The process is very simple, for a condition like Long COVID, we compute the expected number of samples reporting this bacteria (based on people without Long COVID) and compare it to the actual number seen. This can be used to compute a statistical value called Chi-Square (χ²), This is then used to compute the chance of it happening at random. This is possible because we have over 3600 samples from some labs and thus able to detect things better.

Actual example:

• Tetragenococcus halophilus – Species reported by Biomesight
  • Expected to see 15
  • Actually seen 59
• In other words almost 4x more common than expected. The probability is
  • 1.68054690853052E-30
  • or 1 chance in 600,000,000,000,000,000,000,000,000,000 of happening at random.
• This suggests that we should reduce it to remedy Long COVID [with the other 92 bacteria involved]
  • Bacteria Shifts that are Statistically Significant for ME/CFS
  • Bacteria Shifts that are Statistically Significant for Long COVID

Not all symptoms have many bacteria associated

A few examples (using Biomesight data). All samples are P < 0.01 (1 in 1000)

• Myalgia (pain)
  • Mobiluncus — too high
  • Peptoniphilus asaccharolyticus — too high
  • Campylobacter ureolyticus — too high
• Headaches
  • Microbacterium — too high
  • Anaerococcus hydrogenalis — too high
  • Eubacterium limosum — too high
  • Peptoniphilus asaccharolyticus — too high
• Recurrent flu-like symptoms
  • Sphingomonas — too high
  • Chromatium — too high
  • Chromatium weissei — too high
• Excessive adrenaline
  • unclassified Bacteroidetes Order II — too low
  • Bifidobacterium adolescentis — too low. Implies that Bifidobacterium adolescentis probiotics may help
• Difficulty reading
  • Bifidobacterium indicum — too low
• Upset stomach
  • Streptococcus anginosus – too high
  • Viridiplantae (kingdom) – green plants! – too high (how this shows up in results, I will leave to Biomesight to explain)
• Tingling feeling
  • Bifidobacterium indicum – too high
  • Prevotella bivia – too low
• Need to nap during each day
  • Kushneria – too low
  • Prevotella bivia – too low
• Difficulty falling asleep
  • Alkalithermobacter thermoalcaliphilus – too low
  • Paraprevotella xylaniphila – too high
• Absent-mindedness
  • Corynebacterium aurimucosum — too low
  • Streptococcus gordonii — too low
  • Catenibacterium mitsuokai – too low
• Mood swings
  • Glaciecola – too high
• Acne
  • Mogibacterium vescum — too low
  • Listeria — too low
  • Listeria innocua — too low
  • Mogibacterium vescum — too low
• Dry Mouth
  • Prevotella bivia — too low
  • Prevotella disiens — too low
  • Clostridium malenominatum — too low

The reasons that there may be few bacteria associated may originate in symptoms being self-declared and there is a wide variety of actual shifts.