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: Neurocognitive: Slowness of thought_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  neomycin (antibiotic)s[CFS] 0.372
๐Ÿ•ฎ  Hesperidin (polyphenol) 0.341  ๐Ÿ“
๐Ÿ•ฎ  spectinomycin dihydrochloride (antibiotic) 0.293
๐Ÿ•ฎ  high-fat diets 0.279
chitosan,(sugar) 0.268  ๐Ÿ“
๐Ÿ•ฎ  chloramphenicol (antibiotic)s 0.262
๐Ÿ•ฎ  cefaclor hydrate (antibiotic) 0.252
๐Ÿ•ฎ  nitrofurantoin (antibiotic) 0.247
๐Ÿ•ฎ  N-Acetyl Cysteine (NAC), 0.244  ๐Ÿ“
๐Ÿ•ฎ  Vitamin B-12 0.241  ๐Ÿ“
๐Ÿ•ฎ  naproxen,(prescription) 0.238
sucralose 0.237  ๐Ÿ“
๐Ÿ•ฎ  hyoscyamine (l),(prescription) 0.229
๐Ÿ•ฎ  atorvastatin (prescription) 0.229  ๐Ÿ“
๐Ÿ•ฎ  carbamazepine,(prescription) 0.225
๐Ÿ•ฎ  itraconazole,(prescription) 0.224
๐Ÿ•ฎ  sulfamethoxazole (antibiotic) 0.224
๐Ÿ•ฎ  ibuprofen 0.222
๐Ÿ•ฎ  reboxetine mesylate,(prescription) 0.221
๐Ÿ•ฎ  loperamide hydrochloride,(prescription) 0.219
chlorphensin carbamate,(prescription) 0.218
๐Ÿ•ฎ  phentolamine hydrochloride,(prescription) 0.218
๐Ÿ•ฎ  amcinonide,(prescription) 0.218
๐Ÿ•ฎ  tizanidine hcl,(prescription) 0.218
๐Ÿ•ฎ  verapamil hydrochloride,(prescription) 0.218
fluocinolone acetonide,(prescription) 0.218
iobenguane sulfate,(prescription) 0.218
๐Ÿ•ฎ  idoxuridine,(prescription) 0.218
benoxinate hydrochloride,(prescription) 0.218
dipivefrin hydrochloride,(prescription) 0.218
๐Ÿ•ฎ  celecoxib,(prescription) 0.218
๐Ÿ•ฎ  dihydrostreptomycin sulfate (antibiotic) 0.218
clavulanate potassium salt (antibiotic) 0.218
๐Ÿ•ฎ  sulfapyridine (antibiotic) 0.218
dizocilpine maleate,(prescription) 0.218
mianserine hydrochloride,(prescription) 0.218
๐Ÿ•ฎ  thiethylperazine dimalate,(prescription) 0.218
๐Ÿ•ฎ  piroxicam,(prescription) 0.218
dichlorphenamide,(prescription) 0.218
๐Ÿ•ฎ  theophylline monohydrate,(prescription) 0.218
luteolin (flavonoid) 0.218  ๐Ÿ“
๐Ÿ•ฎ  alfuzosin hydrochloride,(prescription) 0.218
๐Ÿ•ฎ  fluvoxamine maleate,(prescription) 0.218
๐Ÿ•ฎ  oxaprozin,(prescription) 0.218
dicyclomine hydrochloride,(prescription) 0.218
๐Ÿ•ฎ  neostigmine bromide,(prescription) 0.218
๐Ÿ•ฎ  diflorasone diacetate,(prescription) 0.218
๐Ÿ•ฎ  tramadol hydrochloride,(prescription) 0.218
๐Ÿ•ฎ  enalapril maleate,(prescription) 0.218
๐Ÿ•ฎ  balsalazide sodium,(prescription) 0.218
๐Ÿ•ฎ  lamivudine,(prescription) 0.218
๐Ÿ•ฎ  prednicarbate,(prescription) 0.218
viloxazine hydrochloride,(prescription) 0.218
๐Ÿ•ฎ  ranitidine hydrochloride,(prescription) 0.218
๐Ÿ•ฎ  clemastine fumarate,(prescription) 0.218
๐Ÿ•ฎ  milrinone,(prescription) 0.218
clodronate,(prescription) 0.218
cromolyn disodium salt,(prescription) 0.218
๐Ÿ•ฎ  pralidoxime chloride,(prescription) 0.218
eucatropine hydrochloride non-drug 0.218

To Remove or Decrease

Modifier Confidence ๐Ÿ“น
๐Ÿ•ฎ  inulin (prebiotic) 1
๐Ÿ•ฎ  Human milk oligosaccharides (prebiotic, Holigos, Stachyose) 0.808
๐Ÿ•ฎ  fructo-oligosaccharides (prebiotic) 0.689
arabinogalactan (prebiotic) 0.64
soy 0.604
๐Ÿ•ฎ  resveratrol (grape seed/polyphenols/red wine) 0.564
resistant starch 0.506
๐Ÿ•ฎ  lactobacillus acidophilus (probiotics) 0.455
๐Ÿ•ฎ  lactulose 0.437
raffinose(sugar beet) 0.427
๐Ÿ•ฎ  lactobacillus plantarum (probiotics) 0.402
jerusalem artichoke (prebiotic) 0.393
wheat bran 0.388
๐Ÿ•ฎ  Burdock Root 0.373
๐Ÿ•ฎ  Glucomannan 0.346
bacillus subtilis (probiotics) 0.336
๐Ÿ•ฎ  galacto-oligosaccharides (prebiotic) 0.335
almonds/ almond skins 0.32
sesame cake/meal 0.319
apple 0.305
clostridium butyricum (probiotics),Miya,Miyarisan 0.304
๐Ÿ•ฎ  pectin 0.285
red wine 0.282
ketogenic diet 0.272
barley,oat 0.27
wheat 0.264
๐Ÿ•ฎ  gum arabic (prebiotic) 0.261
high fiber diet 0.244
๐Ÿ•ฎ  oligosaccharides (prebiotic) 0.244
mediterranean diet 0.242
๐Ÿ•ฎ  zinc 0.236
๐Ÿ•ฎ  lactobacillus plantarum,xylooligosaccharides,(prebiotic) (probiotics) 0.232
๐Ÿ•ฎ  Pulses 0.216
Conjugated Linoleic Acid 0.216
daesiho-tang 0.215
๐Ÿ•ฎ  noni 0.213
๐Ÿ•ฎ  bifidobacterium lactis bb12 (probiotics) 0.212
blueberry 0.212
fish oil 0.209
chondrus crispus,red sea weed 0.209
green tea 0.206
navy bean 0.205
๐Ÿ•ฎ  rifaximin (antibiotic)s 0.202
magnesium 0.196
l-proline 0.189
partially hydrolysed guar gum,fructo-oligosaccharides (prebiotic) 0.188
lupin seeds (anaphylaxis risk, toxic if not prepared properly) 0.184
fasting 0.182
barley 0.181
๐Ÿ•ฎ  lactobacillus paracasei (probiotics) 0.181
๐Ÿ•ฎ  lactobacillus reuteri (probiotics) 0.18
Lactobacillus Johnsonii (probiotic) 0.18
pea (fiber, protein) 0.178
plantago asiatica l. 0.173
oats 0.173
๐Ÿ•ฎ  galactose (milk sugar) 0.172
whey 0.172
Slippery Elm 0.169
quercetin 0.162
chicory (prebiotic) 0.157
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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Permission to data scrap or reverse engineer is explicitly denied to all users. U.S. Code Title 18 PART I CHAPTER 47 ยงโ€ฏ1030, CETS No.185, CFAA
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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