The Ywhab gene exhibited low expression in DLBCL,which was correlated with poor clinical prognosis of DLBCL patients.Compared with normal mouse bone marrow B cells,Ywhab expression was low in 38B9 cells.Overexpression of Ywhab induced apoptosis of 38B9 cells both in vitro and in vivo,promoted the expression of pro-apoptotic proteins Puma,Noxa and Bax at both mRNA and protein levels,and inhibited the mRNA and protein expres-sion of anti-apoptotic protein Bcl2(P＜0.05).The 14-3-3β protein specifically bound to Hsp90aa1 and reduced Hsp90aa1 protein levels,thereby suppressing the growth of 38B9 cells.CONCLUSION:Ywhab promotes the apoptosis of B-cell lymphoma cells by binding to Hsp90aa1 and thereby inhibiting the function of Hsp90aa1.

Objective To explore the effect of nursing intervention based on the integrated theory of health behavior change(ITHBC)on the health behavior of patients with diabetes retinopathy(DR).Methods 157 patients with diabetes retinopathy admitted from July to December 2022 in a tertiary A hospital in Wuhan,Hubei Province,were selected as the experimental group by convenience sampling method,while 156 patients with diabetes retinopathy hospitalized from January to June 2022 were selected as the control group,and routine nursing was caried out.The disease cognition scale scores,self-management scale scores,and quality of life scale scores were compared between the 2 groups before and after 6 months of intervention.Results After 6 months of intervention,the disease cognition scale scores,self-management scale scores,and quality of life scale scores of both groups improved,and the results in the experimental group were better than these in the control group,and the difference is statistically significant(P＜0.05).Conclusion Nursing interventions based on the ITHBC theory can help improve the disease cognition of DR patients,establish and maintain healthy behaviors,improve their self-management level,and improve their quality of life.

Objective: To understand trends and related factors influencing overweight and obesity among primary and secondary school students in Tianjin, so as to provide a basis for formulating overweight and obesity prevention and control strategies. Methods: In September of each year from 2019 to 2023, a survey was conducted among 197 707 primary and secondary school students in 16 districts of Tianjin through a stratified random cluster sampling method. Physical examination was carried out in accordance with the Technical Standard for Physical examination for Student, and overweight and obesity survey was carried out. Basic information, smoking, drinking, diet, physical exercise, and sleep status were collected through questionnaire surveys. Results: The detection rates of overweight and obesity among primary and secondary school students in Tianjin from 2019 to 2023 were 39.07%, 43.33%, 41.54%, 43.92%, and 40.24%, respectively，showing an increasing trend(χ2trend=7.96,P<0.01). The detection rates of overweight increased in both vocational high schools and suburban counties (χ2trends=9.08, 47.18, P<0.01). The detection rates of obesity increased among both male and female students, in primary and vocational high schools and suburban counties (χ2trends=108.34, 15.99, 7.32, 10.95, 14.75, P<0.01). Multivariate Logistic regression analysis showed that smoking, drinking, unhealthful diet, and lack of proper physical exercise had a higher risk of obesity among primary and secondary school students (OR=1.26, 1.13, 1.08, 1.21, P<0.05). Stratified analysis showed that the risk of obesity was higher among boys with unhealthful and moderate lifestyle habits, as well as primary school students with unhealthful lifestyle habits (OR=1.15, 1.11, 1.27, P<0.05). Boys, girls and primary school students with unhealthful lifestyle habits, girls and ordinary high school students with moderate lifestyle habits had higher risk of being overweight (OR=1.14, 1.32, 1.21, 1.18, 1.40, P<0.05). Conclusions The detection rates of overweight and obesity among primary and secondary school students in Tianjin shows an increasing trend. Comprehensive lifestyle should be implemented to better prevent and control the risk of overweight and obesity.

Objective:To explore the value of predicting new-onset heart failure events in patients with hypertrophic cardiomyopathy (HCM) using clinical and cardiac magnetic resonance (CMR) features based on machine learning algorithms.Methods:The study was a retrospective cohort study. Patients with a confirmed diagnosis of HCM who underwent CMR examinations at Beijing Anzhen Hospital from May 2017 to March 2021 were selected and randomly divided into the training set and the validation set in a ratio of 7∶3. Clinical data and CMR parameters (including conventional parameters and radiomics features) were collected. The endpoint events were heart failure hospitalization and heart failure death, with follow-up ending in January 2023. Features with high stability and P value<0.05 in univariate Cox regression analysis were selected. Subsequently, three machine learning algorithms—random forest, decision tree, and XGBoost—were used to build heart failure event prediction models in the training set. The model performance was then evaluated using the independent validation set, with the performance assessed based on the concordance index. Results:A total of 462 patients were included, with a median age of 51 (39, 62) years, of whom 332 (71.9%) were male. There were 323 patients in the training set and 139 in the validation set. The median follow-up time was 42 (28, 52) months. A total of 44 patients (9.5% (44/462)) experienced endpoint events (8 cases of heart failure death and 36 cases of heart failure hospitalization), with 31 events in the training set and 13 in the validation set. Univariate Cox regression analysis identified 39 radiomic features, 4 conventional CMR parameters (left ventricular end-diastolic volume index, left ventricular end-systolic volume index, left ventricular ejection fraction, and late gadolinium enhancement ratio), and 1 clinical feature (history of non-sustained ventricular tachycardia) that could be included in the machine learning model. In the prediction models built with the training set, the concordance indices for the random forest, decision tree, and XGBoost models were 0.966 (95% CI 0.813-0.995), 0.956 (95% CI 0.796-0.992), and 0.973 (95% CI 0.823-0.996), respectively. In the validation set, the concordance indices for the random forest, decision tree, and XGBoost models were 0.854 (95% CI 0.557-0.964), 0.706 (95% CI 0.399-0.896), and 0.703 (95%CI 0.408-0.890), respectively. Conclusion:Integrating clinical and CMR features of HCM patients through machine learning aids in predicting heart failure events, with the random forest model showing superior performance.

Objective:To evaluate the clinical application effects of a domestic bone-level implant system for restoring single tooth loss, and provide clinical evidence for the promotion and application of domestic implants.Methods:A prospective, multicenter clinical trial was conducted from April 2018 to January 2020 in three institutions: Department of Oral Implantology, School & Hospital of Stomatology, Wuhan University, Department of Stomatology, The First Affiliated Hospital of Zhejiang University School of Medicine, and Department of Stomatology, The Third Hospital of Hebei Medical University. The trial planned to include 100 patients for single tooth implantation and restoration, followed up for 1 year, to evaluate the implantation success rate and other related outcomes.Results:This study screened a total of 142 patients and ultimately included 100, comprising 43 males and 57 females with age of (47.0±12.2) years. Ninety-eight out of 100 patients completed a one-year follow-up (98.0%), while 2 patients terminated the trial early due to implant loosening (2.0%). After a one-year follow-up, the implants of the 98 patients were all functioning successfully, with a success rate of 98.0% (98/100). The patients were satisfied with the overall restoration effect.Conclusions:This study indicates that the domestic bone-level implant system has achieved favorable short-term clinical outcomes for single-tooth implantation and restoration.

Temporomandibular joint (TMJ) diseases are common clinical conditions. The number of patients with TMJ diseases is large, and the etiology, epidemiology, disease spectrum, and treatment of the disease remain controversial and unknown. To understand and master the current situation of the occurrence, development and prevention of TMJ diseases, as well as to identify the patterns in etiology, incidence, drug sensitivity, and prognosis is crucial for alleviating patients′suffering.This will facilitate in-depth medical research, effective disease prevention measures, and the formulation of corresponding health policies. Cohort construction and research has an irreplaceable role in precise disease prevention and significant improvement in diagnosis and treatment levels. Large-scale cohort studies are needed to explore the relationship between potential risk factors and outcomes of TMJ diseases, and to observe disease prognoses through long-term follw-ups. The consensus aims to establish a standard conceptual frame work for a cohort study on patients with TMJ disease while providing ideas for cohort data standards to this condition. TMJ disease cohort data consists of both common data standards applicable to all specific disease cohorts as well as disease-specific data standards. Common data were available for each specific disease cohort. By integrating different cohort research resources, standard problems or study variables can be unified. Long-term follow-up can be performed using consistent definitions and criteria across different projects for better core data collection. It is hoped that this consensus will be facilitate the development cohort studies of TMJ diseases.

Objective To investigate the clinical characteristics,treatment methods,and prognosis of a-cute leukemia patients with extramedullary infiltration.Methods The clinical characteristics and treatment methods of 47 acute leukemia patients with extramedullary infiltration admitted to the Affiliated Hospital of Guizhou Medical University from April 2014 to April 2023 were retrospectively analyzed.Subgroup analysis was performed according to whether there was extramedullary infiltration before transplantation,and whether there was isolated extramedullary recurrence after transplantation.Based on this analysis,the patients were di-vided into the pre-transplantation radiotherapy group and pre-transplantation non-radiotherapy group,the post-transplantation radiotherapy group and post-transplantation non-radiotherapy group.According to the treatment methods of central nervous system leukemia(CNSL),the patients were divided into the intrathecal injection group(n=12)and combination of intrathecal injection and radiotherapy group(n=13).The local remission situation,survival duration,and toxic and side effects of radiotherapy and chemotherapy were com-pared.Results For acute leukemia patients with extramedullary infiltration,the overall survival time(OS)in the radiotherapy group was better than that in the non-radiotherapy group(median OS:706 d vs.151 d,P=0.015).Subgroup analysis showed that the OS of the pre-transplantation radiotherapy group was better than that of the pre-transplantation non-radiotherapy group(median OS:592 d vs.386 d,P=0.035).For CNSL,the combination of intrathecal injection and radiotherapy group had a better OS than the intrathecal injection group(median OS:547 d vs.388 d,P=0.045).The event-free survival time(EFS)of the radiotherapy group was better than that of the non-radiotherapy group(median EFS:175 d vs.50 d,P=0.005).The COX pro-portional-hazards model showed that treatment with or without radiotherapy had a significant impact on the OS of acute leukemia patients with extramedullary infiltration.The risk of death in the pre-transplantation non-radiotherapy group was 2.231 times higher than that in the pre-transplantation radiotherapy group(HR=3.231,95％CI:1.021-10.227,P=0.046).Compared with the non-radiotherapy group,the radiother-apy group had a higher local remission and a lower risk of haematological toxicity,infection,and haemorrhage.Conclusion Radiotherapy can rapidly alleviate the local symptoms of acute leukemia complicated with extr-amedullary infiltration,prolong the survival time of these patients,and reduce the risk of hematologic toxicity,infection,and haemorrhage.