Category Technology

RT-PCR is the Gold Standard for testing for covid-19. Samples are taken from the nasal cavity with cotton swabs, nucleic acids are extracted by pre-treatment, and then RT-PCR is performed, but reagents and consumables are running out due to the increase in the number of tests worldwide.
There is a report of how this preprocessing process can be simplified as follows.
https://journals.plos.org/plosone/article/authors?id=10.1371/journal.pone.0243266

As a result, a sample of 20uL to 40uL was heat treated at 95°C to 98°C for 2 to 20 minutes to obtain good RT-PCR results.
The RT-PCR kit evaluated were
ABI TaqMan fast virus 1-Step RT-qPCR kit,
Meridian Bioscience Fast 1-Step RT-qPCR kit.

However, when the sample was suspended using UTM Viral Transport COPAN, this method failed to produce any detectable signal.
The cause is unknown because the composition of the reagent is not disclosed. So, depending on the combination of reagents used in the RT-PCR Kit, it seems that a simplified pre-treatment of heat treatment may not work properly.

A group of  Imperial College London has reported a prototype of a disposable new coronavirus (COVID-19) detecting qPCR chip. Of course, this chip can also be used for other infections.
https://www.nature.com/articles/s41467-020-19911-6

The chip has a very simple structure and is built on a Si substrate (the size of the chip is about 1 cm square). The cost when manufactured with a Si 4-inch wafer was about 40 yen/chip, so it will certainly be disposable.
The temperature control of the chip is done by controlling electric current flowing in the Si substrate, and the detection is performed electrochemically using methylene blue (MB) as a redox reporter. Since MB acts also as an intercalator of DNA, the electrochemical current changes when DNA is amplified.

I also participate in a project targeting elucidation of the mechanism of the sever disease of new coronavirus (COVID-19) and the development of therapeutic drugs.
We promote development of this project by making full use of our experience and wisdom in glycans and lectins.
Thank you for your continuous support on our activities.

https://readyfor.jp/projects/glycotechnica_covid19research/announcements/150515

CRISPR/Cas9 won the 2020 Nobel Prize in Chemistry about a week ago.
We have found several papers that used this technique as a high sensitive detection method for the new coronavirus. The following paper reports that detection using CRISPR/Cas12a could be further 13 times more sensitive by adding Mn2+.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7536916/#advs2020-bib-0005

A brief description of the protocol is as follows:

• Extract an RNA sample from a sample.
• Make cDNA using reverse transcription, and amplify by PCR (dsDNA is made).
• Pre-design crisper RNA (crRNA) using RNA sequences specific to the E protein of the new coronavirus.
• Add Cas12a enzyme and crRNA to the dsDNA sample, the new coronavirus-specific sequence is cut out when it is in the sample dsDNA.
• Thus, since the inhibitory interaction between the fluorescent probe and the quencher that have been incorporated in the PCR process is lost, the fluorescence comes out by applying the excitation light.

Of course, this fluorescence is read by a detector, but it can be confirmed by the eyes.
The addition of Mn2+ to this system increased the sensitivity by 13 times.

Chest X-rays and CT are used as a diagnosis of the new coronavirus (COVID-19). The Univ. of Oklahoma group has succeeded in improved diagnostic accuracy by using Deep Learning techniques to determine whether or not the breast X-ray images are from COVID-19-derived pneumonia.
https://www.sciencedirect.com/science/article/pii/S138650562030959X?via%3Dihub

Deep Learning uses a six-layer Convolutional Neural Network (CNN), with a chest X-ray image resized into 224 x 224 x 3, and a Convolution of 3 x 3. The x3 in the input image indicates that it is three colored (R, G, and B). Since the X-ray image is black and white gray, the three colors of R, G, and B are pre-processed for the image as shown in the figure below. In the figure below, (Ip) is an image with the diaphragm removed, (Ieq) is an image processing method that adjusts the contrast using the intensity histogram of the image, and (Ib) is an image obtained by bilateral filtering on (Ieq). R, G, and B images are simulated by using these three (Ip), (Ib), and (Ieq) images.

The results of Deep Learning are,
with the simple model using the X-ray image as it is, 88% of the accuracy was obtained, and the accuracy was improved to 94.5% by adding the pre-processing of the above mentioned image editing method.

Wouldn’t it be time for diagnosis using Deep Learning to be used more and more in the medical field?