Understanding the impact of your medicines

I just pushed out an update on http://microbiomeprescription.azurewebsites.net/ that may help you understand what various prescription, over the counter and some supplements may be doing to your microbiome.

Select any of the links highlighted below

The next page will show some choices at the top:

Compare Impact

This is intended to allow you to better choice between alternatives – for example Aspirin versus  Paracetamol (acetaminophen). I am sure people will find more uses for it.

The process is simple, search for each item, and put a check beside it. Select the Compare Impact radio button and then click the submit button below it.

This will take you to a page listing the impact side by side. In this case we seel that their impacts are similar, but different on a few items. At the family level there are a few differences

If a family that is important to you is shifted the wrong way, you may wish to consider the better one

Compensate

This is intended when you are prescribed drugs to treat some conditions and wish to reduce the impact on the microbiome by counteracting the drug or drugs impact on the microbiome.

For this example, we pick lovastatin (a statin), Famotidine (Pepcid AC).

We may wish to first see how much impact they have together (do they reinforce or counteract each other)

Bad news — they reinforce each other in decreasing many families

Just pressing back, and changing radio buttons, and submit produces suggestions.

The suggestions are done by creating a virtual microbiome report based on the above shifts and running that through our AI engine.

The suggestion page is the new format with the long lists hidden until you ask to see them.

The Take or Avoid list is defaulted to 100 items (which is one reason that I toggle visibility). Remember – none of these items are guaranteed to work, nor do you need to take all of them. Each item increases your odds

The avoid list values are a lot higher, and thus you may wish by reducing any of these items that you are taking.

Automatic Upload and Login from 3rd Party Sites

An upload from a 3rd party site may be done by posting json to http://microbiomeprescription.azurewebsites.net/api/upload

By uploading, you consent to allow your microbiome data and symptoms to be made available to citizen scientists for further discoveries.

Required consent is cited above. 3rd party is responsible to obtain consent.

Json Structure

The structure is simple:

  • The key is issued by us and identifies where the data is coming from (“source”)
  • logon and password are the authentication pair that you generate. These are used for logging on. Logon and Password should be the same for all samples from the same user (so we can display on a timeline).


“key”:”3rdpartyKey”,
“logon”:”3rdpartyId”,
“Password”:”3rdpartyPassword”,
“taxonomy”:[ 
      { 
“taxon”:2321,
“percent”:0.000304
      },
      { 
“taxon”:2841,
“percent”:0.000983
      }
   ]
}

The taxonomy uses the official taxon numbers and the percentage.

Logon

On your site, create a page that does a post to /email/logon3rd with two elements:

<form method=”post”
action=” http://microbiomeprescription.azurewebsites.net/email/logon3rd“><input type=”hidden” name=”logon” value=”whatever” />
<input type=”hidden” name=”password” value=”whatever” />
<input type=”submit” value=”Logon to MicrobiomePrescription” />
</form>

Atlas Bio Upload Notes

The report file reports only at the strain level, no genus or family levels are given. These total sums up to 100%. The smallest resolution appear to be 0.02% That is 1 in 5,000 bacteria. This is a lot lower resolution than other providers ( 1 in 160,000 is seem in some other reports with a good sample). There is something odd about a large number of bacteria being at 0.02 or 0.04 percent.

While different strains are identified, the naming is not matching the official NCBI name.

It appears that FASTQ downloads from them (alleged to be available if requested) is the prefered way to get better data.

One bacteria was listed as:”(Bifidobacterium catenulatum/Bifidobacterium gallicum/Bifidobacterium kashiwanohense/Bifidobacterium pseudocatenulatum)”

which is with more current tests are 4 different strains.

Bottom Line: Won’t Do

There are too many problems with the data. I have spent almost an entire day fighting it. If they provide a FASTQ file, I have unload for those processed through SequentiaBiotech web site.

To use their CSVs:

  • They must provide the official Taxon Numbers in the Excel File
  • They must provide the full hierarchy with numbers at each level

Without those, their data will pollute the existing contributed base too much. There are no acceptable kludge arounds for these defects.