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: marked weight change _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  loperamide hydrochloride,(prescription) 0.549
๐Ÿ•ฎ  risperidone,(prescription) 0.545
๐Ÿ•ฎ  Hesperidin (polyphenol) 0.535  ๐Ÿ“
๐Ÿ•ฎ  hyoscyamine (l),(prescription) 0.531
๐Ÿ•ฎ  neomycin (antibiotic)s[CFS] 0.521
Vitamin B9,folic acid 0.519  ๐Ÿ“
๐Ÿ•ฎ  itraconazole,(prescription) 0.51
๐Ÿ•ฎ  sulfamethoxazole (antibiotic) 0.51
๐Ÿ•ฎ  spectinomycin dihydrochloride (antibiotic) 0.494
๐Ÿ•ฎ  naproxen,(prescription) 0.494
๐Ÿ•ฎ  gentamicin (antibiotic)s 0.49
๐Ÿ•ฎ  N-Acetyl Cysteine (NAC), 0.49  ๐Ÿ“
๐Ÿ•ฎ  ibuprofen 0.478
๐Ÿ•ฎ  alverine citrate salt,(prescription) 0.468
๐Ÿ•ฎ  phentermine hydrochloride,(prescription) 0.454
raclopride non-drug 0.454
๐Ÿ•ฎ  rimexolone,(prescription) 0.454
๐Ÿ•ฎ  prednisolone,(prescription) 0.454
๐Ÿ•ฎ  benzydamine hydrochloride,(prescription) 0.454
๐Ÿ•ฎ  argatroban,(prescription) 0.454
๐Ÿ•ฎ  dimenhydrinate,(prescription) 0.454
๐Ÿ•ฎ  lisinopril,(prescription) 0.454
pentetic acid non-drug 0.454
๐Ÿ•ฎ  procainamide hydrochloride,(prescription) 0.454
alcuronium chloride,(prescription) 0.454
gallamine triethiodide,(prescription) 0.454
๐Ÿ•ฎ  phenformin hydrochloride,(prescription) 0.454
๐Ÿ•ฎ  acemetacin,(prescription) 0.454
pempidine tartrate,(prescription) 0.454
diphemanil methylsulfate,(prescription) 0.454
๐Ÿ•ฎ  (-) -levobunolol hydrochloride,(prescription) 0.454
๐Ÿ•ฎ  prazosin hydrochloride,(prescription) 0.454
thip hydrochloride non-drug 0.454
etretinate,(prescription) 0.454
๐Ÿ•ฎ  isotretinoin,(prescription) 0.454
atracurium besylate,(prescription) 0.454
๐Ÿ•ฎ  iodixanol,(prescription) 0.454
urosiol,(prescription) 0.454
ioxaglic acid,(prescription) 0.454
๐Ÿ•ฎ  acitretin,(prescription) 0.454
๐Ÿ•ฎ  oxandrolone,(prescription) 0.454
fipexide hydrochloride,(prescription) 0.454
๐Ÿ•ฎ  glimepiride,(prescription) 0.454
๐Ÿ•ฎ  hydralazine hydrochloride,(prescription) 0.454
๐Ÿ•ฎ  domperidone,(prescription) 0.454
๐Ÿ•ฎ  ethosuximide,(prescription) 0.454
๐Ÿ•ฎ  suprofen,(prescription) 0.454
butacaine,(prescription) 0.454
antipyrine; 4-hydroxy non-drug 0.454
๐Ÿ•ฎ  budesonide,(prescription) 0.454
๐Ÿ•ฎ  flunisolide,(prescription) 0.454
(s)-(-)-atenolol,(prescription) 0.454
imidurea non-drug 0.454
๐Ÿ•ฎ  adenosine 5`-monophosphate monohydrate non-drug 0.454
๐Ÿ•ฎ  phthalylsulfathiazole (antibiotic) 0.454
๐Ÿ•ฎ  benzocaine,(prescription) 0.454
denatonium benzoate non-drug 0.454
nitrocaramiphen hydrochloride non-drug 0.454
mebhydroline 1;5-naphtalenedisulfonate,(prescription) 0.454
levalbuterol hydrochloride,(prescription) 0.454

To Remove or Decrease

Modifier Confidence ๐Ÿ“น
๐Ÿ•ฎ  inulin (prebiotic) 1
arabinogalactan (prebiotic) 0.744
Slippery Elm 0.665
resistant starch 0.602
red wine 0.476
๐Ÿ•ฎ  Human milk oligosaccharides (prebiotic, Holigos, Stachyose) 0.425
barley 0.398
๐Ÿ•ฎ  Burdock Root 0.353
walnuts 0.35
wheat bran 0.336
proton-pump inhibitors (prescription) 0.321
๐Ÿ•ฎ  Reduce choline (Beef, Chicken Eggs) 0.311
๐Ÿ•ฎ  Pulses 0.303
๐Ÿ•ฎ  iron 0.298
barley,oat 0.297
apple 0.294
๐Ÿ•ฎ  lactobacillus acidophilus (probiotics) 0.293
lupin seeds (anaphylaxis risk, toxic if not prepared properly) 0.293
๐Ÿ•ฎ  fructo-oligosaccharides (prebiotic) 0.281
PreforPro 0.268
resistant maltodextrin 0.266
๐Ÿ•ฎ  pectin 0.265
plantago asiatica l. 0.265
bacillus subtilis (probiotics) 0.265
raffinose(sugar beet) 0.258
๐Ÿ•ฎ  resveratrol (grape seed/polyphenols/red wine) 0.253
๐Ÿ•ฎ  noni 0.252
๐Ÿ•ฎ  lactobacillus reuteri (probiotics) 0.251
๐Ÿ•ฎ  lactobacillus rhamnosus gg (probiotics) 0.24
dietary fiber 0.238
๐Ÿ•ฎ  cruciferous vegetables (broccoli cabbage) 0.223
๐Ÿ•ฎ  black raspberries 0.223
genistein 0.223
pomegranate 0.221
almonds/ almond skins 0.215
l-proline 0.209
Lactobacillus Johnsonii (probiotic) 0.208
navy bean 0.208
dietary phytoestrogens (isoflavones) 0.2
high fiber diet 0.2
levan 0.197
germinated barley food-stuff 0.196
bacillus,lactobacillus,streptococcus,saccharomyces probiotic 0.196
๐Ÿ•ฎ  ß-glucan 0.195
๐Ÿ•ฎ  saccharomyces boulardii (probiotics) 0.195
soy 0.192
pea (fiber, protein) 0.191
triphala 0.189
๐Ÿ•ฎ  lactobacillus plantarum (probiotics) 0.186
amphotericin 0.184
๐Ÿ•ฎ  galacto-oligosaccharides (prebiotic) 0.18
fish oil 0.173
jerusalem artichoke (prebiotic) 0.17
๐Ÿ•ฎ  berberine 0.169
Conjugated Linoleic Acid 0.167
refined wheat breads 0.164
schisandra chinensis(magnolia berry or five-flavor-fruit) 0.163
๐Ÿ•ฎ  oligosaccharides (prebiotic) 0.162
๐Ÿ•ฎ  galactose (milk sugar) 0.161
green-lipped mussel 0.16
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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