The magic “red line” — a brief introduction to the principle of novel coronavirus antigen detection

Morphological structure of novel coronavirus

The virus is like a mixed jelly wrapped in a gum.

       Rubber candy sandwich “is located in the center of the virus body, and the main component is nucleic acid (ssRNA), which provides genetic information for the replication, inheritance and variation of the virus. The outer layer of “soft candy” is the protein shell of the virus body, called “nucleocapsid” (n), which can protect the virus nucleic acid from external factors and mediate the virus to enter the host cell. Before the entry, the “sugar coating” wrapped around the “soft candy” can be understood as the envelope of the virus body, which is a double-layer membrane wrapped around the nucleocapsid of the virus.

      This layer of “sugar coating” of novel coronavirus is extremely gorgeous. Its membrane contains spike glycoprotein (s), envelope protein (E) and membrane protein (m)

What does the “novel coronavirus antigen test” detect?

      Among these structural proteins, N protein is highly conserved among different sars-cov-2 virus strains and has good immunogenicity. It is the structural protein with the highest expression after the virus infects cells and is a good biomarker for detection. At present, the antigen detection of novel coronavirus is mainly based on the detection of N protein

Principle of antigen detection of novel coronavirus

     A white board, drop several drops of liquid into the sample hole, why can there be a magic “red line” after a few minutes?

     This antigen detection technique is called “colloidal gold method”. Take the novel coronavirus (2019 ncov) antigen detection kit that has been listed in China as an example, let’s take a “shallow” look at the formation process of the “red line”.

     It is assumed that this is a oropharyngeal / nasopharyngeal swab with novel coronavirus specific antigen (N protein). After the swab treatment solution is released and eluted, the N protein is dropped into the sample well with the treatment solution.

      The sample to be tested flows from one end of the test paper to the other end under the pull of the capillary force formed by the paper fiber. In this process, N protein will go through three hurdles.

        Remarks: the first level: gold labeled antibody (gold labeled 2019 ncov antibody); The second level: T-line antibody (2019 ncov antibody); The third level: c-line antibody (“gold label 2019 ncov antibody” secondary antibody)

 What are the types of antigens in the novel coronavirus antigen detection kit?

Definition of antigen：

It is a substance that can combine with TCR of T and BCR (lymphocyte antigen receptor) of B, promote their proliferation and differentiation, produce antibodies or sensitized cells, and combine with them to exert immune effects.

Two characteristics of antigen：

Immunogenicity

The ability of antigen to stimulate the body to produce immune response, induce antibodies or sensitize lymphocytes.

Able to stimulate response

antigenicity：

Antigen has specific binding ability with the its induced antibody or sensitized lymphocyte

The product (antibody, etc.) after the stimulation reaction can specifically bind to itself

The classification guided by this: immunogen = complete antigen, incomplete antigen (only antigenicity) = hapten

Factors influencing immunogenicity

A.Contribution of the antigen

The following are all positively related

1.Size

2.Chemical complexity

3.Chemical composition

4.Conformation and accessibility

5.Physical form

B.Contribution of the interaction between host and antigen

Specificity:

Specificity refers to the specificity that the antigen stimulates the body to produce an immune response and the reaction of its response products = a specific antigen can only stimulate the body to produce specific antibodies or sensitized lymphocytes, and can only specifically bind to the antibody or lymphocytes that respond to the antibody.

the types of epitope

conformational epitope& sequential epitope

TCR & BCR

Type of antigen:

TD Ag (thymus dependent antigen): this kind of antigen stimulates B to produce antibodies that depend on t help, also known as T cell dependent antigen. [most human proteins are]

Ti Ag (thymus independent antigen): this kind of antigen stimulates B to produce antibodies independent of t help, also known as T cell dependent antigen.

Compared with the two, TD AG is more versatile:

Nonspecific immunostimulators:

Not typical but awesome

Superantigens are generally:

Extracellular toxin, retroviral protein

TCR binding site V β \ beta β

Direct stimulation of T cells

The reactive cells were CD4 + T cells

MHC binding site: non polypeptide region

Mitogen

Also known as mitogen, it can cause cells to undergo mitosis and then proliferate.

It is an important mitogen that acts on T and B cells of human and mouse.

 Rapid detection of novel coronavirus infection pneumonia

 In early 2020, coronavirus disease (covid-19) spread worldwide,      causing the world to face a crisis of survival and health. The automatic detection of lung infection through computed tomography     ( C T )    images provides great potential for strengthening the traditional health care strategy to cope with covid-19.   However, segmentation of infected areas from CT sections faces several challenges, including high variability in infection characteristics and low-intensity contrast of infection with normal tissue. In addition, it is unrealistic to collect a large amount of data in a short time,    which hinders the training of the depth model.    In order  to  meet  these challenges, this paper proposes a new covid-19 lung infection segmentation depth network, which can automatically identify the infected areas from chest CT slices. In the network, a parallel partial decoder is used to aggregate high-level features and generate global graphs. Then, implicit inverse attention and explicit edge attention are used to model and enhance the boundary representation. In addition, in order to alleviate the shortage of labeled data,   a semi supervised sequence configuration framework based on random selection propagation strategy is proposed.    This framework only needs a small number of labeled images and mainly uses unlabeled data. Semi supervised framework can improve learning ability and achieve higher performance.       Extensive experiments on covid semiseg and CT show that the proposed inf net is superior to most tip segmentation models and improves the latest performance.

1. The texture, size and position of the infection in CT sections vary greatly, which is challenging to detect. For example, the combined area of the whole piece is too small, which may easily lead to false negative of the whole piece of CT.

2. The variance between classes is small. For example, GGO boundaries are often low in contrast, fuzzy in appearance, and difficult to identify.

3. Due to the emergency of covid-19, it is difficult to collect enough marker data for the training of depth model in a short time.

1. By using the parallel partial decoder (PPD) to aggregate the features from the high level, the combined features obtain the context information and generate a global map as the initial guidance area for the subsequent steps. In order to further explore the boundary clues, we use a set of implicitly repeated reverse attention (RA) modules and explicit edge attention guidance to establish the relationship between regions and boundaries.

2. A semi supervised segmentation system based on the propagation of random selection is introduced to alleviate the short time of labeled data.

3. A covid-19 semi supervised infection segmentation (covid semiseg) dataset was constructed, including 100 labeled CT slices from the covid-19 CT segmentation dataset and 1600 unlabeled images from the covid-19 CT acquisition dataset

Edge map predicted with standard binary cross entropy constraint and edge map of ground truth value (GT):

Parallel partial decoder (PPD)

Reverse Focus module (RA)

    A random sampling strategy is used to gradually expand the training data set using unlabeled data. False labels were generated for unlabeled CT images using the procedure described in the following figure. Then the model is trained using the obtained CT images with false labels.

     The semi information network was extended to a multi class pulmonary infection detection framework to provide more abundant information for the further diagnosis and treatment of covid-19. The extension of the semi information network is based on the multi class marker framework guided by the infected area, as shown in the figure.

 Novel coronavirus (sars-cov-2) antigen detection kit

https://youtu.be/ZCVWNVV

1. Nasopharyngeal swab sample collection

1. Tilt the patient’s head slightly back approximately 70°.

2. Gently insert the sampling swab into the nostril, straight back (not up), along the base of the nasal passage, until it reaches the back wall of the nasopharynx—usually half the distance from the corner of the nose to the front of the ear (approximately 4 to 6 cm) or 1.6-2.5 inches). Note: Do not rub too hard – if you experience a blockage, try the other nostril.

3. Gently rub and roll the swab 5 times.

4. Slowly pull the swab out while rotating the swab. 

2. Swab Specimen Preparation

1. Peel off the aluminum foil seal from the sample extraction tube;

2. Put the swab into the sample extraction tube and stir the swab in the solution at least 5 times;

3. Squeeze the sample extraction tube and move the swab up and down at least 3 times to drain any sample solution in the swab. Discard swabs correctly;

4. Firmly insert the cap on the sample extraction tube. The tubes were left for 1 minute to release viral antigens.

3. Instructions for use

1. Flick the bottom of the tube to mix the sample solution.

2. Remove the test cartridge from the foil bag. Put the test box on the table. Squeeze the sample extraction tube vertically upside down to discharge 3 drops of sample solution into the “S” shaped hole on the test box.

3. Read the results after 15 minutes. Positive results are visible in as little as 1 minute. Negative results must only be confirmed at the end of 15 minutes. Any results interpreted outside the 15-minute window should be considered invalid and must be repeated.

4. Interpretation of results

Positive: * Two colored lines appear. A colored line should always appear in the control line area (C) and another prominent colored line should be adjacent in the test area (T).

*Note: The color intensity of the test line area may vary depending on the concentration of SARS-CoV-2 antigen present in the sample. Therefore, any shade of color in the test line area should be considered a positive.

 Negative: A colored line appears in the control line area (C). No lines appear in the test area (T).

Invalid: The control line failed to appear. Insufficient sample size or incorrect procedure technique are the most likely causes of control line failure. Review the program and repeat the test with the new test. If the problem persists, stop using the test kit immediately and contact your local dealer.