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: Neurological: Executive Decision Making (Difficulty making)_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  gentamicin (antibiotic)s 1
๐Ÿ•ฎ  piperacillin-tazobactam (antibiotic)s 0.775
๐Ÿ•ฎ  amoxicillin (antibiotic)s[CFS] 0.76
๐Ÿ•ฎ  hyoscyamine (l),(prescription) 0.711
๐Ÿ•ฎ  imipenem (antibiotic)s 0.692
๐Ÿ•ฎ  benzylpenicillin sodium (antibiotic) 0.609
๐Ÿ•ฎ  tobramycin (antibiotic)s 0.585
๐Ÿ•ฎ  garlic (allium sativum) 0.56  ๐Ÿ“
๐Ÿ•ฎ  meropenem (antibiotic)s 0.549
๐Ÿ•ฎ  vancomycin (antibiotic)[CFS] 0.546
๐Ÿ•ฎ  Vitamin B-12 0.524  ๐Ÿ“
cinnamon (oil. spice) 0.521  ๐Ÿ“
๐Ÿ•ฎ  reserpine,(prescription) 0.509
๐Ÿ•ฎ  acarbose,(prescription) 0.497
๐Ÿ•ฎ  Hesperidin (polyphenol) 0.493  ๐Ÿ“
๐Ÿ•ฎ  N-Acetyl Cysteine (NAC), 0.482  ๐Ÿ“
๐Ÿ•ฎ  estradiol valerate,(prescription) 0.472
๐Ÿ•ฎ  amikacin (antibiotic)s 0.466
๐Ÿ•ฎ  loperamide hydrochloride,(prescription) 0.465
๐Ÿ•ฎ  Vitamin B1,thiamine hydrochloride 0.462  ๐Ÿ“
๐Ÿ•ฎ  alverine citrate salt,(prescription) 0.457
๐Ÿ•ฎ  itraconazole,(prescription) 0.452
๐Ÿ•ฎ  sulfamethoxazole (antibiotic) 0.452
๐Ÿ•ฎ  atorvastatin (prescription) 0.451  ๐Ÿ“
prednisone,(prescription) 0.45
๐Ÿ•ฎ  ciprofloxacin (antibiotic)s[CFS] 0.449
Caffeine 0.447
neem 0.443  ๐Ÿ“
๐Ÿ•ฎ  lactobacillus paracasei (probiotics) 0.439  ๐Ÿ“
๐Ÿ•ฎ  liothyronine,(prescription) 0.439
๐Ÿ•ฎ  carbidopa non-drug 0.439
prothionamide (antibiotic) 0.439
๐Ÿ•ฎ  pyrazinamide (antibiotic) 0.439
๐Ÿ•ฎ  furosemide,(prescription) 0.439
๐Ÿ•ฎ  procyclidine hydrochloride,(prescription) 0.439
๐Ÿ•ฎ  trichlormethiazide,(prescription) 0.439
๐Ÿ•ฎ  cimetidine,(prescription) 0.439
๐Ÿ•ฎ  bezafibrate,(prescription) 0.439
๐Ÿ•ฎ  trihexyphenidyl-d;l hydrochloride,(prescription) 0.439
๐Ÿ•ฎ  trimipramine maleate salt,(prescription) 0.439
๐Ÿ•ฎ  quinidine hydrochloride monohydrate,(prescription) 0.439
๐Ÿ•ฎ  pirenzepine dihydrochloride,(prescription) 0.439
๐Ÿ•ฎ  dolasetron mesilate,(prescription) 0.439
๐Ÿ•ฎ  diprophylline,(prescription) 0.439
lidoflazine,(prescription) 0.439
๐Ÿ•ฎ  venlafaxine,(prescription) 0.439
canrenone,(prescription) 0.439
๐Ÿ•ฎ  fomepizole,(prescription) 0.439
๐Ÿ•ฎ  terconazole,(prescription) 0.439
๐Ÿ•ฎ  irsogladine maleate,(prescription) 0.439
๐Ÿ•ฎ  fenofibrate,(prescription) 0.439
๐Ÿ•ฎ  metoprolol-(+;-) (+)-tartrate salt,(prescription) 0.439
๐Ÿ•ฎ  flunarizine dihydrochloride,(prescription) 0.439
๐Ÿ•ฎ  darifenacin hydrobromide,(prescription) 0.439
nafronyl oxalate,(prescription) 0.439
๐Ÿ•ฎ  diphenhydramine hydrochloride,(prescription) 0.439
pipenzolate bromide,(prescription) 0.439
nocodazole,(prescription) 0.439
๐Ÿ•ฎ  meptazinol hydrochloride,(prescription) 0.439
๐Ÿ•ฎ  tranilast,(prescription) 0.439

To Remove or Decrease

Modifier Confidence ๐Ÿ“น
proton-pump inhibitors (prescription) 0.849
๐Ÿ•ฎ  lactulose 0.375
arabinogalactan (prebiotic) 0.369
macrolide ((antibiotic)s) 0.329
๐Ÿ•ฎ  inulin (prebiotic) 0.321
Slippery Elm 0.32
ku ding cha tea 0.316
vsl#3 (probiotics) 0.307
raffinose(sugar beet) 0.306
gynostemma pentaphyllum (Jiaogulan) 0.303
๐Ÿ•ฎ  berberine 0.255
๐Ÿ•ฎ  oligosaccharides (prebiotic) 0.243
๐Ÿ•ฎ  saccharomyces boulardii (probiotics) 0.24
apple 0.207
green-lipped mussel 0.201
๐Ÿ•ฎ  Pulses 0.199
lividomycin (antibiotic)s 0.196
๐Ÿ•ฎ  isepamicin (antibiotic)s 0.196
barley,oat 0.195
๐Ÿ•ฎ  tetracycline (antibiotic)s 0.186
๐Ÿ•ฎ  fructo-oligosaccharides (prebiotic) 0.183
non-starch polysaccharides 0.17
๐Ÿ•ฎ  Prescript Assist (2018 Formula) 0.17
high sugar diet 0.165
resistant starch 0.157
red wine 0.157
chondrus crispus,red sea weed 0.155
๐Ÿ•ฎ  pectin 0.146
cholic acid (bile acid) 0.145
symbioflor 2 e.coli probiotics 0.14
levan 0.138
lincosamide (antibiotic)s 0.135
amaranth 0.133
walnuts 0.127
bile (acid/salts) 0.127
Sijunzi decoction 0.126
butirosin 0.126
l-proline 0.126
fat 0.124
๐Ÿ•ฎ  Reduce choline (Beef, Chicken Eggs) 0.117
schisandra chinensis(magnolia berry or five-flavor-fruit) 0.117
resistant maltodextrin 0.115
chemotherapy (prescription) 0.107
lupin seeds (anaphylaxis risk, toxic if not prepared properly) 0.107
magnesium 0.106
๐Ÿ•ฎ  ß-glucan 0.105
palm kernel meal 0.105
animal-based diet 0.104
vitamin a 0.104
๐Ÿ•ฎ  Nicotine, Nicotine Patch 0.101
๐Ÿ•ฎ  fruit 0.096
blueberry 0.095
dairy 0.094
a-glucosidase inhibitors 0.094
aspartame (sweetner) 0.094
xylan (prebiotic) 0.092
fish oil 0.091
๐Ÿ•ฎ  netilmicin (antibiotic)s 0.09
๐Ÿ•ฎ  dibekacin (antibiotic)s 0.089
sodium butyrate 0.086
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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