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Optical Imaging of Ovarian Carcinogenesis in a Rat Menopause Model

Optical Imaging of Ovarian Carcinogenesis in a Rat Menopause Model
大鼠更年期模型中卵巢癌发生的光学成像
批准号:
7208181
负责人:
Jennifer Kehlet Barton
金额:
$27.33万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2006
资助国家:
美国
项目状态:
已结题
起止时间:
2006-12-11 至 2011-11-30

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中文摘要
翻译
描述(由申请人提供):卵巢癌是所有妇科癌症中死亡率最高的,70%的卵巢癌妇女在5年内死于疾病。早期疾病的生存率很高,但卵巢癌通常不会早于III期或IV期被发现,因为其表现模糊,卵巢难以进入,并且对侵袭前病变知之甚少。由于缺乏适当的、特征明确的动物模型,了解卵巢癌病因和开发有效的检测和治疗方法的努力受到了阻碍。最近,直接将啮齿动物卵巢暴露于低剂量致癌物7,12-二甲基苯[a]蒽(DMBA)的模型已被报道产生类似人类的(预)肿瘤病变。我们正在改进这种致癌物质模型,将其与卵泡缺失、卵巢完整的绝经大鼠模型相结合。这种改进的模型将有助于确定绝经后妇女对卵巢癌的不同易感性。本研究的总体目标是开发抗击卵巢癌急需的两种能力:1)与人类疾病非常相似的卵巢癌大鼠模型;2)对早期肿瘤变化敏感的微创成像模式。我们希望我们的研究有助于识别早期肿瘤变化,从而预测癌症。该提案有四个具体目标:1。建立绝经前后大鼠卵巢癌模型。使用4-乙烯基环己烯二氧化物(VCD)和二氧化二苯甲醚(DMBA)创建一种新的卵巢完整大鼠更年期模型,将确定最佳剂量、给药和时间。解剖和生化变化将被评估。2. 发展光学成像技术,以实现高分辨率离体和时间序列在体
英文摘要
DESCRIPTION (provided by applicant): Ovarian cancer has the highest mortality of all gynecologic cancers, and 70% of women with ovarian cancer die of their disease within 5 years. Survival is high with early stage disease, yet ovarian cancer is not usually detected earlier than Stage III or IV because its presentation is vague, the ovary is difficult to access, and little is known about preinvasive lesions. Efforts to understand ovarian cancer etiology and to develop effective detection and treatments for this disease have been hampered by lack of appropriate and wellcharacterized animal models. Recently, models that directly expose the rodent ovary to low doses of the carcinogen 7,12--Dimethylbenz[a]anthracene (DMBA) have been reported to develop human-like (pre)neoplastic lesions. We are improving upon this carcinogen model by combining it with a follicle-deplete, ovary-intact rat model of menopause. This improved model will be useful for determining the differential susceptibility of post-menopausal women to ovarian cancer. The overall goal of this research is to develop 2 capabilities critically needed in the fight against ovarian cancer: 1) a rat model of ovarian cancer that closely resembles the human disease, and 2) minimally-invasive imaging modalities sensitive to early neoplastic changes. We expect our research aid in identification of early neoplastic changes that predict cancer. This proposal has 4 specific aims: 1. Develop a peri-and post-menopausal rat ovarian cancer model. Optimum dosing, administration and timing will be determined for the creation of a novel ovary-intact rat model of menopause using 4-Vinylcyclohexene Diepoxide (VCD) and DMBA. Anatomical and biochemical changes will be evaluated. 2. Develop optical imaging technologies to permit high resolution ex vivo and time-serial in vivo imaging of rat ovary. Miniaturized optical coherence tomography (OCT), laser induced fluorescence (LIF), and high resolution optical coherence microscopy (OCM) will be developed to enable minimally invasive imaging in our improved animal model. 3. Determine mechanisms of contrast in normal, follicle deplete, and cancerous ovarian tissues. We will undertake a comprehensive ex vivo study to elucidate mechanisms of contrast in the 3 imaging modalities developed.in specific aim 2. 4. Perform in vivo, serial imaging studies of ovarian carcinogenesis. We will perform minimallyinvasive, time-serial imaging order to follow carcinogenesis in our rat models. The goal of this study is to determine early anatomical or biochemical changes visible on OCT, OCM, or LIF that predict future development of neoplasms, and to determine if the time sequence of lesions is different for cycling and follicle-deplete animals.
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Ovarian Cancer Detection with Blood- and Imaging-Based Biomarkers
  • 批准号:
    10598251
  • 项目类别:
  • 资助金额:
    $3.45万
  • 财政年份:
    2022
  • 负责人:
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Ovarian Cancer Detection with Blood- and Imaging-Based Biomarkers
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    10737827
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  • 负责人:
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Ovarian Cancer Detection with Blood- and Imaging-Based Biomarkers
  • 批准号:
    10544781
  • 项目类别:
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    2022
  • 负责人:
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  • 依托单位:
Ovarian Cancer Detection with Blood- and Imaging-Based Biomarkers
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    10314537
  • 项目类别:
  • 资助金额:
    $81.19万
  • 财政年份:
    2022
  • 负责人:
    Jennifer Kehlet Barton
  • 依托单位:
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