Improving sensitivity of COVID-19 tests
A multidisciplinary research team at the National Institute of Standards and Technology has developed a way to increase the sensitivity of quantitative polymerase chain reaction (qPCR) tests to detect SARS-CoV-2, the virus that causes COVID-19. The qPCR test is the primary test used to diagnose SARS-CoV-2, but it can lack sensitivity to low viral particle counts. The team employed a mathematical technique that identifies comparatively faint signals in diagnostic test data when the viral load in a patient’s nasal swab is low and helps to amplify the signal. Their findings, published Sept. 20, 2020, in Analytical and Bioanalytical Chemistry, show that positive test data, when depicted in a graph, assume a recognizable shape that never varies, essentially creating a fingerprint for this test. While the shape is always the same, the size and position of the change vary according to the number of viral particles in the sample. Reprogramming computerized test equipment to recognize this shape, regardless of size or position, could improve the ability to identify individuals who are infected but asymptomatic, the researchers said. “Moreover, we demonstrate that the master curve is transferable reference data that can harmonize analyses between different labs and across several years. Application to reverse-transcriptase qPCR measurements of a SARS-CoV-2 RNA construct points to the usefulness of this approach for improving confidence and reducing limits of detection in diagnostic testing of emerging diseases.”
Deep learning algorithm helps triage suspected COVID-19 cases
Containing the COVID-19 pandemic depends on being able to quickly identify people suspected of having the SARS-CoV-2 virus. To that end, researchers in China developed a deep learning algorithm to triage for triage and analysis of lesion burden as seen in chest CT scans. The team trained the artificial intelligence (AI)-aided algorithm on unenhanced chest CT scans from 2,447 patients admitted to a single hospital in Wuhan, China, between Feb. 1 and March 3, 2020, to segment lung opacities and alert cases with COVID-19 imaging manifestations. Of the patients, 1,647 had real-time polymerase chain reaction (RT-PCR)-confirmed COVID-19 and 800 were virus-free. In a large external validation set collected at three fever clinics both within and outside the Wuhan area, with radiological reports as the reference standard, the deep learning triage achieved an area under the curve of 0.953 (95%), sensitivity of 0.923 (95%), specificity of 0.851, positive predictive value of 0.790 and negative predictive value of 0.948. For identification of increases in lesion burden, the tool’s sensitivity and specificity were both 95%. The World Health Organization has recommended chest imaging for symptomatic patients with suspected COVID-19, if RT-PCR testing was unavailable or could not be performed in a timely manner. “In these scenarios, when chest CT is used as a surrogate tool to identify suspected COVID-19 cases, AI-aided triage could facilitate timely isolation of patients with suspected COVID-19 and alleviate pressure on medical staff, especially in with high disease prevalence,” the researchers wrote. “Additionally, in countries where RT-PCR testing is available with timely results, AI-aided triage might help to notify incidental results.” Their work was published in the October 2020 issue of The Lancet Digital Health.
Cancer image analysis tool incorporates HER2 biomarker assay
Santa Clara, Calif.-based Agilent Technologies Inc., a manufacturer of analytical laboratory instruments, said it has expanded its Visiopharm validated image analysis algorithm to incorporate Herceptest mAb pharmdx for Dako Omnis, an advanced staining solution immunohistochemistry and in situ hybridization. Herceptest mAb is CE-IVD marked and was recently released in Europe. The development enables pathologists to use the Visiopharm HER2 APP for objective decision support in the assessment of Herceptest mAb pharmdx stained slides, reducing time for pathologists and patients. The Herceptest mAb pharmdx assay is intended for breast cancer patients where Herceptin treatment is being considered. It includes a rabbit monoclonal antibody that provides strong and reproducible staining of the cancer biomarker, human epidermal growth factor receptor 2 (HER2) in breast cancer tissue. Over-expression of HER2 indicates a potential candidate for Herceptin treatment, which targets HER2 in cancer cells. “Together, the Herceptest mAb pharmdx for Dako Omnis and Visiopharm’s HER2 APP represents an exciting step forward in our shared commitment towards improving end-to-end standardization in tissue diagnostics, enabling our customers to quickly and efficiently generate accurate diagnoses,” said Simon Østergaard, Agilent vice president and general manager of the company’s pathology group.