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.

According to the codon characteristics of the prokaryotic system,we synthesized the optimized Rv2628c-Rv1737c nucleic acid sequence and constructed the expression plasmid pET24a-Rv2628c-Rv1737c.After sequencing,expression in E.coli was induced.The fusion protein was found in inclusion bodies.After renaturation and affinity chromatography purifi-cation,the Rv2628c-Rv1737c recombinant protein,with a molecular weight of 57 kDa and a purity exceeding 90％,was ob-tained.The recombinant protein was used to stimulate PBMC cells from different patients,and the differences in IFN-γ mRNA expression were analyzed with q-PCR.The Rv2628c-Rv1737c fusion protein stimulated patients with TB,particularly latent TB infection(LTBI).The level of IFN-γ mRNA in PBMC cells was higher than that in healthy controls(P＜0.05).Mice immu-nized with BCG+Rv2628c-Rv1737c/DMT showed significant induction of high levels of IgG antibodies.Rv2628c-Rv1737c re-combinant protein,as a latent infection antigen,is recognized by PBMCs cells infected with TB,with strong immunogenicity,and thus may serve as a potential TB subunit vaccine target antigen.This protein may be used to prevent TB infection,particu-larly latent infection,and to perform laboratory diagnosis.

Objective:To conduct a nationwide physician survey to better understand clinicians’ disease awareness, treatment patterns, and experience of Waldenstr?m macroglobulinemia (WM) in China.Methods:This cross-sectional study was conducted from February 2022 to July 2022 by recruiting clinicians with WM treatment experience from hematology, hematology-oncology, and oncology departments throughout China. Quantitative surveys were designed based on the qualitative interviews.Results:The study included 415 clinicians from 219 hospitals spread across thirty-three cities and twenty-two provinces. As for diagnosis, the laboratory tests prescribed by physicians for suspected WM patients were relatively consistent (92% -99% recommendation for laboratory, 79% -95% recommendation for pathology, 96% recommendation for gene testing, and 63% -83% recommendation for imaging examination). However, from a physician's perspective, there was 22% misdiagnosis occurred in clinical practice. The rate of misdiagnosis was higher in lower-level hospitals than in tertiary grade A hospitals (29% vs 21%, P<0.001). The main reasons for misdiagnosis were that WM was easily confused with other diseases, and physicians lacked the necessary knowledge to make an accurate diagnosis. In terms of gene testing in clinical practice, 96% of participating physicians believed that WM patients would require gene testing for MYD88 and CXCR4 mutations because the results of gene testing would aid in confirming diagnosis and treatment options. In terms of treatment, 55% of physicians thought that the most important goal was to achieve remission, while 54% and 51% of physicians wanted to improve laboratory and/or examination results and extend overall survival time, respectively. Among patients with treatment indications, physicians estimated that approximately 21% of them refused to receive treatment, mainly owing to a lack of affordable care and disease awareness. When selecting the most appropriate treatment regimens, physicians would consider patient affordability (63% ), comorbidity (61% ), and risk level (54% ). Regimens containing Bruton tyrosine kinase inhibitor (BTKi) were most widely recommended for both treatment-na?ve and relapsed/refractory patients (94% for all patients, 95% for treatment-na?ve patients, and 75% for relapsed/refractory patients), and most physicians recommended Ibrutinib (84% ). For those patients who received treatment, physicians reported that approximately 23% of patients did not comply with the treatment regimen due to a lack of affordability and disease awareness. Furthermore, 66% of physicians believe that in the future, increasing disease awareness and improving diagnosis rates is critical. Conclusions:This study is the first national physician survey of WM conducted in China. It systematically describes the issues that exist in WM diagnosis and treatment in China, such as a high rate of misdiagnosis, limited access to gene testing and new drugs, and poor patient adherence to treatment. Chinese doctors believe that improving doctors’ and patients’ understanding of WM is one of the most urgent issues that must be addressed right now.

Objective To develop a deep learning model for automated age estimation based on 3D CT reconstructed images of Han population in western China,and evaluate its feasibility and reliability.Methods The retrospective pelvic CT imaging data of 1 200 samples(600 males and 600 females)aged 20.0 to 80.0 years in western China were collected and reconstructed into 3D virtual bone models.The images of the ischial tuberosity feature region were extracted to create sex-specific and left/right site-specific sample libraries.Using the ResNet34 model,500 samples of different sexes were randomly selected as training and verification set,the remaining samples were used as testing set.Initialization and transfer learning were used to train images that distinguish sex and left/right site.Mean absolute error(MAE)and root mean square error(RMSE)were used as primary indicators to evaluate the model.Results Prediction results varied between sexes,with bilateral models outperformed left/right unilateral ones,and transfer learning models showed superior performance over initial models.In the prediction results of bilateral transfer learning models,the male MAE was 7.74 years and RMSE was 9.73 years,the female MAE was 6.27 years and RMSE was 7.82 years,and the mixed sexes MAE was 6.64 years and RMSE was 8.43 years.Conclusion The skeletal age estimation model,utilizing is-chial tuberosity images of Han population in western China and employing the ResNet34 combined with transfer learning,can effectively estimate adult ischium age.

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 explore the association between violent behaviors and emotions in individuals with mental disorders,to evaluate the application value of facial expression analysis technology in violence risk assessment of individuals with mental disorders in supervised settings,and to provide a reference for violence risk assessment.Methods Thirty-nine male individuals with mental disorders in supervised settings were selected,the participant risk of violence,cognitive function,psychiatric symptoms and se-verity were assessed using the Modified Overt Aggression Scale (MOAS),the Historical,Clinical,Risk Management-Chinese version(HCR-CV),the Positive and Negative Syndrome Scale (PANSS) and the Brief Psychiatric Rating Scale (BPRS).An emotional arousal was performed on the participants and the intensity of their emotions and facial expression action units was recorded before,during and after the arousal.One-way analysis of variance (ANOVA) was used to compare the differences in the intensity of emotions and facial expression action units before,during and after the arousal.Pearson correlation analysis was used to calculate the correlations between the intensity of the seven basic emo-tional facial expressions and the scores of the assessment scales.Results The intensity difference of sadness,surprise and fear in different time periods was statistically significant (P<0.05).The intensity of the left medial eyebrow lift action unit was found significantly different before and after the emo-tional arousal (P<0.05).The intensity of anger was positively correlated with the Modified Overt Ag-gression Scale score throughout the experiment (P<0.05).Conclusion Eye action units such as eye-brow lifting,eyelid tightening and upper eyelid lifting can be used as effective action units to identify sadness,anger and other negative emotions associated with violent behaviors.Facial expression analysis technology can be used as an auxiliary tool to assess the potential risk of violence in individuals with mental disorders in supervised settings.

In recent years,the prevalence of carbapenem-resistant Klebsiella pneumoniae(CRKP)infection has become a global public health issue.Ceftazidime/avibactam(CAZ/AVI)has been approved as a novel antimicrobial agent for the treatment of healthcare-associated pneumonia/ventilator-associated pneumonia,bloodstream infection,infection after kidney transplantation,and severe infection combined with liver cirrhosis.However,the use of CAZ/AVI has also led to the emergence of drug-resistant strains.The major mechanisms of drug-resistance include over-expression of blaKPC gene,mutation of β-lactamase and amino acids at key sites,changes in cell permeability caused by loss of membrane porin,and over-expression of efflux pump.This article reviews the research progress of CAZ/AVI in the treatment of CRKP infection,providing reference for clinical diagnosis and treatment.

Objective To explore the magnetic resonance imaging(MRI)manifestations of acute cerebral infarction(ACI)and the correlation between lipoprotein-associated phospholipase A2(Lp-PLA2),cystatin C(Cys C)and short-term prognosis.Methods A total of 110 ACI patients admitted to the department of neurology in our hospital from January 1,2022 to January 1,2023 were selected as the study objects.The clinical data and multimodal MRI were collected,and the serum Lp-PLA2 and Cys C levels of patients were detected.The patients were divided into the good prognosis group and the poor prognosis group according to the modified Rankin scale(mRS)score 90 days after onset.The predictive value of MRI manifestations and Lp-PLA2 and Cys C levels for short-term poor prognosis was analyzed.Results There were statistically significant differences in the time from onset to admission,National Institute of Health Stroke Scale(NIHSS)score on admission,hypertension or diabetes,coronary heart disease or atrial fibrillation of patients between the good prognosis group and the poor prognosis group(P<0.05).The proportions of patients with ischemic penumbra,HV positive,cortical-subcortical infarction,large perforating branch infarction,small perforating branch infarction,bilateral anterior circulation infarction,posterior circulation infarction,anterior-posterior circulation infarction,middle cerebral artery(MCA)stenosis or occlusion,both internal carotid artery(ICA)and MCA stenosis or occlusion,posterior cerebral artery(PCA)or vertebral artery(VA)stenosis or occlusion and hemorrhage transformation in the poor prognosis group were significantly higher than those in the good prognosis group(P<0.05).The Lp-PLA2 and Cys C levels of patients in the poor prognosis group were significantly higher than those in the good prognosis group(P<0.05).Small penetrating branch infarction,posterior circulation infarction,anterior-posterior circulation infarction,MCA stenosis or occlusion,both ICA and MCA stenosis or occlusion,hemorrhage transformation,serum Lp-PLA2 and Cys C had certain predictive value for patients with short-term poor prognosis(P<0.05).Conclusion MRI manifestations(ischemic penumbra,HV positivity,different types of cerebral infarction,and vascular stenosis)and serum Lp-PLA2 and Cys C levels can predict the short-term prognosis of ACI patients and provide important reference for the formulation of clinical treatment plans.

An innovative on-site real-time avian influenza virus(AIV)detection method was estab-lished by integratingrecombinase-aided amplification(RAA)with the clustered regularly inter-spaced short palindromic repeats(CRISPR)/CRISPR-associated protein(Cas)system.After analy-zing 120 sequences of the M gene of avian influenza viruses of different subtypes publicly available on NCBI,the RAA primers and crRNA were designed based on the identified highly conserved segment and used for RAA nucleic acid amplification.After the amplified products were transferred to a CRISPR/Cas13a detection system,the fluorescence values were monitored throughout the re-action process to indicate the results.The sensitivity and specificity of the RAA-CRISPR/Cas13a method were validated using gradient dilutions(106-100 copies/μL)of positive plasmids and sev-en other avian viruses.Fifty clinical samples were tested using this method and compared with the national standard fluorescence RT-PCR method.The results indicated that the detection limit for RAA-CRISPR/Cas13a method was 102 copies/μL,a two-fold improvement over the standard RAA.Specificity assay showed the established method only detected AIV with no cross-reactivity with other seven avian viruses.Compared to the national standard fluorescence RT-PCR method,this method exhibited 100％specificity,95.24％accuracy,and 98.00％consistency in detection of clinical samples.In conclusion,a universal and rapid RAA-CRISPR/Cas13a for detection of AIV was established with the capacity of achieving detection within 60 minutes at 37 ℃,which provides a rapid,sensitive,and specific on-site detection method for AIV.

Objective:To explore the effects of pre-transplant controlling nutritional status(CONUT)and post-transplant minimal residual disease(MRD)on prognosis of patients with multiple myeloma(MM)after autologous hematopoietic stem cell transplantation(auto-HSCT).Methods:The clinical data of 79 patients who received auto-HSCT from 2011 to 2020 in The First Affiliated Hospital of Chongqing Medical University were retrospectively analyzed.The patients were divided into Low-CONUT group(n=62)and High-CONUT group(n=17)according to whether the CONUT score was less than 5.The differences in clinical features,hematopoietic reconstruction,adverse reactions,efficacy and survival between the two groups were compared.In addition,the prognostic risk factors were analyzed and verified by time-dependent ROC curve.Results:The proportions of male patients and bone marrow plasma cells＞30％at initial diagnosis in High-CONUT group were both higher than those in Low-CONUT group(both P＜0.05).While,there were no significant differences in hematopoietic reconstruction and adverse reactions(＞grade 2)between the two groups.The complete response(CR)rate and CR+very good partial response(VGPR)rate before transplantation in Low-CONUT group were both significantly higher than those in High-CONUT group(both P＜0.05).After 3 months of transplantation,the CR+VGPR rate still remained an advantage in Low-CONUT group compared with High-CONUT group(P＜0.01),but CR rate did not(P＞0.05).The overall survival(OS)and progression-free survival(PFS)in Low-CONUT group were both superior to those in High-CONUT group(both P＜0.05).Low CONUT score(0-4)before transplantation and negative MRD at 6 months after transplantation were favorable factors affecting OS and PFS(both P＜0.05),while the International Myeloma Working Group(IMWG)high-risk at initial diagnosis and lactate dehydrogenase(LDH)level＞250 U/L before transplantation were only risk factors for PFS(both P＜0.05).Time-dependent ROC curve analysis showed that pre-transplant CONUT score and MRD status at 6 months after transplantation could independently or jointly predict 1-and 2-year OS and PFS,and the combined prediction was more effective.Conclusion:The combination of pre-transplant CONUT and post-transplant MRD can better predict the prognosis of MM patients.