Objective To systematically review the studies on predictive models for delayed graft function (DGF) after kidney transplantation. Methods Databases including China Biology Medicine Database, China National Knowledge Infrastructure, Wanfang Database, VIP Database, PubMed, Web of Science and CINAHL were searched to collect studies on predictive models for DGF after kidney transplantation published from the establishment of each database to June 29, 2025. Two researchers screened the literatures according to the inclusion and exclusion criteria, evaluated the quality of the literatures using the prediction model risk of bias assessment tool (PROBAST), and conducted a meta-analysis of the common predictors of the models using R software. Results A total of 12 literatures were included, involving 14 predictive models with sample sizes ranging from 103 to 24 653 cases. Donor serum creatinine level, cold ischemia time, donor age and donor body mass index were the top four common predictors. All the predictive models were at high risk of bias and low in applicability. The results of meta-analysis showed that abnormal donor body mass index, advanced donor age, prolonged cold ischemia time and elevated donor serum creatinine level were all associated with an increased risk of DGF after transplantation (all P<0.01), but there was high heterogeneity among the studies. Fixed-effect model and random-effect model were used to re-pool the effect sizes separately. The results indicated that the fixed-effect model and random-effect model had good consistency in terms of donor body mass index, donor age and cold ischemia time, while there was a significant difference in the effect sizes of the two models for donor serum creatinine level. Conclusions The predictive models for DGF risk after kidney transplantation have good predictive performance, but the overall risk of bias is high. In the future, large-sample, multicenter and high-quality prospective clinical studies should be carried out to optimize the predictive models, so as to improve their predictive ability and clinical application value.

The blood-brain barrier （BBB） is a physical and biochemical barrier that precisely regulates brain homeostasis and plays a central role in controlling the transport of endogenous and exogenous drugs and related metabolites across the blood-brain interface. These functions of the BBB are mediated by its major components， including brain microvascular endothelial cells （BMECs）， tight junction protein complexes， and influx and efflux transporter proteins. One of the pathological features of ischemic stroke （IS） is BBB disruption， which plays an important role in the development of post-stroke brain injury and subsequent neurological dysfunction. Therefore， given the increasing incidence of IS， there is an urgent need to develop new therapeutic strategies to prevent BBB dysfunction and thereby protect injured brain tissue after IS. This study describes the pathological mechanisms by which BMEC injury after IS leads to BBB dysfunction and elucidates the association between BMECs and IS， including the regulation of apoptosis， autophagy， inflammatory responses， oxidative stress， neurotoxic effects， and cerebral edema. In addition， this article summarizes Chinese herbal medicines that may prevent and treat IS by targeting BMECs. These include monomeric compounds and single herbs such as flavonoids， glycosides， phenols， phthalides， terpenoids， and Styrax. Traditional Chinese medicine （TCM） compound formulas and preparations include oral formulations such as Buyang Huanwu decoction， Sailuotong， Naoxintong capsules， Dandeng Tongnao capsules， and Shexiang Tongxin dropping pills， as well as injectable preparations such as Tongluo Jiunao injection， Xingnaojing injection， Danshen polyphenolic acid for injection， Yiqi Fumai injection， and Shuxuetong injection. This study aims to explore the protective effects of TCM against IS through targeted regulation of BMEC function， providing new insights into the mechanisms of IS and endovascular therapeutic strategies.

Objective:To establish an arsenic free fully automatic online digestion iodine analyzer detection method for urinary iodine (hereinafter referred to as the method).Methods:Based on the principle of iodine catalyzed antimony cerium redox reaction, a fully automatic online digestion iodine analyzer was used to determine the iodine content in urine. The effectiveness of the method in terms of detection limit, precision, accuracy (determination of urinary iodine primary standard reference materials GBW09108z and GBW09110f and spiked recovery experiment), and interference experiments was validated. The method was compared with the arsenic cerium catalytic spectrophotometry method recommended by the National Reference Laboratory for Iodine Deficiency Disorders.Results:The linear range of the method was 0 - 300 μg/L, with a correlation coefficient │ r│> 0.999 5. The qualitative and quantitative detection limits were 7.41 and 18.01 μg/L, respectively. The relative standard deviation ( RSD) of urine samples with different iodine concentrations ranged from 1.0% to 1.7%. The results of the determination of iodine concentrations in urine using standard substances GBW09108z and GBW09110f were within the given standard range, with RSD < 2.5%. The range of spiked recovery rates for urine samples with different iodine concentrations was 101.3% to 104.8%, with an overall average spiked recovery rate of 103.0%. The average concentration of the baseline iodine standard solution was determined to be 116.21 μg/L, and the relative error of the concentration determination with the addition of interfering substances was less than 5.0%. The comparison results showed that there was no statistically significant difference in the measurement results between the two methods ( t = - 0.06, P = 0.952). Conclusions:The method adopts automated detection, which is simple to operate, labor-saving, and does not require the use of arsenic trioxide. It has high precision and accuracy, and is suitable for detection of large quantities of samples.

Objective To explore the relevant factors affecting the efficacy of traditional Chinese medicine in treating MG,and to develop and validate a nomogram model,in order to personalize the prediction of the probability of benefits for MG patients after traditional Chinese medicine treatment,further guide clinical physicians in targeted medication,and provide guidance for the traditional Chinese medicine treatment of MG.Method Retrospective analysis of MG patients who visited the Affiliated Hospital of Changchun University of Traditional Chinese Medicine from March 2018 to June 2022,13 clinical factors were selected to evaluate the results.To acquire independent factors,univariate and multivariate Logistic analyses were performed.The area under the receiver operating characteristic curve,Harrell's concordance index,calibration curve,and decision curve analyses were used to evaluate the predictive ability,accuracy,and clinical practicability of the prediction model.Results A total of 204 cases were included,divided into derivation cohort 139 cases and temporal validation cohort 65 cases.Multivariate Logistic regression showed 4 independent predictors affecting effectiveness of traditional Chinese medicine in treating MG,including age at onset,repetitive nerve stimulation positive,oral immunosuppressant and anxiety/depression.AUC values for the model group and validation group were 0.76(95%CI:0.68-0.84)and 0.83(95%CI:0.71-0.95),respectively,and based on calibration curve and decision curve analysis,we concluded that the nomogram showed excellent performance.Conclusion MG patients may experience a decrease in short-term efficacy with age,positive RNS,concomitant emotional abnormalities,or recent history of immunosuppressive therapy.This nomogram effectively predicts the possibility of short-term effective traditional Chinese medicine treatment in MG patients.

Objective To investigate the role and mechanism of inflammatory marker C5a in regulating the polarization of M2 macro-phages as well as its clinical application value in the prognosis evaluation of lung cancer.Methods The phorbol 12-myristate 13-ace-tate(PMA)-pulsed human monocytic leukemia cell line THP-1 was stimulated with C5a for 0,6,12,24,and 48 h or 0,1,2,3,6,and 12 h,and then the expression levels of M2 markers CD163 and CD206 mRNA were determined by real-time fluorescence quantita-tive PCR(qRT-PCR).The expression level of general control non-repressed protein 5(GCN5)with histone acetyltransferase(HAT)activity was detected by Western blot.Co-immunoprecipitation(co-IP)was used to determine the acetylation of GATA binding protein 3(GATA3),a key transcription factor for macrophages,and its binding ability to GCN5 and E1A binding protein p300(Ep300/p300).After the macrophages pre-treated with GCN5 inhibitor butyrolactone 3(MB-3)were stimulated with C5a,the expression levels of CD163 and CD206,acetylation of GATA3,and its binding ability to GCN5 were determined by Western blot and co-IP.The clinical significance of the expression of C5a receptor 1(C5aR1)and GCN5 in lung cancer tissues in the prognosis of lung cancer patients was analyzed using the KM plotter database.Results C5a could significantly increase the expression levels of CD163 and CD206 mRNA,expression of GCN5,acetylation of GATA3,and its binding ability to GCN5 in PMA-induced adherent macrophages,but did not affect the binding of GATA3 to p300.Inhibiting the activity of GCN5 not only significantly reduced the acetylation of GATA3 and its binding ability to GCN5,but also down-regulated the expressions of CD163 and CD206.The overall survival rate of lung cancer patients with high expression of C5aR1 or GCN5 was significantly reduced.Conclusion C5a promotes the polarization of M2 macrophages by indu-cing GCN5 to acetylate GATA3.The expressions of C5aR1 and GCN5 in lung cancer tissues may have certain clinical application value for the prognosis of the patients.

Objective To construct YOLOv10 based artificial intelligence(AI)models for the automatic detection in small bowel capsule endoscopy(SBCE)images.Methods SBCE data from two centers was collected,including 23 115 images and 35 412 annotated labels covering 11 categories of small bowel lesions.The images were annotated using the LabelMe tool and converted into the YOLO format required for deep learning model development.The pre-trained YOLOv10 and YOLOv8 models were used for transfer learning training on the constructed dataset.Model performance was comprehensively evaluated using metrics such as precision,accuracy,sensitivity,specificity,false-positive rate,and detection speed.Finally,the models were deployed on local computers for real-time detection of SBCE images and videos.Results Six different versions of YOLO object detection models were developed,namely YOLOv8n,YOLOv8s,YOLOv8m,YOLOv10n,YOLOv10s,and YOLOv10m.On the validation set,YOLOv10s model achieved the best mAP50(0.795);although its inference latency was not the fastest(4.803 ms/img),it met the requirements for clinical application.On the test set,YOLOv10s performed well,with an accuracy of 92.69%,a sensitivity of 89.23%,and a false-positive rate of 4.78%.Especially,in category-specific inference,the highest sensitivity was for"bleeding"at 96.41%,while the lowest was for"narrowing"at 82.29%.Conclusion The model constructed based on YOLOv10 neural network can rapidly and accurately detect and classify various small bowel lesions,exhibiting significant clinical application potential.

Objective To investigate the role and mechanism of inflammatory marker C5a in regulating the polarization of M2 macro-phages as well as its clinical application value in the prognosis evaluation of lung cancer.Methods The phorbol 12-myristate 13-ace-tate(PMA)-pulsed human monocytic leukemia cell line THP-1 was stimulated with C5a for 0,6,12,24,and 48 h or 0,1,2,3,6,and 12 h,and then the expression levels of M2 markers CD163 and CD206 mRNA were determined by real-time fluorescence quantita-tive PCR(qRT-PCR).The expression level of general control non-repressed protein 5(GCN5)with histone acetyltransferase(HAT)activity was detected by Western blot.Co-immunoprecipitation(co-IP)was used to determine the acetylation of GATA binding protein 3(GATA3),a key transcription factor for macrophages,and its binding ability to GCN5 and E1A binding protein p300(Ep300/p300).After the macrophages pre-treated with GCN5 inhibitor butyrolactone 3(MB-3)were stimulated with C5a,the expression levels of CD163 and CD206,acetylation of GATA3,and its binding ability to GCN5 were determined by Western blot and co-IP.The clinical significance of the expression of C5a receptor 1(C5aR1)and GCN5 in lung cancer tissues in the prognosis of lung cancer patients was analyzed using the KM plotter database.Results C5a could significantly increase the expression levels of CD163 and CD206 mRNA,expression of GCN5,acetylation of GATA3,and its binding ability to GCN5 in PMA-induced adherent macrophages,but did not affect the binding of GATA3 to p300.Inhibiting the activity of GCN5 not only significantly reduced the acetylation of GATA3 and its binding ability to GCN5,but also down-regulated the expressions of CD163 and CD206.The overall survival rate of lung cancer patients with high expression of C5aR1 or GCN5 was significantly reduced.Conclusion C5a promotes the polarization of M2 macrophages by indu-cing GCN5 to acetylate GATA3.The expressions of C5aR1 and GCN5 in lung cancer tissues may have certain clinical application value for the prognosis of the patients.

Objective To construct YOLOv10 based artificial intelligence(AI)models for the automatic detection in small bowel capsule endoscopy(SBCE)images.Methods SBCE data from two centers was collected,including 23 115 images and 35 412 annotated labels covering 11 categories of small bowel lesions.The images were annotated using the LabelMe tool and converted into the YOLO format required for deep learning model development.The pre-trained YOLOv10 and YOLOv8 models were used for transfer learning training on the constructed dataset.Model performance was comprehensively evaluated using metrics such as precision,accuracy,sensitivity,specificity,false-positive rate,and detection speed.Finally,the models were deployed on local computers for real-time detection of SBCE images and videos.Results Six different versions of YOLO object detection models were developed,namely YOLOv8n,YOLOv8s,YOLOv8m,YOLOv10n,YOLOv10s,and YOLOv10m.On the validation set,YOLOv10s model achieved the best mAP50(0.795);although its inference latency was not the fastest(4.803 ms/img),it met the requirements for clinical application.On the test set,YOLOv10s performed well,with an accuracy of 92.69%,a sensitivity of 89.23%,and a false-positive rate of 4.78%.Especially,in category-specific inference,the highest sensitivity was for"bleeding"at 96.41%,while the lowest was for"narrowing"at 82.29%.Conclusion The model constructed based on YOLOv10 neural network can rapidly and accurately detect and classify various small bowel lesions,exhibiting significant clinical application potential.

Aim To study the effects of curcumin de-rivative C0818 on the migration,apoptosis and prolifer-ation of non-small cell lung cancer(NSCLC)cells.Methods MTT assay and CFSE were used to detect the inhibitory effect of C0818 on the proliferation of NSCLC cells NCI-H1299 and NCI-H460 in vitro.Flow cytometry was used to detect mitochondrial reactive ox-ygen species(ROS),the mitochondrial membrane po-tential(MMP),cell apoptosis,and the cell cycle.The Transwell chamber assay and scratch wound healing as-say were used to detect the effect of C0818 on the mi-gration ability of NSCLC cells.Western blot was used to detect the expression of apoptosis and migration-re-lated proteins.Results C0818 inhibited the prolifera-tion of NCI-H1299 and NCI-H460 cells in a time-and dose-dependent manner,and arrested the cell cycle at the G2/M phase.C0818 disrupted the MMP.With the increase of the concentration of C0818(0.4-1.6μmol·L-1),the apoptosis rate of cells increased sig-nificantly,accompanied by the up-regulation of pro-ap-optotic proteins Bax,cleaved PARP,and cleaved caspase-3/8,and the down-regulation of anti-apoptotic proteins Bcl-2.C0818 inhibited the migration ability of NSCLC cells and could inhibit the expression of pro-teins related to invasion and metastasis,such as N-cad-herin,β-catenin,and Vimentin.Conclusions The curcumin derivative C0818 induces caspase-mediated and ROS-dependent apoptosis,inhibits cell prolifera-tion,and has a strong anti-metastatic ability in vitro.

Purpose To evaluate the predictive value of multifunctional parameters of single photon emission computed tomography gated myocardial perfusion imaging(SPECT G-MPI)for major adverse cardiovascular events(MACE)in chronic kidney disease(CKD)with abnormal stress myocardial perfusion.Materials and Methods A total of 99 patients diagnosed with CKD from June 2017 to March 2024 who underwent stress and rest G-MPI indicating abnormal myocardial perfusion in Beijing Anzhen Hospital,Capital Medical University.The American Heart Association 17-segment 5-point method and PHASE software were used to obtain the left ventricular myocardial perfusion,functional and synchronization parameters.According to the occurrence of MACE,the patients were divided into MACE group and non-MACE group.Cox regression was used to analyze the predictors related to MACE.The receiver operator characteristic curve was used to analyze the performance of predictors,the survival curves were obtained by the Kaplan-Meier method,Log-rank test was used to compare the differences in different groups.Results Finally,we enrolled 99 CKD patients with abnormal stress myocardial perfusion.35 patients(35.35%)developed MACE during the follow-up period.Cox regression analysis showed that stress phase bandwidth(SPBW)(HR=1.015,95%CI 1.002-1.028)and sum difference score(SDS)(HR=1.105,95%CI 1.008-1.211)were independent risk factors for predicting MACE(both P<0.05).The optimal cut-off value of SPBW and SDS for predicting MACE were 69° and 6 points,the area under the curve was 0.801 and 0.778,respectively.The incidence of MACE in the SPBW≥69° group and SDS≥6 points group was higher than that in SPBW<69° group and SDS<6 points group(66.6%vs.13.2%,53.3%vs.20.4%,both P<0.05).Conclusion SPECT G-MPI multifunctional parameters can be used to predict the prognosis of CKD patients with abnormal stress myocardial perfusion.SPBW and SDS are independent risk factors for MACE in these patients.