Why some suggestions may contradict

RECOMMENDED READING Caveats on using the contents of this page. ๐Ÿ‘จโ€โš•๏ธ

If you need help with this information, here is a list of consultants ๐Ÿ‘จโ€โš•๏ธ๐Ÿ‘ฉโ€โš•๏ธ that are available.

Suggestion Parameters

Sample:A Priori (from theoretical deduction)
Bacteria Selection:Outside of Range
Filter: From Special Studies V2: Physical: Work-Sitting_Drugs
Rank Used: All Ranks
Shifts Used:High and Low Levels
Citations Used:

How do we know if the suggestions are reasonable/valid?

๐Ÿฑ Food Menu Planner ๐Ÿฝ๏ธ ๐Ÿ“น How are suggestions determined


The following will shift items that are too high to lower values and values that are too low to higher values.
Items will feed or starve specific bacteria.

With antibiotics, if there is no significant response, there may be antibiotic resistance. Bacteria do share resistance genes between themselves. Consider moving on to a different one, ideally a different family.

The recommended process to obtain a persistent shift of the microbiome is:
 Generate 4 lists from the suggestions with nothing repeated on another list
  Emphasize one list each week
  After 8 weeks (2 cycles), retest the microbiome to obtains the next set of course corrections
This approach allows the microbiome to stablize towards normal.

To Add or Increase Intake

Modifier (Alt Names on Hover) Confidence ๐Ÿ“น
๐Ÿ•ฎ  Hesperidin (polyphenol) 0.585  ๐Ÿ“
๐Ÿ•ฎ  amoxicillin (antibiotic)s[CFS] 0.567
๐Ÿ•ฎ  gentamicin (antibiotic)s 0.542
๐Ÿ•ฎ  neomycin (antibiotic)s[CFS] 0.524
๐Ÿ•ฎ  ofloxacin (antibiotic)s 0.501
๐Ÿ•ฎ  acarbose,(prescription) 0.493
Caffeine 0.488
๐Ÿ•ฎ  imipenem (antibiotic)s 0.485
๐Ÿ•ฎ  sulfamethoxazole (antibiotic) 0.482
๐Ÿ•ฎ  spectinomycin dihydrochloride (antibiotic) 0.474
๐Ÿ•ฎ  metronidazole (antibiotic)s[CFS] 0.466
๐Ÿ•ฎ  risperidone,(prescription) 0.464
๐Ÿ•ฎ  atorvastatin (prescription) 0.459  ๐Ÿ“
๐Ÿ•ฎ  carbamazepine,(prescription) 0.458
๐Ÿ•ฎ  Vitamin B1,thiamine hydrochloride 0.45  ๐Ÿ“
๐Ÿ•ฎ  N-Acetyl Cysteine (NAC), 0.448  ๐Ÿ“
๐Ÿ•ฎ  naproxen,(prescription) 0.447
๐Ÿ•ฎ  dopamine (prescription) 0.442
๐Ÿ•ฎ  trimethoprim (antibiotic)s 0.44
๐Ÿ•ฎ  streptomycin (antibiotic)s 0.436
๐Ÿ•ฎ  itraconazole,(prescription) 0.428
๐Ÿ•ฎ  alprenolol hydrochloride,(prescription) 0.424
๐Ÿ•ฎ  gabexate mesilate non-drug 0.424
๐Ÿ•ฎ  zileuton,(prescription) 0.424
๐Ÿ•ฎ  dacarbazine,(prescription) 0.424
๐Ÿ•ฎ  levodopa,(prescription) 0.424
๐Ÿ•ฎ  ranolazine,(prescription) 0.424
diphenidol hydrochloride,(prescription) 0.424
tetrahydrozoline hydrochloride,(prescription) 0.424
๐Ÿ•ฎ  halofantrine hydrochloride,(prescription) 0.424
๐Ÿ•ฎ  morantel tartrate,(prescription) 0.424
๐Ÿ•ฎ  telmisartan,(prescription) 0.424
๐Ÿ•ฎ  ketorolac tromethamine,(prescription) 0.424
๐Ÿ•ฎ  diloxanide furoate,(prescription) 0.424
๐Ÿ•ฎ  aminophylline,(prescription) 0.424
sulfamethazine sodium salt (antibiotic) 0.424
๐Ÿ•ฎ  nicergoline,(prescription) 0.424
2-chloropyrazine non-drug 0.424
gbr 12909 dihydrochloride,(prescription) 0.424
cortisol acetate,(prescription) 0.424
๐Ÿ•ฎ  benperidol,(prescription) 0.424
oxfendazol,(prescription) 0.424
๐Ÿ•ฎ  pemirolast potassium,(prescription) 0.424
๐Ÿ•ฎ  naloxone hydrochloride,(prescription) 0.424
๐Ÿ•ฎ  homatropine hydrobromide (r;s),(prescription) 0.424
๐Ÿ•ฎ  nicorandil,(prescription) 0.424
๐Ÿ•ฎ  oxcarbazepine,(prescription) 0.424
๐Ÿ•ฎ  chlorothiazide,(prescription) 0.424
tremorine dihydrochloride non-drug 0.424
trimeprazine tartrate,(prescription) 0.424
๐Ÿ•ฎ  metergoline,(prescription) 0.424
pancuronium bromide,(prescription) 0.424
๐Ÿ•ฎ  guanfacine hydrochloride,(prescription) 0.424
etidronic acid; disodium salt,(prescription) 0.424
๐Ÿ•ฎ  etofenamate,(prescription) 0.424
๐Ÿ•ฎ  reserpine,(prescription) 0.424
๐Ÿ•ฎ  losartan,(prescription) 0.424
methapyrilene hydrochloride,(prescription) 0.424
๐Ÿ•ฎ  alfaxalone,(prescription) 0.424
tiaprofenic acid,(prescription) 0.424

To Remove or Decrease

Modifier Confidence ๐Ÿ“น
๐Ÿ•ฎ  inulin (prebiotic) 1
๐Ÿ•ฎ  lactobacillus plantarum (probiotics) 0.786
red wine 0.755
๐Ÿ•ฎ  berberine 0.754
๐Ÿ•ฎ  Human milk oligosaccharides (prebiotic, Holigos, Stachyose) 0.753
arabinogalactan (prebiotic) 0.739
resistant starch 0.669
Slippery Elm 0.628
๐Ÿ•ฎ  oligosaccharides (prebiotic) 0.534
๐Ÿ•ฎ  Pulses 0.524
๐Ÿ•ฎ  vitamin d 0.498
๐Ÿ•ฎ  lactobacillus acidophilus (probiotics) 0.483
Conjugated Linoleic Acid 0.47
soy 0.403
non-starch polysaccharides 0.385
xylan (prebiotic) 0.382
resistant maltodextrin 0.368
wheat bran 0.367
๐Ÿ•ฎ  Burdock Root 0.358
saccharin 0.356
fish oil 0.355
๐Ÿ•ฎ  fructo-oligosaccharides (prebiotic) 0.355
triphala 0.345
๐Ÿ•ฎ  saccharomyces boulardii (probiotics) 0.343
mediterranean diet 0.331
levan 0.331
๐Ÿ•ฎ  pectin 0.325
apple 0.316
๐Ÿ•ฎ  zinc 0.316
l-citrulline 0.31
๐Ÿ•ฎ  resveratrol (grape seed/polyphenols/red wine) 0.308
๐Ÿ•ฎ  galacto-oligosaccharides (prebiotic) 0.299
๐Ÿ•ฎ  Astragalus polysaccharide 0.296
๐Ÿ•ฎ  lactulose 0.281
barley 0.277
raffinose(sugar beet) 0.277
ketogenic diet 0.275
lupin seeds (anaphylaxis risk, toxic if not prepared properly) 0.274
schisandra chinensis(magnolia berry or five-flavor-fruit) 0.266
high red meat 0.263
gynostemma pentaphyllum (Jiaogulan) 0.261
Lactobacillus Johnsonii (probiotic) 0.253
๐Ÿ•ฎ  bifidobacterium adolescentis,(probiotics) 0.252
๐Ÿ•ฎ  mulberry fruit polysaccharide 0.246
fasting 0.246
fat 0.244
gallic acid (food additive) 0.232
proton-pump inhibitors (prescription) 0.231
stevia 0.225
๐Ÿ•ฎ  rifaximin (antibiotic)s 0.22
๐Ÿ•ฎ  wasabi 0.217
whole grain diet 0.215
bacillus amyloliquefaciens (probiotic) 0.213
๐Ÿ•ฎ  grapes 0.213
almonds/ almond skins 0.211
pea (fiber, protein) 0.207
๐Ÿ•ฎ  bifidobacterium lactis bb12 (probiotics) 0.197
clostridium butyricum (probiotics),Miya,Miyarisan 0.197
๐Ÿ•ฎ  lactobacillus fermentum (probiotics) 0.185
๐Ÿ•ฎ  Prescript Assist (2018 Formula) 0.184
NOTE: (Heparin, hyaluronan, or chondroitin sulfate) and Lactobacillus probiotics should not be taken concurrently.

All suggestions are computed solely on their predicted microbiome impact. Safety, side-effects etc must be evaluated by your medical professionals before starting. Some items suggests have significant risk of adverse consequences for some people.

Special thanks to David F Morrison and Geert Van Houcke for doing Quality Assurance. Special thanks to Oliver Luk, B.Sc. (Biology) from BiomeSight for spot checking the coding of data from the US National Library of Medicine

This is an Academic site. It generates theoretical models of what may benefit a specific microbiome results.

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Caveat emptor: Analysis and suggestions are based on modelling (and thus infererence) based on studies. The data sources are usually given for those that wish to consider alternative inferences. theories and models.
Inventions/Methodologies on this site are Patent Pending.

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