Detection of SARS-CoV-2 by SERS Spectroscopy
试验速览
- 阶段
- 不适用
- 状态
- 进行中（未招募）
- 入组人数
- 712
- 试验地点
- 1
- 主要终点
- Evaluate the performance in terms of sensitivity and specificity of the technique by spectral analysis combined with artificial intelligence for the SARS-CoV-2 virus versus to the reference technique by RT-qPCR
研究概览
简要总结
SARS-CoV-2 infection was identified as responsible for several cases of pneumonia and acute respiratory distress syndromes described in Wuhan, Hubei Province, China in December 2019. A global epidemic has spread since and the Director General of the World Health Organization (WHO) declared in March 2020 the state of a global pandemic.
As the spread of the virus accelerates, several countries are implementing containment strategies to stem the epidemic.
The context of an influx of patients and congestion in healthcare establishments requires rapid and reliable diagnostic solutions for SARS-CoV-2 infection in order to enable patients to be properly referred. These solutions will represent fundamental tools in the management of new epidemic waves, both in terms of health and economics.
详细描述
Spectroscopy is the discipline of studying the interactions between light and matter, in order to perform analyzes unmatched in terms of the speed of data acquisition. Depending on the spectral ranges used by the sensors, it is possible to carry out molecular (molecular and vibrational spectroscopy) or elementary (atomic spectroscopy) analyzes.
As part of this project, GreenTropism has selected Surface Enhanced Raman Scattering (SERS) technology as a spectral technique. The scientific literature reports several cases of use of SERS technology for virus analysis, under variable conditions: variable viral loads, after amplification, use of substrates enriched in antigens.
The SERS allows an analysis of a sample deposited on a substrate on average (from fifteen seconds to 10 minutes depending on the devices and the presence of complementary imaging). Already proven for the identification of viruses on strains pathogenic for humans and animals, its deployment is slowed down by the complexity of the data to be processed.
These spectra acquisition technologies require the joint use of statistical tools and multivariate analyzes to allow sample discrimination (classification) and / or quantification. Until recently, the capacity and performance of statistical tools were limited by the available computational capacities. The lifting of this technological lock allowed the advent and democratization of Artificial Intelligence (AI) techniques theorized in the 1960s and applied today.
GreenTropism's Kaïssa, AI tool, in addition to processing big data, has been designed and trained specifically for processing spectral data and automating all of the algorithmic chains needed to go from spectrum support to its interpretation, and the presentation of the final answer.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Patient aged ≥ 18 years
- •Patient presenting to the GhPSJ for a consultation or hospitalization and for whom a PCR test for SARS-CoV-2 is prescribed as part of his care
- •French-speaking patient.
排除标准
- •Patient under guardianship or curatorship
- •Patient deprived of liberty
- •Patient under legal protection
- •Patient objecting to the use of their data for this research.
结局指标
主要结局
Evaluate the performance in terms of sensitivity and specificity of the technique by spectral analysis combined with artificial intelligence for the SARS-CoV-2 virus versus to the reference technique by RT-qPCR
时间窗: Day 1
Detection of the SARS-CoV-2 virus with the technique by spectral analysis combined with artificial intelligence and with the reference technique by RT-qPCR (Xpert Xpress SARS-CoV-2 or Simplexa™ COVID-19 Direct assay)
次要结局
- Evaluate the detection limit of the technique by spectral analysis combined with artificial intelligence(Day 1)