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        <h2 class="mbr-section-title pb-3 mbr-fonts-style display-2">Our journey</h2>
 
        <h3 class="mbr-section-subtitle pb-5 mbr-fonts-style display-5">We discussed with a formal dengue patients, virologists and other biologists.  In each activities, we notice a new point of view that we had not known. This is the flow chart that we communicated and  history of Modified our trajectory of the Finding Flavi.</h3>
 
 
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                        <h4 class="mbr-timeline-title pb-3 mbr-fonts-style display-5">Interview with former patient</h4>
 
                        <p class="mbr-timeline-text mbr-fonts-style display-7">We met a dengue patient who entered hospital in Republic of the Philippines.<br><br>In the hospital I was investigating the symptoms of severe dengue from the amount of platelets, so  the patient are drawn their bleed twice a day. <br><br>We knew that dengue fever is one of the common disease in tropical countries and that have no specific treatment. Although the number of dead is relativity small, Dengue is one of the Neglected Tropical Diseases.<br></p>
 
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                        <h4 class="mbr-timeline-title pb-3 mbr-fonts-style display-5"><a href="https://2018.igem.org/Team:Tokyo_Tech/Human_Practices#content4-19">Dialogue with
 
Dr. Nukuzuma &nbsp;</a></h4>
 
                        <p class="mbr-timeline-text mbr-fonts-style display-7">We heard about infection mechanism and symptom.
 
<br><br>Infection with different Dengue types causes the severe sympton.
 
<br><br>We discussed ADV &amp; DA of current methods.&nbsp;<br></p>
 
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                        <h4 class="mbr-timeline-title pb-3 mbr-fonts-style display-5">Dengue Type Prediction
 
Model&nbsp;</h4>
 
                        <p class="mbr-timeline-text mbr-fonts-style display-7">We decided to predict the number of patients in the near future with suitable methods.
 
<br><br>Our advisor, Dr. Nishida taught us about how to extract patterns from time-series data.&nbsp;<br></p>
 
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                        <h4 class="mbr-timeline-title pb-3 mbr-fonts-style display-5"><a href="https://2018.igem.org/Team:Tokyo_Tech/Human_Practices#content4-15">Dialogue with
 
Dr. Suzuki&nbsp;</a></h4>
 
                        <p class="mbr-timeline-text mbr-fonts-style display-7">“We need to collect more data to increase the model’s accuracy.”
 
<br><br>He suggested us that we should refer to the system he has developed and develop a new system with fluorescence protein.&nbsp;<br></p>
 
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                        <h4 class="mbr-timeline-title pb-3 mbr-fonts-style display-5">Dengue Type Detection
 
System&nbsp;</h4>
 
                        <p class="mbr-timeline-text mbr-fonts-style display-7">We decided to develop the system to identify which Dengue type the patient is infected with.
 
<br><br>We use different fluorescence protein for each Dengue type so that we can identify it by color at the end.&nbsp;<br></p>
 
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                        <h4 class="mbr-timeline-title pb-3 mbr-fonts-style display-5">Outreach
 
Activities&nbsp;</h4>
 
                        <p class="mbr-timeline-text mbr-fonts-style display-7">We have been engaged with outreach activities to improve the current situation that dengue fever is regarded as one of the Neglected Tropical Diseases.&nbsp;<br></p>
 
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Revision as of 03:10, 18 October 2018

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Description
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PROJECT DESCRIPTION

From our inspiration to future prospects.

Our Inspiration

At first, we thought of using problems in Japan as the theme for this year’s iGEM. However, most of the problems that have been paid attention to are already well researched and finding an angle to do further research is difficult. Therefore, we decided to pick up the theme that can become a problem in Japan in the future.

Amongst the ideas, we noticed the incident in August, 2014 which dengue fever spread from Yoyogi Park, Tokyo. This incident was unique in that people got infected from within Japan, not from overseas. Previously, dengue fever would be reported from the tropics, but with the effect of global warming, Japan would face higher risk of an outbreak, thus we chose dengue fever as our theme.

Information Gathering

Having the theme decided, we started gathering information by not just reading, but also by interviewing a former patient who was infected in the Philippines and discussing with a flavivirus researcher Professor Nukuzuma. We found out the following.

1) Around the world, 4 million people are infected annually, causing the economic loss of more than $40 million. 
2) Previously, dengue virus can be categorized into four categories but another new serotype has just been found. Being infected by one serotype leads to slight fever, but being infected by more than one serotypes can lead to a severe illness.
3) There are four types of vaccine to prevent the four serotypes. However, the vaccine can cause severe illness for some people who have not been infected before.
4) The annual infection ratio of each serotype changes in roughly 10 years cycle in some areas. 

We who gathered these information thought of connecting the modeling (dry lab) to predict infection experience and its serotype, and the experiment (wet lab) to collect many data with specific serotype information. We believe this may help prevent severe dengue fever and improve the efficacy of treatment.

Dengue virus is unique in terms of its four different serotypes. Multiple infection can easily cause severe dengue, appearing hemorrhage and organ damage. It is important to grasp which serotype the patient is infected, however, there is not enough data about each serotype in a year.

To tackle the situation, we took the following two approaches.
In the future, this system can contribute to other flavivirus detection system.

Mobirise

Experiments

We also developed the simple and fast testing kit that can detect serotype with fluorescence, so that we can check the patient easily and get enough data to estimate the patients’ serotypes more accurate.

Mobirise

Modelling

We succeeded in the development of the serotype prediction system using stochastic process analysis. This system can predict the patient's serotype by simulating the past data.

Modeling

We successfully developed a model to predict change in the number and ratio of patients in each serotype. This model can predict an epidemic of each serotype of dengue fever, thus can be used by health care institutions to prepare vaccine production and distribution. Furthermore, kit created by experiment is absolutely limited in quantity. Under such circumstances, anticipating trends can send Kit preferentially to areas expected to increase the number of seriously injured people.

Experiment

In order to improve the prediction accuracy of our model, a lot of data should be available. However, few serotype-specific data was reported, and we noticed that we cannot predict many areas as it is now. Therefore, we wanted to make a kit which can identify serotype cheaply and conveniently.

Mr. Suzuki of National Institute of Infectious Disease gave us advice. Then, we created a concept of serotype identification kit using single-round infectious pseudo-viruses, and conducted experiments to actually produce them.

Consider that there is a limited quantity of this kit, we would first send a small amount to many areas. By predicting epidemic serotypes based on the data,we are planning to define areas to distribute more.

Future Prospects

The prototype will be made soon taking the advantage of both of our wet lab and dry lab technique. Accurate serotype epidemic predictions can be achieved in many areas, so it will be possible to efficiently distribute vaccines and adjust production as mentioned above.In addition to this, this detection kit will be also useful for studying interactions between two serotypes, studies of the strength between serotypes, as well as identifying the potential patients who should be vaccinated with a quadrivalent vaccine (one with infection experience). In addition, we plan to evolve into a kit that also supports other flaviviruses as future goals.


In the future, this system can contribute to other flavivirus detection system.

Address

2 Chome-12-1
Ookayama, Meguro, Tokyo

Contacts

Email: igem2018tokyotech@gmail.com