For me it is the simplicity of it (transparent minimal system prompts and harnest), you can extend it the way you like, I don't have to install a (buggy) Electron app (CC or Codex app), it integrates where I work, because it's simple (like in a standard terminal on VS code). I'm not locked in with any vendor and can switch models whenever I want, and most importantly, I can effectively use it within apps that are themselves using it as coding agent (the meta part - like a chat UI for very specific business cases). Being in TypeScript, it integrates very well with the browser and one can leverage the browser sandbox around it.
Reasons are mainly: No incentive to develop antibiotics from a legal perspective (FDA), as insurance companies prefer to reimburse the cheap and generic, still working mostly "well enough" for now.
Insurers pay for in-patient antibiotics as part of a lump sum to hospitals known as a Diagnosis Related Group (DRG). Using a cheap antibiotic increases hospital profit margins, while using an expensive new drug could mean that a hospital might lose money by treating a given patient. As a result, hospitals are incentivized to use cheaper antibiotics whenever possible. This puts significant pricing pressure on new antibiotics, which are one of the only type of medicines paid for like this.
I worked a few years in pharma R&D (Roche, largest R&D budget in this industry).
In a pharma setting, the 3D structure of a protein is mostly used to perform drug design (https://en.wikipedia.org/wiki/Drug_design#Computer-aided_dru...), i.e. trying to understand how a chemical will physically interact with a protein and thereby modify it's physiological function, in order to treat a disease.
The biggest problem comes from the fact that proteins are (1) non-static and very flexible and (2) don't exist in vacuum, they interact with a myriad of other entities in a living system. In other words, it's not because you know the structure of a protein and how to theoretically perturb it with a small molecule, that you have a drug. The large majority of structures predicted to be active against a protein target are not, when tested in a biological assay. The process helps, but ultimately it's a very empirical endeavor (test a ton of different chemicals in actual experiments, try to abstract some logic and move on from that). As a result, simply knowing the structure of a protein will not get you far down the line into finding a new drug.
On the resource topic: Even in a very large pharma setting, you will find only a dozen of scientists or so dedicated to the topic (out of tens of thousands employees), supporting many projects and with very little time to perform their own research. As a result, any team fully dedicated to the problem (like AlphaFold) can easily over-compete pharma. Most of the cost in drug discovery comes from dealing with patients and clinical trial. It's only at this stage that you'll know how your drug really works, and how it fits in the existing market and society (think of neuroscience for instance).
I don't want to undermine the protein structure field and AlphaFold results (it's fascinating), but pharma business model de facto relies very little on knowing a protein structure or not. It's also mostly useful to design small molecules, a class a bit out of fashion (biologics are the top-sellers in 2018, and new modalities are coming-up, like RNAs and gene editing for instance).
Nano-bubbles and particles have been used since a long time to diagnose diseases and enhance the quality of ultrasound analysis (contrast agents). You can also link an antibody on their surface to target them against a particular protein or reveal a zone of interest (e.g. a tumor revealed by a specific biomarker). Nano-bubbles are even use to destroy adjacent tissue on purpose (sonoporation).
Usually such particles are constructed via a mix of lipids and have a very short life time in the blood stream. I wonder how they're going to keep them alive in the blood stream for what seems to be a very long time and their design regarding toxicity.
The post mentions that "The FDA will continually work to identify additional public datasets to make available through openFDA" - do you guys already have an idea on what datasets are coming next?
The article shows that buying Windows with a laptop doesn't cost you much more than no Windows at all. Based on this and my experience, I suggest to get a Windows first and install whatever Linux you like as dual-boot. The reasons are:
1) Having a Windows installed strongly guarantees that you are going to have a functional machine no matter what. Depending on your experience with Linux you might screw things up. If it happens you still have Windows as a back-up.
2) If your aim is to communicate research results, it is very likely that you are going to need to use Office at some point, to interact with your supervisor, colleagues, that don't care about OS and just run Windows (the majority of people). You can use Office within Linux but I find it much easier to use directly from Windows (reduces the pipework and focus on the science).
3) It maybe no longer holds, but a few years ago it was really frustrating to use and connect machines running Linux on a projector. For instance let's say you have to give a talk at a conference, you want to be 100% sure that it will work out of the box and that you don't have to fiddle around to show your slides. Windows does that really well (drivers are primarily developed for it I guess) and allows you to focus on the presentation only (stressful enough). I've witnessed numerous times good science being badly communicated because of this issue, where people try to tune the resolution for 10 minutes before starting and the slides end-up being half-cropped.
In summary, dual-boots guarantees compatibility with the outside world (science research perspective), and you can use your Linux the rest of the time :-)
I wanted to reach out to you about the FDA letter that was sent to 23andMe last Friday.
It is absolutely critical that our consumers get high quality genetic data that they can trust. We have worked extensively with our lab partner to make sure that the results we return are accurate. We stand behind the data that we return to customers - but we recognize that the FDA needs to be convinced of the quality of our data as well.
23andMe has been working with the FDA to navigate the correct regulatory path for direct-to-consumer genetic tests. This is new territory, not just for 23andMe, but for the FDA as well. The FDA is an important partner for 23andMe and we will be working hard to move forward with them.
I apologize for the limited response to the questions many of you have raised regarding the letter and its implications for the service. We don't have the answers to all of those questions yet, but as we learn more we will update you.
I am committed to providing each of you with a trusted consumer product rooted in high quality data that adheres to the best scientific standards. All of us at 23andMe believe that genetic information can lead to healthier lives.
Thank you for your loyalty to 23andMe. Please refer to our 23andMe blog for updates on this process.