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: Comorbid: Small intestinal bacterial overgrowth (SIBO)_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  imipenem (antibiotic)s 1
๐Ÿ•ฎ  gentamicin (antibiotic)s 0.874
๐Ÿ•ฎ  piperacillin-tazobactam (antibiotic)s 0.788
๐Ÿ•ฎ  ofloxacin (antibiotic)s 0.672
๐Ÿ•ฎ  ciprofloxacin (antibiotic)s[CFS] 0.599
๐Ÿ•ฎ  amikacin (antibiotic)s 0.583
fluoroquinolone (antibiotic)s 0.568
foeniculum vulgare,fennel 0.474
๐Ÿ•ฎ  ceftriaxone (antibiotic)s 0.471
๐Ÿ•ฎ  amoxicillin (antibiotic)s[CFS] 0.458
๐Ÿ•ฎ  vancomycin (antibiotic)[CFS] 0.428
๐Ÿ•ฎ  trimethoprim (antibiotic)s 0.414
๐Ÿ•ฎ  benzylpenicillin sodium (antibiotic) 0.409
๐Ÿ•ฎ  cefotaxime sodium salt (antibiotic) 0.405
๐Ÿ•ฎ  tobramycin (antibiotic)s 0.39
๐Ÿ•ฎ  norfloxacin (antibiotic)s 0.348
๐Ÿ•ฎ  meropenem (antibiotic)s 0.323
oregano (origanum vulgare, oil) | 0.3
intesti-bacteriophage 0.27
๐Ÿ•ฎ  ampicillin (antibiotic)s[CFS] 0.244
๐Ÿ•ฎ  sparfloxacin (antibiotic) 0.238
๐Ÿ•ฎ  chloramphenicol (antibiotic)s 0.223
๐Ÿ•ฎ  Vitamin B-12 0.223  ๐Ÿ“
syzygium aromaticum (clove) 0.221
๐Ÿ•ฎ  garlic (allium sativum) 0.221  ๐Ÿ“
cinnamon (oil. spice) 0.22  ๐Ÿ“
clostridium butyricum (probiotics),Miya,Miyarisan 0.206  ๐Ÿ“
Curcumin 0.203  ๐Ÿ“
๐Ÿ•ฎ  hyoscyamine (l),(prescription) 0.198
๐Ÿ•ฎ  aztreonam (antibiotic) 0.198
๐Ÿ•ฎ  Hesperidin (polyphenol) 0.182  ๐Ÿ“
nigella sativa seed (black cumin) 0.179  ๐Ÿ“
๐Ÿ•ฎ  ceftazidime (antibiotic)s 0.177
๐Ÿ•ฎ  Vitamin B1,thiamine hydrochloride 0.172  ๐Ÿ“
๐Ÿ•ฎ  reserpine,(prescription) 0.168
๐Ÿ•ฎ  atorvastatin (prescription) 0.166  ๐Ÿ“
Caffeine 0.164
laser trilobum l.,kefe cumin 0.162
neem 0.161  ๐Ÿ“
๐Ÿ•ฎ  itraconazole,(prescription) 0.16
๐Ÿ•ฎ  N-Acetyl Cysteine (NAC), 0.16  ๐Ÿ“
๐Ÿ•ฎ  sulfamethoxazole (antibiotic) 0.16
๐Ÿ•ฎ  loperamide hydrochloride,(prescription) 0.159
๐Ÿ•ฎ  clindamycin (antibiotic)s[CFS] 0.156
๐Ÿ•ฎ  lactobacillus casei (probiotics) 0.153  ๐Ÿ“
๐Ÿ•ฎ  acarbose,(prescription) 0.152
๐Ÿ•ฎ  alverine citrate salt,(prescription) 0.151
๐Ÿ•ฎ  phentermine hydrochloride,(prescription) 0.146
raclopride non-drug 0.146
๐Ÿ•ฎ  isotretinoin,(prescription) 0.146
atracurium besylate,(prescription) 0.146
๐Ÿ•ฎ  iodixanol,(prescription) 0.146
urosiol,(prescription) 0.146
ioxaglic acid,(prescription) 0.146
๐Ÿ•ฎ  acitretin,(prescription) 0.146
๐Ÿ•ฎ  oxandrolone,(prescription) 0.146
fipexide hydrochloride,(prescription) 0.146
๐Ÿ•ฎ  glimepiride,(prescription) 0.146
๐Ÿ•ฎ  tiabendazole,(prescription) 0.146
๐Ÿ•ฎ  carbachol,(prescription) 0.146

To Remove or Decrease

Modifier Confidence ๐Ÿ“น
aspartame (sweetner) 0.33
Slippery Elm 0.271
๐Ÿ•ฎ  inulin (prebiotic) 0.251
arabinogalactan (prebiotic) 0.224
resistant starch 0.212
๐Ÿ•ฎ  berberine 0.207
๐Ÿ•ฎ  Human milk oligosaccharides (prebiotic, Holigos, Stachyose) 0.177
๐Ÿ•ฎ  lactulose 0.133
navy bean 0.132
penicillin-moxalactam (antibiotic)s 0.123
red wine 0.12
๐Ÿ•ฎ  fructo-oligosaccharides (prebiotic) 0.12
๐Ÿ•ฎ  Pulses 0.119
๐Ÿ•ฎ  Reduce choline (Beef, Chicken Eggs) 0.118
blueberry 0.116
non-starch polysaccharides 0.113
macrolide ((antibiotic)s) 0.112
๐Ÿ•ฎ  iron 0.109
saccharin 0.106
wheat bran 0.106
levan 0.106
l-proline 0.105
symbioflor 2 e.coli probiotics 0.098
proton-pump inhibitors (prescription) 0.096
resistant maltodextrin 0.096
fasting 0.095
๐Ÿ•ฎ  galacto-oligosaccharides (prebiotic) 0.089
๐Ÿ•ฎ  oligosaccharides (prebiotic) 0.085
๐Ÿ•ฎ  pectin 0.082
mediterranean diet 0.076
soy 0.075
๐Ÿ•ฎ  saccharomyces boulardii (probiotics) 0.072
lupin seeds (anaphylaxis risk, toxic if not prepared properly) 0.072
๐Ÿ•ฎ  glucose (sugar) 0.071
๐Ÿ•ฎ  Burdock Root 0.07
ku ding cha tea 0.068
xylan (prebiotic) 0.065
green-lipped mussel 0.064
schisandra chinensis(magnolia berry or five-flavor-fruit) 0.064
๐Ÿ•ฎ  netilmicin (antibiotic)s 0.063
jerusalem artichoke (prebiotic) 0.062
bile (acid/salts) 0.061
high fiber diet 0.061
a-glucosidase inhibitors 0.061
barley,oat 0.061
almonds/ almond skins 0.061
l-citrulline 0.059
gynostemma pentaphyllum (Jiaogulan) 0.058
catecholamines (polyphenol) 0.057
red alga Laurencia tristicha 0.056
๐Ÿ•ฎ  tetracycline (antibiotic)s 0.055
sodium butyrate 0.055
pea (fiber, protein) 0.054
๐Ÿ•ฎ  lactobacillus fermentum (probiotics) 0.053
plantago asiatica l. 0.053
carob 0.052
๐Ÿ•ฎ  dibekacin (antibiotic)s 0.051
hypocaloric hyperproteic diet 0.05
salt (sodium chloride) 0.05
colinfant e.coli probiotics 0.049
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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