1.Discriminating Tumor Deposits From Metastatic Lymph Nodes in Rectal Cancer: A Pilot Study Utilizing Dynamic Contrast-Enhanced MRI
Xue-han WU ; Yu-tao QUE ; Xin-yue YANG ; Zi-qiang WEN ; Yu-ru MA ; Zhi-wen ZHANG ; Quan-meng LIU ; Wen-jie FAN ; Li DING ; Yue-jiao LANG ; Yun-zhu WU ; Jian-peng YUAN ; Shen-ping YU ; Yi-yan LIU ; Yan CHEN
Korean Journal of Radiology 2025;26(5):400-410
Objective:
To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer.
Materials and Methods:
A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs.
Results:
All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027).
Conclusion
The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.
2.Discriminating Tumor Deposits From Metastatic Lymph Nodes in Rectal Cancer: A Pilot Study Utilizing Dynamic Contrast-Enhanced MRI
Xue-han WU ; Yu-tao QUE ; Xin-yue YANG ; Zi-qiang WEN ; Yu-ru MA ; Zhi-wen ZHANG ; Quan-meng LIU ; Wen-jie FAN ; Li DING ; Yue-jiao LANG ; Yun-zhu WU ; Jian-peng YUAN ; Shen-ping YU ; Yi-yan LIU ; Yan CHEN
Korean Journal of Radiology 2025;26(5):400-410
Objective:
To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer.
Materials and Methods:
A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs.
Results:
All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027).
Conclusion
The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.
3.Discriminating Tumor Deposits From Metastatic Lymph Nodes in Rectal Cancer: A Pilot Study Utilizing Dynamic Contrast-Enhanced MRI
Xue-han WU ; Yu-tao QUE ; Xin-yue YANG ; Zi-qiang WEN ; Yu-ru MA ; Zhi-wen ZHANG ; Quan-meng LIU ; Wen-jie FAN ; Li DING ; Yue-jiao LANG ; Yun-zhu WU ; Jian-peng YUAN ; Shen-ping YU ; Yi-yan LIU ; Yan CHEN
Korean Journal of Radiology 2025;26(5):400-410
Objective:
To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer.
Materials and Methods:
A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs.
Results:
All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027).
Conclusion
The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.
4.Discriminating Tumor Deposits From Metastatic Lymph Nodes in Rectal Cancer: A Pilot Study Utilizing Dynamic Contrast-Enhanced MRI
Xue-han WU ; Yu-tao QUE ; Xin-yue YANG ; Zi-qiang WEN ; Yu-ru MA ; Zhi-wen ZHANG ; Quan-meng LIU ; Wen-jie FAN ; Li DING ; Yue-jiao LANG ; Yun-zhu WU ; Jian-peng YUAN ; Shen-ping YU ; Yi-yan LIU ; Yan CHEN
Korean Journal of Radiology 2025;26(5):400-410
Objective:
To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer.
Materials and Methods:
A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs.
Results:
All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027).
Conclusion
The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.
5.Discriminating Tumor Deposits From Metastatic Lymph Nodes in Rectal Cancer: A Pilot Study Utilizing Dynamic Contrast-Enhanced MRI
Xue-han WU ; Yu-tao QUE ; Xin-yue YANG ; Zi-qiang WEN ; Yu-ru MA ; Zhi-wen ZHANG ; Quan-meng LIU ; Wen-jie FAN ; Li DING ; Yue-jiao LANG ; Yun-zhu WU ; Jian-peng YUAN ; Shen-ping YU ; Yi-yan LIU ; Yan CHEN
Korean Journal of Radiology 2025;26(5):400-410
Objective:
To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer.
Materials and Methods:
A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs.
Results:
All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027).
Conclusion
The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.
6.Analysis of Serum Metabolic Biomarkers in Adult Patients with Kashin-Beck Disease and Degenerative Osteoarthritis in Qinghai Province.
Jia le XU ; Qiang LI ; Chuan LU ; Xin ZHOU ; Yan Mei ZHAO ; Jian Ling WANG ; Ji Quan LI ; Li MA ; Zhi Jun ZHAO ; Ke Wen LI
Biomedical and Environmental Sciences 2025;38(9):1173-1177
7.Causes and global, regional, and national burdens of traumatic brain injury from 1990 to 2019
Xiao-Fei HUANG ; Shuai-Feng MA ; Xu-Heng JIANG ; Ren-Jie SONG ; Mo LI ; Ji ZHANG ; Tian-Jing SUN ; Quan HU ; Wen-Rui WANG ; An-Yong YU ; He LI
Chinese Journal of Traumatology 2024;27(6):311-322
Purpose::Traumatic brain injury (TBI), currently a major global public health problem, imposes a significant economic burden on society and families. We aimed to quantify and predict the incidence and severity of TBI by analyzing its incidence, prevalence, and years lived with disability (YLDs). The epidemiological changes in TBI from 1990 to 2019 were described and updated to provide a reference for developing prevention, treatment, and incidence-reducing measures for TBI.Methods::A secondary analysis was performed on the incidence, prevalence, and YLDs of TBI by sex, age group, and region ( n =21,204 countries and territories) between 1990 and 2019 using the Global Burden of Diseases, Injuries, and Risk Factors Study 2019. Proportions in the age-standardized incidence rate due to underlying causes of TBI and proportions of minor and moderate or severe TBI were also reported. Results::In 2019, there were 27.16 million (95% uncertainty intervals ( UI): 23.36 -31.42) new cases of TBI worldwide, with age-standardized incidence and prevalence rates of 346 per 100,000 population (95% UI: 298 -401) and 599 per 100,000 population (95% UI: 573 -627), respectively. From 1990 to 2019, there were no significant trends in global age-standardized incidence (estimated annual percentage changes: -0.11%, 95% UI: -0.18% --0.04%) or prevalence (estimated annual percentage changes: 0.01%, 95% UI: -0.04% -0.06%). TBI caused 7.08 million (95% UI: 5.00 -9.59) YLDs in 2019, with age-standardized rates of 86.5 per 100,000 population (95% UI: 61.1 -117.2). In 2019, the countries with higher incidence rates were mainly distributed in Central Europe, Eastern Europe, and Australia. The 2019 global age-standardized incidence rate was higher in males than in females. The 2019 global incidence of moderate and severe TBI was 182.7 per 100,000 population, accounting for 52.8% of all TBI, with falls and road traffic injuries being the main causes in most regions. Conclusions::The incidence of moderate and severe TBI was slightly higher in 2019, and TBI still accounts for a significant portion of the global injury burden. The likelihood of moderate to severe TBI and the trend of major injury under each injury cause from 1990 to 2019 and the characteristics of injury mechanisms in each age group are presented, providing a basis for further research on injury causes in each age group and the future establishment of corresponding policies and protective measures.
8.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.
9.Traditional Use, Phytochemistry, Pharmacology, Toxicology and Clinical Applications of Persicae Semen: A Review.
Yu-Quan LIU ; Hui-Li WU ; Zhi-Qiang ZHANG ; Wen-le WANG ; Guo-Qing HAN ; Chun-Hong ZHANG ; Xin-Liang LYU ; Chun-Jie MA ; Min-Hui LI
Chinese journal of integrative medicine 2024;30(12):1137-1147
Persicae Semen (Taoren), the seed of mature peaches consumed as both food and medicine, is native to the temperate regions of China, distributed in the provinces of North and East China, and currently cultivated worldwide. The primary components of Persicae Semen include volatile oil, protein, amino acids, amygdalin, and prunasin, all of which have pharmacological properties, such as anti-inflammatory, antioxidant, and immune regulatory effects, and are clinically used in the treatment of gynecological, cardiovascular, cerebrovascular, orthopedic, and digestive system diseases. This review provides a comprehensive perspective on the resource status, ethnopharmacology, phytochemistry, pharmacology, and toxicology, as well as the trend of Persicae Semen patent, global distribution, and clinical applications. This review will help facilitate the development and utilization of Persicae Semen in clinical settings.
Humans
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Phytochemicals/chemistry*
;
Animals
;
Drugs, Chinese Herbal/therapeutic use*
;
Medicine, Chinese Traditional/methods*
;
Medicine, Traditional/methods*
10.EPCs-exos combined with tanshinone Ⅱ_A protect vascular endothelium cells from oxidative damage via PI3K/Akt pathway.
Lu MA ; Lei YANG ; Chang-Qing DENG ; Wei ZHANG ; Huang DING ; Xiao-Dan LIU ; Wan-Yu LI ; Jiang WEN ; Wei TAN ; Yan-Ling LI ; Yan-Yan ZHANG ; Xin-Ying FU ; Lin-Quan LIU ; Cai-Xia LIU ; Zhao-Wen ZENG
China Journal of Chinese Materia Medica 2023;48(23):6423-6433
This study aims to investigate the molecular mechanism of tanshinone Ⅱ_(A )(TaⅡ_A) combined with endothelial progenitor cells-derived exosomes(EPCs-exos) in protecting the aortic vascular endothelial cells(AVECs) from oxidative damage via the phosphatidylinositol 3 kinase(PI3K)/protein kinase B(Akt) pathway. The AVECs induced by 1-palmitoyl-2-(5'-oxovaleroyl)-sn-glycero-3-phosphocholine(POVPC) were randomly divided into model, TaⅡ_A, EPCs-exos, and TaⅡ_A+EPCs-exos groups, and the normal cells were taken as the control group. The cell counting kit-8(CCK-8) was used to examine the cell proliferation. The lactate dehydrogenase(LDH) cytotoxicity assay kit, Matrigel assay, DCFH-DA fluorescent probe, and laser confocal microscopy were employed to examine the LDH release, tube-forming ability, cellular reactive oxygen species(ROS) level, and endothelial cell skeleton morphology, respectively. The enzyme-linked immunosorbent assay was employed to measure the expression of interleukin(IL)-1β, IL-6, and tumor necrosis factor(TNF)-α. Real-time fluorescence quantitative PCR(qRT-PCR) and Western blot were employed to determine the mRNA and protein levels, respectively, of PI3K and Akt. Compared with the control group, the model group showed decreased cell proliferation and tube-forming ability, increased LDH release, elevated ROS level, obvious cytoskeletal disruption, increased expression of IL-1β, IL-6, and TNF-α, and down-regulated mRNA and protein levels of PI3K and Akt. Compared with the model group, TaⅡ_A or EPCs-exos alone increased the cell proliferation and tube-forming ability, reduced LDH release, lowered the ROS level, repaired the damaged skeleton, decreased the expression of IL-1β, IL-6, and TNF-α, and up-regulated the mRNA and protein levels of PI3K and Akt. TaⅡ_A+EPCs-exos outperformed TaⅡ_A or EPCs-exos alone in regulating the above indexes. The results demonstrated that TaⅡ_A and EPCs-exos exerted a protective effect on POVPC-induced AVECs by activating the PI3K/Akt pathway, and the combination of the two had stronger therapeutic effect.
Proto-Oncogene Proteins c-akt/metabolism*
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Phosphatidylinositol 3-Kinases/metabolism*
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Signal Transduction
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Reactive Oxygen Species/metabolism*
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Tumor Necrosis Factor-alpha/metabolism*
;
Interleukin-6/metabolism*
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Endothelium, Vascular
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Oxidative Stress
;
Endothelial Progenitor Cells
;
RNA, Messenger/metabolism*
;
Abietanes

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