The Journey Begins with your microbiome

Thanks for joining me!

This is a companion site to the analysis site at:

The intent of this site to assist people with health issues that are, or could be, microbiome connected. There are MANY conditions known to have the severity being a function of the microbiome dysfunction, including Autism, Alzheimer’s, Anxiety and Depression. See this list of studies from the US National Library of Medicine. Individual symptoms like brain fog, anxiety and depression have strong statistical association to the microbiome. A few of them are listed here.

The base rule of the site is to avoid speculation, keep to facts from published studies and to facts from statistical analysis(with the source data available for those wish to replicate the results). Internet hearsay is avoid like the plague it is.

Open data and Open source are our mottos!

Continue reading “The Journey Begins with your microbiome”

The foolishness of Numeric Health Measures for the Microbiome

Today I was asked if the numbers shown on various sights like the one below, ” if this result is correlated with the severity of the patient?”. The answer is No. The usual reason that such numbers appear on sites is to satisfy customers asking for such numbers. Simple, easy to understand number.

Businesses want to make customers happy — so they literally cook-up a number to show on their reports. There is no research supporting any of the magical numbers that I have seen. Some one put together some numeric formula to generate the numbers.

For Biomesight, the logic is shown on the page with the right display

If you sum up the values for each dial, you get the total. When you view the percentage, it is not so obvious: 100%, 69%, 88%, 85% looks like a complex formula is being used.

Analogy: Give me a Health Measure for a Person

The microbiome is very much like a person. How would you create a single value for a person?

  • Probiotics –> Income
  • Commensals –> Savings
  • Pathobionts –> Debts
  • Diversity –> Health?
    • Married and marriage status
    • Chronic Conditions
    • Health Status
    • Height
    • Weight
    • Gender
    • etc

Would a person with no debt, good savings, a low income and married with 5 kids be a higher or lower measure than someone with moderate debt, high income, small savings and no relationships?

Are there any studies?

What about the ratios used in the literature?

  • Bacteroides/Bifidobacterium Ratio
  • Bacteroides/Clostridium Ratio
  • Blautia/Bacteroides Ratio
  • Firmicutes/Bacteroidetes Ratio 
  • Prevotella/Bacteroides Ratio

Well, the sample that I am looking at have 43%ile, 98%ile, 7.2%ile, 58%ile and 19%ile. The numbers are all over the place!

We also have two studies:

Their latest study states: ” accuracy of 80% in distinguishing healthy (no disease) from non-healthy (diseased)” So 1 in five times, they will misclassify a healthy person as having a disease.

An example of the algorithm being used is below.

This is just trying to determine if the person is healthy or not — nothing about severity.

My approach is simple, I simply flagged the bacteria deemed to be unhealthy. Just list them.

How to use it:

Same Sample – 2 Labs: 16s vs Shotgun

Back Story

Latest microbiome results are in. Seems like my microbiome is stubborn and stuck these last few tests. Wondering if I should just use this test suggestions exclusively or combine with my prior Thorne test 

We have two sample – one via BiomeSight and one via Thorne. This post is going to do two things:

  • Look at Suggestions – by combining both sets of suggestions using the Uber Consensus
  • Look at the differences between the reports.

We also review “which is better”. My focus is clinical application to individuals — not research papers; answer at bottom.

Uber Consensus

The process has become very simple — “Just give me Suggestions!” on both samples and then going to uber consensus as illustrated by the video below.

The result was excellent agreement on suggestion between each set of results. The CSV files are attached below.

Differences between Reports

I compared two things between the reports:

  • Percentage of the bacteria in the microbiome
  • Percentile of the bacteria in the microbiome

At the Phylum Level

Items less than 100 should be ignored (accuracy of measurement limits). There are a few dramatic differences.

Bacteria NameThorne CountBiomeSight Count
By Count

Looking at Percentiles next

Bacteria NameThorne %ileBiomeSight %ile
By Percentile ranking

We have Bacteroidetes in agreement with both — but for the rest…

At the genus level

Bacteria NameThorne CountBiomeSight Count
By Counts

Looking at the percentile rankings — the absolute numbers may vary greatly, but what about relative percentiles?

Bacteria NameThorne %ileBiomesight %ile
By Percentile

Bottom Line

I have never had much belief in the absolute accuracy of the bacteria named or the count. Why? Simple, I understand the statistical process being used and its weakness. I will leave arguments over “which is better” and “which is accurate” to others.

See The taxonomy nightmare before Christmas… for more information.

My focus and concern is to improve the microbiome. With sparse data and the great complexity involved, I am actually very pleased that the suggestions are in agreement. The suggestions are computed using fuzzy logic expert systems. The noise in the data and the statistical processes involved seem to be smoothed out by this Artificial Intelligence engine approach.

Score: Labs: -2, Microbiome Prescription 2

Which is better?. My focus is clinical application to individuals, both give similar suggestions using the Fuzzy Logic Expert System. There is no difference in that sense.

ME/CFS recovery short circuited by COVID

I have been doing periodic review of this person’s sample. He just got his latest results and it was a shocker (of the wrong type!). This is worth a review.

Comparing Samples Overtime

At the typical analysis level there has been no change in these broad criteria since the last sample:

  • Outside Range from JasonH
  • Outside Range from Medivere
  • Outside Range from Metagenomics
  • Outside Range from MyBioma
  • Outside Range from Nirvana/CosmosId
  • Outside Range from XenoGene

Why compare over 90%ile to under 10%ile? The reason is probability – we are converting the data to a uniform distribution for all of the bacteria. This allows for reliable statistical significance to be determined for all of the bacteria. If things are “normal” then the ratio should be 1.0 The further from 1, the more abnormal. This is independent of any assumptions on bacteria distributions.

Looking at over 90% and under 10%, our expected count are 64 for both (10% of 639)

  • We have 20 over 90%ile, so we have under representation of dominant
  • We have 273 under 10%ile, the typical over representation of low levels of many bacteria seenwith most ME/CFS people
  • The ratios is higher at 13.7 compare to prior ratios (8.2, 11,3,3,6.5)

Where we see differences

  • Outside Kaltoft-Moldrup count returned to the size of the very first sample.
  • Compounds over 90%ile to under 10%ile (which should be 1.0 theoretically) had been close to 1.0 on the prior 3 samples, jumped up to 8.8:1. This was not as bad as the first sample with a 12.9:1 ration.
  • Enzymes over 90%ile to under 10%ile, continue to be bias towards low with a 2.7:1 ratio (prior 3.5, 1.7, 2.9, 3.2)
  • Conditions: jumped from none over 90%ile to 13!

My general impression is that ground has been lost. This is the first time that subsequent results appear to be worse. WHAT HAPPENED!???!!???!!!

Lab Read Quality9.
Bacteria Reported By Lab639461593445551
Bacteria Over 99%ile433515
Bacteria Over 95%ile1113112423
Bacteria Over 90%ile2023214035
Bacteria Under 10%ile273189237123227
Bacteria Under 5%ile21910714366192
Bacteria Under 1%ile17523449167
Lab: BiomeSight
Rarely Seen 1%671423
Rarely Seen 5%22143379
Outside Range from JasonH77446
Outside Range from Medivere1515151519
Outside Range from Metagenomics88667
Outside Range from MyBioma77778
Outside Range from Nirvana/CosmosId2323181821
Outside Range from XenoGene3232363639
Outside Lab Range (+/- 1.96SD)786914
Outside Box-Plot-Whiskers3833385841
Outside Kaltoft-Moldrup210111123100211
Condition Est. Over 99%ile50007
Condition Est. Over 95%ile900015
Condition Est. Over 90%ile1300029
Enzymes Over 99%ile3510301972
Enzymes Over 95%ile1006821982162
Enzymes Over 90%ile191183296126192
Enzymes Under 10%ile520645514369616
Enzymes Under 5%ile375423264186450
Enzymes Under 1%ile219864937272
Compounds Over 99%ile2347622844
Compounds Over 95%ile7225423112786
Compounds Over 90%ile12633829830798
Compounds Under 10%ile11043082972271265
Compounds Under 5%ile10681732241111241
Compounds Under 1%ile10456567471206

What Happened?

This person sent the following notes

  • I’ve got COVID in October, I feel as I have fully recovered.
  • I have a little bit more energy than before.
  • My body feels extremely stressed, I have started to get a pressure over the neck / thyroid when I get totally stressed out.
  • My sleep is much better. I have been following Andrew Hubermans protocol for sleep, which had a great impact on me. 
  • Would be great if I could get some recommendations for food, supplements, antibiotics etc.

Ah, the person feels like he has recovered but his microbiome is still recovering. We have a clean explanation for the regression! Our goal is now to try to stop Long COVID from setting in.

The Conditions matches include: hypercholesterolemia (High Cholesterol), Hyperlipidemia (High Blood Fats), Hypertension (High Blood Pressure, Nonalcoholic Fatty Liver Disease (nafld) Nonalcoholic and Atherosclerosis. None of those are concerning –they were not matches last time and thus should be viewed as transient red herrings. Looking at PUBMED Long COVID explicitly, we see good news: Long COVID   (29 %ile) 37 of 212. However when we go over to Special Studies, the very top one is

  •  41 % match COVID19 (Long Hauler), the next match was 21% –this really sticks out!
    • All Prior samples also had Long Hauler at the top too.

Going Forward

To build our consensus, we will do the usual and toss in our top Special Studies one.

Remember that we have a massive over representation of low %ile bacteria — hence some selection methods produce must larger numbers.

The suggestions downloads are below.

The top items are two similar probiotic mixtures from studies:

Looking at probiotics and the components above — bifidobacterium breve (probiotic) and propionibacterium freudenreichii appear to be the best of the components. The last one is used to make Emmental and Jarlsberg cheese or is available as a probiotic: Nutricology/Securil.

Given the general hostility between lactobacillus and E.Coli, plus the risk or lactic acid issue with lactobacillus, I would suggest avoiding lactobacillus casei initially, perhaps try it in a later cycle. A similar contradiction happened with different forms of cranberry as shown below. We want absolutely clean positive choices.

If different results from similar versions of something — then AVOID.

I reran the suggestions with everything — in case any prescription items may be of special interest.

The top antibiotics included:

As always, I prefer the Cecile Jadin approach of taking a single course, take a break and then take a different antibiotics.

Looking at Foods

The top food suggested was Protein powder, whey based, protein >70%, unfortified (ignore the unfortified – go for the standard used by ME/CFS Physicians: Undenatured Whey). This is a recommended with good results by many ME/CFS physicians like Teitelbaum and Dr. Paul Cheney. The second choice was duck liver which is not easy to obtain in many places (with other liver below). #3 items was Rye, whole grain flour, and near the top of the list: Confectionery, peanut, chocolate-coated which I chuckled over: See my 2012 post, Honestly! Chocolate!!! (Yes 11 years ago!) and 2013 post, Peanut Butter – a complex food?

I personally have always love Liver pâté! There was something that always felt so good after eating. Liverwurst is essentially the same food.

So for a Scandinavian, this is almost going to a church social!! Liver pâté on Dark 100% Rye bread with Jarlsberg cheese also on Dark 100% Rye bread!

Postscript – and Reminder

I am not a licensed medical professional and there are strict laws where I live about “appearing to practice medicine”.  I am safe when it is “academic models” and I keep to the language of science, especially statistics. I am not safe when the explanations have possible overtones of advising a patient instead of presenting data to be evaluated by a medical professional before implementing.

I can compute items to take, those computations do not provide information on rotations etc.

I cannot tell people what they should take or not take. I can inform people items that have better odds of improving their microbiome as a results on numeric calculations. I am a trained experienced statistician with appropriate degrees and professional memberships. All suggestions should be reviewed by your medical professional before starting.

The answers above describe my logic and thinking and is not intended to give advice to this person or any one. Always review with your knowledgeable medical professional.

Beyond simple naïve microbiome analysis?


This post started out with a title of “Post-Acute COVID-19 Syndrome vs Myalgic Encephalomyelitis – Similarities and Differences“. It scope was pretty obvious — compare microbiome shifts from these two sibling conditions. Expectations was a bland informational review.

The result was calling into question the typical assumption that we could isolate symptoms and conditions to specific bacteria. I follow the statistics and discovered that you can get a magnitude better statistical significance by moving beyond bacteria. In coming weeks, I hope to code up suggestions AI based on this enlightenment.

My starting point

In my last post, Long COVID – an update, I did a comparison between the citizen science data and the literature published on the US Library of Medicine. In terms of symptoms, these two appear the same — but at the microbiome (and enzyme levels) how similar are they?

  • Post-Acute COVID-19 Syndrome (PCAS), also known as Long COVID
  • Myalgic Encephalomyelitis (ME), also known as Chronic Fatigue Syndrome (CFS)

One frustrating aspect of many studies on the US Library of Medicine for many conditions is simple: results are not replicated in subsequent studies for the same condition. Historically I have viewed this as a result of different equipment and different reference libraries. In many cases the bacteria deemed significant are often different and when they did report the same bacteria, they report opposite shifts!

This post explores some of these issues, and came to an interesting conclusion.

Study Caveats

The studies on the US Library of Medicine compare people with the condition to healthy controls. With the citizen science data that is almost impossible to do. If a person has gotten a microbiome test, they likely have some condition(s) and thus are not healthy controls!!

This is not all bad. It means that when we find things that are statistically significant they are differentiators against other people with microbiome issues. That is, how are people with ME different than people with FM and IBS. Conceptually, we are more likely to identify the key features for these conditions and not key features for auto-immune conditions or a gut disturbance in general. It is a nuisance difference, but may be a very important nuisance.

Comparison that we will review are from:

  • US Pubmed — bacteria reported by both with direction
  • KEGG Enzymes shifts from Citizen Science (using only Biomesight data)
  • Bacteria shifts from Citizen Science (using only Biomesight data)
Citizen Science Samples
Studies on US National Library of Medicine

For citizen science we may have many uploaded samples annotated both with PCAS and ME. To resolve this conflict, ME will contain only samples with ME and without PCAS. Both ME and PCAS have many, many comorbid symptoms which may also come into play. Many of the pure ME samples are before COVID swept the world, hence relatively clean. PCAS are more recent samples.

For PACS citizen science data, we have only significance difference identified from Biomesight data, hence we will compare those only.

US National Library of Medicine6823325
Enzymes – Citizen Science with p < 0.00122819931
Bacteria – Citizen Science109360
Entities reported as significant or found significant

I must admit that finding no bacteria in common with the same lab and the same reference library was a little bit of a surprise. One explanation is that microbiome dysfunctions evolve over time. People with PACS have had it less then 3 years, likely an average of just 1 year. People with ME has had it often for 30+ years. Comparing the two may be similar to comparing a one bottle of grape juice to a bottle of vintage wine.

Details for Common Bacteria from US National Library of Medicine

In the table below: H indicates High, L indicates Low.

Note that Bacteroides are reported high and low in different studies, suggesting there are subsets of each condition

speciesAnaerobutyricum halliiL
speciesEnterocloster bolteaeH
speciesFaecalibacterium prausnitziiL
speciesRuminococcus gnavusH

Details for Shared Enzymes with p < 0.001

In recent posts for conditions comorbid with ME, PACS, I found that enzyme analysis had greater statistical significance than bacteria. All of these posts reported higher enzyme levels were significant with these conditions.

The result for items shared that had p < 0.001 was almost overwhelming!

ECKeyEnzymeName,5-anhydro-D-mannitol:NADP+ oxidoreductase oxidoreductase oxidoreductase,CoM:ferredoxin oxidoreductase,CoM,ferredoxin:coenzyme F420 oxidoreductase,CoM,ferredoxin:H2 oxidoreductase B,coenzyme M,ferredoxin:formate oxidoreductase,3-dideoxy-alpha-D-glucuronate N-acetyltransferase very-long-chain (2’R)-2′-hydroxy-phytoceramide phosphoinositoltransferase[(2R)-glycerophospho]-N-acetyl-beta-D-mannosaminyl-(1->4)-N-acetyl-alpha-D-glucosaminyl-diphospho-ditrans,octacis-undecaprenol glycerophosphotransferase,N’-diacetylbacillosamine:tritrans,heptacis-undecaprenyl-phosphate N,N’-diacetylbacillosamine transferase acetate acetohydrolase phosphohydrolase sulfohydrolase 3-sulfohydrolase sulfohydrolase 6-glucanohydrolase endo-beta-mannosidase,2-D-mannoside mannohydrolase mannohydrolase oligopeptidase indole-lyase (deaminating; pyruvate-forming) endolyase exolyase beta-D-mannuronate—uronate lyase lyase ammonia-lyase (acetaldehyde-forming) epimerase 2-epimerase aldose-ketose-isomerase ligase (AMP-forming)

One of them caught my eye, heparin-sulfate lyase, because micro-clots and “thick blood” are associated with these conditions with good results reported from the use of heparin for some patients.

 HSGAGs are widely distributed on the cell surface and extracellular cell matrix of virtually every mammalian cell type and play critical role in regulating numerous functions of blood vessel wall, blood coagulation, inflammation response and cell differentiation.

Microbial heparin/heparan sulphate lyases: potential and applications [2012]

Bacteria – Citizen Science

This blew me away — we have over 150 people with PCAS and over 250 with ME giving us superior sample sizes. We have 145 bacteria deemed significant for one or the other. We had NONE that was in common. This gut punch gives two main options: denial and look for an excuse to disregard. or roll with the punch and roll with enzymes.

Bottom Line

The enzyme aspect is the strongest association between PACS and ME. The count is higher, but more important, we are dealing with p < 0.001 data making false positives unlikely. This leads to a model that there is no ‘magical list of bacteria involved’ but a diverse array of bacteria that may be different for each person — but united in the over production of enzymes. This shifts the microscope of research into a different light spectrum. This is very interesting and may require some new brain cells to be used.

Using this information to improve..

If Enzymes estimate trumps bacteria levels (in a statistical sense), then we need to look at the enzyme levels and deduce for each one of concern, which collection of bacteria needs reduction — even when they are in the normal range. It is the aggregation of bacteria where the issue arises, not individual bacteria or specific subsets of bacteria.

A few examples may illustrate things a little

Example: (S)-3-hydroxy-3-methylglutaryl-CoA acetoacetate-lyase (acetyl-CoA-forming) a.k.a. EC, which was the most significant for PACS in the post: Long COVID – an update. There are some 2000+ taxon associated with it. We look at the averages for these below.

RankTax_NameWith PACSWithout PACSTScoreDF
speciesFaecalibacterium prausnitzii1381511096043.796775667
speciesPseudomonas viridiflava53252.62810832
speciesComamonas kerstersii125402.60038854
speciesPseudomonas aeruginosa62311.82464443
speciesEmticicia oligotrophica23039671.727619455
speciesDenitratisoma oestradiolicum42241.65065722
speciesGranulicella tundricola29211.6122548
speciesBacillus subtilis40191.37443117
speciesNiabella soli31240.96394316
speciesRalstonia insidiosa53380.91487436
speciesOligella ureolytica51320.8976819
speciesGlaciecola nitratireducens27240.6789966
speciesBacillus halotolerans32280.37121958
speciesAcidaminococcus intestini7496240.357674146
speciesAcinetobacter guillouiae67630.09895618
Key Contributors to EC,

For another one, we see the pattern stronger. Below we see the difference of Faecalibacterium prausnitzii is around 30,000 units. Looking at the other contributors, we see an additional 40,000 units. These extra units doubles the shift (and thus significance) of the enzyme above that of a single bacteria. Some of these are deemed healthy usually, for example: Akkermansia muciniphila which was at the 78%ile for Without PACS and 83%ile with PCAS. Neither would be deem to be outliers.

RankTaxon NameWith
speciesFaecalibacterium prausnitzii1381511096043.796775667
speciesSutterella wadsworthensis962667722.380718452
speciesAliarcobacter skirrowii3756212.22360217
speciesAkkermansia muciniphila19096122901.896922547
speciesDesulfovibrio desulfuricans14234691.76977232
speciesEmticicia oligotrophica23039671.727619455
speciesEnterococcus casseliflavus1965811.63566638
speciesPorphyromonas asaccharolytica13502541.59988186
speciesBacteroides fragilis808055951.523991489
speciesBifidobacterium dentium14544611.433823239
speciesPhocaeicola dorei35482290751.396731649
speciesCorynebacterium aurimucosum11054071.27523496
speciesBacteroides eggerthii14379103451.108857263
speciesCorynebacterium jeikeium18977230.85828270
speciesPhocaeicola coprophilus649636420.856783152
speciesDesulfovibrio piger203215340.848976141
speciesMegamonas funiformis167711300.62028390
speciesHathewaya histolytica289027290.467066660
speciesHaemophilus parainfluenzae134312500.282656500
speciesMesoplasma entomophilum118210690.230055294
speciesPhocaeicola vulgatus51403512130.034398665
Key Contributors for EC6.1.1.6

Going Forward

The logical approach is simple to describe. For a person with the symptom, determine the enzymes which are abnormal. Determine the bacteria that are too high (even if only a little). Then use the suggestions AI Engine to determine the substances that will affect the greatest number of these bacteria to shift in the desired direction without encouraging other bacteria that could contribute to these enzymes to increase.

Now, the mathematics and complexities of this computation is a different matter but well within the power of today’s computer.

German CFS Patient got COVID….

This request came from the person discussed in Follow up to: A German CFS Patient Experience and Analysis.

I don’t know, if you remember me, we did two reports together, and your suggestions really helped to get my microbiome back on track (which shows in the samples).

And then I got COVID in November 2021-December 2021. But I felt better with it, but unfortunately I couldn’t give up my sample while having COVID.

Anyway I wanted to ask you whether you may be interested in my case, because I had a huge, irreversible it seems, crash from 20-30 on Bell CFIDS disability scale to now under 10 and my microbiome crashed along with me . (The crash also resulted in a high number of Lorazepam intake from which I’m slowly withdrawing now. But I didn’t get a clear idea of the effect of Lorazepam on the microbiome, other then they make the slowed gut motility worse of course.)

I have a very severe and have a progressive form of ME/CFS in the way that whenever I really crash I always go down to a lower baseline and do not recover. And with most crashes I loose about 50 % of my functionality, so it just took me one year to go from very mild to very severe.

From my lowest point onwards I’ve always had to take about 4 Lorazepam to guarantee a minimum of a bearable quality of life in bed. I succeeded for 4 years not to have a major crash and did did not build up a tolerance towards Lorazepam in that time.

A lot of things seem to have reversed, what I should take before are often things that I now should avoid. What Biomesight says seems to contradict slightly from what I can gather from your site. (Yes, I know you explained why there can be contradictory results).

And I have difficulties getting the suggestions for the handpicked criteria to show. Of course I would be super glad, if you could help, but I understand if you have more interesting projects to work on. (I would of course donate for your effort, as this is the only or easiest way to say thank you),

I believe one of the differences between Microbiome Prescription(MP) and Biomesight is simply the number of studies used to make suggestions. At present, we have over 11,000 studies coded into MP, I do not know the number that Biomesight uses, but I expect less than 1%. Also, MP suggestions was written by a person that has worked professionally in Artificial Intelligence. I suspect Biomesight lacks that skill set for development. Regardless, put items not in disagreement as first priority.

Analysis — The Numbers

There is no magic number that answers questions about the microbiome. Usually, I look for abnormalities. Since the earlier post, she had 6 more microbiome samples done periodically and shown below. She is wise to regularly monitor and ideally take moderate steps (diet and supplements) to counter any concerning trends.

Shannon Diversity Index78.294.367.153.998.984.70
Simpson Diversity Index30.740.744.417.542.948.90
Chao1 Index53.666.881.536.765.161.90
Lab Read Quality4.
Bacteria Reported By Lab612653717536636642
Bacteria Over 99%ile2159684
Bacteria Over 95%ile42050333212
Bacteria Over 90%ile294469506838
Bacteria Under 10%ile44181181405343
Bacteria Under 5%ile1216416592014
Bacteria Under 1%ile1140148130
Rarely Seen 1%537022
Rarely Seen 5%161121101915
Outside Range from JasonH556688
Outside Range from Medivere121219191919
Outside Range from Metagenomics99101066
Outside Range from MyBioma666699
Outside Range from Nirvana/CosmosId202014142121
Outside Range from XenoGene363636363939
Outside Lab Range (+/- 1.96SD)2122416189
Outside Box-Plot-Whiskers67831069410658
Outside Kaltoft-Moldrup641832188710675
Condition Est. Over 99%ile000000
Condition Est. Over 95%ile020000
Condition Est. Over 90%ile035000
Enzymes Over 99%ile0021000
Enzymes Over 95%ile19066151736
Enzymes Over 90%ile68131193427118
Enzymes Under 10%ile302852039420080
Enzymes Under 5%ile13225130418127
Enzymes Under 1%ile1164802211
Compounds Over 99%ile1017000
Compounds Over 95%ile1803531018
Compounds Over 90%ile49573131764
Compounds Under 10%ile78987696511241135998
Compounds Under 5%ile77984892710921057959
Compounds Under 1%ile77383290410691018930
0 – 974%4626%4323%75%116%96%
20 – 292615%148%158%1611%148%2314%
30 – 39138%137%168%128%159%1811%
40 – 49148%137%147%1712%1810%159%
50 – 59148%169%147%107%159%1610%
60 – 692213%2011%189%107%1710%1811%
70 – 792313%158%2212%1913%169%138%
80 – 892313%1911%189%1812%2112%159%
90 – 99116%116%2212%1913%2112%138%
0 – 95%1028%6027%707%1410%228%16
20 – 2913%278%186%169%1810%2312%25
30 – 397%154%86%1512%2310%2211%23
40 – 499%198%169%248%158%1810%21
50 – 5912%259%2012%309%1810%2212%25
60 – 698%1712%258%217%148%176%12
70 – 7910%209%197%1911%229%2010%21
80 – 8913%279%197%1916%309%219%19
90 – 997%158%1712%308%1512%287%15

We lack any data on Lorazepam and other Benzodiazepines impacts on the microbiome which complicates interpretations. I did a search on the US National Library of Medicine and found nothing useful.

More History of Patient

I discovered Pregabalin in March 22 which brought me from Bell CFIDS disability scale below 10 up to nearly bell 20-30. The ditch in the curve around May 22nd is probably me taking too much Lorazepam and Pregabalin, as I for the first time I could take care of things that needed to betaken care of.

At that time I took about 500mg metformin (which did give me energy) most of the day and stayed with my Thorne Fibre mend, Inulin (in the beginning Inulin from the Argave helped dramatically with nausea and headaches)and Acacia Fibre, sometime an Amino Acid complex, but they make me jittery. Usually completely constipated I suddenly developed a strong diarrhea along with an unbearable itching of my whole skin in August / September for which Famotidine(Pepcid) and Cromoglicic acid (Cromolyn – prescription in US) worked best. That was a time where I ate lots of cake and carbohydrates and would take Metformin (I am always hovering around the entrance point to prediabetic) afterwards. That seemed to be too much sugar, my body couldn’t deal with. After I stopped the cake , eat more vegetables again, it went away. 

I have got restless legs, which are kind of turned on or off with every mayor crash. Now unfortunately they are turned on, and the only thing apart from medication that helps is when I eat complex carbohydrates lie brown rice, whole food, pasta, oat flakes etc, when I don’t I use Pramipexole.

Pregabalin been used with Fibromyalgia, a sibling condition for some, and suggested by the American Family Physician journal in 2023. Pregabalin with Lorazepam has known interactions: ” increase side effects such as dizziness, drowsiness, confusion, and difficulty concentrating.”[Src] so she is right about her loss of effectiveness.

Of the many items cited, we know what a few of them likely shifts. Others we lack data.

This missing data illustrates the challenge of trying to manipulate the microbiome — an absence of data. For antibiotics we have a reasonable amount of information, thus we can negotiate with MDs between their desired goal for the antibiotic and our goal of improving the microbiome to find a mutually acceptable compromise.

Going Forward

As part of my learning process, I evaluated each against the “Just Give Me Suggestions” consensus to see it that provide any insight. I also looked at the top items in three other classes.

cromolyn disodium salt275.5243.9368.9504.3391.1393.3
Best Probioticlactobacillus caseilactobacillus caseilactobacillus caseilactobacillus caseilactobacillus caseilactobacillus casei
Best Amino Acidpolymannuronic acidpolymannuronic acidmelatonin supplementmelatonin supplementmelatonin supplementpolymannuronic acid
Best Vitamin/MineralVitamin B7Vitamin CVitamin CVitamin B1Vitamin B1Vitamin B-12
Great Consistency across the samples!

This helps us evaluate possible (we do not know for certain) impact on various microbiome.

I am not a medical professional and have no clinical experience, so picking items tend to be arbitrary in most cases. I am familiar with the literature for ME/CFS and if the person has ME/CFS, I will tend to pick items that studies reporting helping.

My preference is simple.

My suggestion (given all of the fuzziness and items being taken) is to persist with the prescription items — they help both her symptoms and her microbiome! I would suggest adding the following items (see Dosages for Supplements for literature on dosage):

  • lactobacillus casei – at least 48 BCFU/day — this is the suggested serving size from Custom Probiotics product. Or a Yakult bottle with each meal (each bottle is 20 BCFU). Depending on availability and cost.
  • melatonin – 10 mg/d – in three dosages, i.e. one with each meal.
  • Vitamin B1, B12, C7 and C. (see above for dosages)

One additional item that I would suggest, being prediabetic is to take the Pendulum Akkermansia muciniphila probiotic. This may be a challenge to obtain in Germany (if someone is visiting the US, that may be a backdoor to get it).

Postscript – and Reminder

I am not a licensed medical professional and there are strict laws where I live about “appearing to practice medicine”.  I am safe when it is “academic models” and I keep to the language of science, especially statistics. I am not safe when the explanations have possible overtones of advising a patient instead of presenting data to be evaluated by a medical professional before implementing.

I can compute items to take, those computations do not provide information on rotations etc.

I cannot tell people what they should take or not take. I can inform people items that have better odds of improving their microbiome as a results on numeric calculations. I am a trained experienced statistician with appropriate degrees and professional memberships. All suggestions should be reviewed by your medical professional before starting.

The answers above describe my logic and thinking and is not intended to give advice to this person or any one. Always review with your knowledgeable medical professional.

The Transcribed Tests – a New Option: Condition Matching

As a result of doing an analysis for a 19 month old toddler, I added a new option that can also be used with Transcribed tests. This post applied to the following tests:

You must save your input.


Here’s an example

When you logged in, you will see your saved tests, CLICK ON Review.

And then we have the details you entered below with an important column, taxon number.

Below this are conditions where your pattern matches at least 5 shifts reported in Published Studies.

There may be many items listed. This is by pattern matching and is not predictive.

If you have any of these conditions, or suspect you may have. Just click the appropriate button.

An example is below. These are tuned safest-suggestions for the matches. What do I mean by safest? It means the items are not reported in any study in the database to adversely impact any of bacteria listed. Many substances have contradictory reports on shifts — this substances are excluded.

Not Listed Condition?

This person believes they may have Autoimmune, so going to we find that it is listed.

If it is not listed, search for bacteria shifts reported and use those (please send me the studies so I may add them).

The bacteria are shown in a tree. You have to manually match between the two.

In this we have:

  • Escherichia     ⬇️  but our sample is high,
  • Roseburia intestinalis   ⬆️  - we are high on Roseburia, we will include it

We have only one match — this tests with limited reporting is not a good fit for this condition. Doing a test like Biomesight, Xenogene, Thorne or Ombre is likely the best choice.

We just copy the taxon number into the form at the bottom of the page, and then click suggestions.

In this case, we get a short list. Remember, doing a single bacteria means you are ignoring a lot of interactions and factors. The suggestions could feed other bacteria that are too high.

19 month old Toddler with GI-Issues


A sample result dated 29/10/2023 it’s for 19month old son born via C section and having lots of ongoing tummy pain since birth.

Fully breastfed for well over 12 months but the microbiome doesn’t appear that way.


This is a very much “flying by the seats of my pants” analysis. Why? From birth for the next 10-20 years the microbiome has dramatic natural changes. The Fuzzy Logic Expert System on Microbiome Prescription is tuned for adults and not these age ranges. If you are dealing with a child, the approach below is suggested.

I am going to use ChatGPT selectively to make analysis easier, checking that it’s answers agree with my memories from reading studies..

So, first some literature to frame this analysis in. There are 400+ studies on C Sections and Infant microbiome.

  • “There is certainly a transient difference in the gut microbiota of infants born by Cesarean delivery compared to their VD counterparts. While this difference appears to be corrected after weaning, it may have lifelong impacts on the development of the immune system. ” [2018]
  • “When comparing the gut microbiota composition of CSD babies with vaginally delivered (VD) babies, the former show a microbiome that closely resembles that found in the environment and the mother’s skin, while VD babies show a microbiome more similar to the vaginal microbiome. Although these alterations of normal gut microbiota establishment tend to disappear during the first months of life, they still affect host health in the mid–long term since CSD has been correlated with a higher risk of early life infections and non-transmissible diseases, such as inflammatory diseases, allergies, and metabolic diseases.” [2021]
    • Too late, but important for any future babies “Lab analysis showed that the microbiota of the C-section babies swabbed with their mother’s vaginal fluids was close to that of vaginally born babies” [2021]

Bifidobacteria and Firmicutes Dominance: In healthy infants and toddlers, the gut microbiome often shows dominance of beneficial bacteria like Bifidobacteria and Firmicutes. These bacteria play crucial roles in digestion, immune system development, and protection against pathogens.

Generally, Bifidobacterium can comprise anywhere from 10% to 40% or more of the total gut microbial population in toddlers.

In healthy toddlers, Firmicutes can typically constitute a substantial portion of the gut microbiota, often ranging from around 30% to 60% or more of the total bacterial population

From ChatGpt

Looking at Bifidobacterium, the numbers are lower (57%ile) than expected for a child of this age but with a rich diversity of species. Bifidobacterium is 2.4%, well below the expected 10-40%. Bacillota (formerly known as Firmicutes at 83%ile) is 79.4%, well above expected 30-60%.

Switch to consequence, ChatGpt reports:
“Some of the health implications of low Bifidobacterium levels in toddlers may include:

  1. Digestive Issues: Bifidobacterium species contribute to the breakdown of complex carbohydrates, aiding in the digestion of certain fibers and sugars. Low levels of these bacteria may result in digestive problems such as constipation, diarrhea, or irregular bowel movements.
  2. Weakened Immune Function: Bifidobacterium species are involved in regulating and supporting the immune system. Insufficient levels of these beneficial bacteria may compromise the immune response, making toddlers more susceptible to infections and illnesses.
  3. Increased Risk of Allergies and Asthma: Some research suggests that alterations in the gut microbiota, including low levels of Bifidobacterium, might be associated with a higher risk of developing allergies and asthma in children.
  4. Potential Weight and Metabolic Effects: The gut microbiome, including Bifidobacterium, can influence metabolism and energy regulation. Low levels of these beneficial bacteria in toddlers’ gut microbiota might potentially be linked to metabolic issues or an increased risk of obesity later in life, although this connection is still an area of ongoing research.
  5. Impact on Neurodevelopment: Emerging studies suggest a possible link between the gut microbiota and brain development. Some evidence indicates that alterations in gut bacteria, including Bifidobacterium, might influence cognitive development and behavior in children.
  6. Susceptibility to Infections and Antibiotics: A decrease in beneficial bacteria like Bifidobacterium can leave the gut more vulnerable to colonization by harmful bacteria. Moreover, toddlers with low levels of these beneficial bacteria may face a higher risk of complications after antibiotic treatments, as antibiotics can further disrupt the delicate balance of the gut microbiota.
  7. Digestive Imbalances: In some cases, an overabundance of certain Firmicutes strains might affect the balance of gut microbiota and contribute to digestive problems, including irregular bowel movements, bloating, or discomfort.”

At this point, using the Fuzzy-Logic Expert System on Microbiome Prescription is not a good choice to get suggestions. It is tuned for adults and not toddlers. All of the values are in the normal range for an adult, but definitely out of range for a toddler.

What we want is to increase one bacteria and decrease another bacteria without looking at the percentile. I just added a subsection on the Research Features tab to make that available. It requires the the taxon numbers be entered. In this case: Decrease: 1239, Increase 1678 (Bifidobacterium).

See this video for a walk thru of the process.

This results in this page

You can click on each modifier to verify that it only impacts the bacteria named by taxon in the desired way.

In toddlers, several Bifidobacterium species are commonly found in their gastrointestinal tract. Among these species, Bifidobacterium longum, Bifidobacterium breve, and Bifidobacterium infantis are frequently observed in the gut microbiota of toddlers. These species play essential roles in maintaining gut health, aiding in digestion, and supporting the immune system during early childhood.


We can take this one step further, picking specific children :

With a deeper set of suggestions:

Feed Back

I usually send drafts to the person for comments, concerns etc. This was the response:

I wondered whether prevotella/segatella buccae was a concern as it was the highest species in the sample and bacteroides was extremely low. The practitioner we saw prescribed HMO and lactulose after reviewing Biomesight raw data.

Mother of child

The HMO suggestion is reasonable if you do not check all of the literature. We have contradictory results from studies for HMO. Remember Bacillota is the modern name for Firmicutes.

Similarly, we have some contradiction in results with Bifidobacterium — so it was not deemed ultra safe.

This suggests adding segatella buccae (NCBI 28126) be added.

The results are similar, with less items on the to avoid.

Trying a different combinations, for example

We get different ordering and a few changes.

Bottom Line

We have various sets of suggestions, doing a consensus is likely the best path forward.

Postscript – and Reminder

I am not a licensed medical professional and there are strict laws where I live about “appearing to practice medicine”.  I am safe when it is “academic models” and I keep to the language of science, especially statistics. I am not safe when the explanations have possible overtones of advising a patient instead of presenting data to be evaluated by a medical professional before implementing.

I can compute items to take, those computations do not provide information on rotations etc.

I cannot tell people what they should take or not take. I can inform people items that have better odds of improving their microbiome as a results on numeric calculations. I am a trained experienced statistician with appropriate degrees and professional memberships. All suggestions should be reviewed by your medical professional before starting.

The answers above describe my logic and thinking and is not intended to give advice to this person or any one. Always review with your knowledgeable medical professional.Posted on  b

Chronic Lyme Microbiome Analysis

Back Story Section

April 2022 strong antibiotic treatment against another pathogen flared my chronic borrelia/babesia/bartonella. [I had Ceftriaxone iv and 1500mg of azithromycin as a single dose].

Shortly after this stinging started in the belly and burning when passing a stool and urinating. Its yeast symptoms. I have mthfr mutation and low bifido bacteria.

“chronic borrelia/babesia/bartonella” is also known as Chronic Lyme disease. See Lyme Disease Co-Infections | It is a close sibling to ME/CFS, Long COVID and Occult Rickettsia. There are 77 samples uploaded marked with Lyme, 45 of these also indicate ME/CFS (58% overlap). There was no statistically significance difference in the microbiome between these two groups.

This person requested a video walkthrough due to cognitive issues with reading.


Looking at the Percentile-Percentage distribution, we see the common pattern with ME/CFS and Long COVID: over representation of the 0-9%ile range. The numbers in each percentile range should be about the same. They are not.

Looking at the new Anti inflammatory Bacteria Score [Score: 12.56 or 16.9 %ile], we see that bacteria controlling inflammation appears to be very deficient. Dr. Jason Hawrelak Recommendations is at 89%ile with the following anti-inflammation bacteria being flagged as low: Roseburia, Bifidobacterium, Lactobacillus and Akkermansia.

Looking at the Potential Condition lists, we see many that we would expect to see

  • Allergic Rhinitis (Hay Fever) 100%ile
  • Hyperlipidemia (High Blood Fats) 97%ile
  • Chronic Fatigue Syndrome 96%ile
  • Allergies 95%ile
  • Irritable Bowel Syndrome 94%ile
  • Functional constipation / chronic idiopathic constipation 93%ile

Going Forward

I checked the KEGG suggested probiotics none of the suggestions were strong. On the other hand we have a good number of supplement suggestions from KEGG (shown below). The higher the Z-Score, the more important they are.

Looking at probiotics we see the best ones being bifidobacterium (which is good because many lactobacillus produce d-lactic acid that causes brain fog).

  • bifidobacterium pseudocatenulatum,
  • bifidobacterium infantis,
  • bifidobacterium breve

There are some lactobacillus also suggested:

  • lactobacillus casei — documented to be good for allergies and hay fever. Usually I suggest Yakult, one vial around each meal.
  • lactobacillus reuteri — biogaia (reported not to produce d-lactic acid)

For supplements, checking the items from the KEGG list above, we found that all items suggested which we have data on, agreement that they should help:

  • N-Acetyl Cysteine (NAC), +185
  • l-proline + 161
  • l-glutamine + 76
  • l-arginine +45
  • l-phenylalanine +40

Postscript – and Reminder

I am not a licensed medical professional and there are strict laws where I live about “appearing to practice medicine”.  I am safe when it is “academic models” and I keep to the language of science, especially statistics. I am not safe when the explanations have possible overtones of advising a patient instead of presenting data to be evaluated by a medical professional before implementing.

I can compute items to take, those computations do not provide information on rotations etc.

I cannot tell people what they should take or not take. I can inform people items that have better odds of improving their microbiome as a results on numeric calculations. I am a trained experienced statistician with appropriate degrees and professional memberships. All suggestions should be reviewed by your medical professional before starting.

The answers above describe my logic and thinking and is not intended to give advice to this person or any one. Always review with your knowledgeable medical professional.

Bad Diet and Antibiotics? ME/CFS like symptoms

Back Story

  • 38yr Old now, Issues started around the age of 24-27 i think [gradual onset]
  • From the age of 17-35 my diet has been really bad ( Coca cola, pizza, burger, fries, candy and sweets etc)
  • From 23-25 I started getting really tired everyday followed by pains in various locations
  • Later, started loosing weight in the face, eyes started to sink deeper and deeper, my face become really gaunt. All my life i have been thin and could never gain weight nomatter how much i ate.
  • I also startet getting extremly fatigue after eating.
  • definitive stomach issues started around the age of 30-33, may have been before.
  • Since I turned 34, i have been trying to figure out what is wrong with me.
    • Allof the standard checks at the docs Office(ultrasound of organs and stomach area, CT/MRI of stomach area, Colonscophy and gastroscophy)
    • Nothing found

So everything points towards gut dysbiosis or something like that

I started to change around with my diet July 2022. Details of various attempted changes (Gluten free, no dairy, no sugar, carnivore diet) — currently on Keto with resistant carbs.

But many symptoms are still there.

I have been taken multiple rounds of antibiotics from november 2022 until Jan. 2023 (80 days) because i had a sinus and deviated septum surgery. I have also taken 7 days of metrodinazole  and amoxicilin 12 weeks ago because of the H Pylori infection i had. Retest was negative for H. Pylori
Got diagnosed with methane SIBO via breath test in september 2023

I have been diagnosed by a GI Map test in May 2023 with:

  • candida
  • E coli overgrowth
  • Streptococcus overgrowth
    by a gimap test in May 2023

I feel like my body is destroying itself.
A long list of symptoms was given


Potential Medical Conditions Detected

Nothing stood out. By this I mean that the Percentile ranking is well into the Prevalence. The closest was SIBO where the borderline would be 100-52= 58%ile. He was reasonably over that. He wrote “Got diagnosed with methane SIBO via breath test in September 2023”, so this was a definite matching forecast from PubMed literature.

Bacteria deemed Unhealthy

The one item of interest was Faecalibacterium prausnitzii, which was 19% of his microbiome and associated with increased Candida risk (which he has had).

Dr. Jason Hawrelak Recommendations

Percentages of Percentiles

This is my quick way to statistically determine if there is statistically significant dysfunction. The significance is 0.99999.. etc, so yes.

Forecast Symptoms

In the top ones we had the following agreements with reality:

  • cold extremities
  • Rapid muscular fatigability
  • Joint pain
  • Sinus issues with headaches 
  • Onset: Gradual 
  • Sinus issue
  • Onset: 2010-2020 
  • Gender: Male
  • General: Headaches
  • Post-exertional malaise

The ones that did not match were connected to cognitive issues.

Pattern appear to match a subset of myalgic encephalomyelitis/chronic fatigue syndrome. Many MDs will suspect it, but will not give a diagnosis if the person is not totally disabled. The reason is simple, no treatment plan and likely a negative psychological impact.

Going Forward

This looks likes a good candidate for a two stage building a consensis:

  • “Just Give Me Suggestions”
  • THEN using special studies (everything at once – skipping Gender) to add a fifth set of suggestions

The suggestions are short and tight. Barley porridge with Walnuts for breakfast for most days.

I would suggest taking Danish product Biogaia Lactobacillus Reuteri just before bed each night for two weeks, then switch to clostridium butyricum for two weeks. The other probiotics – do 1 at a time for 1-2 weeks, take them 1-2 hours after breakfast.

Akkermansia Muciniphila probiotics and Swedish Filmjölk (on your porridge?) are two probiotics with no known negative impact and some positive impact. The list above are the highest predicted impact.

What to avoid

Keep up the no alcohol but reduce/drop beef in your carnivore diet. Go for herring, eels and other fish product. It is interesting that the two E.Coli probiotics are listed as avoid (the logic does not look at E.Coli levels, but other bacteria levels to make that suggestion)

Prescription Items (if you have a willing MD)

Doing antibiotics is usually consider if the above do not cause sufficiently improvement over time. I mentioned that the history looks quasi-ME/CFS. I was not surprise to see many ME/CFS antibiotics on the list, including:


If you and your MD decide to try antibiotics, I would suggest on of those (using Dr. Jadin approach of pulsing).

Browsing the Details

High value was 701, low as -391. Usually these two numbers are about the same magnituded. Items spotted of note:

Note that inulin (prebiotic), jerusalem artichoke (prebiotic) etc are of low (but positive) value.


Q: Regarding your sugestion of all the probiotics. Usually the probiotic comes in bottles where there is like 4-10 different strains. Should i avoid that and only buy single strains in each bottle of all the ones you mentioned?

A: Each strain impacts things in different ways. My preference is always single strains, ideally ones that have been researched with the ideal being ones researched for your condition or symptoms and found effective. See , There are reports of some probiotics making people worse. A major issue is that probiotics are not well regulated Many “retail mixtures” have over 60% of their contents misidentified. See Deceptive Probiotic Labels or Assessment of commercial probiotic bacterial contents and label accuracy, When the bottle gives an explicit strain (not species), then the owner of that strain has motivation to insure quality control.

Looking at the challenges of getting probiotics in Denmark. What may be an acceptable compromise is to find a probiotic mixture that does not contain any probiotics with an estimated adverse risk. In your case these are:

  • symbioflor 2 e.coli probiotics
  • colinfant e.coli probiotics
  • bacillus subtilis natto (probiotics)
  • bifidobacterium longum,lactobacillus helveticus (probiotics)
  • lactobacillus paracasei,lactobacillus acidophilus,bifidobacterium animalis (probiotics)
  • General Biotics Equilibrium
  • bifidobacterium (probiotics)
  • lactobacillus rhamnosus gg,lactobacillus,rhamnosus,propionibacterium
  • reudenreichii,bifidobacterium breve (probiotics)
  • bifidobacterium bifidum (probiotics)
  • bacillus licheniformis,(probiotics)
    Prescript Assist (2018 Formula)
  • lactobacillus bulgaricus (probiotics)
  • lactobacillus gasseri (probiotics)
  • lactobacillus rhamnosus (probiotics)
  • lactobacillus casei shirota (probiotics)
  • lactobacillus fermentum (probiotics)
  • lactobacillus sakei (probiotics)
  • lactobacillus delbrueckii bulgaricus,bifidobacterium bifidum,enterococcus faecium,candida pintolopesii,aspergillus oryzae (probiotics)
  • lactobacillus brevis (probiotics)
  • bifidobacterium adolescentis,(probiotics)
  • bifidobacterium lactis,streptococcus thermophilus probiotic

Example:  lactobacillus rhamnosus gg (probiotics) is an explicit strain (“GG”) is the second highest positive, while generic  lactobacillus rhamnosus is # 54 and negative.

Postscript – and Reminder

I am not a licensed medical professional and there are strict laws where I live about “appearing to practice medicine”.  I am safe when it is “academic models” and I keep to the language of science, especially statistics. I am not safe when the explanations have possible overtones of advising a patient instead of presenting data to be evaluated by a medical professional before implementing.

I can compute items to take, those computations do not provide information on rotations etc.

I cannot tell people what they should take or not take. I can inform people items that have better odds of improving their microbiome as a results on numeric calculations. I am a trained experienced statistician with appropriate degrees and professional memberships. All suggestions should be reviewed by your medical professional before starting.

The answers above describe my logic and thinking and is not intended to give advice to this person or any one. Always review with your knowledgeable medical professional.

Studies Quality Assurance Launch

Over the last two weeks, there has been a couple of email pointing out possible errors in some citations. I am not surprised. I expect 90-95% correctness (i.e. 1 in 10 or 1 in 20) may be incorrectly entered. To improve the quality, we need independent review of the data. In one amusing case, I quoted my source correctly but that review study incorrectly cited it’s source. The data entry was right, the source document was wrong.

The articles are technical studies which often require advance reading skills and knowledge of this topic. Some of the sources are available in full on the web for free, others are behind a paywall. If you are connected with a university or college, you may have access thru your institution.

If you cannot access the full text of the source, then skip it. Extracts and summaries can contain errors.


Just email me from the email account that you logged in with and I will add auditor or QA permissions to your account.

Doing it

When you logged in, you should see:

When you look at citations, you will see the ⚖️ icon (or a ✅ if someone has already checked) beside the citation.

Example for a list of citations

Note that there may be more in the study then what the titles implies. Often data is from Appendix and tables.

Clicking ⚖️ will take you to a page showing what was extracted and gives you an opportunity to correct it,

Click [Report above Issue] will send emails to me and to your self. If all of the information is correct, then click [I have verified..] and the next time you see the citation, there will be a ✅ beside it. Your email is stored beside the citation as the reviewer.

You will get an email confirming stuff

If there is information that was missed (more likely) please include the TAXON numbers of the bacteria. This speeds up the process. Often information was missed because of alternative spelling.

That’s the process. Short, simple and with the ability for me to quickly make corrections.