The terminator may not come at any time soon but medicines should be coming soon at a printer near you …
Mid last year, Gartner mentioned “medical applications [of 3D printing] will have the biggest impact in the next two to five years“. With 3D printing you can already create a lot of physical artifacts and medical applications go from building medical equipments to prosthetic parts, but also blood vessels, bone, heart valve, cartilage, etc. Complete organs are not too far, with companies like Organovo already printing functional liver assays, prospects to restore a body by replacing or consolidating personalized parts seem interesting.
On the other side, restoring a body function by providing personalized molecules was a dream so far. Preventing body malfunction via similar systems is too.
I recently watched and read about Lee Cronin’s laboratory work and these dreams may come true, one of these days. In a TEDxGateway video in 2013, Prof. Cronin explained briefly how he did it. Last December, they published their method with a basic application in Nature Communications. What I also liked is that, beyond the technical capabilities, this research is based on common components (right) and free software that are available for everyone. And Cronin also insisted on compatibility between “recipes” and the possibilities to exchange them as well as source code – one day, will their software be released on Github like some of their 3D models as STL files?
Cronin also talks about pharmaceutical companies releasing blueprints for drugs that could save plenty of lives in emerging economies, for instance. In my opinion, this is however where the technology goes much faster than the ideological framework we live in: pharma companies will not likely suddenly release recipes for drugs that bring them money (no for-profit company in any other sector would, by the way) and the regulatory framework for healthcare is far from ready to accommodate these advances.
Prevention could also benefit from these advances. Synthetic vaccines are in production since two decades at least. If safety is the first argument often put forward in their favor, rapid prototyping and versatile production could one day become possible. It seems it was already tested for flu vaccines. Now imagine to move the “engineering” part in a computer, sending the recipe for the best-adapted vaccine directly to “vaccine printers” in regions where health hazards are likely to occur or as early as they occur … We would also face many corporate and regulatory hurdles. But it wouldn’t be the first field where technology would push broader changes …
With more than 2,400€ collected, our team – Bordet’s angels – can be proud, for a first participation! We are 12th of more than 100 Belgian teams. One key learning is that the gold, old paper display still works better than anything else to raise money.
And it was fun for me, a bit itchy in the end. But with the right trimming tools, this goes away very quickly. Thanks for all my supporters ;-) – your support is worth a thousand thank-you!
And a bonus video that was fun to create …
We’re nearly halfway through Movember, the month we grow our moustache in order to raise awareness about men’s health. I am in Amsterdam, for a congress and this was the hardest day of the month so far: since 8am, nearly every single person I met said it didn’t look good. And this can be harsh when you talk with (potential) business partners! However, practically, when you have time, this is an unique opportunity to initiate discussions with others about prostate cancer.
So this is a plea to make it worth! Please donate to prostate cancer research via my profile page: http://mobro.co/jepoirrier. Belgian men lives on average 5 years less than Belgian women. Belgium has the 4th highest cancer rate for men diagnosed in 2012 worldwide. Survival rates are however good but together we can do better!
Still want to see how it looks like so far? Here you see I will soon need to use wax, scissors and all sorts of precision instruments to tame it ;-)
And again, donate to a good cause! Many thanks in advance for your help!
A contact matrix is a representation of contacts between individuals. For instance, in order to model the spread of rumors on social media, you ideally have to rely on contact matrices to compute the strength of bonds between types of individual agents. In the infectious disease world, a contact matrix is used to approximate contacts between individuals, e.g. between grand-parents and grand-children.
In this blog post, after a short explanation of POLYMOD contact matrices, I will show how to get the data, process it and 3D print these matrices. Ready?
1. Finding contact matrices
The most used contact matrices in epidemiological modelling are coming from the POLYMOD study, published by Mossong et al. in 2008. The study is a population-based prospective survey of mixing patterns in eight European countries (Belgium, Germany, Finland, Great Britain, Italy, Luxembourg, The Netherlands, and Poland). For that purpose their method consisted in common paper-diaries used by individuals to record information about their daily contacts (you might think this is so old fashion but nobody reproduced this study or did better so far!).
So what does it look like (I’ll take Belgium as an example here)?
You can see above a heatmap of physical contacts between participants and their contacts. The more towards the blue indicates fewer contacts. The more towards white indicates more contacts. Therefore the diagonal towards the top right shows that most Belgian participants have contacts with people of the same age. And this diagonal has two “wings”, representing interactions between parents in their 30s and their children. There are also two “bumps”, representing interactions between grand-parents and their grand-children.
So these heatmaps are already something pleasant to the eye. But what if you could actually touch them? Can you actually physically play with them? This was made possible thanks to 3D printing, a manufacturing process that transform practically any custom 3D model created on a computed into a physical artifact.
We’ll first need to get the data, process it in a suitable format and finally print it …
There Dr. Gammino describes the hurdles faced by healthcare workers in countries where census data is often missing, where political, seasonal and geographical variations are making these more difficult. The description of the different social structures in urban or rural areas was also interesting. But the post also highlights how “social mapping” and geographic information systems (GIS) are helping understanding where the population resides and helping reaching them (here for polio vaccination but this could be for other purposes: maternity care, child care, etc.).
In this respect, modelling could help determine the best strategy to reach still unknown population, where settlers could move, where to concentrate efforts e.g. And a few papers actually address these issues. For instance, Rahmandad et al. studied the impact of network types (networks between individuals) on the dynamics of a polio outbreak. Or Tony Wragg reported the influence of information campaigns on polio eradication in India (one could use information as an infectious agent).
Now it would be interesting to see the two worlds collide: having these geotagged information feed a prediction model and reverting back predictions to healthcare workers in the field to inform them of potential areas to visit. This would have some implications for logistics and these efforts should also address privacy questions. But it would potentially help eradicating polio too.
With a few days of interval I received two very different ways of reviewing data collected by users of “activity trackers”.
The first one came from Jawbone (although I don’t own the UP, I might have subscribed to one of their mailing-lists earlier) and is also publicly available here. Named “2013, the big sleep” it a kind of infographics of how public (and mostly American) events influenced sleep of the “UP Community”. Here data about all (or at least a lot of) UP users were aggregated and shown. This is Big Data! This is a wonderful and quantitative insight on the impact of public event on sleep! But this is also a public display of (aggregated) individual data (something that UP users most probably agreed by default when accepting the policy, sometimes when they first used their device).
The second way came from Fitbit, also via e-mail. There was written how many steps I took in total as well as my most and least active periods / days of 2013. At the bottom there was a link to a public page comparing distances traveled in general with what it could mean in the animal kingdom (see below or here). This is not Big Data (although I am sure Fitbit have access to all these data). But at the same time (aggregated) individual data are not shared with the general public (although here again I am sure a similar policy apply to Fitbit users).
Different companies, different ways to handle the data … I hope people will realise the implication of sharing their data in an automated ways in such centralized services.
After a bit less than 2 hours, jepsfitbitapp retrieved my sleep data from Fitbit for the whole 2013 (read previous post for the why (*)). Since this dataset covers the period I didn’t have a tracking device and, more broadly, I always slept at least a little bit at night, I removed all data point where it indicates I didn’t sleep.
So I slept 5 hours and 37 minutes on average in 2013 with one very short night of 92 minutes and one very nice night of 12 hours and 44 minutes. Fitbits devices do not detect when you go to sleep and when you wake up: you have to tell tem (for instance by tapping 5 times on the Flex) that you go to sleep or you wake up (by the way this is a very clever way to use the Flex that has no button). Once told you are in bed the Flex manages to determine the number of minutes to fall asleep, after wakeup, asleep, awake, … The duration mentioned here is the real duration the Fitbit device considers I sleep (variable
Visually it looks like there is a tendency to sleep more as 2013 passes. But, although the best linear fit shows an angle, the difference between sleep in March and sleep in December is not significant.
R allows to study the data in many different ways (of course!). When plotting the distribution of durations asleep it seems this may be distributed like a normal (Gaussian) distribution (see the graph below). But the Shapiro-wilk normality test shows that the data doesn’t belong to a normal distribution.
The simple plot of the number of awakenings over time shows the same non-significant trend as the sleep duration (above). The histogram of these awakenings shows a more skewed distribution to the left (to a low number of awakenings) (than the sleep duration). This however shows there is a relation between the two variables: the more I sleep the more the Flex detects awakenings (see second graph below).
Sleep efficiency is the ratio between the total time asleep by the total time in bed from the moment I fell asleep. This is therefore not something related to the different sleep stages. However it may indicate an issue worth investigating with a real doctor. In my case, although I woke up 9 time per night on average in 2013, my sleep efficiency is very high (93.7% on average) …
… or very low. There are indeed some nights where my sleep efficiency is below 10% (see the 4 points at the bottom of the chart). These correspond with nights when I didn’t sleep a lot and also with very little awakenings (since these are related).
There is no mood tracking with Fitbit (except one additional tracker that you can define by yourself and must enter a value manually): everything tracked has to be a numerical value either automatically tracked or manually entered. It would be interesting to couple these tracked variables with the level of fatigue at wake-up time or the mood you feel during the subsequent day. I guess there are apps for that too …
(*) Note: I just discovered that there is in fact a specific call in the API for time series … This is for a next post!
Previous data belong to the “activities” category. In this category it is easy to get data about a specific activity over several days in one request. All parameters related to sleep are not in the same category and I couldn’t find a way to get all the sleep durations (for instance) in one query (*). So I updated the code to requests all sleep parameters for each and every day of 2013 … and I hit the limit of 150 requests per hours.
This graph is what I achieved so far. I didn’t sleep much in March-April 2013: on average 4.9 hours per night. The interesting thing is that I can understand why by going back to my agenda at that time (work, study, family …). As soon as I can get additional data it would be interesting to see if sleep durations will increase later on.
(*) If you know how to get all sleep durations for 2013 in one query, let me know!
I continue to explore data about my physical activity in 2013 (see part 1). We moved from an apartment (on the third floor of a building) to a house (with two floors) on July 1st, 2013. I was wondering if the change would have an impact on the number of floors I climbed: I now have to climb to reach bedrooms and go down to go in the living room. A standard house.
Two things before diving into data … First I sometimes used to climb the stairs to the 3rd floor in my building (and I worked all the time at the same floor at the office). Then only the Fitbit One is collecting the number of floors you climb, not the Flex (you can enter them in the web interface but I don’t). So I don’t value the data after I lost my Fitbit One (Sep. 16). I don’t really know how the One determines the number of stairs I climb but I felt it was fairly accurate. For instance when I climbed 3 stairs in my building, the One always indicated +3 stairs on its counter.
So now the data. I updated the R scripts and here is what I get for the number of floors.
On average I did not climb a lot of stairs. In general it is below 20. And if I compare the data before and after the move there is indeed a significant difference (p=2.49e-06)! But I was climbing more floors when I was in my apartment than when I was/am in a house (respective means of 12.59 and 7.37 floors)!
There are a few outliers, days when I climbed relatively more than others. Going back to my agenda, it corresponded to:
- one day I took holidays just after the move in order to arrange things at home (strangely the days of the move doesn’t correspond to more of that activity);
- one day when I came back from a business trip (I had to walk a lot to/in/from airports);
- two days with no particular event.
The lessons I take are that you don’t necessarily need stairs in the area where you live to actually climb more floors (in my case it appears to be the opposite). And I don’t necessarily need to have a specific activity to climb more floors, hence it’s a question of willingness more than anything else.
Next post: how much sleep did I get in 2013!