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 analyze the influencing factors of chronic obstructive pulmonary disease(COPD)complicating pulmonary interstitial fibrosis,and to establish a nomogram prediction model.Methods The clinical data of 195 patients with COPD admitted and treated in this hospital from January 2019 to Feb-ruary 2021 were retrospectively analyzed.The factors possibly affecting the patients with COPD complicating pulmonary interstitial fibrosis were collected,and the patients were divided into 2 groups according to whether having pulmonary interstitial fibrosis.The independent risk factors were analyzed and screened by the multi-variate logistic regression.Then the nomogram prediction model was constructed.Results Among the includ-ed 195 cases of COPD in this study,there were 50 cases(25.64％)of complicating pulmonary interstitial fi-brosis.The univariate and multivariate logistic regression analysis results showed that the smoking history,duration of COPD,frequency of acute exacerbations onset,serum transforming growth factor β1(TGF-β1),basic fibroblast growth factor(bFGF)and angiotensin Ⅱ(AngⅡ)were the independent influencing factors of COPD complicating pulmonary interstitial fibrosis(P＜0.05).The nomogram model was constructed accord-ing to the results of multivariate analysis results,and the area under the receiver operating characteristic(ROC)curve was 0.956(95％CI:0.930-0.983),the average absolute error of internal verification by the Bootstrap method was 0.025,and the prediction model performance basically fitted the ideal model.Conclusion The nomogram model constructed by this study for predicting the pulmonary interstitial fibrosis in COPD patients has high accuracy and distinction degree.

In recent years， the incidence of pulmonary nodules has kept rising. To give full play to the advantages of traditional Chinese medicine （TCM） in the treatment of pulmonary nodules and identify the breakthrough points of integrating TCM with Western medicine， the China Association of Chinese Medicine organized medical experts in TCM and western medicine to carry out in-depth discussion regarding this disease. The discussion encompassed the modern medical advances， TCM theories of etiology and pathogenesis， the role and advantages of TCM in the whole course management of pulmonary nodules， contents and methods of research on pulmonary nodules， and science popularization work， aiming to provide a reference for clinical practice and scientific research. After discussion， the experts concluded that the occurrence of pulmonary nodules was rooted in the deficiency of the lung and spleen and triggered by phlegm dampness， blood stasis， and Qi stagnation. TCM can treat pulmonary nodules by controlling and reducing nodules， improving physical constitution， ameliorating multi-system nodular diseases， reducing anxiety and avoiding excessive diagnosis and treatment， and serving as an alternative for patients who are unwilling or unfit for surgical treatment. At present， the optimal diagnosis and treatment strategy for pulmonary nodules has not been formed， which needs to be further studied from multiple perspectives such as clinical epidemiology， biology， and evidence-based medicine. The primary task of current research is to find out the advantages， effective prescriptions， and target populations and determine the effective outcomes of TCM in the treatment of pulmonary nodules. At the same time， basic research should be carried out to explore the etiology and biological behaviors of pulmonary nodules. The expert consensus on the diagnosis and treatment of pulmonary nodules with integrated TCM and Western medicine needs to be continuously revised to guide clinicians to conduct standardized， scientific， and accurate effective diagnosis and treatment.

Transcatheter aortic valve replacement(TAVR)has become one of the effective methods for treating patients with aortic valve disease.With the continuous maturity of technology,innovation of instruments and increasing experience,the indications for TAVR has been expanded.Following international trends,the number of TAVR in China has steadily increased with each passing year.In 2023,the long-term follow-up results of TAVR in low-risk AS patients further confirm the long-term benefits of TAVR.The relevant research on TAVR for patients with aortic regurgitation and patients with bicuspid aortic stenosis provide evidence support for the expansion of TAVR indications.At the same time,the development of valve devices and new technological innovations are emerging in an endless stream,and the new concept of full life cycle management is increasingly being valued.Especially in China,the development of local devices is progressing rapidly,and multiple devices have entered the clinical research stage.The clinical manifestations and research results are worth pursuing.

20-hydroxyeicosatetraenoic acid(20-HETE)and epoxyeicosatrienoic acids(EETs)are products of enzyme metabolism of arachidonic acid(AA)by cytochrome P450(CYP).20-HETE is mainly produced by CYP4A,CYP4F metabolism of AA,which has a certain toxic effect on the cardiovascular and cerebrovascular system.EETS is mainly produced by CYP2J,CYP2C metabolizes AA,which has a certain protective effect on the cardiovascular and cerebrovascular system.This article reviews the effects and mechanisms of drugs related to AA-CYP-HETE/EETs metabolic pathway on cardiovascular diseases such as myocardial hypertrophy,hypertension,heart failure,and myocardial infarction,in order to provide a reference for the clinical use of cardiovascular diseases and provide ideas and directions for the basic research and development of cardiovascular disease treatment drugs.

Objective To explore whether the cognitive function of central obese mice is decreased by affecting the expression of brain-derived neurotrophic factor(BDNF)in hippocampus.Methods Healthy mice at the neonatal stage were divided into normal group and model group at random.To obtain the obese models,model group mice were injected at cervical subcutaneous with 10％L-monosodium glutamate(MSG;3 mg·g-1·d-1)for 5 days.The normal group was injected with the same dose of 0.9％NaCl.In addition,mice were removed according to the requirements.Finally,we got 8 mice in each group.The following parameters were compared:body weight,Lee's index and levels of the serum lipid.The BDNF expression levels in hippocampal tissue were measured using western blotting.Results At the 8th weekend,the body weight of the model and normal groups was(49.01±2.47)and(41.27±3.28)g;the Lee's indexes were(357.14±9.24)and(330.15±7.37)g1/3·cm-1;triglyceride levels were(1.37±0.52)and(0.73±0.31)mmol·L-1;total cholesterol levels were(2.98±0.18)and(1.98±0.30)mmol·L-1;low-density lipoprotein levels were(0.31±0.03)and(0.24±0.02)mmol·L-1;high-density lipoprotein levels were(2.70±0.15)and(1.98±0.40)mmol·L-1;the differences were statistically significant(P＜0.05,P＜0.01),which were consistent with the characteristics of the central obesity model.The BDNF protein expression levels in the hippocampus of the model and normal groups were 6.02 x 104±626.53 and 7.04 x 104±1 440.81,which has statistically significant(P＜0.01).Conclusion The cognitive function of central obese mice may be decreased by down-regulating the expression of BDNF in hippocampus.

ObjectiveTo explore whether miR-375 regulates the malignant characteristics of osteosarcoma (OS) by influencing the expression of MMP13. MethodsPlasmid DNAs and miRNAs were transfected into OS cells and HEK293 cells using Lipofectamine 3000 reagent. Real-time quantitative polymerase chain reaction was performed to measure the expression of miR-375 and MMP13 in OS patients and OS cells. Western blot was performed to analyze the MMP13 protein in the patients with OS and OS cells. The targeting relationship between miR-375 and MMP13 was analyzed by luciferase assay. Migration and invasion were analysed by heal wound and transwell assays, respectively. ResultsmiR-375 expression in OS tissues was lower than that in normal tissues. The expression of MMP13 was upregulated in OS tissues. MMP13 expression was negatively correlated withmiR-375 expression in patients with OS. Migration and invasion were significantly inhibited in OS cells with the miR-375 mimic compared with OS cells with the miRNA control. MMP13 partially reversed the inhibition of migration and invasion induced by miR-375 in the OS cells. ConclusionmiR-375 attenuates migration and invasion by downregulating the expression of MMP13 in OS cells.

Objective To investigate the effect of early postoperative abnormal blood glucose on the short-term prognosis of non-small cell lung cancer(NSCLC),and to analyze the clinical characteristics and risk factors related to poor early prognosis.Methods A total of 897 patients with NSCLC who underwent thoracoscopic surgery in Huadong Hospital,Fudan University from Jan 2020 to Aug 2021 were divided into hyperglycemia(HG)group(>7.8 mmol/L)and normal blood glucose(NG)group(≤7.8 mmol/L and≥3.9 mmol/L)according to the early postoperative blood glucose values.Additionally,the patients were divided into higher blood glucose fluctuation group(≥4 mmol/L)and the group with lower blood glucose fluctuation(<4 mmol/L)basing on the fasting blood glucose.Using Logistic regression models,column line charts,ROC curves and other methods,we aimed to clarify the impact of early postoperative blood glucose abnormalities on short-term prognosis,explore clinical characteristics associated with poor short-term outcomes,identify other high-risk factors,and establish relevant risk prediction models.Results Compared with the NG group,the incidence of postoperative pneumonia,thromboembolism,ICU admission rate,total length of hospital stay and hospital cost were significantly higher in the HG group(P<0.05).Higher blood glucose fluctuation group had a greater risk of ICU admission(P=0.003).Logistic regression analysis showed that age,preoperative fasting glucose,white blood cell count and cytokeratin 19 fragment antigen 21-1(CYFRA21-1)were risk factors for postoperative hyperglycemia(P<0.05).Contrary to the effect of BMI,diabetes,male patients,higher blood glucose fluctuation,white blood cell count and age were the risk factors for postoperative adverse events(P<0.05).The AUC of the column line chart model was 0.661(95%CI:0.624-0.698),indicating good discriminative ability for predicting poor short-term prognosis postoperatively.Calibration curves also demonstrated good consistency between predicted and actual probabilities.Conclusion Early postoperative blood glucose fluctuations independently impact the short-term prognosis of thoracoscopic NSCLC patients.Blood glucose combined with gender,BMI,white blood cell count,age and diabetes history can serve as predictive factors for poor short-term prognosis postoperatively.Additionally,a column line chart constructed based on these factors may aid clinicians in early intervention for NSCLC patients with indications.

Purpose: The increase in thyroid cancer incidence has inevitably led to an increase in thyroid cancer surgeries. This meta-regression analysis aimed to determine if the rate of post-thyroidectomy complications changes by year. Materials and Methods: PubMed and Embase databases were used to perform a systematic literature search of studies published from January 1, 2005, using the keywords “thyroidectomy” and “complication.” A meta-regression was performed for post-thyroidectomy hypocalcemia and bleeding. Results: This meta-analysis included 25 studies involving 927751 individuals. Through the years of publications in this study, there was no significant difference in the proportion of post-thyroidectomy hypocalcemia and bleeding (p=0.9978, 0.6393). Conclusion Although the number of thyroid surgeries has recently increased, the incidence of post-thyroidectomy hypocalcemia and bleeding did not significantly increase.

The objective of this study was to compare recommendations of the Korean Medication Algorithm Project for Bipolar Disorder 2022 (KMAP-BP 2022) with other recently published guidelines for treating bipolar disorder. We reviewed a total of six recently published global treatment guidelines and compared treatment recommendation of the KMAP-BP 2022 with those of other guidelines. For initial treatment of mania, there were no significant differences across treatment guidelines. All guidelines recommended mood stabilizer (MS) or atypical antipsychotic (AAP) monotherapy or a combination of an MS with an AAP as a first-line treatment strategy in a same degree for mania. However, the KMAP-BP 2022 recommended MS + AAP combination therapy for psychotic mania, mixed mania and psychotic depression as treatment of choice. Aripiprazole, quetiapine and olanzapine were the first-line AAPs for nearly all phases of bipolar disorder across guidelines. Some guideline suggested olanzapine is a second-line options during maintenance treatment, related to concern about long-term tolerability. Most guidelines advocated newer AAPs (asenapine, cariprazine, long-acting injectable risperidone, and aripiprazole once monthly) as first-line treatment options for all phases while lamotrigine was recommended for depressive and maintenance phases. Lithium and valproic acid were commonly used as MSs in all phases of bipolar disorder. KMAP-BP 2022 guidelines were similar to other guidelines, reflecting current changes in prescription patterns for bipolar disorder based on accumulated research data. Strong preference for combination therapy was characteristic of KMAP-BP 2022, predominantly in the treatment of psychotic mania, mixed mania and psychotic depression.