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So here's the thing: developing a new drug and getting it to you can take a long time.
大家都知道：一种新药从研发到上市需要花费很长时间。
When we have to work out the cause of a condition -- for example, with multiple sclerosis or heart disease
当我们还要研究病因时（例如多发性硬化症或心脏病），
developing a new drug takes significant trial and error and lots of money.
研发新药就需要大量的试错和资金。
Which is why we only have drugs for a small proportion of diseases. But you could change all this.
这也是为什么只有少部分疾病有治疗药物。但你可以改变现状。
You could help discover new, cheaper drugs for currently untreatable diseases. It's all about medical crowdsourcing.
你的一臂之力能有助于为目前的不治之症发现更便宜的新药。这一切都与医疗众筹相关。
However, researchers aren't asking you to donate your money, they're asking you to donate something more personal...
不过研究人员并不是要让你捐钱，而是想请你捐出某件更私人的东西...
First, though, some drug development history. Many of the first medicines were discovered by chance.
不过首先，让我们回顾一下药物研发的历史。许多最早的药物都是偶然发现的。
Natural philosophers then took these and identified the active chemicals inside. And pharmaceutical companies then turned those into drugs.
自然哲学家们随后鉴定出了其中的活性成分。之后药企再把这些成分制作成药。
The thing is, for a long time, we didn't know why those drugs worked.
可问题是，在很长一段时间里，我们并不知道这些药为什么会起效。
Until scientists figured out that disease happens when the molecular machines that keep your body going -- your proteins -- start misbehaving.
直到科学家们发现，当保持你身体运作的分子机器，即蛋白质，开始作祟的时候，你就会生病。
Drugs treat disease by targeting those disruptive proteins.
药品通过靶向作用于那些破坏性蛋白质来治疗疾病。
Researchers realized that if they can identify which malfunctioning proteins cause a specific disease,
研究人员意识到，如果他们可以辨别哪些功能失常的蛋白质能引起某种特定疾病，
they can then try to find or develop a drug that stops those proteins acting up, and that will prevent the disease.
他们就可以尝试寻找或发明一种能阻止那些蛋白质作怪的药，从而预防疾病。
It's a great plan, but it's a slow process. So far, they've only identified these therapeutic targets for a small proportion of diseases.
这是一个很棒的计划，但过程漫长。至今，科学家们只识别了很小一部分疾病的药物靶点。
However, this is where you can help.
不过这就是你能帮忙的地方。
Researchers are now turning their attention to DNA, to the genetic instruction manual that tells our bodies how to make our proteins.
研究人员现在将注意力转向了DNA：DNA是一份指导我们身体如何生产蛋白质的遗传说明书。
They want to know which small changes in someone's genome can lead to the production of those dodgy proteins that cause a disease.
他们想知道基因组中有哪些微小变化会导致生成有缺陷的蛋白质，从而引发疾病。
The thing is, that's a big job. DNA is huge, and each disease is likely to have hundreds, possibly thousands, of proteins involved.
这可是一项浩大的工程。DNA存储了大量信息，而且每一种疾病都可能涉及上百，甚至上千个蛋白质。
But if they have lots of people's genomes, they can compare them and spot patterns.
但如果研究人员有很多人的基因组，他们就能加以比对，并发现规律。
They can look at multiple people suffering from the same currently untreatable disease, find any small genetic changes they share,
他们可以研究多个患有目前无法治疗的同一种疾病的人，找出他们共有的微小的遗传突变，
identify the faulty proteins they code for, and there you go: those are brand new therapeutic targets for a currently untreatable disease.
识别这些突变所编码的故障蛋白质，那么这些突变就是目前不治之症的全新药物靶点。
Now the researchers have three options: 1. Has one of those new target proteins been previously linked to a different disease that is treatable?
现在研究人员有三个选项：1.这些新的靶蛋白中，是否有哪个曾与另一种可治疗的疾病相关联？
If so, the drug for that disease may target this protein and work for this disease, too. To find out, start a clinical trial.
如果有，治疗那种疾病的药物或许能针对这一蛋白质，并治疗当前这种疾病。可以启动临床试验进行验证。
2. If not, has one of those new target proteins being previously linked to a different disease that had a promising drug that didn't ultimately work?
2.如果没有，这些新的靶蛋白中是否有哪个曾与另一种疾病相关联；该疾病有过前景良好的药物，但最终未能凑效？
If so, its promise may have come from successfully targeting this protein and it may work for this disease. Start a clinical trial to find out.
如果有，当时的前景可能是因为成功靶向了这一蛋白，或许能对当前疾病起效。可以启动临床试验来求证。
3. If this is a brand new protein target never identified before for any disease could they design a new drug to affect it?
3.如果这是一个全新的蛋白质靶点，从未和任何疾病有所关联，他们是否能设计一种新药来影响这种蛋白质？
This involves AI machine learning and some very cool chemistry. And a lot of time, effort, and cost too.
这个问题涉及人工智能、机器学习，以及一些很酷的化学研究，并且同样需要耗费大量的时间、努力和金钱。
Researchers are excited about all this because they think 1 in 5 of the proteins in your body either have, or are likely to have, a drug that will bind to them.
研究人员对这些选项非常激动，因为他们认为人体内五分之一的蛋白质都已有，或可能发现，能与之结合的药物。
And, as any common disease is likely to have hundreds, possibly thousands, of proteins involved,
而且，由于任何一种普通疾病都可能涉及成百上千的蛋白质，
they're hopeful they'll be able to identify a few of those proteins they've already got a drug for.
研究人员希望他们能识别出几种已有对应药物的蛋白质。
But this all relies on finding those new therapeutic targets, and that's why they need you.
但这一切都依赖于找到那些新的药物靶点，这就是为什么研究人员需要你，
Well, your data -- both your genetic data and your health history data, so they can compare the genomes of people with similar conditions.
或者说，你的数据--包括你的基因数据和病史数据，这样他们就可以比对患有类似疾病的人的基因组。
So would you give your data for research? There are two questions you may have: who will have access to my data, and what could they do with it?
那么，你会为研究贡献你的数据吗？对此，你可能会有两个问题：谁有权访问我的数据？他们能拿我的数据做什么？
One group is health care providers who are starting to consider using genetic analysis to give patients more personal care.
一个群体是医疗保健提供者，他们已经开始考虑利用遗传分析为患者提供更加个性化的护理服务。
Another group is private consumer genetic testing companies.
另一个群体是私营的消费级基因检测公司。
Some have already sold genetic data on to pharmaceutical companies for profit, but that was with their customers consent.
有一些公司已经向药企出售了基因数据以获取利润，不过前提是已经取得了顾客的同意。
However, it raises another question: if your data goes towards making new drugs, should pharmaceutical companies recognize that contribution and offer drugs more cheaply?
然而，这就引发了另一个问题：如果你的数据被用于研发新药，药企是否应该承认这一贡献，并以更实惠的价格销售药品？
Your best bet is to research the organizations who are asking for your data to find out what they will do with it and how they will protect it.
最好的办法，就是研究那些请求你提供数据的机构，了解他们将如何使用以及保护这些数据。
We'll each have our own take on this,
我们每个人对此都会有自己的看法，
but what is clear is genomics could be a powerful tool to cut the current time and cost it takes to develop new drugs for currently untreatable diseases.
但明确的是，基因组学可以成为一个强大的工具，对于当前无药可医的疾病，它能削减目前开发新药所需的时间和成本。
So, what do you think?
所以，你怎么看？
