Hepatocellular carcinoma (HCC) is a leading cause of cancer-associated death globally. Liver transplantation (LT) has emerged as a key treatment for patients with HCC, and the Milan criteria have been adopted as the cornerstone of the selection policy. To allow more patients to benefit from LT, a number of expanded criteria have been proposed, many of which use radiologic morphological characteristics with larger and more tumors as surrogates to predict outcomes. Other groups developed indices incorporating biological variables and dynamic markers of response to locoregional treatment. These expanded selection criteria achieved satisfactory results with limited liver supplies. In addition, a number of prognostic models have been developed using clinicopathological characteristics, imaging radiomics features, genetic data, and advanced techniques such as artificial intelligence. These models could improve prognostic estimation, establish surveillance strategies, and bolster long-term outcomes in patients with HCC. In this study, we reviewed the latest findings and achievements regarding the selection criteria and post-transplant prognostic models for LT in patients with HCC.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-associated death globally. Liver transplantation (LT) has emerged as a key treatment for patients with HCC, and the Milan criteria have been adopted as the cornerstone of the selection policy. To allow more patients to benefit from LT, a number of expanded criteria have been proposed, many of which use radiologic morphological characteristics with larger and more tumors as surrogates to predict outcomes. Other groups developed indices incorporating biological variables and dynamic markers of response to locoregional treatment. These expanded selection criteria achieved satisfactory results with limited liver supplies. In addition, a number of prognostic models have been developed using clinicopathological characteristics, imaging radiomics features, genetic data, and advanced techniques such as artificial intelligence. These models could improve prognostic estimation, establish surveillance strategies, and bolster long-term outcomes in patients with HCC. In this study, we reviewed the latest findings and achievements regarding the selection criteria and post-transplant prognostic models for LT in patients with HCC.

Objective: To explore the current status of hyperopic reserve and its related factors among non-myopia preschool and primary school students aged 5 to 12 years in Guangdong Province, so as to provide a basis for formulating intervention strategies for the pre myopia stage of children. Methods: From October to December 2023, by using stratified cluster random sampling method, a survey on hyperopic reserve among preschool children and primary school students in Guangdong were conducted. And a total of 10 567 children from the senior class of kindergarten to the sixth grade of primary school who completed autorefraction measurements with and without cycloplegia and the questionnaire survey were included in the study. The prevalence characteristics of low hyperopic reserve among non-myopia children were analyzed, and multivariable Logistic regression was used to analyze the related factors. Results: The prevalence rate of low hyperopic reserve among 8 790 non-myopia children was 62.4%. The average spherical equivalent (SE) for children aged 5 to 12 years was 0.88 (0.25, 1.25)D, decreasing from 1.13 ( 0.75 , 1.50)D in senior kindergarten to -1.00 (-2.50, 0.38)D in sixth grade, with the difference was statistically significant ( H=2 475.3, P <0.01). Multivariable Logistic regression analysis, after adjusting for confounders including gender, urban and rural, and grade, revealed that parental myopia was a risk factor for low hyperopic reserve in the preschool stage (one parent with myopia: OR=1.62, 95%CI =1.35-1.93; both parents with myopia: OR=2.05, 95%CI = 1.66 -2.55); in the lower primary school stage, parental myopia (one parent with myopia: OR=1.46, 95%CI =1.27-1.68; both parents with myopia: OR=1.58, 95%CI =1.33-1.89), frequently or always reading or using electronic screens while lying down or on one s stomach ( OR=1.43, 95%CI =1.13-1.81), and never or occasionally maintaining a viewing distance of over 3 meters when watching TV/playing video games ( OR=1.34, 95%CI =1.04-1.72) were risk factors; in the higher primary school stage, failing to take a break every hour during near work ( OR=1.79, 95%CI =1.16-2.75) was a risk factor (all P <0.05). Conclusions The emmetropization of children aged 5-12 years in Guangdong Province is accelerated, and non-myopia children generally exhibit insufficient hyperopic reserve. The contributing factors for insufficient hyperopia reserve in non-myopia children vary across different educational stages, necessitating targeted precision interventions.

Objective To evaluate the anticoagulation efficacy of non-vitamin K antagonist oral anticoagulants (NOACs) and vitamin K antagonists (VKAs) in patients with high-risk atrial fibrillation (AF) undergoing transcatheter aortic valve implantation (TAVI). Methods A computer-based search was conducted on PubMed, EMbase, The Cochrane Library, CNKI, SinoMed, and VIP databases to identify studies on the application of NOACs and VKAs in high-risk AF patients after TAVI. The search period was from database inception to January 2023. The quality of the included studies was assessed using the Cochrane risk assessment tool and the Newcastle-Ottawa Scale (NOS). Meta-analysis was performed using RevMan 5.4 software. Results A total of 7 studies involving 24 592 patients were included. The meta-analysis results showed that compared to patients using VKAs, those treated with NOACs had a significantly lower risk of all-cause mortality [RR=0.74, 95%CI (0.58, 0.94), P=0.01]. Subgroup analysis indicated that when the follow-up period was less than 1 year, there was no significant difference in all-cause mortality between the NOAC and VKA groups [RR=0.57, 95%CI (0.17, 1.88), P=0.35]; however, when the follow-up period was ≥1 year, the VKA group had a higher all-cause mortality rate than the NOAC group, with a statistically significant difference [RR=0.73, 95%CI (0.57, 0.95), P=0.02]. No significant differences were found between the two groups regarding early stroke [RR=0.50, 95%CI (0.19, 1.28), P=0.15], stroke during follow-up [RR=1.04, 95%CI (0.88, 1.22), P=0.64], bleeding [RR=0.94, 95%CI (0.73, 1.21), P=0.61], major or life-threatening bleeding [RR=0.80, 95%CI (0.49, 1.31), P=0.38], or acute kidney injury [RR=0.51, 95%CI (0.16, 1.59), P=0.24]. Conclusion Compared to VKAs, the use of NOACs in patients with high-risk AF undergoing TAVI may reduce the risk of all-cause mortality, especially during long-term anticoagulation therapy, potentially offering greater benefits. However, further evidence from randomized controlled trials is needed to confirm these findings.

Liver diseases cannot be easily detected in the early stage， and although invasive diagnostic methods， such as liver biopsy， are relatively accurate， they tend to have a low degree of acceptance， which greatly limits the improvement in diagnosis and treatment techniques for liver diseases. Therefore， it is of great importance to search for new biomarkers and therapeutic targets. As an emerging biomarker for liquid biopsy， tRNA-derived small RNA （tsRNA） is abnormally expressed in various liver diseases including viral hepatitis， fatty liver disease， liver injury， and liver cancer， and it can affect the development and progression of liver diseases by regulating the biological functions such as gene expression， epigenetic regulation， and protein translation. This article reviews the origin， classification， and biological function of tsRNA， as well as the research advances in tsRNA as biomarkers and potential therapeutic targets for liver diseases， so as to provide ideas for the early diagnosis and treatment of liver diseases.

Transfer RNA-derived small RNAs(tsRNAs)are a novel class of non-coding small RNAs,which have various biological functions such as regulating gene expression,protein translation,epigenetic inheritance,cell proliferation and apoptosis,and communication.tsRNA is abnormally expressed in respiratory system cancers,lung injury,pulmonary hypertension,childhood obstructive sleep apnea-hypopnea syndrome and respiratory virus infections,affecting the occurrence and development of these diseases.However,the regulatory mechanism of tsRNAs in respiratory diseases has not been fully elucidated yet.Therefore,this review introduces the sources,classification and detection methods of tsRNA,summarizes the biological functions of tsRNA in respiratory diseases,related regulatory mechanisms,and its application prospects as biomarker in clinical diagnosis and prognosis evaluation,aiming to provide new ideas for the diagnosis and treatment of respiratory diseases,and to provide reference for in-depth research on tsRNA regulation of respiratory diseases.

Objective To investigate the mechanism of DNA methyltransferase 1(DNMT1)inhibitor combined with extracellular signal-regulated kinase 1(ERK1),homeodomain-interacting protein kinase 2(HIPK2),and glycogen synthase kinase 3 β(GSK3 β)inhibitors in synergistically inducing apoptosis and protein arrest in relapsed and refractory acute myeloid leukemia(AML)cells.Methods Three therapeutic targets,including ERK1,HIPK2,and GSK3β,were screened based on the Gene Expression Omnibus(GEO),The Cancer Genome Atlas(TCGA)database,and Ex-pression 2 Kinases database.Human AML cell line U937 cells were treated with DNMT1 inhibitor a-lone or combined with ERK1,HIPK2,or GSK3β inhibitors.Cell viability was detected using the CCK-8 method.Apoptosis rate was analyzed by flow cytometry,and cell cycle distribution was de-termined by propidium iodide(PI)staining.The mRNA expression levels of DNMT1,ERK1,HIPK2,and GSK3β were detected by real-time fluorescent quantitative reverse transcriptionpoly-merase chain reaction(RT-qPCR).Protein expressionlevels of DNMT1,ERK1,HIPK2,and GSK3β were detected by immunoblotting.Results DNMT1 inhibitor significantly inhibited the cell viability of U937 cells,and significantly induced apoptosis and cell cycle arrest in U937 cells(P＜0.05).When DNMT1 inhibitor was combined with ERK1,HIPK2,or GSK3β inhibitors,cell via-bility and apoptosis rate were significantly reduced(P＜0.05).DNMT1 inhibitor alone or its com-bination with ERK1,HIPK2,and GSK3β inhibitors induced U937 cell arrest in the G0/G,phase,with a significant increase in the proportion of cells in the G0/G1 phase in the combination group(P＜0.05).The combination of DNMT1 inhibitor with ERK1,HIPK2,and GSK3β inhibitors sig-nificantly reduced the mRNA expression levels in DNMT1,ERK1,HIPK2,and GSK3β in U937 cells(P＜0.05).Similarly,the combination therapy significantly reduced the protein expression levels of DNMT1,ERK1,HIPK2,and GSK3β in U937 cells(P＜0.05).Conclusion DNMT1 inhibitor combined with ERK1,HIPK2,and GSK3 β inhibitors can synergistically induce apoptosis and protein arrest in relapsed and refractory AML cells,providing a novel strategy for combined tar-geted therapy of AML.

Objective:To investigate the value of nomogram model based on CT features in differentiating ectopic pancreas (EP) from gastrointestinal stromal tumors (GIST) with a long diameter less than 3 cm.Methods:This study was a case-control study. The clinical and imaging data of 43 patients with EP and 90 patients with GIST confirmed by pathology in the Affiliated Hospital of Guizhou Medical University from August 2013 to March 2024 were retrospectively analyzed. Preoperative CT images were analyzed to obtain qualitative features (number of lesions, location, morphology, growth pattern, borders, cystic degeneration, calcification, ulceration, catheter sign, central umbilication) and quantitative features (lesion long diameter, short diameter, long/short diameter, lesion and normal pancreas arterial-phase and venous-phase CT values, and enhancement ratio). Statistical analyses, including independent sample t-tests, Mann-Whitney U tests, χ2 tests, and Fisher exact tests, were performed to compare CT characteristics between the two groups. Binary logistic regression analysis was used to obtain independent predictors to identify the two groups, to establish a joint model, and to draw a nomogram. The discriminative performance of the independent predictors and the combined model was assessed using receiver operating characteristic (ROC) curves, while calibration curves were used to evaluate model fit. Results:The differences in age, location, morphology, border, catheter sign, central umbilication, short diameter, long/short diameter, arteriovenous phase enhancement CT value and arteriovenous phase enhancement ratio were statistically significant between the EP group and the GIST group (all P<0.05). The logistic analysis showed that the differences in age ( OR=0.920, 95% CI 0.885-0.956, P<0.001), border ( OR=5.994, 95% CI 2.111-17.022, P=0.001), long/short diameter ( OR=7.820, 95% CI 1.841-33.224, P=0.005), and venous phase enhancement ratio ( OR=8.847, 95% CI 1.103-70.972, P=0.040) were the independent predictors for distinguishing EP from GIST, and the area under the ROC curve (AUC) were 0.782 (95% CI 0.698-0.866), 0.684 (95% CI 0.600-0.767), 0.705 (95% CI 0.607-0.803), and 0.693 (95% CI 0.605-0.781), respectively. Combined age, border, long diameter/short diameter and venous phase enhancement ratio were plotted in a nomogram with an AUC of 0.881 (95% CI 0.817-0.945), sensitivity and specificity of 74.4% and 93.3%, respectively. The calibration curve demonstrated a strong agreement between predicted and actual probabilities (Hosmer-Lemeschow test, P=0.267). Conclusions:CT imaging reveals significant differences between EP and small GISTs (<3 cm). EP is more likely when patients are younger and lesions exhibit indistinct borders, a higher long-to-short diameter ratio, and greater venous-phase enhancement. The nomogram derived from CT features provides a valuable tool for differentiating EP from GIST.

Objective To observe the value of T1 mapping combining diffusion weighted imaging(DWI)for noninvasive preoperative predicting tumor-infiltrating lymphocyte(TIL)level in invasive breast cancer.Methods Totally 143 patients with invasive breast cancer were retrospectively collected and divided into high group(TIL≥10％,n=73)and low group(TIL＜10％,n=70)according to TIL level by postoperation pathology.Clinicopathological information were collected,MRI features of breast cancer lesions were documented,mean T1 values(T1mean)and mean ADC values(ADCmean)were measured,and then were compared between groups.Multivariate logistic regression analysis was used to identify independent predictive factors of TIL levels,and a nomogram was constructed based on regression model.The receiver operating characteristic(ROC)curve and the area under the curve(AUC)were used to evaluate the predictive value for TIL levels.Results Compared with low group,high group had higher proportion of human epidermal growth factor receptor 2(HER2)positivity(P＜0.05),and showed more circular/oval shapes and more smooth margins but less peritumoral edema(all P＜0.05).Significant differences of lesions enhancement pattern was found between groups(P＜0.05).T1mean and ADCmean were both higher in high group than those in low group(both P＜0.05).Lesions enhancement pattern,T1mean and ADCmean were all independent predictors of TIL levels in breast cancer.The AUC of nomogram combining the above 3 factors for predicting TIL level was 0.848,significantly higher than that of lesions enhancement pattern(AUC=0.569,Z=5.384,P＜0.05)and T1mean(AUC=0.662,Z=3.876,P＜0.05),but not statistically different with that of ADCmean(AUC=0.814,Z=1.578,P=0.115).Decision curve analysis showed that this nomogram had good clinical application value.Conclusion Combining T1 mapping and DWI could effectively predict level of TIL level in breast cancer before surgery.

Imaging examination is a crucial part in diagnosis and treatment of central nervous system infection(CNSI),involving complex imaging sequences and parameters.This consensus was jointly written by multiple CNSI imaging experts in China,aimed to standardize imaging examination of CNSI.