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

Objective To investigate the role of transcription factor EB(TFEB)in endotoxin-tolerant macrophages.Methods The RAW264.7 cells were divided into blank group(DMEM medium),LPS 5 group(5 ng/mL LPS treatment for 4 h),LPS 100 group(100 ng/mL LPS treatment for 4 h),and tolerance group(5 ng/mL LPS for 12 h followed by 100 ng/mL LPS for 4 h).The releases of inflammatory factors TNF-α and IL-6 were measured using ELISA.Western blotting and immunofluorescence assay were used to evaluate the distribution of autophagy-related proteins LC3 and P62,as well as TFEB in the cytoplasm and nucleus.Lentiviral overexpression of TFEB or siRNA-mediated knockdown of TFEB were performed to observe the changes in autophagy levels and bacterial clearance ability in the tolerant cells.Results The cells in the tolerance group had significantly lower contents of TNF-α and IL-6,as well as reduced bacterial clearance ability(P<0.01),down-regulated LC3 expression while up-regulated P62 level,and decreased expression of TFEB in both the cytoplasm and nucleus(P<0.01)when compared with the cells of the LPS 100 group.Overexpression of TFEB significantly increased LC3 level,reduced P62 level,and enhanced bacterial clearance ability in the endotoxin-tolerant cells(P<0.01).In contrast,siRNA-mediated knockdown of TFEB had no significant impacts on LC3 and P62 expression levels or bacterial clearance ability.Conclusion Overexpression of TFEB can restore the autophagy of endotoxin-tolerant cells and enhance their bacterial clearance capacity,thereby alleviating the immunosuppressive state of sepsis.These findings suggest that TFEB holds promise as a potential therapeutic target for the prevention and treatment of sepsis.

Objective To investigate the role and underlying mechanism of activating transcription factor 3(ATF3)in suppressing lipopolysaccharide(LPS)-induced autophagy and inflammatory responses in macrophages.Methods Firstly,the gene expression omnibus(GEO)database was used to analyze ATF3 expression in peripheral blood mononuclear cells(PBMCs)from sepsis patients,and gene set enrichment analysis(GSEA)was performed to identify enriched signaling pathways.Secondly,RAW264.7 macrophages were divided into a blank control group and an LPS-stimulated group(100 ng/mL LPS).Western blotting and immunofluorescence assay were used to detect ATF3 protein expression and observe its subcellular localization,respectively.Lentiviral transduction was used to generate ATF3 knockdown and overexpression cell lines to evaluate their effects on cytokine release and bacterial clearance.Cleavage Under Targets and Tagmentation(CUT&Tag)sequencing was employed to identify downstream target genes transcriptionally regulated by ATF3.Furthermore,the impact of ATF3 knockdown or overexpression on autophagy-related gene 5(ATG5),autophagy-related gene 16-like 1(ATG16L1),and autophagy levels was evaluated.Results GEO analysis revealed that ATF3 expression was significantly elevated in PBMCs from sepsis patients(P<0.01),and GSEA showed significant enrichment of autophagy-related and inflammation-related pathways(P<0.01).In RAW264.7 cells,100 ng/mL LPS stimulation significantly increased ATF3 expression in the nucleus than the blank control group(P<0.01).ATF3 knockdown led to increased secretions of TNF-α and IL-6 and enhanced bacterial clearance of macrophages(P<0.01),whereas ATF3 overexpression significantly suppressed TNF-α and IL-6 releases,and remained bacterial clearance at a low level when compared with the conditions in the negative control(NC)group(P<0.01).CUT&Tag results demonstrated that ATF3 was enriched at the promoter regions of key autophagy genes Atg5 and Atg16l1.Compared with the NC group,ATF3 knockdown significantly up-regulated the protein levels of LC3-II/I,ATG5,and ATG16L1 while decreased p62 expression(P<0.01).Conversely,ATF3 overexpression inhibited the expression of LC3-II/I,ATG5,and ATG16L1(P<0.01),but had no significant effect on p62 level.Conclusion Sepsis induces elevated ATF3 expression in macrophages,and suppresses autophagic activity and down-regulates pro-inflammatory cytokines TNF-α and IL-6,which probably mediated by ATF3 regulating transcription of ATG5 and ATG16L1,suggesting ATF3 as a potential therapeutic target for autophagy-inflammation imbalance.

Objective To analyze the basic conditions and pathological characteristics of the samples in the Human Brain Bank of Hebei Medical University,which were pathologically diagnosed as cerebral amyloid angiopathy,and to provide reference for the research of related diseases.Methods The basic data of gender,age,apolipoprotein E genotype,pathological classification of cerebral amyloid angiopathy,Alzheimer's disease-related pathological change score,comorbidities and other pathological information were analyzed.Results Up to October 2024,twenty samples were confirmed by pathological diagnosis,with a male to female ratio of 3:1 and an average age of(80.90±8.08)years.Involve three kinds of apolipoprotein E subtype,5 kinds of genotypes(ε2/ε3 xε2/ε4、ε3/ε3 xε3/ε4、ε4/ε4);There were 2 pathologic types,including 6 cases of type 1 and 14 cases of type 2.The pathological grade included 3 grades.The severity grade and subtype classification of cerebral amyloid vascular disease were correlated with the degree of pathological changes of Alzheimer's disease.Cerebral amyloid angiopathy samples could coexist with other degenerative diseases with high comorbidity.Conclusion The incidence of cerebral amyloid angiopathy is higher in the aged samples collected based on Brain Bank,which coexists with conditions such as Alzheimer's disease and microbleeds,etc.It provides more detailed pathological diagnosis basis for further scientific research sharing of samples.

Objective:To investigate the diagnostic and prognostic value of systemic immune inflammatory index (SII) for severe traumatic brain injury secondary to acute lung injury (sTBI-ALI).Methods:A retrospective study was conducted on patients with severe traumatic brain injury admitted to the trauma center of the First Affiliated Hospital of Soochow University from January 2021 to November 2023. Patients received standard treatments including hemostasis and intracranial pressure management. Vital signs and blood routine data were collected upon admission. Patients were categorized into sTBI group and sTBI-ALI group based on established clinical diagnostic criteria for ALI to evaluate the diagnostic utility of SII. Subsequently, within the sTBI-ALI group, patients were stratified into survival and non-survival groups based on their 30-day outcomes to assess the prognostic value of SII.Results:A total of 260 sTBI patients were enrolled, of whom 113 developed ALI. Among the sTBI-ALI patients, 73 survived at 30 days. Compared to the sTBI group, the sTBI-ALI group exhibited significantly higher respiratory rates, heart rates, white blood cell counts, neutrophil counts, platelet counts, and SII levels (all P<0.05). Multivariate logistic regression analysis showed that SII index ( OR=1.003, 95% CI: 1.002-1.004, P<0.001) was an independent risk factor for ALI development in sTBI patients. The combined predictive model incorporating SII and heart rate yielded an AUC of 0.801 (95% CI: 0.740-0.862). The non-survival group had significantly higher neutrophil counts and SII levels, and significantly lower Glasgow Coma Scale scores than the survival group (all P<0.05). Multifactorial regression analysis indicated that SII index ( OR=1.002, P=0.004, 95% CI: 1.000-1.003) served as an independent risk factor for 30-day mortality in sTBI-ALI patients. The combined predictive model of SII and GCS achieved an AUC of 0.904 (95% CI: 0.848-0.960). Conclusions:SII demonstrates potential as a biomarker for predicting the development of ALI following sTBI. Furthermore, incorporating SII into predictive models significantly enhances the ability to forecast mortality risk in sTBI-ALI patients.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.