1.Safety analysis of Yttrium-90 resin microsphere selective internal radiation therapy on malignant liver tumors
Jia CAI ; Shiwei TANG ; Rongli LI ; Mingxin KONG ; Hongyan DING ; Xiaofeng YUAN ; Yuying HU ; Ruimei LIU ; Xiaoyan ZHU ; Wenjun LI ; Haibin ZHANG ; Guanwu WANG
Chinese Journal of Clinical Medicine 2025;32(1):24-29
Objective To explore the safety of Yttrium-90 resin microsphere selective internal radiation therapy (90Y-SIRT) on malignant liver tumors. Methods A retrospective analysis was conducted on 64 patients with malignant liver tumors who underwent 90Y-SIRT from February 2023 to November 2024 at Weifang People’s Hospital. The clinical characteristics of the patients and the occurrence of adverse reactions after treatment were analyzed to assess the safety of 90Y-SIRT. Results Among the 64 patients, there were 52 males (81.25%) and 12 females (18.75%); the average age was (56.29±11.08) years. Seven patients (10.94%) had tumors with maximum diameter of less than 5 cm, 38 patients (59.38%) had tumors with maximum diameter of 5-10 cm, and 19 patients (29.68%) had tumors with maximum diameter of greater than 10 cm. There were 47 cases (73.44%) of solitary lesions and 17 cases (26.56%) of multiple lesions; 53 cases (82.81%) were primary liver cancers and 11 cases (17.19%) were metastatic liver cancers. Of the 64 patients, 63 successfully completed the Technetium-99m macroaggregated albumin (99mTc-MAA) perfusion test and received the 90Y-SIRT; one patient received 90Y-SIRT after the second 99mTc-MAA perfusion test due to a work error. The most common adverse reactions included grade 1 alanine aminotransferase (ALT) elevation in 26 cases (40.62%) and grade 2 in 2 cases (9.37%), grade 1 aspartate aminotransferase (AST) elevation in 27 cases (42.18%) and grade 2 in 7 cases (10.93%); grade 1 nausea in 17 cases (26.56%) and grade 2 in 6 cases (9.37%); grade 1 abdominal pain in 12 cases (18.75%), grade 2 in 5 cases (7.81%), and grade 3 in 1 case (1.56%); grade 1 vomiting in 11 cases (17.18%), grade 2 in 5 cases (7.81%), and grade 3 in 1 case (1.56%). Conclusion The adverse reactions of 90Y-SIRT for treating malignant liver tumors are mild, indicating good safety.
2.Human infection with Gongylonema pulchrum: a case report and review of relevant literature during the recent 10 years
Feng TANG ; Xiaofeng SUN ; Xiangzhen XU ; Fanzhen MAO ; Yaobao LIU
Chinese Journal of Schistosomiasis Control 2025;37(3):332-336
This article presents the diagnosis and treatment processes, and morphological and genetic testing of Gongylonema pulchrum in a case with G. pulchrum found in the oral mucosa. In addition, this article reviews publications pertaining to G. pulchrum human infections by Chinese scientists during the recent 10 years and summarizes the demographic and clinical characteristics, location and number of parasites, diagnosis and treatment processes, and epidemiological surveys of cases infected with G. pulchrum, so as to provide insights into improving the diagnostic capability among clinicians.
3.POU2F1 inhibits miR-29b1/a cluster-mediated suppression of PIK3R1 and PIK3R3 expression to regulate gastric cancer cell invasion and migration.
Yizhi XIAO ; Ping YANG ; Wushuang XIAO ; Zhen YU ; Jiaying LI ; Xiaofeng LI ; Jianjiao LIN ; Jieming ZHANG ; Miaomiao PEI ; Linjie HONG ; Juanying YANG ; Zhizhao LIN ; Ping JIANG ; Li XIANG ; Guoxin LI ; Xinbo AI ; Weiyu DAI ; Weimei TANG ; Jide WANG
Chinese Medical Journal 2025;138(7):838-850
BACKGROUND:
The transcription factor POU2F1 regulates the expression levels of microRNAs in neoplasia. However, the miR-29b1/a cluster modulated by POU2F1 in gastric cancer (GC) remains unknown.
METHODS:
Gene expression in GC cells was evaluated using reverse-transcription polymerase chain reaction (PCR), western blotting, immunohistochemistry, and RNA in situ hybridization. Co-immunoprecipitation was performed to evaluate protein interactions. Transwell migration and invasion assays were performed to investigate the biological behavior of GC cells. MiR-29b1/a cluster promoter analysis and luciferase activity assay for the 3'-UTR study were performed in GC cells. In vivo tumor metastasis was evaluated in nude mice.
RESULTS:
POU2F1 is overexpressed in GC cell lines and binds to the miR-29b1/a cluster promoter. POU2F1 is upregulated, whereas mature miR-29b-3p and miR-29a-3p are downregulated in GC tissues. POU2F1 promotes GC metastasis by inhibiting miR-29b-3p or miR-29a-3p expression in vitro and in vivo . Furthermore, PIK3R1 and/or PIK3R3 are direct targets of miR-29b-3p and/or miR-29a-3p , and the ectopic expression of PIK3R1 or PIK3R3 reverses the suppressive effect of mature miR-29b-3p and/or miR-29a-3p on GC cell metastasis and invasion. Additionally, the interaction of PIK3R1 with PIK3R3 promotes migration and invasion, and miR-29b-3p , miR-29a-3p , PIK3R1 , and PIK3R3 regulate migration and invasion via the phosphatidylinositol 3-kinase/protein kinase B/mammalian target of rapamycin (PI3K/Akt/mTOR) pathway in GC cells. In addition, POU2F1 , PIK3R1 , and PIK3R3 expression levels negatively correlated with miR-29b-3p and miR-29a-3p expression levels in GC tissue samples.
CONCLUSIONS
The POU2F1 - miR-29b-3p / miR-29a-3p-PIK3R1 / PIK3R1 signaling axis regulates tumor progression and may be a promising therapeutic target for GC.
MicroRNAs/metabolism*
;
Humans
;
Stomach Neoplasms/pathology*
;
Cell Line, Tumor
;
Cell Movement/physiology*
;
Phosphatidylinositol 3-Kinases/metabolism*
;
Animals
;
Mice
;
Octamer Transcription Factor-1/metabolism*
;
Mice, Nude
;
Class Ia Phosphatidylinositol 3-Kinase/metabolism*
;
Neoplasm Invasiveness
;
Gene Expression Regulation, Neoplastic/genetics*
;
Male
;
Immunohistochemistry
;
Female
4.m6A modification regulates PLK1 expression and mitosis.
Xiaoli CHANG ; Xin YAN ; Zhenyu YANG ; Shuwen CHENG ; Xiaofeng ZHU ; Zhantong TANG ; Wenxia TIAN ; Yujun ZHAO ; Yongbo PAN ; Shan GAO
Chinese Journal of Biotechnology 2025;41(4):1559-1572
N6-methyladenosine (m6A) modification plays a critical role in cell cycle regulation, while the mechanism of m6A in regulating mitosis remains underexplored. Here, we found that the total m6A modification level in cells increased during mitosis by the liquid chromatography-mass spectrometry/mass spectrometry and m6A dot blot assays. Silencing methyltransferase-like 3 (METTL3) or METTL14 results in delayed mitosis, abnormal spindle assembly, and chromosome segregation defects by the immunofluorescence. By analyzing transcriptome-wide m6A targets in HeLa cells, we identified polo-like kinase 1 (PLK1) as a key gene modified by m6A in regulating mitosis. Specifically, through immunoblotting and RNA pulldown, m6A modification inhibits PLK1 translation via YTH N6-methyladenosine RNA binding protein 1, thus mediating cell cycle homeostasis. Demethylation of PLK1 mRNA leads to significant mitotic abnormalities. These findings highlight the critical role of m6A in regulating mitosis and the potential of m6A as a therapeutic target in proliferative diseases such as cancer.
Humans
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Polo-Like Kinase 1
;
Cell Cycle Proteins/metabolism*
;
Proto-Oncogene Proteins/metabolism*
;
Protein Serine-Threonine Kinases/metabolism*
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Mitosis/physiology*
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HeLa Cells
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Adenosine/genetics*
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Methyltransferases/metabolism*
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RNA, Messenger/metabolism*
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RNA-Binding Proteins/metabolism*
5.Individualized prediction model of tacrolimus dose/weight-adjusted trough concentration based on machine learning approach
Hui Jiang ; Liang Tang ; Xin Wang ; Fan Jiang ; Deguang Wang ; Xiaofeng Lan ; Xiang Xie
Acta Universitatis Medicinalis Anhui 2025;60(2):344-350
Objective:
To utilize machine learning(ML) algorithms to develop accurate and effective prediction models for TAC dose/weight-adjusted trough concentration(C0/D).
Methods:
Data were collected on 264 TAC blood concentration monitoring data from 72 patients undergoing kidney transplantation. The effects of population statistical data, clinical features, combined medication, and ultrasound feature parameters on TAC C0/D were analyzed. Features with a significance level less than 0.05 in the univariate analysis of TAC C0/D were selected for inclusion in the random forest(RF) algorithm to identify significant features. These features were interpreted using partial dependency plots. Five ML algorithms, including RF, support vector regression(SVR), extreme gradient boosting(XGBoost), decision trees(DT) and artificial neural networks(ANN), were employed to establish the TAC C0/D prediction model. Hyper-parameter tuning was performed on the RF model that performed the best.
Results :
Ten characteristic variables with importance scores>5 were retained and included in the ML model: transglutaminase, red blood cell count, blood urea nitrogen, weight, serum creatinine, renal segmental arterial resistance index, renal aortic resistance index, hematocrit, renal pelvic Young′s modulus value, and time after transplantation. The partial dependence plots showed that all 10 important variables screened were positively correlated with TAC C0/D. The tuned RF model outperformed the other models with aR2of 0.81, aRMSEof 43.93, and aMAEof 29.97.
Conclusion
The ML models demonstrate good performance in predicting TAC C0/D and provide innovative interpretations using partial dependence plot. The optimized RF model shows optimal performance and offers a novel tool for individualized medication adjustment for TAC in renal transplant patients.
6.Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients (version 2024)
Yao LU ; Yang LI ; Leiying ZHANG ; Hao TANG ; Huidan JING ; Yaoli WANG ; Xiangzhi JIA ; Li BA ; Maohong BIAN ; Dan CAI ; Hui CAI ; Xiaohong CAI ; Zhanshan ZHA ; Bingyu CHEN ; Daqing CHEN ; Feng CHEN ; Guoan CHEN ; Haiming CHEN ; Jing CHEN ; Min CHEN ; Qing CHEN ; Shu CHEN ; Xi CHEN ; Jinfeng CHENG ; Xiaoling CHU ; Hongwang CUI ; Xin CUI ; Zhen DA ; Ying DAI ; Surong DENG ; Weiqun DONG ; Weimin FAN ; Ke FENG ; Danhui FU ; Yongshui FU ; Qi FU ; Xuemei FU ; Jia GAN ; Xinyu GAN ; Wei GAO ; Huaizheng GONG ; Rong GUI ; Geng GUO ; Ning HAN ; Yiwen HAO ; Wubing HE ; Qiang HONG ; Ruiqin HOU ; Wei HOU ; Jie HU ; Peiyang HU ; Xi HU ; Xiaoyu HU ; Guangbin HUANG ; Jie HUANG ; Xiangyan HUANG ; Yuanshuai HUANG ; Shouyong HUN ; Xuebing JIANG ; Ping JIN ; Dong LAI ; Aiping LE ; Hongmei LI ; Bijuan LI ; Cuiying LI ; Daihong LI ; Haihong LI ; He LI ; Hui LI ; Jianping LI ; Ning LI ; Xiying LI ; Xiangmin LI ; Xiaofei LI ; Xiaojuan LI ; Zhiqiang LI ; Zhongjun LI ; Zunyan LI ; Huaqin LIANG ; Xiaohua LIANG ; Dongfa LIAO ; Qun LIAO ; Yan LIAO ; Jiajin LIN ; Chunxia LIU ; Fenghua LIU ; Peixian LIU ; Tiemei LIU ; Xiaoxin LIU ; Zhiwei LIU ; Zhongdi LIU ; Hua LU ; Jianfeng LUAN ; Jianjun LUO ; Qun LUO ; Dingfeng LYU ; Qi LYU ; Xianping LYU ; Aijun MA ; Liqiang MA ; Shuxuan MA ; Xainjun MA ; Xiaogang MA ; Xiaoli MA ; Guoqing MAO ; Shijie MU ; Shaolin NIE ; Shujuan OUYANG ; Xilin OUYANG ; Chunqiu PAN ; Jian PAN ; Xiaohua PAN ; Lei PENG ; Tao PENG ; Baohua QIAN ; Shu QIAO ; Li QIN ; Ying REN ; Zhaoqi REN ; Ruiming RONG ; Changshan SU ; Mingwei SUN ; Wenwu SUN ; Zhenwei SUN ; Haiping TANG ; Xiaofeng TANG ; Changjiu TANG ; Cuihua TAO ; Zhibin TIAN ; Juan WANG ; Baoyan WANG ; Chunyan WANG ; Gefei WANG ; Haiyan WANG ; Hongjie WANG ; Peng WANG ; Pengli WANG ; Qiushi WANG ; Xiaoning WANG ; Xinhua WANG ; Xuefeng WANG ; Yong WANG ; Yongjun WANG ; Yuanjie WANG ; Zhihua WANG ; Shaojun WEI ; Yaming WEI ; Jianbo WEN ; Jun WEN ; Jiang WU ; Jufeng WU ; Aijun XIA ; Fei XIA ; Rong XIA ; Jue XIE ; Yanchao XING ; Yan XIONG ; Feng XU ; Yongzhu XU ; Yongan XU ; Yonghe YAN ; Beizhan YAN ; Jiang YANG ; Jiangcun YANG ; Jun YANG ; Xinwen YANG ; Yongyi YANG ; Chunyan YAO ; Mingliang YE ; Changlin YIN ; Ming YIN ; Wen YIN ; Lianling YU ; Shuhong YU ; Zebo YU ; Yigang YU ; Anyong YU ; Hong YUAN ; Yi YUAN ; Chan ZHANG ; Jinjun ZHANG ; Jun ZHANG ; Kai ZHANG ; Leibing ZHANG ; Quan ZHANG ; Rongjiang ZHANG ; Sanming ZHANG ; Shengji ZHANG ; Shuo ZHANG ; Wei ZHANG ; Weidong ZHANG ; Xi ZHANG ; Xingwen ZHANG ; Guixi ZHANG ; Xiaojun ZHANG ; Guoqing ZHAO ; Jianpeng ZHAO ; Shuming ZHAO ; Beibei ZHENG ; Shangen ZHENG ; Huayou ZHOU ; Jicheng ZHOU ; Lihong ZHOU ; Mou ZHOU ; Xiaoyu ZHOU ; Xuelian ZHOU ; Yuan ZHOU ; Zheng ZHOU ; Zuhuang ZHOU ; Haiyan ZHU ; Peiyuan ZHU ; Changju ZHU ; Lili ZHU ; Zhengguo WANG ; Jianxin JIANG ; Deqing WANG ; Jiongcai LAN ; Quanli WANG ; Yang YU ; Lianyang ZHANG ; Aiqing WEN
Chinese Journal of Trauma 2024;40(10):865-881
Patients with severe trauma require an extremely timely treatment and transfusion plays an irreplaceable role in the emergency treatment of such patients. An increasing number of evidence-based medicinal evidences and clinical practices suggest that patients with severe traumatic bleeding benefit from early transfusion of low-titer group O whole blood or hemostatic resuscitation with red blood cells, plasma and platelet of a balanced ratio. However, the current domestic mode of blood supply cannot fully meet the requirements of timely and effective blood transfusion for emergency treatment of patients with severe trauma in clinical practice. In order to solve the key problems in blood supply and blood transfusion strategies for emergency treatment of severe trauma, Branch of Clinical Transfusion Medicine of Chinese Medical Association, Group for Trauma Emergency Care and Multiple Injuries of Trauma Branch of Chinese Medical Association, Young Scholar Group of Disaster Medicine Branch of Chinese Medical Association organized domestic experts of blood transfusion medicine and trauma treatment to jointly formulate Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients ( version 2024). Based on the evidence-based medical evidence and Delphi method of expert consultation and voting, 10 recommendations were put forward from two aspects of blood support mode and transfusion strategies, aiming to provide a reference for transfusion resuscitation in the emergency treatment of severe trauma and further improve the success rate of treatment of patients with severe trauma.
7.Construction and verification of dynamic prognosis graph of gallbladder cancer patients
Zhiyang JIANG ; Haile CAN ; Yafen TANG ; Xiaogang LI ; Xiaofeng LIAO
Journal of Clinical Surgery 2024;32(2):182-187
Objective To construct a nomogram to predict the prognosis of patients with gallbladder cancer(GBC).Methods The clinicopathological data of GBC patients were extracted from the SEER database,and the independent prognostic factors of GBC patients were analyzed by Cox regression,and a nomogram was constructed.Finally,the column diagrams in the training queue and validation queue are verified.Results Age,T stage,M stage,histological grade,radiotherapy,surgery and tumor size were independent prognostic factors in GBC patients,and the differences were statistically significant(P<0.05).In the training cohort,the C index was 0.735(95%CI=0.721~0.749),and the AUC values at 1,3 and 5 years were 0.821,0.820 and 0.833,respectively.In the verification group,the C index was 0.733(95%CI=0.711~0.755),and the AUC values for 1,3 and 5 years were 0.816,0.807 and 0.827,respectively.The calibration curve shows that the predicted values of the nomogram are in good agreement with the observed values.The decision curve shows that the nomogram model has better prediction ability than TNM staging system.Conclusion The constructed dynamic prognosis nomogram of GBC patients has high accuracy and reliability.
8.Dynamic monitoring and esthetic evaluation of dimensional changes in the peri-implant soft tissue contour after the immediate implant placement and provisionalization with the modified socket-shield technique in the esthetic zone
Xiaofeng TANG ; Qingqing WU ; Xi CHEN ; Yuan WANG ; Gang FU
Chinese Journal of Stomatology 2024;59(6):551-558
Objective:To accurately measure the dynamic changes of peri-implant soft tissue within one year after the immediate implant placement and provisionalization with the modified socket-shield technique (MSST) in the esthetic zone, and to provide a basis for evaluating the effect of the modified socket-shield technique on the maintenance of peri-implant soft tissue.Methods:A total of 22 patients (22 implants) were prospectively included 1 year after completion of immediate implant placement and provisionalization (IIPP) within MSST in the esthetic zone from January 2022 to January 2024 at the Department of Oral Implantology in the Stomatological Hospital of Chongqing Medical University. The intraoral optical models of patients were obtained by an intraoral scanner system preoperatively and at 3, 6, and 12 months postoperatively, respectively. The standard tessellation language files of intraoral optical models at multiple time points were imported to Geomagic Studio 2013 to be superimposed and aligned for analyzing the peri-implant soft tissue contour on the labial side of the implant site at multiple levels. The amount of gingival margin recession, gingival papilla change, and thickness change of the labial side of the soft tissues at each postoperative point in time were measured at each postoperative time point, as well as evaluating the esthetic effect by the pink esthetic score (PES).Results:The patients were (40±13) years old (21-75 years), including 9 males and 13 females. No adverse events occurred in all the implants during the 12-month follow-up period. The recession level of the gingival margin of the implant site (GL) was 0.08 (0.07) mm, the recession level of the mesial papilla (ML) was 0.19 (0.25) mm, and the recession level of the distal papilla (DL) was 0.19 (0.10) mm. The average collapse thickness of the soft tissue contour on the labial side of the implant (ΔD) was (0.39±0.09) mm, mainly occurring within 2 mm of the root of the gingival margin. The height of the alveolar bone was reduced by (0.17±0.08) mm. The thickness of the labial alveolar bone at 1, 3, and 5 mm root side of the implant shoulder was reduced by (0.13±0.08), (0.12±0.10) and 0.04 (0.17) mm, respectively. The postoperative pink esthetic score was 13.00 (2.25) points at 12 months, which suggested that all implant sites achieved ideal esthetic results.Conclusions:The labial soft tissue contour at implant sites shows minimal change following immediate implant placement and provisionalization using the modified socket-shield technique for 1 year in the esthetic zone.
9.The predictive value of dynamic contrast-enhanced MRI quantitative analysis for perineural invasion in peripheral prostate cancer
Erpeng CAI ; Kai TANG ; Xiaofeng HU ; Hu ZHANG ; Xianfeng ZHU ; Yan WANG
Journal of Practical Radiology 2024;40(10):1649-1652,1657
Objective To investigate the value of quantitative parameters(Kep and Ktrans)of dynamic contrast-enhanced magnetic resonance imaging(DCE-MRI),in predicting perineural invasion(PNI)in peripheral prostate cancer(PCa).Methods The clinical and preoperative MRI data of 45 patients with peripheral PCa who underwent radical prostatectomy(RP)were analyzed retrospectively.According to the pathological results,the patients were divided into PNI group(n=27)and non-PNI group(n=18).Various parameters,including age,total prostate specific antigen(tPSA),Ktrans value,Kep value,apparent diffusion coefficient(ADC)value,prostate volume,maximum lesion diameter,and prostate-specific antigen density(PSAD)were compared between the two groups.Multivariate logistic regression analysis was used to identify independent predictors of PNI,and a joint prediction model was established.The DeLong test was used to compare differences in the area under the curve(AUC)between the joint prediction model and each independent predictor.Results The univariate analysis identified statistically significant differences in the tPSA,Ktrans value,ADC value,maximum lesion diameter,and PSAD between the two groups(P<0.01).The multivariate analysis showed that the Ktrans value and the maximum lesion diameter were independent predictors of PNI,with AUC of 0.854 and 0.874,respectively(P<0.01).The AUC of the joint prediction model for PNI diagnosis was 0.955(P<0.001).The DeLong test showed that the AUC of the joint prediction model for PNI diagnosis was better than that of the Ktrans and the maximum lesion diameter(P<0.05).Conclusion The Ktrans value can be used to predict PNI.Furthermore,the combination of the Ktrans value and the maximum lesion diameter is more effective for predicting PNI than traditional methods.This provides more reference basis for the selection of clinical treatment methods.
10.RP11-789C1.1 inhibits gastric cancer cell proliferation and accelerates apoptosis via the ATR/CHK1 signaling pathway
Wenwei LIU ; Wei FENG ; Yongxin ZHANG ; Tianxiang LEI ; Xiaofeng WANG ; Tang QIAO ; Zehong CHEN ; Wu SONG
Chinese Medical Journal 2024;137(15):1835-1843
Background::Long non-coding RNAs (lncRNAs) plays an important role in the progression of gastric cancer (GC). Their involvement ranges from genetic regulation to cancer progression. However, the mechanistic roles of RP11-789C1.1 in GC are not fully understood.Methods::We identified the expression of lncRNA RP11-789C1.1 in GC tissues and cell lines by real-time fluorescent quantitative polymerase chain reaction. A series of functional experiments revealed the effect of RP11-789C1.1 on the proliferation of GC cells. In vivo experiments verified the effect of RP11-789C1.1 on the biological behavior of a GC cell line. RNA pull-down unveiled RP11-789C1.1 interacting proteins. Western blot analysis indicated the downstream pathway changes of RP11-789C1.1, and an oxaliplatin dosing experiment disclosed the influence of RP11-789C1.1 on the drug sensitivity of oxaliplatin. Results::Our results demonstrated that RP11-789C1.1 inhibited the proliferation of GC cells and promoted the apoptosis of GC cells. Mechanistically, RP11-789C1.1 inhibited checkpoint kinase 1 (CHK1) phosphorylation by binding ataxiatelangiectasia mutated and Rad3 related (ATR), a serine/threonine-specific protein kinase, promoted GC apoptosis, and mediated oxaliplatin sensitivity.Conclusion::In general, we discovered a tumor suppressor molecule RP11-789C1.1 and confirmed its mechanism of action, providing a theoretical basis for targeted GC therapy.


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