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: DePaul University Fatigue Questionnaire : Easily irritated_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
๐Ÿ•ฎ  piperacillin-tazobactam (antibiotic)s 0.758
๐Ÿ•ฎ  amoxicillin (antibiotic)s[CFS] 0.675
๐Ÿ•ฎ  imipenem (antibiotic)s 0.667
๐Ÿ•ฎ  tobramycin (antibiotic)s 0.586
๐Ÿ•ฎ  benzylpenicillin sodium (antibiotic) 0.557
๐Ÿ•ฎ  amikacin (antibiotic)s 0.524
๐Ÿ•ฎ  meropenem (antibiotic)s 0.523
๐Ÿ•ฎ  hyoscyamine (l),(prescription) 0.513
๐Ÿ•ฎ  ciprofloxacin (antibiotic)s[CFS] 0.509
๐Ÿ•ฎ  garlic (allium sativum) 0.477  ๐Ÿ“
cinnamon (oil. spice) 0.446  ๐Ÿ“
๐Ÿ•ฎ  Vitamin B-12 0.436  ๐Ÿ“
๐Ÿ•ฎ  vancomycin (antibiotic)[CFS] 0.426
๐Ÿ•ฎ  ampicillin (antibiotic)s[CFS] 0.42
foeniculum vulgare,fennel 0.418
๐Ÿ•ฎ  ofloxacin (antibiotic)s 0.41
neem 0.404  ๐Ÿ“
๐Ÿ•ฎ  Hesperidin (polyphenol) 0.388  ๐Ÿ“
๐Ÿ•ฎ  cefotaxime sodium salt (antibiotic) 0.387
๐Ÿ•ฎ  reserpine,(prescription) 0.373
๐Ÿ•ฎ  N-Acetyl Cysteine (NAC), 0.372  ๐Ÿ“
๐Ÿ•ฎ  trimethoprim (antibiotic)s 0.364
๐Ÿ•ฎ  sulfamethoxazole (antibiotic) 0.359
Caffeine 0.353
intesti-bacteriophage 0.35
๐Ÿ•ฎ  Vitamin B1,thiamine hydrochloride 0.343  ๐Ÿ“
๐Ÿ•ฎ  thyme (thymol, thyme oil) 0.342
๐Ÿ•ฎ  atorvastatin (prescription) 0.34  ๐Ÿ“
๐Ÿ•ฎ  itraconazole,(prescription) 0.339
๐Ÿ•ฎ  metronidazole (antibiotic)s[CFS] 0.339
๐Ÿ•ฎ  acarbose,(prescription) 0.336
๐Ÿ•ฎ  alverine citrate salt,(prescription) 0.334
๐Ÿ•ฎ  lactobacillus reuteri (probiotics) 0.331  ๐Ÿ“
๐Ÿ•ฎ  estradiol valerate,(prescription) 0.33
๐Ÿ•ฎ  dopamine (prescription) 0.329
๐Ÿ•ฎ  loperamide hydrochloride,(prescription) 0.328
๐Ÿ•ฎ  streptomycin (antibiotic)s 0.327
๐Ÿ•ฎ  pranlukast,(prescription) 0.325
phensuximide,(prescription) 0.325
๐Ÿ•ฎ  pheniramine maleate,(prescription) 0.325
๐Ÿ•ฎ  propofol,(prescription) 0.325
๐Ÿ•ฎ  promethazine hydrochloride,(prescription) 0.325
๐Ÿ•ฎ  cyclophosphamide,(prescription) 0.325
๐Ÿ•ฎ  tolbutamide,(prescription) 0.325
๐Ÿ•ฎ  vincamine,(prescription) 0.325
๐Ÿ•ฎ  rimantadine hydrochloride,(prescription) 0.325
๐Ÿ•ฎ  dextromethorphan hydrobromide monohydrate,(prescription) 0.325
๐Ÿ•ฎ  cyclizine hydrochloride,(prescription) 0.325
๐Ÿ•ฎ  tolazoline hydrochloride,(prescription) 0.325
clidinium bromide,(prescription) 0.325
aminohippuric acid,(prescription) 0.325
(-)-mk 801 hydrogen maleate,(prescription) 0.325
๐Ÿ•ฎ  alprostadil,(prescription) 0.325
๐Ÿ•ฎ  methocarbamol,(prescription) 0.325
methylhydantoin-5-(l) non-drug 0.325
๐Ÿ•ฎ  mifepristone,(prescription) 0.325
๐Ÿ•ฎ  norgestimate,(prescription) 0.325
๐Ÿ•ฎ  clozapine,(prescription) 0.325
๐Ÿ•ฎ  cetirizine dihydrochloride,(prescription) 0.325

To Remove or Decrease

Modifier Confidence ๐Ÿ“น
aspartame (sweetner) 0.424
arabinogalactan (prebiotic) 0.403
๐Ÿ•ฎ  lactulose 0.361
proton-pump inhibitors (prescription) 0.325
๐Ÿ•ฎ  inulin (prebiotic) 0.305
red wine 0.275
๐Ÿ•ฎ  Reduce choline (Beef, Chicken Eggs) 0.27
Slippery Elm 0.246
vsl#3 (probiotics) 0.242
raffinose(sugar beet) 0.236
macrolide ((antibiotic)s) 0.222
๐Ÿ•ฎ  saccharomyces boulardii (probiotics) 0.202
๐Ÿ•ฎ  fructo-oligosaccharides (prebiotic) 0.197
ku ding cha tea 0.196
๐Ÿ•ฎ  Pulses 0.195
๐Ÿ•ฎ  galacto-oligosaccharides (prebiotic) 0.19
๐Ÿ•ฎ  berberine 0.184
non-starch polysaccharides 0.177
chondrus crispus,red sea weed 0.174
apple 0.172
green-lipped mussel 0.171
๐Ÿ•ฎ  oligosaccharides (prebiotic) 0.169
barley,oat 0.164
gynostemma pentaphyllum (Jiaogulan) 0.16
๐Ÿ•ฎ  isepamicin (antibiotic)s 0.151
lividomycin (antibiotic)s 0.151
wheat bran 0.151
resistant starch 0.15
xylan (prebiotic) 0.149
mediterranean diet 0.14
l-proline 0.136
๐Ÿ•ฎ  zinc 0.128
fasting 0.125
levan 0.122
symbioflor 2 e.coli probiotics 0.12
๐Ÿ•ฎ  pectin 0.111
fish oil 0.109
l-citrulline 0.108
amaranth 0.106
resistant maltodextrin 0.106
almonds/ almond skins 0.094
palm kernel meal 0.094
butirosin 0.091
Sijunzi decoction 0.091
lincosamide (antibiotic)s 0.091
๐Ÿ•ฎ  alcoholic beverages 0.091
schisandra chinensis(magnolia berry or five-flavor-fruit) 0.089
jerusalem artichoke (prebiotic) 0.088
lupin seeds (anaphylaxis risk, toxic if not prepared properly) 0.085
๐Ÿ•ฎ  netilmicin (antibiotic)s 0.084
blueberry 0.078
sesame cake/meal 0.076
๐Ÿ•ฎ  Human milk oligosaccharides (prebiotic, Holigos, Stachyose) 0.075
a-glucosidase inhibitors 0.075
pea (fiber, protein) 0.074
fat 0.074
ketogenic diet 0.073
๐Ÿ•ฎ  lactobacillus gasseri (probiotics) 0.073
partially hydrolysed guar gum,fructo-oligosaccharides (prebiotic) 0.072
grape seed extract 0.072
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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