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 effect of nano-hydroxyapatite/collagen composite (nHAC) on bone graft fusion after anterior cervical discectomy and fusion (ACDF) in patients with cervical spondylosis and low bone mass. METHODS: A retrospective analysis was conducted on 47 patients with low bone mass who underwent ACDF from 2017 to 2021. They were divided into the nHAC group and the allogeneic bone group according to different bone graft materials. The nHAC group included 26 cases, with 8 males and 18 females;aged 50 to 78 years old with an average of (62.81±7.79) years old;the CT value of C₂-C₇ vertebrae was (264.16±36.33) HU. The allogeneic bone group included 21 cases, with 9 males and 12 females;aged 54 to 75 years old with an average of (65.95±6.58) years old;the CT value of C₂-C₇ vertebrae was (272.39±40.44) HU. The visual analogue scale (VAS), neck disability index (NDI), and Japanese Orthopaedic Association (JOA) spinal cord function score were compared before surgery, 1 week after surgery, and at the last follow-up to evaluate the clinical efficacy. Imaging assessment included C₂-C₇ Cobb angle, surgical segment height, intervertebral fusion, and whether the cage subsidence occurred at 1 week after surgery and the last follow-up. RESULTS: The follow-up duration ranged from 26 to 39 months with an average of (33.27±3.34) months in the nHAC group and 26 to 41 months with an average of (31.86±3.57) months in the allogeneic bone group. At 1 week after surgery and the last follow-up, the VAS, NDI scores, and JOA scores in both groups were significantly improved compared with those before surgery, with statistically significant differences (P<0.05). At 1 week after surgery, the C₂-C₇ Cobb angles in the nHAC group and the allogeneic bone group were (14.26±10.32)° and (14.28±8.20)° respectively, which were significantly different from those before surgery (P<0.05). At the last follow-up, the C₂-C₇ Cobb angles in both groups were smaller than those at 1 week after surgery, with statistically significant differences (P<0.05). At 1 week after surgery, the height of the surgical segment in the nHAC group was (31.65±2.55) mm, and that in the allogeneic bone group was (33.63±3.26) mm, which were significantly different from those before surgery (P<0.05). At the last follow-up, the height of the surgical segment in both groups decreased compared with that at 1 week after surgery, with statistically significant differences (P<0.05). At the last follow-up, 39 surgical segments were fused and 6 cages subsided in the nHAC group;40 surgical segments were fused and 7 cages subsided in the allogeneic bone group;there was no statistically significant difference between the two groups (P>0.05). Compared with the CT value of vertebrae without cage subsidence, the CT value of vertebrae with cage subsidence in both groups was significantly lower, with a statistically significant difference (P<0.05). CONCLUSION The application of nHAC in ACDF for patients with low bone mass can achieve effective fusion of the surgical segment. There is no significant difference in improving clinical efficacy, intervertebral fusion, and cage subsidence compared with the allogeneic bone group. With the extension of follow-up time, the C₂-C₇ Cobb angle decreases, the height of the surgical segment is lost, and the cage subsides in both the nHAC group and the allogeneic bone group, which may be related to low bone mass. Low bone mass may be one of the risk factors for cervical spine sequence changes, surgical segment height loss, and cage subsidence after ACDF.
Humans ; Male ; Female ; Middle Aged ; Spondylosis/physiopathology* ; Spinal Fusion/methods* ; Cervical Vertebrae/surgery* ; Aged ; Diskectomy ; Durapatite ; Retrospective Studies ; Collagen/chemistry*

Hearing loss is the most prevalent disabling disease. Cochlear implantation（CI） serves as the primary intervention for severe to profound hearing loss. This consensus systematically explores the value of genetic diagnosis in the pre-operative assessment and efficacy prognosis for CI. Drawing upon domestic and international research and clinical experience, it proposes an evidence-based medicine three-tiered prognostic classification system（Favorable, Marginal, Poor）. The consensus focuses on common hereditary non-syndromic hearing loss（such as that caused by mutations in genes like GJB2, SLC26A4, OTOF, LOXHD1） and syndromic hereditary hearing loss（such as Jervell & Lange-Nielsen syndrome and Waardenburg syndrome）, which are closely associated with congenital hearing loss, analyzing the impact of their pathological mechanisms on CI outcomes. The consensus provides recommendations based on multiple round of expert discussion and voting. It emphasizes that genetic diagnosis can optimize patient selection, predict prognosis, guide post-operative rehabilitation, offer stratified management strategies for patients with different genotypes, and advance the application of precision medicine in the field of CI.
Humans ; Cochlear Implantation ; Prognosis ; Hearing Loss/surgery* ; Consensus ; Connexin 26 ; Mutation ; Sulfate Transporters ; Connexins/genetics*

Two novel diketopiperazines (1 and 5), along with ten known compounds (2-4, 6-12) demonstrating significant skin inflammation inhibition, were isolated from a marine-derived fungus identified as Aspergillus sp. FAZW0001. The structural elucidation and configurational reassessments of compounds 1-5 were established through comprehensive spectral analyses, with their absolute configurations determined via single crystal X-ray diffraction using Cu Kα radiation, Marfey's method, and comparison between experimental and calculated electronic circular dichroism (ECD) spectra. Compounds 1, 2, and 8 exhibited significant anti-inflammatory activities in Propionibacterium acnes (P. acnes)-induced human monocyte cell lines. Compound 8 demonstrated the ability to down-regulate interleukin-1β (IL-1β) expression by inhibiting Toll-like receptor 2 (TLR2) expression and modulating the activation of myeloid differentiation factor 88 (MyD88), mitogen-activated protein kinase (MAPK), and nuclear factor κB (NF-κB) signaling pathways, thus reducing the cellular inflammatory response induced by P. acnes. Additionally, compound 8 showed the capacity to suppress mitochondrial reactive oxygen species (ROS) production and nucleotide-binding oligomerization domain-like receptor protein 3 (NLRP3) inflammasome activation, thereby reducing IL-1β maturation and secretion. A three-dimensional quantitative structure-activity relationships (3D-QSAR) model was applied to compounds 5-12 to analyze their anti-inflammatory structure-activity relationships.
Humans ; Aspergillus/chemistry* ; Diketopiperazines/isolation & purification* ; Anti-Inflammatory Agents/isolation & purification* ; Interleukin-1beta/genetics* ; Toll-Like Receptor 2/immunology* ; Propionibacterium acnes/drug effects* ; NF-kappa B/genetics* ; Molecular Structure ; Myeloid Differentiation Factor 88/immunology* ; Monocytes/immunology* ; NLR Family, Pyrin Domain-Containing 3 Protein/genetics* ; Cell Line

OBJECTIVE: Pneumoconiosis, a lung disease caused by irreversible fibrosis, represents a significant public health burden. This study investigates the causal relationships between gut microbiota, gene methylation, gene expression, protein levels, and pneumoconiosis using a multi-omics approach and Mendelian randomization (MR). METHODS: We analyzed gut microbiota data from MiBioGen and Esteban et al. to assess their potential causal effects on pneumoconiosis subtypes (asbestosis, silicosis, and inorganic pneumoconiosis) using conventional and summary-data-based MR (SMR). Gene methylation and expression data from Genotype-Tissue Expression and eQTLGen, along with protein level data from deCODE and UK Biobank Pharma Proteomics Project, were examined in relation to pneumoconiosis data from FinnGen. To validate our findings, we assessed self-measured gut flora from a pneumoconiosis cohort and performed fine mapping, drug prediction, molecular docking, and Phenome-Wide Association Studies to explore relevant phenotypes of key genes. RESULTS: Three core gut microorganisms were identified: Romboutsia ( OR = 0.249) as a protective factor against silicosis, Pasteurellaceae ( OR = 3.207) and Haemophilus parainfluenzae ( OR = 2.343) as risk factors for inorganic pneumoconiosis. Additionally, mapping and quantitative trait loci analyses revealed that the genes VIM, STX8, and MIF were significantly associated with pneumoconiosis risk. CONCLUSIONS This multi-omics study highlights the associations between gut microbiota and key genes ( VIM, STX8, MIF) with pneumoconiosis, offering insights into potential therapeutic targets and personalized treatment strategies.
Humans ; Male ; East Asian People/genetics* ; Europe ; Gastrointestinal Microbiome ; Lung ; Macrophage Migration-Inhibitory Factors/metabolism* ; Mendelian Randomization Analysis ; Multiomics ; Pneumoconiosis/microbiology* ; Intramolecular Oxidoreductases

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 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.

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.