This is a brief post that draws on the analytical approach from the methodology used in Mast Cell Activation Syndrome and Multiple Chemical Sensitivity. Atypically, we are able to determine which probiotics are likely better than others. Rather than delve into the technical details—which can overwhelm those experiencing brain fog—I’m going straight to the results for this set of symptoms:

• ME/CFS – not specific
• ME/CFS – with IBS
• ME/CFS – without IBS
• IBS
• Long COVID
• IBD
• Crohn’s Disease

We are filtering to P < 0.0001 (ZScore of +/-3.72). We are also restricting to strictly safe, that is no predicted inappropriate shifts to keep the list shorted and easier to handle for the brain fogged.

The Good Count below are the number of bacterium that it has the desired effect upon. The Good value is an estimate of the amount of influence.

• The Good Count is the number of bacteria that are likely to shift in a positive direction direction.

• Good is an scaled aggregation of the R2 values for these bacteria. One bactieria may have a R2 and slope of (.9 and 1.5) another of (.2 and 5). The result is .9 * 1.5 + .2 * 5 =2.35.

ME/CFS (General)

Name	Good	Good Count
Clostridium beijerinckii	136	9
Niallia circulans	79	5

This is not unexpected because the vagueness of description results in loss of clarity.

Condition: ME/CFS with IBS

Name	Good	Good Count
Bifidobacterium adolescentis	1678	61
Bifidobacterium catenulatum	1108	40
Bifidobacterium bifidum	750	31
Clostridium beijerinckii	136	9
Niallia circulans	79	5

Condition: ME/CFS without IBS

Name	Good	Good Count
Lactococcus lactis	765	35
Clostridium beijerinckii	136	9
Niallia circulans	79	5

Irritable Bowel Syndrome

Name	Good	Good Count
Christensenella minuta	2796	98
Enterococcus faecium	2334	93
Anaerobutyricum hallii	2317	93
Lactococcus cremoris	1909	77
Bifidobacterium adolescentis	1678	61
Blautia wexlerae	1645	71
Bifidobacterium catenulatum	1108	40
Lactococcus lactis	765	35
Bifidobacterium bifidum	750	31
Clostridium beijerinckii	136	9
Niallia circulans	79	5

Long COVID

Name	Good	Good Count
Christensenella minuta	2796	98
Enterococcus faecium	2334	93
Anaerobutyricum hallii	2317	93
Lactococcus cremoris	1909	77
Bifidobacterium adolescentis	1678	61
Blautia wexlerae	1645	71
Bifidobacterium catenulatum	1108	40
Lactococcus lactis	765	35
Bifidobacterium bifidum	750	31
Clostridium beijerinckii	136	9
Niallia circulans	79	5

Inflammatory Bowel Disease (IBD)

Name	Good	Good Count
Enterococcus faecium	2334	93
Lactococcus cremoris	1909	77
Bifidobacterium adolescentis	1678	61
Bifidobacterium catenulatum	1108	40
Lactococcus lactis	765	35
Bifidobacterium bifidum	750	31
Clostridium beijerinckii	136	9
Niallia circulans	79	5

Crohn’s Disease

Name	Good	Good Count
Christensenella minuta	2796	98
Enterococcus faecium	2334	93
Anaerobutyricum hallii	2317	93
Lactococcus cremoris	1909	77
Bifidobacterium adolescentis	1678	61
Blautia wexlerae	1645	71
Bifidobacterium catenulatum	1108	40
Lactococcus lactis	765	35
Bifidobacterium bifidum	750	31
Clostridium beijerinckii	136	9
Niallia circulans	79	5

Summary

This feature will not be added to the web site because the computations has taken hours to run and require a large amount of memory (most of the 32 GB available). In general, too low amounts dominated as the most significant pattern. It is not killing off high bacteria but encouraging low bacteria that seems to apply for these conditions.

It is interesting to note that Lactobacillus never appears. You may notice that some conditions are very similar which is not unexpected to me. There are commonality of low bacteria across conditions.

Note: ME/CFS With IBS suggestions include ME/CFS and IBS suggestions. IBS and IBD have some similarity but are different. Crohn’s disease seems more likely to be a progression of IBS and not IBD. The data model may be useful for seeing likely disease progression paths.