ObjectiveThis study aims to explore the effect of Feiyanning granules on ferroptosis in lung cancer cells and its regulatory function within the nuclear transcription factor E₂-related factor 2 （Nrf2）/mouse solute carrier family 7 member 11 （SLC7A11）/glutathione peroxidase 4 （GPX4） signaling pathway. MethodsThe cell counting kit-8 （CCK-8） method was used to detect the effect of Feiyanning granule on the proliferation of A549 lung cancer cells. A549 lung cancer cells were categorized into a blank group， a ferroptosis inhibitor-1 （Fer-1） group （10 μmol·L^-1）， a Feiyanning granules （600 mg·L^-1） group， and a Feiyanning granules + Fer-1 group. After 48 hours of intervention， the activity and morphology of the cells were observed. The CCK-8 method was employed to measure cell viability. Biochemical assays were carried out to measure the levels of reactive oxygen species （ROS）， malondialdehyde （MDA）， glutathione （GSH）， and ferrous ions （Fe²⁺） in A549 cells. Western blot was utilized to evaluate the expression levels of Kelch-like ECH-associated protein 1 （Keap1）， Nrf2， SLC7A11， and GPX4 proteins. A549 lung cancer cells were categorized into a blank group and a Feiyanning Granule group （600 mg·L^-1）， and mitochondrial morphology was examined via transmission electron microscopy （TEM）. ResultsAfter the intervention of Feiyaning granules， the proliferation of A549 cells was significantly inhibited in a concentration-dependent manner compared with that in the blank group （P<0.01）. Compared with the blank group， the Feiyanning granules group exerted an significantly inhibitory effect on the viability of lung cancer cells （P<0.01）. Compared with that in the Feiyanning granules group， the cell viability in the Feiyanning granules +Fer-1 group was obviously restored （P<0.05）. Compared with the blank group， the Feiyanning Granule group showed a significant increase in the levels of ROS， MDA， and Fe²⁺ （P<0.01）， a significant decrease in the GSH level （P<0.01）， and facilitated ferroptosis. Compared with the blank group， the Feiyanning granules group showed significantly decreased expression of Nrf2， SLC7A11， and GPX4 proteins and enhanced expression of Keap1 （P<0.01）. Compared with those in the Feiyanning Granule group， the protein levels of Nrf2， SLC7A11， and GPX4 increased significantly （P<0.01）， and the expression of Keap1 decreased significantly in the Feiyanning granules + Fer-1 group （P<0.01）. Compared with the blank group， the Feiyaning granules group exhibited reduced mitochondrial size and increased matrix electron density. ConclusionFeiyanning granules can induce ferroptosis in lung cancer cells， and its underlying mechanism might be associated with the inhibition of the Nrf2/SLC7A11/GPX4 signaling pathway.

Objective To construct a dynamic prediction model for JC polyomavirus (JCV) reactivation after kidney transplantation. Methods A retrospective analysis was conducted on the clinical data of 128 recipients who met the inclusion criteria and received kidney transplantation in Nanfang Hospital, Southern Medical University, from June 2021 to December 2024. Dynamic Cox regression and dynamic restricted mean survival time (RMST) regression based on the landmark method were adopted to establish the dynamic prediction models, and Monte Carlo cross-validation was used to evaluate model performance. Results The dynamic models could predict the incidence and onset time of JCV reactivation within the subsequent 3 months according to covariates at each landmark time point from the 1st to the 6th month after transplantation. Remuzzi score, body mass index and warm ischemia time were independent risk factors for JCV reactivation, while a history of urinary BK polyomavirus reactivation served as a protective factor. The median values of the area under the curve, C-index and Brier score of the dynamic Cox model were 0.873, 0.851 and 0.065 respectively, and the C-index of the dynamic RMST model was 0.829, all of which were superior to those of the static model. Conclusions The landmark-based dynamic models exhibit excellent predictive performance, which may integrate baseline data and post-transplant follow-up information to realize dynamic assessment of the risk and time window of JCV reactivation, thereby providing evidence for optimizing post-operative monitoring and individualized intervention strategies.

Cholangiocarcinoma (CCA) is a malignancy characterized by tumor cells originating in the liver or bile ducts, exhibiting features of cholangiocyte differentiation. It poses a significant clinical challenge due to the limited diagnostic and therapeutic options available. Robust animal models are essential for advancing our understanding of CCA pathogenesis and developing effective treatments. This review provides a comprehensive overview of CCA mouse models, highlighting various approaches, including chemical induction, genetically engineered models, and tumor xenografts. Each model is discussed in terms of its establishment techniques, pathological characteristics, and research significance, with a focus on intrahepatic CCA. Chemical induction models, such as diethylnitrosamine- and azoxymethane-induced models, offer insights into tumorigenesis processes, whereas genetically modified models involving alterations in key genes such as Kirsten rat sarcoma viral oncogene homolog, tumor protein 53, and isocitrate dehydrogenase serve as important tools for studying the molecular mechanisms underlying CCA. Xenograft models, including patient-derived xenografts, bridge the gap between experimental research and clinical applications, allowing for precise therapeutic evaluations. By comparing these models, this review underscores their respective advantages and limitations, paving the way for future studies aiming to optimize and innovate CCA modeling strategies.

[Objective] To conduct a retrospective statistical comparison of alanine aminotransferase (ALT) test values in blood donors prior to blood collection, aiming to analyze the objective characteristics of the population with elevated ALT levels (ALT>50 U/L) and provide reference data for adjusting the screening eligibility threshold for ALT. [Methods] The preliminary ALT screening data of 30 341 blood donor samples collected prior to blood donation from three smart blood donation sites at the Shenzhen Blood Center between 2022 and 2023 were extracted and compared with data from a health examination department of a tertiary hospital in Shenzhen (representing the general population, n=24 906). Both datasets were categorized and statistically described. A retrospective analysis was conducted to examine the associations between ALT test results and factors such as donors' gender, age, ethnicity, donation site, donation season, and frequency of blood donation. [Results] The ALT levels in both blood donors and the general population were non-normally distributed. The 95th percentile of ALT values was calculated as 61.4 U/L (male: 67.8 U/L, female: 39.3 U/L) for blood donors and 58.1 U/L (male: 63.7 U/L, female: 51.2 U/L) for the general population. The non-compliance rates (ALT>50 U/L) were 7.65% (2 321/30 341) in blood donors and 7.08% (1 763/24 906) in the general population. There were significant differences (P<0.05) in the ALT failure rate among blood donors based on gender, age, and donation site, but no significant differences (P>0.05) during the blood donation season. There was no statistically significant difference (P>0.05) in the positive rates of four serological markers (HBsAg, anti HCV, HIV Ag/Ab, anti TP) for blood screening pathogens between ALT unqualified and qualified individuals (2.05% vs 1.5%). If the ALT qualification threshold was raised from 50 U/L to 90 U/L, the non qualification rates of male and female blood donors would decrease from 9.82% (2 074/21 125) to 2.23% (471/21 125) and from 2.70% (249/9 216) to 0.75% (69/9 216), respectively. Among the 154 blood donors who donated blood more than 3 times, 88.31% of the 248 ALT test results were in the range of 50-90 U/L. Among them, 9 cases had ALT>130 U/L, and ALT was converted to qualified in subsequent blood donations. [Conclusion] There are differences in the ALT failure rate among blood donors of different genders and ages, and different blood donation sites and operators can also affect the ALT detection values of blood donors. The vast majority of blood donors with ALT failure are caused by transient and non pathological factors. With the widespread use of blood virus nucleic acid testing, appropriately increasing the ALT qualification threshold for blood donors can expand the qualified population and alleviate the shortage of blood sources, and the risk of blood safety will not increase.

Macrophages are innate immune cells connected with the development of inflammation. Retinoic acid has previously been proved to have anti-inflammatory and anti-arthritic properties. However, the exact mechanism through which retinoic acid modulates arthritis remains unclear. This study aimed to investigate whether retinoic acid ameliorates rheumatoid arthritis by modulating macrophage polarization. This study used retinoic acid to treat mice with adjuvant arthritis and evaluated anti-inflammatory effects by arthritis score, thermal nociceptive sensitization test, histopathologic examination and immunofluorescence assays. In addition, its specific anti-arthritic mechanism was investigated by flow cytometry, cell transfection and inflammatory signaling pathway assays in RAW264.7 macrophages in vitro. Retinoic acid significantly relieved joint pain and attenuated inflammatory cell infiltration in mice. Furthermore, this treatment modulated peritoneal macrophage polarization, increased levels of arginase 1, as well as decreased inducible nitric oxide synthase expression. In vitro, we verified that retinoic acid promotes macrophage transition from the M1 to M2 type by upregulating mitogen-activated protein kinase (MAPK) phosphatase 1 (MKP-1) expression and inhibiting P38, JNK and ERK phosphorylation in lipopolysaccharide-stimulated RAW264.7 cells. Notably, the therapeutic effects of retinoic acid were inhibited by MKP-1 knockdown. Retinoic acid exerts a significant therapeutic effect on adjuvant arthritis in mice by regulating macrophage polarization through the MKP-1/MAPK pathway, and play an important role in the treatment of rheumatic diseases.

Macrophages are innate immune cells connected with the development of inflammation. Retinoic acid has previously been proved to have anti-inflammatory and anti-arthritic properties. However, the exact mechanism through which retinoic acid modulates arthritis remains unclear. This study aimed to investigate whether retinoic acid ameliorates rheumatoid arthritis by modulating macrophage polarization. This study used retinoic acid to treat mice with adjuvant arthritis and evaluated anti-inflammatory effects by arthritis score, thermal nociceptive sensitization test, histopathologic examination and immunofluorescence assays. In addition, its specific anti-arthritic mechanism was investigated by flow cytometry, cell transfection and inflammatory signaling pathway assays in RAW264.7 macrophages in vitro. Retinoic acid significantly relieved joint pain and attenuated inflammatory cell infiltration in mice. Furthermore, this treatment modulated peritoneal macrophage polarization, increased levels of arginase 1, as well as decreased inducible nitric oxide synthase expression. In vitro, we verified that retinoic acid promotes macrophage transition from the M1 to M2 type by upregulating mitogen-activated protein kinase (MAPK) phosphatase 1 (MKP-1) expression and inhibiting P38, JNK and ERK phosphorylation in lipopolysaccharide-stimulated RAW264.7 cells. Notably, the therapeutic effects of retinoic acid were inhibited by MKP-1 knockdown. Retinoic acid exerts a significant therapeutic effect on adjuvant arthritis in mice by regulating macrophage polarization through the MKP-1/MAPK pathway, and play an important role in the treatment of rheumatic diseases.

Objective:To investigate the application value of risk prediction model for acute kidney injury (AKI) after donation of cardiac death (DCD) liver transplantation based on machine learning algorithm.Methods:The retrospective cohort study was conducted. The clinicopathological data of 1 001 pairs of DCD liver transplant donors and recipients at five hospitals, including The First Affiliated Hospital of Zhejiang University School of Medicine et al, in the Chinese Liver Transplan-tation Registry from January 2015 to December 2023 were collected. Of the donors, there were 825 males and 176 females. Of the recipients, there were 806 males and 195 females, aged 52 (range, 18-75)years. There were 281 recipients included using oversampling technique, and all 1 282 recipients were divided to the training set of 897 recipients and the validation set of 385 recipients by a ratio of 7∶3 using computer-generated random numbers. Seven prediction models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), and Categorical Boosting (CatBoost), were constructed for AKI after liver transplantation based on machine learning algorithm. Observation indicators: (1) comparison of clinicopathological characteristics between recipients with and without AKI and donors; (2) follow-up and survival of recipients with and without AKI; (3) construction and validation of nomogram prediction model of AKI after liver transplantation; (4) construction and validation of machine learning prediction model of AKI after liver transplantation. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test, and comparison among groups was conducted using the Kruskal-Wallis H test. Comparison of count data between groups was conducted using the chi-square test or corrected chi-square test. Kaplan-Meier method was used to calculate survival rates and plot survival curves. Logistic regression model was performed for univariate and multivariate analyses. The receiver operating characteristic (ROC) curve was plotted to calculate area under curve (AUC) and 95% confidence interval ( CI). The performance of prediction model was evaluated using DeLong test, accuracy, sensitivity, specificity. The calibration curve was plotted to evaluate the performance of predicted probability and actual probability. The interpretability analysis of machine learning algorithm and SHapley Additive exPlanations was used to explain the model decision separately. Results:(1) Comparison of clinicopathological characteristics between recipients with and without AKI and donors. Of 1 001 recipients, there were 360 cases with AKI and 641 cases without AKI after liver transplantation. There were significant differences in body mass index (BMI), hepatic encepha-lopathy, hepatitis B surfact antigen (HBsAg), hepatorenal syndrome (HRS) and donor diabetes, donor blood urea nitrogen, donor alanine aminotransferase, donor aspartate aminotransferase, mass of graft, volume of blood loss during liver transplantation, warm ischema time of donor liver, and operation time between recipients with and without AKI ( Z=-4.337, χ2=9.751, 9.088, H=11.142, χ2=5.286, Z=-3.360, -2.539, -3.084, -1.730, -3.497, -1.996, -2.644, P<0.05). (2) Follow-up and survival of recipients with and without AKI. All the 1 001 recipients received follow-up. The recipients with AKI after liver transplantation were followed up for 18.6(range, 0-102.3)months, and recipients without AKI after liver transplantation were followed up for 31.9(range, 0.1-105.5)months. The 1-, 3-, and 5-year overall survival rates were 72.1%, 63.5%, and 59.3% of recipients with AKI, versus 86.7%, 76.7%, and 72.5% of recipients without AKI, respectively, showing a significant difference in overall survival between them ( χ2=26.028, P<0.05). (3) Construction and validation of nomogram predic-tion model of AKI after liver transplantation. Results of multivariate analysis showed that recipient BMI, recipient creatinine, recipient HBsAg, recipient HRS, donor blood urea nitrogen, donor crea-tinine, anhepatic phase and volume of blood loss during liver transplantation were independent risk factors for AKI of recipients after liver transplantation ( odds ratio=1.113, 0.998, 0.605, 1.580, 1.047, 0.998, 1.006, 1.157, 95% CI as 1.070-1.157, 0.996-1.000, 0.450-0.812, 1.021-2.070, 1.021-1.074, 0.996-0.999, 1.000-1.012, 1.045-1.281, P<0.05). The nomogram prediction model of AKI after liver transplantation was constructed based on the results of multivariate analysis. Results of ROC curve showed that the AUC of 0.666 (95% CI as 0.637-0.696). (4) Construction and validation of machine learning prediction model of AKI after liver transplantation. Based on the Lasso regression analysis, seven machine learning algorithm prediction models, including RF, XGBoost, SVM, LR, DT, KNN, and CatBoost, were constructed, with ROC curves of the validation set plotted. The AUC of above models were 0.863, 0.841, 0.721, 0.637, 0.620, 0.708, 0.731, accuracies were 0.764, 0.782, 0.701, 0.592, 0.605, 0.605, 0.681, sensitivities were 0.764, 0.789, 0.719, 0.588, 0.694, 0.694, 0.704, specificities were 0.763, 0.774, 0.683, 0.597, 0.511, 0.511, 0.656, respectively. Delong test showed that the RF model with the highest AUC of 0.863(95% CI as 0.828-0.899). Calibration curve analysis showed the predicted probability closest to the actual probability of RF model, indicating the model with a good validation value. Further sorting of SHAP of different clinical factors based on RF model showed that recipient BMI, donor blood urea nitrogen, volume of blood loss during liver transplantation, donor age had large effects on the output outcomes. Conclusion:The nomogram prediction model and seven machine learning algorithm prediction models for AKI after DCD liver transplantation are constructed, and the RF model based on machine learning has a better predictive performance.

Aim To investigate the protective effects of Tilianin on lipopolysaccharide(LPS)-induced septice-mia-induced myocardial injury in rats and to explore the related mechanisms.Methods Animals were ran-domly grouped and a rat septicemia-induced myocardial injury model was constructed.Echocardiography was used to assess the cardiac function of rats,left ventricu-lar internal diameter at end-diastole(LVIDd)and left ventricular internal diameter at end-systole(LVIDs)were measured,and left ventricular ejection fraction(LVEF)and left ventricular fractional shortening(LVFS)were calculated;the kits were used to detect the serum activity of the relevant cardiac enzymes and the level of inflammatory factors;HE staining was used to observe the morphological changes of myocardium;immunofluorescence staining of cardiac tissues was used to detect the nuclear translocation of NF-κB p65;Western blot was used to detect the expression of TLR4,MyD88,p-NF-κB p65,and NLRP3 proteins in cardiac tissues.Results Compared with the model group,each administration group differently upregulated LVEF,LVFS,and LVIDs,and improved the coordina-tion of LV wall fluctuations in the model group of rats;cardiac enzymes LDH and CK-MB levels increased,and levels of inflammatory factors TNF-α,IL-6,and IL-1 β were reduced,exerting cardioprotective effects;HE staining showed that myocardial tissue cell gap was re-duced,myocardial fiber breakage was reduced,cardio-myocyte arrangement tended to be normal,and inflam-matory cell infiltration was reduced;NF-κB p65 entry into the nucleus was reduced,and phosphorylated NF-κB p65(p-NF-κB p65)expression was reduced;and Western blot results showed that the expression of TLR4,MyD88,and NLRP3 proteins was reduced.Conclusions Tilianin pretreatment reduces serum my-ocardial enzymes and inflammatory factors and im-proves myocardial injury in rats with septicemia-in-duced myocardial injury,which may be related to the Tilianin anti-TLR4/NF-κB/NLRP3 inflammatory signa-ling pathway.

Objective To analyze the correlation between right atrial strain at various stages and various influencing factors in patients with pulmonary hypertension,and to explore the role of right atrial strain in the assessment of pulmonary hypertension.Methods A total of 239 cases diagnosed with pulmonary hypertension who underwent echocardiography and complete right heart catheterization at hospital from October 2021 to December 2023 were included.Conventional ultrasound parameters such as right heart strain,right atrial area(RA area),inferior vena cava diameter(IVC diameter),and collapse rate of the inferior vena cava(IVC diameter changes)were measured.The heart rate(HR)corresponding to the ultrasound images were recorded.General information such as age and gender,as well as catheter data including mean right atrial pressure(mRAP),mean pulmonary artery pressure(mPAP),and pulmonary vascular resistance(PVR),were collected.The relationship between right atrial strain and its influencing factors was analyzed,and further analysis was conducted by dividing into shunt group and non-shunt group based on the presence or absence of left-to-right shunt disease.Results The correlation with RA reservoir strain(RASr)from high to low is RV global strain(RV4CSL),RV free wall strain(RVFWSL),RA area,IVC diameter,mRAP,age,HR,and PVR;the correlation with RAconduit strain(RAScd)from high to low is RV4CSL,RVFWSL,RA area,IVC diameter,mRAP,age,PVR,and HR;the correlation with RA contraction strain(RASct)from high to low is RA area,RV4CSL,RVFWSL,mRAP,IVC diameter,and HR.The collapse rate of the inferior vena cava is correlated with strain at various stages of the right atrium;gender is correlated with RASr and RASct.Conclusions Right atrial strain can reflect changes in right atrial function,with the highest correlation to right ventricular strain and right atrial area.Right atrial strain can indicate the severity of right ventricular function and right atrial remodeling,serving as an evaluative index for the condition and treatment outcomes of pulmonary arterial hypertension.

Aim To detect the non-coding RNA(ncRNA)expression profile of neuroglioma cells via whole transcriptome sequencing,establish the ceRNA network and reveal the molecular mechanism of ncRNA participating in the inhibition of neuroglioma cell prolif-eration by melatonin.Methods Neuroglioma cells were intervened with by 0,2,4,6 and 8 mmol·L-1 melatonin for 24,48 and 72 h,and the inhibitory effect of melatonin on cell proliferation was detected via CCK-8;after the intervention of 0 and 4 mmol·L-1 melatonin to U251 cells for 24 h,differentially ex-pressed miRNA(DEmiRNA),lncRNA(DElncRNA)and mRNA(DEmRNA)were detected through whole transcriptome sequencing,along with GO and KEGG enrichment analysis of DEmRNA;the ceRNA network was constructed,and the key gene expression of ceR-NA was verified through qRT-PCR.Results Melato-nin exerts a time-dose-dependent inhibitory effect on the proliferation of neuroglioma cells;a total of 5049 DEmRNA,635 DElncRNA and 146 DEmiRNA in 0 and 4 mmol·L-1 melatonin groups were screened out via whole transcriptome sequencing;DEmRNAs were mainly enriched in cancer-related signaling pathways,such as ferroptosis,mTOR signaling pathway,FoxO signaling pathway and cell cycle;the ceRNA network included 4 lncRNAs,3 miRNAs and 48 mRNAs.As verified through real-time PCR,the expressions of hsa-miR-129-5p,hsa-miR-362-5p,LINC00707 and SLC16A1-AS1 of U251 cells were consistent with the sequencing results,and the gene expression of U87 cells was basically consistent with the sequencing re-sults.Conclusions Melatonin affects cancer-related signaling pathways through the differential expression of ncRNA so as to inhibit the proliferation of U251 cells;the ceRNA network composed of LINC00707,SLC16A1-AS1,hsa-miR-129-5p and hsa-miR-362-5p may take a part in the molecular mechanism of melato-nin in inhibiting neuroglioma cell proliferation.