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: Sleep: Waking up early in the morning (e.g. 3 AM)_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) 0.52  ๐Ÿ“
๐Ÿ•ฎ  neomycin (antibiotic)s[CFS] 0.505
๐Ÿ•ฎ  amoxicillin (antibiotic)s[CFS] 0.486
๐Ÿ•ฎ  N-Acetyl Cysteine (NAC), 0.463  ๐Ÿ“
๐Ÿ•ฎ  metronidazole (antibiotic)s[CFS] 0.438
๐Ÿ•ฎ  loperamide hydrochloride,(prescription) 0.436
Caffeine 0.426
๐Ÿ•ฎ  spectinomycin dihydrochloride (antibiotic) 0.42
๐Ÿ•ฎ  itraconazole,(prescription) 0.418
๐Ÿ•ฎ  sulfamethoxazole (antibiotic) 0.418
๐Ÿ•ฎ  naproxen,(prescription) 0.411
๐Ÿ•ฎ  ibuprofen 0.411
๐Ÿ•ฎ  carbamazepine,(prescription) 0.405
๐Ÿ•ฎ  risperidone,(prescription) 0.398
sucralose 0.396  ๐Ÿ“
๐Ÿ•ฎ  ketoconazole,(prescription) 0.395
๐Ÿ•ฎ  Vitamin B1,thiamine hydrochloride 0.395  ๐Ÿ“
๐Ÿ•ฎ  streptomycin (antibiotic)s 0.379
prednisone,(prescription) 0.377
๐Ÿ•ฎ  Vitamin B-12 0.377  ๐Ÿ“
๐Ÿ•ฎ  ofloxacin (antibiotic)s 0.376
๐Ÿ•ฎ  levocabastine hydrochloride,(prescription) 0.373
๐Ÿ•ฎ  repaglinide,(prescription) 0.373
clocortolone pivalate,(prescription) 0.373
๐Ÿ•ฎ  sildenafil,(prescription) 0.373
๐Ÿ•ฎ  oxybutynin chloride,(prescription) 0.373
๐Ÿ•ฎ  sotalol hydrochloride,(prescription) 0.373
๐Ÿ•ฎ  sulfaquinoxaline sodium salt,(prescription) 0.373
๐Ÿ•ฎ  sulfadimethoxine (antibiotic) 0.373
propoxycaine hydrochloride,(prescription) 0.373
๐Ÿ•ฎ  aniracetam,(prescription) 0.373
lanatoside c,(prescription) 0.373
๐Ÿ•ฎ  scopolamine hydrochloride,(prescription) 0.373
methylatropine nitrate,(prescription) 0.373
do 897/99 non-drug 0.373
๐Ÿ•ฎ  proscillaridin a,(prescription) 0.373
diperodon hydrochloride non-drug 0.373
๐Ÿ•ฎ  chlormadinone acetate,(prescription) 0.373
๐Ÿ•ฎ  oxaprozin,(prescription) 0.373
๐Ÿ•ฎ  indapamide,(prescription) 0.373
๐Ÿ•ฎ  deferoxamine mesylate,(prescription) 0.373
azacyclonol,(prescription) 0.373
๐Ÿ•ฎ  clobetasol propionate,(prescription) 0.373
๐Ÿ•ฎ  balsalazide sodium,(prescription) 0.373
๐Ÿ•ฎ  imipramine hydrochloride,(prescription) 0.373
levopropoxyphene napsylate non-drug 0.373
๐Ÿ•ฎ  cyclopenthiazide,(prescription) 0.373
๐Ÿ•ฎ  sulfaphenazole (antibiotic) 0.373
(+;-)-synephrine,(prescription) 0.373
๐Ÿ•ฎ  sibutramine hcl,(prescription) 0.373
canrenoic acid potassium salt,(prescription) 0.373
๐Ÿ•ฎ  dropropizine (r;s),(prescription) 0.373
๐Ÿ•ฎ  argatroban,(prescription) 0.373
๐Ÿ•ฎ  dimenhydrinate,(prescription) 0.373
๐Ÿ•ฎ  lisinopril,(prescription) 0.373
pentetic acid non-drug 0.373
๐Ÿ•ฎ  budesonide,(prescription) 0.373
๐Ÿ•ฎ  flunisolide,(prescription) 0.373
(s)-(-)-atenolol,(prescription) 0.373
imidurea non-drug 0.373

To Remove or Decrease

Modifier Confidence ๐Ÿ“น
๐Ÿ•ฎ  Human milk oligosaccharides (prebiotic, Holigos, Stachyose) 1
๐Ÿ•ฎ  inulin (prebiotic) 0.995
arabinogalactan (prebiotic) 0.829
Slippery Elm 0.666
๐Ÿ•ฎ  lactobacillus plantarum (probiotics) 0.591
soy 0.549
resistant starch 0.547
๐Ÿ•ฎ  resveratrol (grape seed/polyphenols/red wine) 0.539
wheat bran 0.528
๐Ÿ•ฎ  Burdock Root 0.528
red wine 0.508
๐Ÿ•ฎ  fructo-oligosaccharides (prebiotic) 0.487
๐Ÿ•ฎ  lactobacillus acidophilus (probiotics) 0.45
๐Ÿ•ฎ  Pulses 0.408
๐Ÿ•ฎ  berberine 0.406
๐Ÿ•ฎ  lactulose 0.386
triphala 0.372
๐Ÿ•ฎ  galacto-oligosaccharides (prebiotic) 0.353
๐Ÿ•ฎ  vitamin d 0.35
almonds/ almond skins 0.345
mediterranean diet 0.343
apple 0.339
raffinose(sugar beet) 0.334
ketogenic diet 0.326
๐Ÿ•ฎ  pectin 0.312
pea (fiber, protein) 0.308
proton-pump inhibitors (prescription) 0.301
xylan (prebiotic) 0.285
๐Ÿ•ฎ  lactobacillus fermentum (probiotics) 0.284
jerusalem artichoke (prebiotic) 0.279
fasting 0.274
sesame cake/meal 0.272
plantago asiatica l. 0.271
saccharin 0.263
๐Ÿ•ฎ  oligosaccharides (prebiotic) 0.261
๐Ÿ•ฎ  saccharomyces boulardii (probiotics) 0.261
wheat 0.261
lupin seeds (anaphylaxis risk, toxic if not prepared properly) 0.252
non-starch polysaccharides 0.251
barley 0.248
๐Ÿ•ฎ  Glucomannan 0.243
resistant maltodextrin 0.242
๐Ÿ•ฎ  gum arabic (prebiotic) 0.237
Lactobacillus Johnsonii (probiotic) 0.235
Conjugated Linoleic Acid 0.231
fat 0.231
clostridium butyricum (probiotics),Miya,Miyarisan 0.228
walnuts 0.227
schisandra chinensis(magnolia berry or five-flavor-fruit) 0.226
magnesium 0.223
l-citrulline 0.222
daesiho-tang 0.22
๐Ÿ•ฎ  bifidobacterium lactis bb12 (probiotics) 0.219
laminaria digitata,oarweed - seaweed 0.214
gallic acid (food additive) 0.213
high red meat 0.208
barley,oat 0.206
bacillus subtilis (probiotics) 0.204
๐Ÿ•ฎ  lactobacillus paracasei (probiotics) 0.203
fish oil 0.2
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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Use of data on this site is prohibited except under written license. There is no charge for individual personal use. Use for any commercial applications or research requires a written license.
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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