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: Autonomic: Heart rate increase after standing_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

Suggestions

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
foeniculum vulgare,fennel 0.704
๐Ÿ•ฎ  piperacillin-tazobactam (antibiotic)s 0.65
๐Ÿ•ฎ  imipenem (antibiotic)s 0.64
๐Ÿ•ฎ  benzylpenicillin sodium (antibiotic) 0.602
๐Ÿ•ฎ  amikacin (antibiotic)s 0.584
๐Ÿ•ฎ  thyme (thymol, thyme oil) 0.583
๐Ÿ•ฎ  vancomycin (antibiotic)[CFS] 0.575
๐Ÿ•ฎ  meropenem (antibiotic)s 0.542
๐Ÿ•ฎ  ciprofloxacin (antibiotic)s[CFS] 0.517
๐Ÿ•ฎ  amoxicillin (antibiotic)s[CFS] 0.505
intesti-bacteriophage 0.485
cinnamon (oil. spice) 0.478  ๐Ÿ“
fluoroquinolone (antibiotic)s 0.475
laser trilobum l.,kefe cumin 0.472
๐Ÿ•ฎ  hyoscyamine (l),(prescription) 0.454
neem 0.45  ๐Ÿ“
๐Ÿ•ฎ  ofloxacin (antibiotic)s 0.435
๐Ÿ•ฎ  hypericin, St. John's Wort 0.425
๐Ÿ•ฎ  tobramycin (antibiotic)s 0.422
๐Ÿ•ฎ  chloramphenicol (antibiotic)s 0.421
๐Ÿ•ฎ  ampicillin (antibiotic)s[CFS] 0.415
syzygium aromaticum (clove) 0.41
Curcumin 0.389  ๐Ÿ“
๐Ÿ•ฎ  neomycin (antibiotic)s[CFS] 0.389
๐Ÿ•ฎ  Hesperidin (polyphenol) 0.387  ๐Ÿ“
nigella sativa seed (black cumin) 0.386  ๐Ÿ“
๐Ÿ•ฎ  garlic (allium sativum) 0.377  ๐Ÿ“
๐Ÿ•ฎ  azithromycin,(antibiotic)s[CFS] 0.377  ๐Ÿ“
๐Ÿ•ฎ  risperidone,(prescription) 0.374
oplopanax horridus(Devil's Club) 0.369
๐Ÿ•ฎ  loperamide hydrochloride,(prescription) 0.367
๐Ÿ•ฎ  N-Acetyl Cysteine (NAC), 0.364  ๐Ÿ“
๐Ÿ•ฎ  trimethoprim (antibiotic)s 0.363
๐Ÿ•ฎ  minocycline (antibiotic)s[CFS] 0.363
๐Ÿ•ฎ  jatropha curcas [can be poisonous] 0.358
vitamin B3,niacin 0.357  ๐Ÿ“
๐Ÿ•ฎ  naproxen,(prescription) 0.341
๐Ÿ•ฎ  Vitamin B-12 0.34  ๐Ÿ“
๐Ÿ•ฎ  alverine citrate salt,(prescription) 0.339
moxalactam disodium salt (antibiotic) 0.334
๐Ÿ•ฎ  dopamine (prescription) 0.332
๐Ÿ•ฎ  melatonin supplement 0.331  ๐Ÿ“
Caffeine 0.328
๐Ÿ•ฎ  fenbendazole,(prescription) 0.323
๐Ÿ•ฎ  reboxetine mesylate,(prescription) 0.323
๐Ÿ•ฎ  spectinomycin dihydrochloride (antibiotic) 0.321
salvia officinalis (sage) 0.307
๐Ÿ•ฎ  reserpine,(prescription) 0.306
๐Ÿ•ฎ  cefotaxime sodium salt (antibiotic) 0.298
oregano (origanum vulgare, oil) | 0.298
midodrine hydrochloride,(prescription) 0.298
mepenzolate bromide,(prescription) 0.298
๐Ÿ•ฎ  pridinol methanesulfonate salt,(prescription) 0.298
๐Ÿ•ฎ  famotidine,(prescription) 0.298
๐Ÿ•ฎ  piperidolate hydrochloride,(prescription) 0.298
pentolinium bitartrate,(prescription) 0.298
betazole hydrochloride,(prescription) 0.298
chlorpheniramine maleate,(prescription) 0.298
๐Ÿ•ฎ  warfarin,(prescription) 0.298

To Remove or Decrease

Modifier Confidence ๐Ÿ“น
๐Ÿ•ฎ  inulin (prebiotic) 0.693
proton-pump inhibitors (prescription) 0.602
arabinogalactan (prebiotic) 0.589
๐Ÿ•ฎ  fructo-oligosaccharides (prebiotic) 0.414
๐Ÿ•ฎ  lactulose 0.379
wheat bran 0.37
mediterranean diet 0.369
red wine 0.366
navy bean 0.356
Slippery Elm 0.354
๐Ÿ•ฎ  Reduce choline (Beef, Chicken Eggs) 0.348
๐Ÿ•ฎ  Human milk oligosaccharides (prebiotic, Holigos, Stachyose) 0.342
non-starch polysaccharides 0.336
blueberry 0.333
aspartame (sweetner) 0.325
๐Ÿ•ฎ  Pulses 0.316
๐Ÿ•ฎ  resveratrol (grape seed/polyphenols/red wine) 0.301
raffinose(sugar beet) 0.298
pea (fiber, protein) 0.292
resistant starch 0.291
apple 0.261
ketogenic diet 0.261
๐Ÿ•ฎ  Burdock Root 0.24
fat 0.237
Conjugated Linoleic Acid 0.232
๐Ÿ•ฎ  galacto-oligosaccharides (prebiotic) 0.225
xylan (prebiotic) 0.217
fasting 0.216
walnuts 0.215
๐Ÿ•ฎ  pectin 0.207
bacillus subtilis (probiotics) 0.203
almonds/ almond skins 0.201
soy 0.2
l-citrulline 0.2
lupin seeds (anaphylaxis risk, toxic if not prepared properly) 0.198
๐Ÿ•ฎ  Prescript Assist (2018 Formula) 0.195
sesame cake/meal 0.192
Lactobacillus Johnsonii (probiotic) 0.182
barley,oat 0.176
๐Ÿ•ฎ  gum arabic (prebiotic) 0.167
๐Ÿ•ฎ  oligosaccharides (prebiotic) 0.164
jerusalem artichoke (prebiotic) 0.158
macrolide ((antibiotic)s) 0.154
penicillin-moxalactam (antibiotic)s 0.154
chemotherapy (prescription) 0.151
l-proline 0.149
plantago asiatica l. 0.145
resistant maltodextrin 0.144
๐Ÿ•ฎ  bifidobacterium lactis bb12 (probiotics) 0.135
schisandra chinensis(magnolia berry or five-flavor-fruit) 0.132
๐Ÿ•ฎ  galactose (milk sugar) 0.131
magnesium 0.127
๐Ÿ•ฎ  lactobacillus fermentum (probiotics) 0.126
sodium butyrate 0.123
๐Ÿ•ฎ  lactobacillus acidophilus (probiotics) 0.122
blackcurrant 0.121
high fiber diet 0.12
๐Ÿ•ฎ  lactobacillus gasseri (probiotics) 0.119
๐Ÿ•ฎ  lactobacillus plantarum,xylooligosaccharides,(prebiotic) (probiotics) 0.113
Alpha-Ketoglutarate 0.108
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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