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.

BACKGROUND:In recent years,some scholars in the field of tendon bone injury have attached stromal cell-derived factor 1 to tissue engineering scaffolds to promote tendon bone healing,and achieved good results.However,whether stromal cell-derived factor 1 promotes tendon bone healing mechanisms and participates in the repair of natural healing has not yet been defined. OBJECTIVE:To study the expression of stroma-cell derived factor 1 during tendon bone healing after rupture of the whole supraspinatus muscle of the rabbit rotator cuff and its migration effect and optimal in vitro migration promoting concentration on stem cells during tendon bone injury. METHODS:Totally 18 adult New Zealand rabbits were randomly selected to establish rotator cuff injury models,and an additional 3 rabbits were selected as blank controls.At 3,5,7,14,21,and 28 days after modeling,three rabbits were executed separately and the rabbits in the blank group were sacrificed.The tissues of tendon bone junction were taken and stored in a-80℃refrigerator.The expression of stromal cell-derived factor 1 was detected by ELISA at each time point after injury.Mesenchymal stem cells were isolated from the bone marrow of young rabbit femur,cultured,and identified.Transwell assay was performed to verify the migration-promoting effect of stromal cell-derived factor 1 on stem cells and the optimal migration-promoting concentration in vitro.The stem cells cultured to P3 were co-cultured with BrdU and injected into the rabbit ear marginal vein,and immunohistochemical staining was used to verify whether the stem cells migrated to the injury site. RESULTS AND CONCLUSION:(1)Stromal cell-derived factor 1 gene expression was bimodal during rotator cuff tendon bone healing.Stromal cell-derived factor 1 gene expression increased significantly at 3 days post-injury(P<0.01)and then decreased,reaching a minimum at 5 days post-injury.It increased again and reached a peak 14 days after injury(P<0.01)and then decreased.(2)Cell immunohistochemical staining displayed that stem cells labeled with BrdU did migrate to the injury site.(3)The results of the transwell experiment exhibited that 60-80 ng/mL stromal cell-derived factor 1 had the best effect on promoting migration of stem cells,while a concentration of 200 ng/mL inhibited migration.(4)Stromal cell-derived factor 1 is involved in the healing of rotator cuff tendon bone during the inflammatory response phase and the proliferation phase.The mechanism of action may be to promote the migration of stem cells to the injury and their differentiation into various types of cells to promote repair.In addition,the pro-migration effect of stromal cell-derived factor 1 exists at a range of concentrations,beyond which it may act as an inhibitor.

Objective By population survey,to explore the epidemiological characteristics of gastric precancerous lesions in Lishui District of Nanjing and provide objective basis for the prevention and treatment of early gastric cancer.Methods From July 2021 to December 2022,21 977 patients who received endoscopy and/or 13C-UBT in Lishui District People's Hospital and 6 medical community units in Nanjing City were retrospectively analyzed for demography characteristics,detection rate of gastric precancerous lesions,and H.Pylori infection rate.Results(1)590 cases of gastric precancerous lesions were detected(detection rate 2.68%);(2)The total detection rate of precancerous lesions and three pathological types in males were all higher than those in females(all P<0.001);(3)The minimum age for the total detection rate of precancerous lesions in males and the mini-mum age for each pathological type were lower than in females(P<0.001,0.009,0.005,0.002);(4)The popu-lation total H.pylori infection rate was 23.10%,the H.pylori infection rate in patients with precancerous lesions was higher than that in non-precancerous lesions(P<0.001),both H.pylori infection rate of male and female in precancerous lesions were all higher than those of non-precancerous lesions of the same sex(all P<0.001),in addition,the H.pylori infection rate of male whether in precancerous or non-precancerous lesions was higher than that of female(all P<0.001);(5)The precancerous lesions detection rate in male,female,and the overall age range of 20～29 to 70～79 years is positively correlated with age growth(P<0.001),and rapidly decreases after the age of 79,the of H.pylori infection rate was also positively correlated with age growth(P<0.001),and the trend of age change(P<0.001)was parallel to the precancerous lesions detection rate.Conclusions The detec-tion rate of gastric precancerous lesions in this region is above the average level in China;the total H.pylori infec-tion rate is at a relatively low level in China;the H.pylori infection rate is parallel to the age trend of the detection rate of gastric precancerous lesions,and increases with age.

Background::Very low birth weight (VLBW) infants are the key populations in neonatology, wherein morbidity and mortality remain major challenges. The study aimed to analyze the clinical characteristics of VLBW infants.Methods::A retrospective cohort study was conducted in West China Second Hospital between January 2016 and December 2021. Neonates with a birth weight of <1500 g were included. Mortality, care practices, and major morbidities were analyzed, and compared with those of previous 7 years (2009-2015).Results::Of the total 1750 VLBW, 1386 were infants born with birth weight between 1000-1499 g and 364 infants were born with weight below 1000 g; 42.9% (751/1750) required delivery room resuscitation; 53.9% (943/1750) received non-invasive ventilation only; 38.2% (669/1750) received invasive ventilation; 1517 VLBW infants received complete treatment. Among them, 60.1% (912/1517) of neonates had neonatal respiratory distress syndrome (NRDS), 28.7% (436/1517) had bronchopulmonary dysplasia (BPD), 22.0% (334/1517) had apnea, 11.1% (169/1517) had culture-confirmed sepsis, 8.4% (128/1517) had pulmonary hemorrhage, 7.6% (116/1517) had severe intraventricular hemorrhage (IVH)/periventricular leukomalacia (PVL), 5.7% (87/1517) had necrotizing enterocolitis (NEC), and 2.0% (31/1517) had severe retinopathy of prematurity. The total and in-hospital mortality rates were 9.7% (169/1750) and 3.0% (45/1517), respectively. The top three diagnoses of death among those who had received complete treatment were sepsis, NRDS, and NEC. In 2009-2015, 1146 VLBW were enrolled and 895 infants received complete treatment. The proportions of apnea, IVH, and IVH stage ≥3/PVL, were higher in 2009-2015 compared with those in 2016-2021, while the proportions of NRDS and BPD were characterized by significant increases in 2016-2021. The total and in-hospital mortality rates were 16.7% (191/1146) and 5.6% (50/895) respectively in 2009-2015.Conclusion::Among VLBW infants born in 2016-2021, the total and in-hospital mortality rates were lower than those of neonates born in 2009-2015. Incidences of NRDS and BPD increased in 2016-2021, which affected the survival rates and long-term prognosis of VLBW.

Objective To describe the current state of research and future research hotspots through a metrological analysis of the literature in the field of forensic anthropological remains identification re-search.Methods The data retrieved and extracted from the Web of Science Core Collection (WoSCC),the core database of the Web of Science information service platform (hereinafter referred to as "WoS"),was used to analyze the trends and topic changes in research on forensic identification of human re-mains from 1991 to 2022.Network visualisation of publication trends,countries (regions),institutions,authors and topics related to the identification of remains in forensic anthropology was analysed using python 3.9.2 and Gephi 0.10.Results A total of 873 papers written in English in the field of forensic anthropological remains identification research were obtained.The journal with the largest number of publications was Forensic Science International (164 articles).The country (region) with the largest number of published papers was China (90 articles).Katholieke Univ Leuven (Netherlands,21 articles) was the institution with the largest number of publications.Topic analysis revealed that the focus of forensic anthropological remains identification research was sex estimation and age estimation,and the most commonly studied remains were teeth.Conclusion The volume of publications in the field of forensic anthropological remains identification research has a distinct phasing.However,the scope of both international and domestic collaborations remains limited.Traditionally,human remains identifica-tion has primarily relied on key areas such as the pelvis,skull,and teeth.Looking ahead,future re-search will likely focus on the more accurate and efficient identification of multiple skeletal remains through the use of machine learning and deep learning techniques.

Objective To understand the current situation of human brain donation in Hebei Province by analyzing the basic information of Human Brain Bank samples of Hebei Medical University in order to provide basic data support for subsequent scientific research.Methods The samples collected from the Human Brain Bank of Hebei Medical University were analyzed(from December 2019 to February 2024),including gender,age,cause of death,as well as quality control data such as postmortem delay time,pH value of cerebrospinal fluid and and RNA integrity number and result of neuropathological diagnosis.Results Until February 2024,30 human brain samples were collected and stored in the Human Brain Bank of Hebei Medical University,with a male to female ratio of 9∶1.Donors over 70 years old accounted for 53％.Cardiovascular and cerebrovascular diseases(36.67％)and nervous system diseases(23.33％)accounted for a high proportion of the death causes.The location of brain tissue donors in Shijiazhuang accounted for 90％donations,and the others were from outside the city.The postmortem delay time was relatively short,90％within 12 hours and 10％more than 12 hours.69.23％of the brain samples had RNA integrity values greater than 6.Cerebrospinal fluid pH values ranged from 5.8 to 7.5,with an average value of 6.60±0.45.Brain weights ranged from 906-1496 g,with an average value of(1210.78±197.84)g.Three apolipoprotein E(APOE)alleles were detected including five genotypes(ε2/ε3,ε2/ε4,ε3/ε3,ε3/ε4,ε4/ε4).Eleven staining methods related to neuropathological diagnosis had been established and used.A total of 12 cases were diagnosed as neurodegenerative diseases(including Alzheimer's disease,Parkinson's disease,multiple system atrophy,corticobasal degeneration and progressive supranuclear palsy,etc.),accounting for 40％donated brains.The comorbidity rate of samples over 80 years old was 100％.Conclusion The summary and analyses of the data of brain donors in the Human Brain Bank of Hebei Medical University can reflect the current situation of the construction and operation of the brain bank in Hebei Province,and it can also be more targeted to understand and identify potential donors.Our information can provide reference for the construction of brain bank and provides more reliable materials and data support for scientific research.

Objective To explore the diagnostic efficacy of serum 14-3-3β protein combined with fractional exhaled nitric oxide(FeNO)and conventional ventilatory lung function parameters in diagnosing bronchial asthma(referred to as"asthma")in children.Methods A prospective study included 136 children initially diagnosed with asthma during an acute episode as the asthma group,and 85 healthy children undergoing routine health checks as the control group.The study compared the differences in serum 14-3-3β protein concentrations between the two groups,analyzed the correlation of serum 14-3-3β protein with clinical indices,and evaluated the diagnostic efficacy of combining 14-3-3β protein,FeNO,and conventional ventilatory lung function parameters for asthma in children.Results The concentration of serum 14-3-3β protein was higher in the asthma group than in the control group(P<0.001).Serum 14-3-3β protein showed a positive correlation with the percentage of neutrophils and total serum immunoglobulin E,and a negative correlation with conventional ventilatory lung function parameters(P<0.05).Cross-validation of combined indices showed that the combination of 14-3-3β protein,FeNO,and the percentage of predicted value of forced expiratory flow at 75%of lung volume had an area under the curve of 0.948 for predicting asthma,with a sensitivity and specificity of 88.9%and 93.7%,respectively,demonstrating good diagnostic efficacy(P<0.001).The model had the best extrapolation.Conclusions The combination of serum 14-3-3β protein,FeNO,and the percentage of predicted value of forced expiratory flow at 75%of lung volume can significantly improve the diagnostic efficacy for asthma in children.

Objective To observe the value of artificial intelligence(AI)models based on non-contrast chest CT for measuring bone mineral density(BMD).Methods Totally 380 subjects who underwent both non-contrast chest CT and quantitative CT(QCT)BMD examination were retrospectively enrolled and divided into training set(n=304)and test set(n=76)at a ratio of 8∶2.The mean BMD of L1-L3 vertebrae were measured based on QCT.Spongy bones of T5-T10 vertebrae were segmented as RO1,radiomics(Rad)features were extracted,and machine learning(ML),Rad and deep learning(DL)models were constructed for classification of osteoporosis(OP)and evaluating BMD,respectively.Receiver operating characteristic curves were drawn,and area under the curves(AUC)were calculated to evaluate the efficacy of each model for classification of OP.Bland-Altman analysis and Pearson correlation analysis were performed to explore the consistency and correlation of each model with QCT for measuring BMD.Results Among ML and Rad models,MLBagging OP and RadBagging-OP had the best performances for classification of OP.In test set,AUC of MLBagging-OP,RadBagging-op and DLOP for classification of OP was 0.943,0.944 and 0.947,respectively,with no significant difference(all P＞0.05).BMD obtained with all the above models had good consistency with those measured with QCT(most of the differences were within the range of(x)±1.96s),which were highly positively correlated(r=0.910-0.974,all P＜0.001).Conclusion AI models based on non-contrast chest CT had high efficacy for classification of OP,and good consistency of BMD measurements were found between AI models and QCT.

Objective:To explore the risk factors related to delayed pleural effusion in multiple trauma patients.Methods:A retrospective cohort study was conducted to analyze the clinical data of 145 multiple trauma patients admitted to the First Affiliated Hospital of Soochow University from January 2022 to October 2023, including 99 males and 46 females, aged 18-81 years [56.0(46.5, 64.5)years]. Based on whether delayed pleural effusion developed after injury, the patients were divided into delayed pleural effusion group ( n=66) and non-delayed pleural effusion group ( n=79). The clinical data of the patients in both groups were collected, including gender, age, underlying disease (diabetes mellitus and hypertension), cause of injury (traffic injury, blow injury, fall from height, and others), comorbid injuries (traumatic brain injury, maxillofacial fracture, clavicular fracture, scapular fracture, sternal fracture, spinal fracture, multiple rib fracture, pneumothorax, lung contusion, and pelvic fracture), severity of injury [injury severity score (ISS) and abbreviated injury scale (AIS) score for the chest], location and number of rib fractures, vital signs at admission (body temperature, heart rate, respiration, systolic blood pressure, diastolic blood pressure), and clinical test indices [white blood cells (WBC), hemoglobin (Hb), platelets (PLT), total protein (TP), albumin (ALB), C-reactive protein (CRP), procalcitonin (PCT), fibrinogen (FIB), fibrin degradation product (FDP), D-dimer (D-D), aspartate transaminase (AST), alanine transferase (ALT), and creatinine (Cr)]. Univariate analysis was conducted to assess the correlation between the forementioned factors and the development of delayed pleural effusion after multiple traumas. Multivariate Logistic regression analysis was used to determine the independent risk factors for the development of delayed pleural effusion after multiple traumas. Results:The results of univariate analysis showed that multiple rib fracture, pneumothorax, pulmonary contusion, chest AIS score, posterior rib fracture, number of rib fractures, TP, ALB, CRP, PCT and FDP were correlated with delayed pleural effusion in multiple trauma patients ( P<0.05 or 0.01); whereas gender, age, underlying disease, cause of injury, sternal fracture, spinal fracture, clavicular fracture, scapular fracture, pelvic fracture, maxillofacial fracture, traumatic brain injury, anterior rib fracture, ISS, vital signs at admission, WBC, Hb, PLT, FIB, D-D, AST, ALT, and Cr were not correlated with delayed pleural effusion in multiple trauma patients ( P>0.05). The results of multivariate Logistic regression analysis revealed that lung contusion ( OR=3.96, 95% CI 1.59, 9.85, P<0.01), ALB ( OR=0.79, 95% CI 0.66, 0.94, P<0.01), and CRP ( OR=1.02, 95% CI 1.01, 1.03, P<0.01) were significantly correlated with delayed pleural effusion in multiple trauma patients. Conclusion:Lung contusion, ALB, and CRP are the independent risk factors for delayed pleural effusion in multiple trauma patients.

Objective To construct and validate a risk prediction model for delayed healing of venous leg ulcer(VLU),so as to provide a reference basis for early identification of people at high risk of delayed healing.Methods Using a convenience sampling method,331 VLU patients attending vascular surgery departments in 2 tertiary A hospitals in Sichuan Province from January 2018 to December 2022 were selected as a modeling group and an internal validation group,and 112 patients admitted to another tertiary A hospital were selected as an external validation group.Risk factors for delayed healing in VLU patients were screened using univariate analysis,LASSO regression,and multivariate logistic regression analysis,and a risk prediction model was constructed using R software,and the predictive effects of the models were examined using the area under the receiver operating characteristic curve,the Hosmer-Lemeshow test,decision curve,and the bootstrap resampling for internal validation and spatial external validation were performed,respectively.Results The predictors that ultimately entered the prediction model were diabetes(OR=4.752),deep vein thrombosis(OR=4.104),lipodermatosclerosis(OR=5.405),ulcer recurrence(OR=3.239),and ankle mobility(OR=5.520).The model had good discrimination(AUC:0.819 for internal validation and 0.858 for external validation),calibration(Hosmer-Lemeshow test:χ2=13.517,P=0.095 for internal validation and χ2=3.375,P=0.909 for external validation)and clinical validity.Conclusion The model constructed in this study has good differentiation and calibration,and it can effectively predict people at high risk of delayed healing of VLU,which facilitates targeted clinical interventions to improve ulcer outcomes and reduce the risk of delayed ulcer healing.