KS-Detect: A sample-in, answer-out solution to the Diagnosis of Kaposi's Sarcoma

摘要 该项目的目标是开发一个完整的“样本输入-回答”解决方案，以诊断 通过开发我们的KS-Detect系统，在有限的资源环境中治疗卡波西肉瘤（KS）。 卡波西肉瘤是撒哈拉以南非洲地区男性的主要癌症，女性的第二大癌症。 非洲KS很难与其他血管增生性疾病区分开来，特别是在非洲， 训练有素的病理学家仅限于少数几家医院，免疫组织化学几乎不存在。多 研究表明，皮肤中卡波西肉瘤疱疹病毒（KSHV）的基于PCR的核酸鉴定 活组织检查是在缺乏明确诊断的情况下进行明确诊断的最佳方法。 免疫组化 KS-Detect系统结合了我们的（1）“注射器实验室”技术，用于活检提取和样本采集 处理，（2）用于极低功率核酸扩增的太阳热PCR，以及（3）纳米颗粒 基于比色智能手机测定的结果的量化。该系统旨在用于 并解决了一些挑战与低资源设置诊断卡波西氏 肉瘤包括：在现场采集和处理活检样本的能力，提供至少10倍的 通过使用，可在单次电池充电时执行的诊断反应的数量增加 太阳光驱动热循环过程，并消除对专用仪器的依赖 仅需要智能电话和透镜，从而使得系统能够由操作者固定在现场。 我们已经展示了KS-Detect系统中使用的三种技术。这样做的目的是 R21的工作是优化每种技术，同时将它们集成到工作流程中，并执行 使用人体样本进行的一系列系统级验证实验。这种临床前优化至关重要 在临床实施之前。

Abstract The goal of this project is to develop a complete "sample-in-answer-out" solution to the diagnosis of Kaposi's sarcoma (KS) in limited resource settings through the development of our KS-Detect system. Kaposi's sarcoma is the leading cancer in men and the second leading cancer in women in sub-Saharan Africa. KS is difficult to distinguish from other angioproliferative diseases, particularly in Africa where access to trained pathologists is limited to few hospitals and immunohistochemistry is practically non-existent. Multiple studies have shown that PCR based nucleic acid identification of Kaposi's sarcoma herpesvirus (KSHV) in skin biopsies represents the best method of performing an unambiguous diagnosis in the absence of immunohistochemistry. The KS-Detect system combines our (1) "lab-on-a-syringe" technology for biopsy extraction and sample processing, (2) solar-thermal PCR for extremely low-power nucleic acid amplification, and (3) a nanoparticle based colorimetric smartphone assay for the quantification of the results. The system is designed to be used by a field nurse and addresses a number of challenges with low resource setting diagnosis of Kaposi's sarcoma including: the ability to take and process a biopsy samples in the field, providing at least a 10-fold increase in the number of diagnostic reactions that can be performed on a single battery charge by using sunlight to drive the thermal cycling process, and elimination of the reliance on specialized instrumentation requiring only a smartphone and a lens thereby enabling the system to be fixed in the field by the operator. We have already demonstrated each of the three technologies used in the KS-Detect system. The aims of this R21 effort are to optimize each of the technologies while integrating them into the workflow, and to perform a series of system level validation experiments using human samples. This pre-clinical optimization is critical prior to clinical implementation.