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.

In cases where a tracheal injury exceeds half the length of the adult trachea or one-third of the length of the child trachea, it becomes difficult to perform end-to-end anastomosis after tracheal resection due to excessive tension at the anastomosis site. In such cases, tracheal replacement therapy is required. Advances in tissue engineering technology have led to the development of tissue engineering tracheal substitutes, which have promising applications. Hydrogels, which are highly hydrated and possess a good three-dimensional network structure, biocompatibility, low immunogenicity, biodegradability, and modifiability, have had wide applications in the field of tissue engineering. This article provides a review of the characteristics, advantages, disadvantages, and effects of various hydrogels commonly used in tissue engineering trachea in recent years. Additionally, the article discusses and offers prospects for the future application of hydrogels in the field of tissue engineering trachea.

Heart failure is a complex clinical syndrome and pediatric heart failure (PHF) has a high mortality rate. Early diagnosis is crucial for treatment and management of PHF. In clinical practice, various tests and examinations play a key role in the diagnosis of PHF, including continuously updated biomarkers, echocardiography, and cardiac magnetic resonance imaging. This article focuses on summarizing relevant research on biomarkers, examinations, combined testing, clinical models, and the grading and staging of PHF diagnosis, aiming to provide insights and directions for the diagnosis of PHF.
Humans ; Heart Failure/diagnosis* ; Child ; Biomarkers/blood* ; Echocardiography ; Magnetic Resonance Imaging

OBJECTIVE: To retrospectively analyze the clinical characteristics, co-mutated genes in newly diagnosed acute myeloid leukemia (AML) patients with NRAS and KRAS gene mutations, and the impact of NRAS and KRAS mutations on prognosis. METHODS: The clinical data and next-generation sequencing results of 80 newly diagnosed AML patients treated at our hospital from December 2018 to December 2023 were collected. The clinical characteristics, co-mutated genes of NRAS and KRAS , and the impact of NRAS and KRAS mutations on prognosis in newly diagnosed AML patients were analyzed. RESULTS: Among 80 newly diagnosed AML patients, NRAS mutations were detected in 20 cases(25.0%), and KRAS mutations were detected in 9 cases(11.3%). NRAS mutations predominantly occurred at codons 12 and 13 of exon 2, as well as codon 61 of exon 3, while KRAS mutations were most commonly occurred at codons 12 and 13 of exon 2, all of which were missense mutations. There were no statistically significant differences observed in terms of age, sex, white blood cell count(WBC), hemoglobin(Hb), platelet count(PLT), bone marrow blasts, first induction chemotherapy regimen, CR1/CRi1 rates, chromosome karyotype, 2022 ELN risk classification and allogeneic hematopoietic stem cell transplantation(allo-HSCT) among the NRAS mutation group, KRAS mutation group and NRAS/KRAS wild-type group (P >0.05). KRAS mutations were significantly correlated with PTPN11 mutations (r =0.344), whereas no genes significantly associated with NRAS mutations were found. Survival analysis showed that compared to the NRAS/KRAS wild-type group, patients with NRAS mutation had a relatively higher 5-year overall survival (OS) rate and relapse-free survival (RFS) rate, though the differences were not statistically significant (P =0.097, P =0.249). Compared to the NRAS/KRAS wild-type group, patients with KRAS mutation had a lower 5-year OS rate and RFS rate, with no significant differences observed (P =0.275, P =0.442). There was no significant difference in the 5-year RFS rate between the KRAS mutation group and NRAS mutation group (P =0.157), but the 5-year OS rate of patients with KRAS mutation was significantly lower than that of patients with NRAS mutation (P =0.037). CONCLUSION In newly diagnosed AML patients, KRAS mutation was significantly correlated with PTPN11 mutation. Compared to patients with NRAS/KRAS wild-type, those with NRAS mutation showed a more favorable prognosis, while patients with KRAS mutation showed a poorer prognosis; however, these differences did not reach statistical significance. Notably, the prognosis of AML patients with KRAS mutation was significantly inferior compared to those with NRAS mutation.
Humans ; Leukemia, Myeloid, Acute/diagnosis* ; Mutation ; Prognosis ; Proto-Oncogene Proteins p21(ras)/genetics* ; GTP Phosphohydrolases/genetics* ; Retrospective Studies ; Membrane Proteins/genetics* ; Female ; Male ; Middle Aged ; Adult ; Aged

OBJECTIVE: Hepatocellular carcinoma (HCC) is sensitive to ferroptosis, a new form of programmed cell death that occurs in most tumor types. However, the mechanism through which ferroptosis modulates HCC remains unclear. This study aimed to investigate the oncogenic role and prognostic value of FANCD2 and provide novel insights into the prognostic assessment and prediction of immunotherapy. METHODS: Using clinicopathological parameters and bioinformatic techniques, we comprehensively examined the expression of FANCD2 macroscopically and microcosmically. We conducted univariate and multivariate Cox regression analyses to identify the prognostic value of FANCD2 in HCC and elucidated the detailed molecular mechanisms underlying the involvement of FANCD2 in oncogenesis by promoting iron-related death. RESULTS: FANCD2 was significantly upregulated in digestive system cancers with abundant immune infiltration. As an independent risk factor for HCC, a high FANCD2 expression level was associated with poor clinical outcomes and response to immune checkpoint blockade. Gene set enrichment analysis revealed that FANCD2 was mainly involved in the cell cycle and CYP450 metabolism. CONCLUSION To the best of our knowledge, this is the first study to comprehensively elucidate the oncogenic role of FANCD2. FANCD2 has a tumor-promoting aspect in the digestive system and acts as an independent risk factor in HCC; hence, it has recognized value for predicting tumor aggressiveness and prognosis and may be a potential biomarker for poor responsiveness to immunotherapy.
Humans ; Carcinoma, Hepatocellular/diagnosis* ; Liver Neoplasms/diagnosis* ; Immunotherapy ; Fanconi Anemia Complementation Group D2 Protein/metabolism* ; Prognosis ; Male ; Female ; Middle Aged ; Biomarkers, Tumor/metabolism*

From an aqueous extract of the Angelica sinensis root head (guitou), nine pairs of lignanoid enantiomers [(+)-/(-)-1-(+)-/(-)-9], including three pairs of new structures [(+)-/(-)-1-(+)-/(-)-3] and two pairs of chiral separated enantiomers for the first time [(+)-/(-)-4 and (+)-/(-)-5], were isolated and chirally separated by column chromatography over different types of resin, normal and reversed phase silica gels, together with HPLC techniques using reversed phase and chiral columns. Their structures were determined by spectroscopic data analysis, theoretic calculation of electronic circular dichroism (ECD) spectra, and single-crystal X-ray diffraction. The chiral separated new enantiomers named (+)-/(-)-angelignanins Q-T [(+)-/(-)-1-(+)-/(-)-4] and (+)-/(-)-daphneresinol [(+)-/(-)-5], respectively.

Objective To explore characteristics of clinical parameters and cytokines in patients with drug-induced liver injury(DILI)caused by different drugs and their correlation with clinical indicators. Method The study was conducted on patients who were up to Review of Uncertainties in Confidence Assessment for Medical Tests(RUCAM)scoring criteria and clinically diagnosed with DILI.Based on Chinese herbal medicine,cardiovascular drugs,non-steroidal anti-inflammatory drugs(NSAIDs),anti-infective drugs,and other drugs,patients were divided into five groups.Cytokines were measured by Luminex technology.Baseline characteristics of clinical biochemical indicators and cytokines in DILI patients and their correlation were analyzed. Results 73 patients were enrolled.Age among five groups was statistically different(P=0.032).Alanine aminotransferase(ALT)(P=0.033)and aspartate aminotransferase(AST)(P=0.007)in NSAIDs group were higher than those in chinese herbal medicine group.Interleukin-6(IL-6)and tumor necrosis factor alpha(TNF-α)in patients with Chinese herbal medicine(IL-6:P<0.001;TNF-α:P<0.001)and cardiovascular medicine(IL-6:P=0.020;TNF-α:P=0.001)were lower than those in NSAIDs group.There was a positive correlation between ALT(r=0.697,P=0.025),AST(r=0.721,P=0.019),and IL-6 in NSAIDs group. Conclusion Older age may be more prone to DILI.Patients with NSAIDs have more severe liver damage in early stages of DILI,TNF-α and IL-6 may partake the inflammatory process of DILI.

Objective sampling method was used to conduct a questionnaire survey on outpatients in two hospitals in Guangdong province in order to evaluate patients’ satisfaction with the quality of medical service. This paper explored the factors that affect patients’ evaluation of medical service quality, and found that patients’ age was negatively correlated with the evaluation of medical service quality. It is suggested that the establishment of friendly medical institutions should be carried out according to the national policy. At the same time, the management mechanism of hospital should be improved, the number of medical service centers for "efficient" should be increased, and the medical service personnel should be regularly trained; carry out medical knowledge education in community, improve the popularization of personal medical knowledge and close the cognitive gap between doctors and patients.

Objective To investigate the morphological typing and clinical significance of the distal tibiofibular syndesmosis fibular notch based on CT images. Methods According to the inclusion and exclusion ceiteria‚ the imaging data of patients undergoing ankle joint CT examination were analyzed‚ and the inferior tibiofibular joint fibula notch was classified according to the morphological characteristics. The measurements included 8 distances. There were 123 males and 102 females‚ all of whom were Han nationality‚ aged 18-60 years old. Results Retrospectively analyzed the result of 225 patients from December 2013 to December 2022. The distal tibiofibular syndesmosis fibular notch was divided into four types according to morphological characteristics‚ C-shaped (50. 67%)‚ V-shaped (26. 67%)‚ flat-shaped (15. 11%) and L-shaped (7. 56%). The angle between the anterior and posterior facets of the flat shape (145. 56 ± 9. 25)° was the largest and the angle between the anterior and posterior facets of the L shape (125. 07 ± 13. 54)° was the smallest(P< 0. 05); the depth of the notch in the flat shape (3. 11 ± 0. 83) mm was the smallest and in the L shape (4. 47±1. 11) mm was the largest(P<0. 05);The posterior facet length (13. 06 ± 3. 56) mm and anterior tibiofibular gap (3. 83±1. 49) mm on left were larger than on the right side (P<0. 05); The posterior facet length (13. 36 ± 3. 46) mm‚ fibular notch depth (3. 93 ± 1. 10) mm and vertical distance of tibiofibular overlap (9. 10 ± 2. 55) mm larger in men than in women (P<0. 05). Conclusion In this study‚ the data related to the inferior tibiofibular syndesmosis notch were measured and divided into four types according to the shape. The flat inferior tibiofibular syndesmosis notch is more likely to have chronic ankle instability‚ and the fibula is more likely to move forward during anatomical reduction. The inferior tibiofibular syndesmosis of L-shaped and C-shaped notches is more prone to posterior displacement of fibula or poor rotation reduction during anatomical reduction.