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 understand the current situation of healthcare-associated infection(HAI)in China,pro-vide data support and decision-making basis for formulating scientific and effective strategies for HAI prevention and control.Methods A nationwide cross-sectional survey on HAI was conducted among various types and levels of medical institutions in China according to a unified protocol of bedside surveys and case investigations.Results In 2024,a total of 5 736 medical institutions and 2 751 765 patients were surveyed.Among them,34 889 HAI cases were identified,with a prevalence rate of 1.27％.The number of HAI episodes was 38 032,and case prevalence rate was 1.38％.The prevalence rate of HAI in medical institutions in different regions of China ranged from 0.66％to 2.35％.Among medical institutions of different scales,those with a bed capacity of ≥900 had the high-est incidence of HAI,reaching 1.65％.The most common infection site was the lower respiratory tract(44.66％),followed by the urinary tract(12.94％),surgical site(9.32％),upper respiratory tract(7.02％),and bloodstream infection(5.78％).The top 3 departments with the highest HAI rates were the general intensive care unit(10.02％),department of neurosurgery(5.51％),and department(group)of hematology(5.34％).A total of 23 238 strains of HAI pathogens were detected,with 10 714 strains(46.10％)from lower respiratory tract speci-mens.The top 5 detected strains were Klebsiella pneumoniae(14.76％),Pseudomonas aeruginosa(13.33％),Escherichia coli(12.79％),Acinetobacter baumannii(9.23％),and Staphylococcus aureus(7.88％).231 944 pa-tients underwent class Ⅰ incision surgery were monitored,with 1 647 cases experienced surgical site infection,and the prevalence rate of surgical site infection was 0.71％.The number of patients who should undergo pathogen de-tection(patients receiving therapeutic and therapeutic combined prophylactic antimicrobial agents)was 715 179,while the actual number was 480 492,with a pathogen detection rate of 67.18％.425 225 patients received patho-genic detection before treatment,with a detection rate of 59.46％.Conclusion The overall HAI prevalence in Chi-na is lower,showing disparities among medical institutions of different regions and scales.Therefore,precise imple-mentation of measures is necessary for HAI prevention and control,with a focus on high-risk institutions and high-risk departments,key areas,and critical procedures.All levels of medical institutions should continuously reduce the incidence of HAI by strengthening monitoring,standardizing the use of antimicrobial agents,and reinforcing basic HAI prevention and control measures.

Objective To understand the current situation of healthcare-associated infection(HAI)in China,pro-vide data support and decision-making basis for formulating scientific and effective strategies for HAI prevention and control.Methods A nationwide cross-sectional survey on HAI was conducted among various types and levels of medical institutions in China according to a unified protocol of bedside surveys and case investigations.Results In 2024,a total of 5 736 medical institutions and 2 751 765 patients were surveyed.Among them,34 889 HAI cases were identified,with a prevalence rate of 1.27％.The number of HAI episodes was 38 032,and case prevalence rate was 1.38％.The prevalence rate of HAI in medical institutions in different regions of China ranged from 0.66％to 2.35％.Among medical institutions of different scales,those with a bed capacity of ≥900 had the high-est incidence of HAI,reaching 1.65％.The most common infection site was the lower respiratory tract(44.66％),followed by the urinary tract(12.94％),surgical site(9.32％),upper respiratory tract(7.02％),and bloodstream infection(5.78％).The top 3 departments with the highest HAI rates were the general intensive care unit(10.02％),department of neurosurgery(5.51％),and department(group)of hematology(5.34％).A total of 23 238 strains of HAI pathogens were detected,with 10 714 strains(46.10％)from lower respiratory tract speci-mens.The top 5 detected strains were Klebsiella pneumoniae(14.76％),Pseudomonas aeruginosa(13.33％),Escherichia coli(12.79％),Acinetobacter baumannii(9.23％),and Staphylococcus aureus(7.88％).231 944 pa-tients underwent class Ⅰ incision surgery were monitored,with 1 647 cases experienced surgical site infection,and the prevalence rate of surgical site infection was 0.71％.The number of patients who should undergo pathogen de-tection(patients receiving therapeutic and therapeutic combined prophylactic antimicrobial agents)was 715 179,while the actual number was 480 492,with a pathogen detection rate of 67.18％.425 225 patients received patho-genic detection before treatment,with a detection rate of 59.46％.Conclusion The overall HAI prevalence in Chi-na is lower,showing disparities among medical institutions of different regions and scales.Therefore,precise imple-mentation of measures is necessary for HAI prevention and control,with a focus on high-risk institutions and high-risk departments,key areas,and critical procedures.All levels of medical institutions should continuously reduce the incidence of HAI by strengthening monitoring,standardizing the use of antimicrobial agents,and reinforcing basic HAI prevention and control measures.

Objective:To investigate the correlation between frailty and telomere length in the elderly.Methods:Cross-sectional study.A total of 128 elderly patients aged ≥65 years who were hospitalized in the Second Hospital of Shanxi Medical University from January to June 2024 were selected.Frailty assessment was conducted using the Edmonton Scale, and patients were divided into the non-frail group(n=64)and the frail group(n=64)based on frailty score.The telomere length of peripheral blood leukocyte was detected by real-time fluorescent quantitative polymerase chain reaction(qPCR), and the correlation analysis between frailty and telomere length was conducted.Results:Compared with the non-frailty group, the frailty group had older age, higher body mass index(BMI), higher American Society of Anesthesiologists(ASA)grade, an increased proportion of diabetes and hypertension, and shorter telomere length( P<0.05). Logistic regression analysis: ageing, increased BMI, hypertension and diabetes were risk factors for frailty in the elderly( OR=1.280, 1.135, 1.543, 1.081, P=0.001, 0.036, 0.010, 0.021), while ASA grade and telomere length were not related to the occurrence of frailty.After stratification, frailty scores were weakly correlated with telomere length in the age ≤73 years old ( r=-0.344, P=0.005)and the BMI ≥24 kg/m 2( r=-0.336, P=0.001). The correlation between frailty scores and telomere length was strengthened in the poorly controlled hypertension group( r=-0.571, P=0.042)and the group with poorly controlled diabetes( r=-0.613, P=0.045)showed a stronger correlation between frailty scores and telomere length. Conclusions:In the overall analysis, there was no definitive association between telomere length and frailty.After stratification, it was found that telomere length was weakly to moderately correlated with frailty, suggesting that telomere length may interact with other factors, and strong risk factors such as age, BMI, hypertension and diabetes may mask the weak effect of telomere length on frailty.

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*

Objective:To investigate the correlation between frailty and telomere length in the elderly.Methods:Cross-sectional study.A total of 128 elderly patients aged ≥65 years who were hospitalized in the Second Hospital of Shanxi Medical University from January to June 2024 were selected.Frailty assessment was conducted using the Edmonton Scale, and patients were divided into the non-frail group(n=64)and the frail group(n=64)based on frailty score.The telomere length of peripheral blood leukocyte was detected by real-time fluorescent quantitative polymerase chain reaction(qPCR), and the correlation analysis between frailty and telomere length was conducted.Results:Compared with the non-frailty group, the frailty group had older age, higher body mass index(BMI), higher American Society of Anesthesiologists(ASA)grade, an increased proportion of diabetes and hypertension, and shorter telomere length( P<0.05). Logistic regression analysis: ageing, increased BMI, hypertension and diabetes were risk factors for frailty in the elderly( OR=1.280, 1.135, 1.543, 1.081, P=0.001, 0.036, 0.010, 0.021), while ASA grade and telomere length were not related to the occurrence of frailty.After stratification, frailty scores were weakly correlated with telomere length in the age ≤73 years old ( r=-0.344, P=0.005)and the BMI ≥24 kg/m 2( r=-0.336, P=0.001). The correlation between frailty scores and telomere length was strengthened in the poorly controlled hypertension group( r=-0.571, P=0.042)and the group with poorly controlled diabetes( r=-0.613, P=0.045)showed a stronger correlation between frailty scores and telomere length. Conclusions:In the overall analysis, there was no definitive association between telomere length and frailty.After stratification, it was found that telomere length was weakly to moderately correlated with frailty, suggesting that telomere length may interact with other factors, and strong risk factors such as age, BMI, hypertension and diabetes may mask the weak effect of telomere length on frailty.

Objective:To investigate the specimen quality of intersphincteric resection with transabdominal transanal mixed approach for rectal cancer in the Chinese Transanal Total Mesorectal Excision Registry Collaborative (CTRC) database.Methods:The retrospective case-control study was conducted. Based on the concept of real-world research, the clinicopathological data of 281 pati-ents with rectal cancer in the CTRC database who underwent intersphincteric resection with trans-abdominal transanal mixed approach in 19 medical centers, including the Beijing Friendship Hospital of Capital Medical University et al, from November 15,2017 to December 31,2023 were collected. There were 196 males and 85 females, aged 61(range, 27-87)years. Observation indicators: (1) preoperative examinations; (2) neoadjuvant therapy; (3) postoperative examinations; (4) analysis of influencing factors for positive circumferential margin in surgical specimen of intersphincteric resec-tion for rectal cancer. Measurement data with normal distribution were represented as Mean±SD. Measurement data with skewed distribution were represented as M(range). Count data were described as absolute numbers or percentages. The chi-square test was used for univariate analysis. Logistic regression model was used for multivariate analysis. Results:(1) Preoperative examinations. Of the 281 patients, 234 cases underwent preoperative pelvic magnetic resonance imaging (MRI) examina-tion. There were 2 cases in clinical stage T0, 3 cases in clinical stage T1, 58 cases in clinical stage T2, 137 cases in clinical stage T3, 24 cases in clinical stage T4, 3 cases in clinical stage Tx, 7 cases missing clinical T staging data. There were 87 cases in clinical stage N0, 68 cases in clinical stage N1, 60 cases in clinical stage N2, 9 cases in clinical stage Nx, 10 cases missing clinical N staging data. There were 30 cases with mesorectal fascia invasion, 53 cases with extramural venous invasion. The distance from lower margin of tumor to anal margin was 41.9(range, 1.0-80.0)mm. (2) Neoadjuvant therapy. Of the 281 patients, 125 cases underwent neoadjuvant therapy, including 39 cases receiving chemo-therapy alone, 6 cases receiving short-course simultaneous chemoradiotherapy, 5 cases receiving short-course simultaneous chemoradiotherapy and delayed surgery, 48 cases receiving long-course simultaneous chemoradiotherapy, 2 cases receiving other treatments, and 25 cases missing neoadju-vant therapy data. (3) Postoperative examinations. Of the 281 patients, 249 cases achieved R 0 resection, 9 cases achieved R 1 resection, and there were 23 cases missing surgical margin data. The maximum tumor diameter, the number of lymph nodes harvested and positive rate of vessel carcinoma embolus were 30.0(range, 0.5-200.0)mm, 13(range, 0-70) and 27.55%(73/265) in 281 patients. There were 252 patients with circumferential margin records, showing positive in 15 cases, with a positive rate as 5.95%(15/252). The minimum distance from deep part of tumor to circumferential margin was 7.0(range, 0-150.0)mm in 252 patients. There were 85 cases with distal margin records, showing positive in 1 case, and the distance from lower margin of tumor to distal margin was 10.0(range, 0-202.0)mm. There were 273 patients with specimen integrity records, which showed intact specimen in 208 cases, fair specimen in 58 cases, poor specimen in 4 cases, unevaluated specimen in 3 cases. There were 7 cases with rectal perforation. Of the 281 patients, cases in pathological stage T0, Tis, T1, T2, T3, T4 were 14, 5, 22, 107, 113, 12, respectively, and there were 8 cases missing pathological T staging data. Of the 281 patients, cases in pathological stage N0, N1a, N1b, N1c, N2a, N2b were 176, 27, 27, 11,20, 12, respectively, and there were 8 cases missing pathological N staging data. Of the 281 patients, there were 4 cases with distant metastasis, 262 cases without distant metastasis, 5 cases not evaluated, and 10 cases missing tumor metastasis data. Of the 125 patients undergoing neoadjuvant therapy, there were 85 cases with tumor regression grade records, including 16 cases as grade 1, 27 cases as grade 2, 19 cases as grade 3, 15 cases as grade 4, 8 cases as grade 5. (4) Analysis of influencing factors for positive circumferential margin in surgical specimen of intersphincteric resection for rectal cancer. Results of univariate analysis showed that preoperative T staging on preoperative pelvic MRI, mesorectal fascia invasion, extramural venous invasion, pathological T staging, and pathological N staging were related factors for positive circumferential margin in surgical specimen of intersphincteric resection for rectal cancer ( P<0.05). Conclusions:Intersph-incteric resection with transabdominal transanal mixed approach has good specimen quality and low positive rate of surgical margin. T staging on preoperative pelvic MRI may be related to positive circumferential margin after intersphincteric resection for rectal cancer.

Ameloblastoma is a benign tumor characterized by locally invasive phenotypes,leading to facial bone destruction and a high recurrence rate.However,the mechanisms governing tumor initiation and recurrence are poorly understood.Here,we uncovered cellular landscapes and mechanisms that underlie tumor recurrence in ameloblastoma at single-cell resolution.Our results revealed that ameloblastoma exhibits five tumor subpopulations varying with respect to immune response(IR),bone remodeling(BR),tooth development(TD),epithelial development(ED),and cell cycle(CC)signatures.Of note,we found that CC ameloblastoma cells were endowed with stemness and contributed to tumor recurrence,which was dominated by the EZH2-mediated program.Targeting EZH2 effectively eliminated CC ameloblastoma cells and inhibited tumor growth in ameloblastoma patient-derived organoids.These data described the tumor subpopulation and clarified the identity,function,and regulatory mechanism of CC ameloblastoma cells,providing a potential therapeutic target for ameloblastoma.

Actinomycosis is a rare chronic granulomatous disease characterized by granuloma formation and tissue fibrosis with sinus tracts,often misdiagnosed due to its similarity to many infectious and non-infectious diseases.This report presents a case of a 60-year-old female with more than 10 years history of rheumatoid arthritis who developed actinomycosis infection after long-term treatment with immunosuppressants and biologics,including methotrexate,leflunomide,and infliximab,leading to recurrent joint pain,poorly controlled rheumatoid arthritis activity,and persistent elevation of white blood cell counts.Abdominal CT revealed a pelvic mass and right ureteral dilation.Pathological examination of cervical tissue showed significant neutrophil infiltration and sulfur granules,indicating actinomycosis.The patient received 18 months of doxycycline treatment for the infection and continued rheumatoid arthritis therapy with leflunomide,hydroxychloroquine sulfate,and tofacitinib,resulting in improved joint symptoms and normalized white blood cell counts.After 2 years of follow-up,the patient remained stable with no recurrence.This case highlights the importance of clinicians being vigilant for infections,particularly chronic,occult infections from rare pathogens,in rheumatoid arthritis patients on potent immunosuppressants and biologics,advocating for early screening and diagnosis.

Objective:To evaluate the diagnostic efficacy and practicality of the Jaundice color card (JCard) as a screening tool for neonatal jaundice.Methods:Following the standards for reporting of diagnostic accuracy studies (STARD) statement, a multicenter prospective study was conducted in 9 hospitals in China from October 2019 to September 2021. A total of 845 newborns who were admitted to the hospital or outpatient department for liver function testing due to their own diseases. The inclusion criteria were a gestational age of ≥35 weeks, a birth weight of ≥2 000 g, and an age of ≤28 days. The neonate′s parents used the JCard to measure jaundice at the neonate′s cheek. Within 2 hours of the JCard measurement, transcutaneous bilirubin (TcB) was measured with a JH20-1B device and total serum bilirubin (TSB) was detected. The Pearson′s correlation analysis, Bland-Altman plots and the receiver operating characteristic (ROC) curve were used for statistic analysis.Results:Out of the 854 newborns, 445 were male and 409 were female; 46 were born at 35-36 weeks of gestational age and 808 were born at ≥37 weeks of gestational age. Additionally, 432 cases were aged 0-3 days, 236 cases were aged 4-7 days, and 186 cases were aged 8-28 days. The TSB level was (227.4±89.6) μmol/L, with a range of 23.7-717.0 μmol/L. The JCard level was (221.4±77.0) μmol/L and the TcB level was (252.5±76.0) μmol/L. Both the JCard and TcB values showed good correlation ( r=0.77 and 0.80, respectively) and agreements (96.0% (820/854) and 95.2% (813/854) of samples fell within the 95% limits of agreement, respectively) with TSB. The JCard value of 12 had a sensitivity of 0.93 and specificity of 0.75 for identifying a TSB ≥205.2?μmol/L, and a sensitivity of 1.00 and specificity of 0.35 for identifying a TSB ≥342.0?μmol/L. The TcB value of 205.2?μmol/L had a sensitivity of 0.97 and specificity of 0.60 for identifying TSB levels of 205.2 μmol/L, and a sensitivity of 1.00 and specificity of 0.26 for identifying TSB levels of 342.0 μmol/L. The areas under the ROC curve (AUC) of JCard for identifying TSB levels of 153.9, 205.2, 256.5, and 342.0 μmol/L were 0.96, 0.92, 0.83, and 0.83, respectively. The AUC of TcB were 0.94, 0.91, 0.86, and 0.87, respectively. There were both no significant differences between the AUC of JCard and TcB in identifying TSB levels of 153.9 and 205.2 μmol/L (both P>0.05). However, the AUC of JCard were both lower than those of TcB in identifying TSB levels of 256.5 and 342.0 μmol/L (both P<0.05). Conclusions:JCard can be used to classify different levels of bilirubin, but its diagnostic efficacy decreases with increasing bilirubin levels. When TSB level are ≤205.2 μmol/L, its diagnostic efficacy is equivalent to that of the JH20-1B. To prevent the misdiagnosis of severe jaundice, it is recommended that parents use a low JCard score, such as 12, to identify severe hyperbilirubinemia (TSB ≥342.0 μmol/L).