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: Immune Manifestations: Inflammation (General)_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  neomycin (antibiotic)s[CFS] 0.423
๐Ÿ•ฎ  Hesperidin (polyphenol) 0.35  ๐Ÿ“
๐Ÿ•ฎ  spectinomycin dihydrochloride (antibiotic) 0.329
๐Ÿ•ฎ  naproxen,(prescription) 0.285
๐Ÿ•ฎ  cefaclor hydrate (antibiotic) 0.271
๐Ÿ•ฎ  hyoscyamine (l),(prescription) 0.267
๐Ÿ•ฎ  reboxetine mesylate,(prescription) 0.265
๐Ÿ•ฎ  ibuprofen 0.263
๐Ÿ•ฎ  carbamazepine,(prescription) 0.262
๐Ÿ•ฎ  loperamide hydrochloride,(prescription) 0.259
๐Ÿ•ฎ  sulfamethoxazole (antibiotic) 0.257
๐Ÿ•ฎ  fenbendazole,(prescription) 0.257
๐Ÿ•ฎ  N-Acetyl Cysteine (NAC), 0.254  ๐Ÿ“
๐Ÿ•ฎ  estradiol valerate,(prescription) 0.252
๐Ÿ•ฎ  guanabenz acetate,(prescription) 0.25
๐Ÿ•ฎ  famciclovir,(prescription) 0.25
๐Ÿ•ฎ  ketoprofen,(prescription) 0.25
pinacidil,(prescription) 0.25
๐Ÿ•ฎ  colchicine,(prescription) 0.25
estradiol-17 beta,(prescription) 0.25
๐Ÿ•ฎ  iohexol,(prescription) 0.25
๐Ÿ•ฎ  sulfisoxazole (antibiotic) 0.25
๐Ÿ•ฎ  dexamethasone acetate,(prescription) 0.25
๐Ÿ•ฎ  gliclazide,(prescription) 0.25
methimazole,(prescription) 0.25
๐Ÿ•ฎ  aceclofenac,(prescription) 0.25
๐Ÿ•ฎ  sulindac,(prescription) 0.25
cycloheximide non-drug 0.25
๐Ÿ•ฎ  trazodone hydrochloride,(prescription) 0.25
๐Ÿ•ฎ  chlorambucil,(prescription) 0.25
picotamide monohydrate,(prescription) 0.25
androsterone,(prescription) 0.25
๐Ÿ•ฎ  doxazosin mesylate,(prescription) 0.25
pargyline hydrochloride,(prescription) 0.25
glycopyrrolate,(prescription) 0.25
๐Ÿ•ฎ  nizatidine,(prescription) 0.25
๐Ÿ•ฎ  triamcinolone,(prescription) 0.25
indoprofen,(prescription) 0.25
promazine hydrochloride,(prescription) 0.25
๐Ÿ•ฎ  fluorometholone,(prescription) 0.25
benztropine mesylate,(prescription) 0.25
๐Ÿ•ฎ  xylazine,(prescription) 0.25
indatraline hydrochloride non-drug 0.25
piromidic acid (antibiotic) 0.25
levonordefrin,(prescription) 0.25
(s)-(-)-cycloserine (antibiotic) 0.25
๐Ÿ•ฎ  gliquidone,(prescription) 0.25
๐Ÿ•ฎ  labetalol hydrochloride,(prescription) 0.25
digitoxigenin,(prescription) 0.25
๐Ÿ•ฎ  fenoprofen calcium salt dihydrate,(prescription) 0.25
clofibric acid non-drug 0.25
pronethalol hydrochloride non-drug 0.25
xamoterol hemifumarate,(prescription) 0.25
๐Ÿ•ฎ  efavirenz,(prescription) 0.25
hycanthone,(prescription) 0.25
๐Ÿ•ฎ  aripiprazole,(prescription) 0.25
(+)-isoproterenol (+)-bitartrate salt,(prescription) 0.25
proparacaine hydrochloride,(prescription) 0.25
๐Ÿ•ฎ  methenamine (antibiotic) 0.25
acefylline,(prescription) 0.25

To Remove or Decrease

Modifier Confidence ๐Ÿ“น
๐Ÿ•ฎ  inulin (prebiotic) 1
๐Ÿ•ฎ  Human milk oligosaccharides (prebiotic, Holigos, Stachyose) 0.801
๐Ÿ•ฎ  fructo-oligosaccharides (prebiotic) 0.785
arabinogalactan (prebiotic) 0.702
soy 0.683
resistant starch 0.597
๐Ÿ•ฎ  resveratrol (grape seed/polyphenols/red wine) 0.463
๐Ÿ•ฎ  lactobacillus acidophilus (probiotics) 0.449
๐Ÿ•ฎ  lactulose 0.446
bacillus subtilis (probiotics) 0.444
raffinose(sugar beet) 0.422
๐Ÿ•ฎ  lactobacillus plantarum (probiotics) 0.42
wheat bran 0.415
jerusalem artichoke (prebiotic) 0.392
๐Ÿ•ฎ  Glucomannan 0.367
๐Ÿ•ฎ  galacto-oligosaccharides (prebiotic) 0.365
apple 0.348
almonds/ almond skins 0.34
sesame cake/meal 0.326
๐Ÿ•ฎ  Burdock Root 0.322
barley,oat 0.307
high fiber diet 0.296
๐Ÿ•ฎ  gum arabic (prebiotic) 0.294
green tea 0.28
wheat 0.279
clostridium butyricum (probiotics),Miya,Miyarisan 0.279
red wine 0.278
proton-pump inhibitors (prescription) 0.274
blueberry 0.259
๐Ÿ•ฎ  pectin 0.258
๐Ÿ•ฎ  lactobacillus plantarum,xylooligosaccharides,(prebiotic) (probiotics) 0.25
mediterranean diet 0.25
๐Ÿ•ฎ  lactobacillus paracasei (probiotics) 0.246
Conjugated Linoleic Acid 0.246
๐Ÿ•ฎ  Cacao 0.243
chondrus crispus,red sea weed 0.236
whey 0.228
magnesium 0.226
๐Ÿ•ฎ  noni 0.223
barley 0.22
l-proline 0.218
๐Ÿ•ฎ  zinc 0.217
๐Ÿ•ฎ  rifaximin (antibiotic)s 0.214
ketogenic diet 0.211
daesiho-tang 0.208
rosmarinus officinalis,rosemary 0.208
๐Ÿ•ฎ  oligosaccharides (prebiotic) 0.202
๐Ÿ•ฎ  bifidobacterium lactis bb12 (probiotics) 0.196
Slippery Elm 0.196
fish oil 0.195
pea (fiber, protein) 0.194
plantago asiatica l. 0.193
oats 0.191
quercetin 0.19
navy bean 0.184
lupin seeds (anaphylaxis risk, toxic if not prepared properly) 0.178
fasting 0.177
๐Ÿ•ฎ  enterococcus faecium (probiotic) 0.175
๐Ÿ•ฎ  lactobacillus reuteri (probiotics) 0.174
partially hydrolysed guar gum,fructo-oligosaccharides (prebiotic) 0.173
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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