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: Neuroendocrine Manifestations: Painful menstrual periods_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  gentamicin (antibiotic)s 1
๐Ÿ•ฎ  piperacillin-tazobactam (antibiotic)s 0.816
๐Ÿ•ฎ  vancomycin (antibiotic)[CFS] 0.794
๐Ÿ•ฎ  benzylpenicillin sodium (antibiotic) 0.69
๐Ÿ•ฎ  amoxicillin (antibiotic)s[CFS] 0.689
๐Ÿ•ฎ  imipenem (antibiotic)s 0.656
๐Ÿ•ฎ  thyme (thymol, thyme oil) 0.601
๐Ÿ•ฎ  hyoscyamine (l),(prescription) 0.571
cinnamon (oil. spice) 0.546  ๐Ÿ“
๐Ÿ•ฎ  tobramycin (antibiotic)s 0.521
๐Ÿ•ฎ  meropenem (antibiotic)s 0.518
๐Ÿ•ฎ  risperidone,(prescription) 0.495
๐Ÿ•ฎ  ciprofloxacin (antibiotic)s[CFS] 0.491
neem 0.481  ๐Ÿ“
๐Ÿ•ฎ  Hesperidin (polyphenol) 0.479  ๐Ÿ“
๐Ÿ•ฎ  N-Acetyl Cysteine (NAC), 0.474  ๐Ÿ“
๐Ÿ•ฎ  acarbose,(prescription) 0.433
Caffeine 0.427
syzygium aromaticum (clove) 0.423
๐Ÿ•ฎ  naproxen,(prescription) 0.414
๐Ÿ•ฎ  ampicillin (antibiotic)s[CFS] 0.412
๐Ÿ•ฎ  sulfamethoxazole (antibiotic) 0.412
๐Ÿ•ฎ  ibuprofen 0.409
๐Ÿ•ฎ  alverine citrate salt,(prescription) 0.406
๐Ÿ•ฎ  reboxetine mesylate,(prescription) 0.405
๐Ÿ•ฎ  melatonin supplement 0.399  ๐Ÿ“
๐Ÿ•ฎ  itraconazole,(prescription) 0.395
๐Ÿ•ฎ  streptomycin (antibiotic)s 0.392
fluoroquinolone (antibiotic)s 0.391
๐Ÿ•ฎ  spectinomycin dihydrochloride (antibiotic) 0.39
๐Ÿ•ฎ  garlic (allium sativum) 0.39  ๐Ÿ“
๐Ÿ•ฎ  loperamide hydrochloride,(prescription) 0.387
๐Ÿ•ฎ  minocycline (antibiotic)s[CFS] 0.385
๐Ÿ•ฎ  fenbendazole,(prescription) 0.382
Vitamin B9,folic acid 0.381  ๐Ÿ“
๐Ÿ•ฎ  trimethoprim (antibiotic)s 0.38
vitamin B3,niacin 0.38  ๐Ÿ“
๐Ÿ•ฎ  estradiol valerate,(prescription) 0.374
๐Ÿ•ฎ  ketoconazole,(prescription) 0.372
๐Ÿ•ฎ  telmisartan,(prescription) 0.37
thioguanosine,(prescription) 0.37
๐Ÿ•ฎ  dorzolamide hydrochloride,(prescription) 0.37
๐Ÿ•ฎ  procaine hydrochloride,(prescription) 0.37
amidopyrine,(prescription) 0.37
l(-)-vesamicol hydrochloride non-drug 0.37
๐Ÿ•ฎ  flavoxate hydrochloride,(prescription) 0.37
gabazine bromide non-drug 0.37
r(-) apomorphine hydrochloride hemihydrate,(prescription) 0.37
mesalamine,(prescription) 0.37
dexfenfluramine hydrochloride,(prescription) 0.37
๐Ÿ•ฎ  danazol,(prescription) 0.37
๐Ÿ•ฎ  flurbiprofen,(prescription) 0.37
niflumic acid,(prescription) 0.37
๐Ÿ•ฎ  ticlopidine hydrochloride,(prescription) 0.37
๐Ÿ•ฎ  khellin non-drug 0.37
๐Ÿ•ฎ  imatinib,(prescription) 0.37
n-acetyl-dl-homocysteine thiolactone,(prescription) 0.37
amiprilose hydrochloride non-drug 0.37
๐Ÿ•ฎ  pentoxifylline,(prescription) 0.37
๐Ÿ•ฎ  cytarabine,(prescription) 0.37

To Remove or Decrease

Modifier Confidence ๐Ÿ“น
๐Ÿ•ฎ  inulin (prebiotic) 0.908
arabinogalactan (prebiotic) 0.617
Slippery Elm 0.614
๐Ÿ•ฎ  lactulose 0.514
apple 0.456
๐Ÿ•ฎ  Human milk oligosaccharides (prebiotic, Holigos, Stachyose) 0.419
๐Ÿ•ฎ  fructo-oligosaccharides (prebiotic) 0.406
wheat bran 0.398
proton-pump inhibitors (prescription) 0.392
raffinose(sugar beet) 0.366
red wine 0.336
resistant starch 0.329
๐Ÿ•ฎ  Pulses 0.323
pea (fiber, protein) 0.281
๐Ÿ•ฎ  berberine 0.277
xylan (prebiotic) 0.269
bacillus subtilis (probiotics) 0.269
๐Ÿ•ฎ  saccharomyces boulardii (probiotics) 0.263
๐Ÿ•ฎ  resveratrol (grape seed/polyphenols/red wine) 0.263
๐Ÿ•ฎ  pectin 0.244
vsl#3 (probiotics) 0.242
macrolide ((antibiotic)s) 0.241
mediterranean diet 0.236
fasting 0.235
barley,oat 0.229
๐Ÿ•ฎ  lactobacillus plantarum (probiotics) 0.224
barley 0.218
fat 0.213
๐Ÿ•ฎ  galacto-oligosaccharides (prebiotic) 0.213
schisandra chinensis(magnolia berry or five-flavor-fruit) 0.211
Lactobacillus Johnsonii (probiotic) 0.207
gynostemma pentaphyllum (Jiaogulan) 0.204
non-starch polysaccharides 0.198
walnuts 0.195
high red meat 0.195
l-citrulline 0.19
almonds/ almond skins 0.189
fish oil 0.185
ketogenic diet 0.179
๐Ÿ•ฎ  oligosaccharides (prebiotic) 0.174
Conjugated Linoleic Acid 0.172
lupin seeds (anaphylaxis risk, toxic if not prepared properly) 0.171
๐Ÿ•ฎ  bifidobacterium lactis bb12 (probiotics) 0.167
resistant maltodextrin 0.167
๐Ÿ•ฎ  zinc 0.165
palm kernel meal 0.16
jerusalem artichoke (prebiotic) 0.158
plantago asiatica l. 0.153
Alpha-Ketoglutarate 0.153
๐Ÿ•ฎ  galactose (milk sugar) 0.152
๐Ÿ•ฎ  lactobacillus reuteri (probiotics) 0.15
bile (acid/salts) 0.15
laminaria digitata,oarweed - seaweed 0.141
cholic acid (bile acid) 0.14
sesame cake/meal 0.137
low-fat diets 0.136
blackcurrant 0.135
๐Ÿ•ฎ  gum arabic (prebiotic) 0.134
clostridium butyricum (probiotics),Miya,Miyarisan 0.132
lincosamide (antibiotic)s 0.131
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.
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