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.
A reader wrote asked about apparent inconsistencies in suggestions. That is a very valid request.
What’s the difference between ‘General suggestions’ on the odds ratio suggestions page and ‘Probiotic suggestions’? The results seem almost unrelated. Two examples: in my results, Bifidobacterium breve is in the ‘General suggestions’ listed as having a negative impact, yet in ‘Probiotic suggestions’ it has a highly positive impact, is even mentioned as the second best probiotic (see image down below)! The one there mentioned as the top probiotic, Enterococcus faecalis, is in my ‘General suggestions’ hardly a suggestion, having barely any positive impact (0.87, with many probiotics having more impact).
Clinical Studies Based versus Modelled Probiotics
When I saw an opportunity to model the impact of probiotics instead of relying on published studies, I jumped at the opportunity. The key reasons are below:
Clinical studies often given contradictory results on the impact of a probiotic on other bacteria.
There are many reasons that this would occur naturally:
The studies were in the context of an existing condition (thus altered microbiome to start with)
The studies used different reference libraries to determine bacteria (See Nightmare post)
The studies usually gives a simple increase or decrease.
Example: for Pseudomonas, we have 2 studies saying it is increased and 2 studies saying it is decreased by B. subtilis
Modelled uses:
Healthy individuals for modelling (thus no existing conditions!)
The same reference library for all samples
The model gives a numeric estimate of how much changes is expected (R2)
What is the practical implementation? Looking at the differences below (The full tables are bottom) we see that the model shows impact on a magnitude more of different bacteria.
In theory, over time, with enough studies on healthy individual with sufficient size of each study, there will likely be convergence of the numbers. Studies are time consuming to do — so these results are likely not likely to be fully available until the next millenium.
Microbiome Prescription generates suggestions using both methods. There is no mechanism to determine which is better. Personally, I prefer the model because there is a lot more data available and the data is quantitative and not a binary of (increase/decrease).
The model assumes this logic:
If you take a (living) probiotic, then the amount in your microbiome will increase and all of the cascading impacts of this increase will likely match the impact of healthy individuals who naturally have more of that bacteria.
Why Contradictions?
There are massive interactions occurring. If you ignore (or have no data) on some impacts, then you can easily go very off course.
Consider Lactobacillus brevis: Assuming that 10% of your bacteria are out of wack, the recommendation with studies would be based on just 2 bacteria (10% of 23). Recommendations from the model would use around 16 bacteria (10% of 157). In short, more factors would be considered.
Back in my Uni days, one of my favorite profs taught probability and was a specialist in models of disease spread. A few of her papers below
Epidemic highs and lows: a stochastic diffusion model for active cases. Journal of Biological Dynamics
The effect of patterns of infectiousness on epidemic size. Mathematical Biosciences and Engineering 5 (2008), 429-435.
Bimodal epidemic sizedistributions for near-critical SIR with vaccination. Bulletin of Mathematical Biology 70(2008), 589-602.
Stochastic epidemic modeling. In: Mathematical and Statistical Estimation Approaches in Epidemiology, Ed. G. Chowell, Springer (2009), 31-52.
Often her work includes the use of Markovian chains. This mathematical framework was a foundation for the work on Microbiome Prescription dealing with bacteria.
Contemporary Pandemics
There are three potential pandemics in scope as summarized in the table below
Disease
Total Cases
Death Rate
Primary Transmission
Incubation Period
Presymptomatic Transmission
SARS
8,422
9.6-11%
Respiratory droplets, aerosols, fomites
2-10 days (median 4-6)
Minimal
COVID-19
779+ million
Variable (~1-2% overall)
Respiratory droplets, aerosols, surfaces
4.9-7.5 days
40-80% of transmission occurs 2-4 days before symptoms
Andes Hantavirus
Hundreds (regional)
36-38%
Rodent excreta inhalation; person-to-person (unique among hantaviruses)
7-39 days (median 18)
Yes, during early prodromal phase
SARS (2002-2003)
The SARS outbreak resulted in 8,422 cases worldwide with 916 deaths, yielding a case fatality rate of approximately 9.6-11%. The virus transmits primarily through respiratory droplets, aerosols, and contact with contaminated surfaces (fomites). The incubation period ranges from 2-10 days (median 4-6 days), with most estimates around 5.3 days. SARS transmission occurs primarily after symptom onset, particularly fever, with minimal evidence of presymptomatic transmission.
COVID-19 (2019-Present)
COVID-19 has caused over 779 million confirmed cases and 7.1 million deaths globally as of 2026, with a variable case fatality rate depending on healthcare access and population demographics. The virus spreads through respiratory droplets, aerosols, and surface contact. The mean incubation period is approximately 4.9-7.5 days, depending on the variant and population studied. Critically, 40-80% of COVID-19 transmission occurs 2-4 days before symptom onset, with presymptomatic individuals consistently accounting for over 50-52% of daily new infections.
Andes Hantavirus
Andes virus causes Hantavirus Cardiopulmonary Syndrome (HCPS) with a case fatality rate of 36-38%. While most hantaviruses transmit only through inhalation of aerosolized rodent excreta, Andes virus is unique among hantaviruses in its capacity for person-to-person transmission, which occurs during the early prodromal phase. The incubation period ranges from 7-39 days (median 18 days), with most cases showing symptoms within 14-32 days after brief exposure. A recent cruise ship outbreak in May 2026 reported 8 cases with 3 deaths. Person-to-person transmission has been documented in household clusters and confirmed through genetic sequencing in Argentina and Chile.
Public Health Official Misinformation
Over the last week, I have seen a constant ignorance (failing to read the literature) as well as “calm the masses” speeches. “All Hanta virus are the same”. I did see one news program that did an interview with an informed Harvard professor.
Causes for Anxiety
As you see above, N95 masks are being used for protection for Hanta virus. Properly fitted N95 respirators have a filtration efficiency of 95-99% for viral particles, translating to a failure rate of 1-5% under optimal conditions. To translate it, with 1 person on a flight with 100 souls, up to 5 new infection could be expected. If every one was wearing N-95 properly , then the odds of another new infection become 1 in 400. Personally, I use P100 masks. The failure rate of P100 respirators is approximately 0.03% for viral particles, compared to N95’s 1-5% failure rate. T
Protection Against Viral Infections
N95 masks reduce the risk of coronavirus infections (SARS-CoV-1 and SARS-CoV-2) by 70% compared to surgical masks (OR 0.30, 95% CI 0.20-0.44). When worn by infected individuals, duckbill N95 masks block 98-99% of COVID-19 viral particles from escaping into the air, reducing transmission risk by up to ninefold when used population-wide and threefold with individual use. [source]
Failure Rates and Limitations
While N95 respirators are highly effective, some penetration occurs at the most challenging particle size (~50 nm). Studies found that penetration rates can slightly exceed 5% at this size, though this may include viral fragments rather than viable infectious particles. The primary failure mode is improper fit rather than filter inadequacy—N95 masks with suboptimal fit still maintain >90% filtration efficiency, but leakage around the edges significantly reduces overall protection. [source]
What will the future reveal?
Detection issue:
For Andes virus specifically, RT-qPCR can detect viral RNA in peripheral blood cells 5-15 days before symptom onset and before antibodies appear. The test demonstrates 94.9% sensitivity and 100% specificity with a very low detection limit of approximately 10 viral copies [source]
So with 42 days before symptoms, a person with Hanta virus will test negative for 27 days (while being contagious), This is very different from the other two virus. The significance of this depends on other factors in the Markov matrix. The prior Chile and Argentina outbreaks was for a localized area (effectively local isolation). The current outbreaks have possible cases flying across the world.
Timeline of the 2026 Andes Hantavirus Cruise Ship Outbreak
Pre-Outbreak Period
November 27, 2025 – April 1, 2026: The index case (Case 1), a Dutch adult male passenger, traveled for four months on a road trip through Chile, Uruguay, and Argentina, where he likely contracted the virus.
April 2026
April 1: MV Hondius, a Dutch-flagged cruise ship, departed from Ushuaia, Argentina with 147 passengers and crew from 23 countries.ecdc.europa+1
April 11: Case 1 died onboard the ship; he is considered a probable case (no microbiological tests were performed).
April 24: The ship stopped at Saint Helena, where Case 1’s body was removed and his wife disembarked; 30 passengers total disembarked at this port.wikipedia
April 26: Case 1’s wife died in a Johannesburg, South Africa hospital.wikipedia
May 2026
May 2: The cluster of severe respiratory illness was officially reported to the World Health Organization (WHO) and CDC; at this time, 34 passengers and crew had disembarked from the ship.cdc+2
May 4: WHO confirmed the outbreak publicly and reported seven infections with three fatalities.pbs
May 6: WHO confirmed the specific hantavirus strain as Andes virus (ANDV) through PCR and sequencing; one additional case was identified.cdc+1
May 7: CDC sent a team to meet the cruise ship in the Canary Islands following its travel from Cape Verde; three ill passengers were evacuated.cdc+1
May 8: WHO reported eight total cases (six confirmed, two probable) including three deaths, for a 38% case fatality ratio; all confirmed cases tested positive for Andes virus.
May 9: CDC issued a Level 3 emergency response and classified the situation as a current outbreak; CDC began coordinating repatriation of American passengers to a specialized medical facility in Nebraska.
May 10: MV Hondius arrived at the port of Granadilla, Tenerife, Canary Islands; disembarkation and repatriation flights began.ecdc.europa
May 11 (as of 14:00): European Centre for Disease Prevention and Control (ECDC) reported nine total cases (seven confirmed, two probable).ecdc.europa
May 15th End of Isolation for persons who meet Patient 1 and did not sail on MV Hondius
June 25th: End of Isolation for persons who sailed on MV Hondius
June 25th: End of Isolation for persons who transferred people from MV Hondius (if N95 failure is considered)
Current Status
As of May 11, 2026, passengers are hospitalized across multiple countries including South Africa, the Netherlands, Germany, Saint Helena, Spain, France, and Switzerland. International contact tracing is ongoing through IHR National Focal Points for all passengers and crew who had contact with confirmed cases. The outbreak has drawn global attention as one of the largest and most high-profile hantavirus clusters in recent history, particularly concerning due to confirmed person-to-person transmission of Andes virus.
Worse Case Scenario
An airline staff flying patients home gets infected from N95 mask failure. This person proceed to fly for the next 5 weeks before becoming sick. This is estimated to having 1,680-2,880 unique passenger contacts. This person is likely to also infect all of their fellow workers, yielding over 10,000 exposures.
Fortunately, the airplane’s air filters do better than N95 so the actual numbers would be significantly less,
N95: ≥95% removal (often higher in practice, but certified at 95%).
The Saving Factor
R₀ (basic reproduction number): Average number of people one infected person will infect in a fully susceptible population.
basic reproduction number
Current Estimates from Literature
SARS
2-4
COVID
2-3
HANTA
< 1.0
If a mutation happens to increase R₀ then we are heading to a new lock down.. We have 8 cases from 1 individual (in a unique environment) which gives a possibility of R₀ being over 1.
Early this year I took one week of antibiotics to deal with possible developing cellulitis. About a year prior, I took the antibiotic for a prior incidence of cellulitis. I have been taking just one regular prescription drug (L) for the last few years.
About two weeks after finishing the antibiotics, I developed itching in the legs which I expected to just fade away. Suddenly I had eye edema as shown, rashes, etc. A lot of other symptoms that my wife (with verified by tests) Mast Cell Activation Syndrome.
At peak
Calming down
Our medical professional prescribed high dosage of multiple anti-histamines. After two weeks there was little progress. After a lot of prompting of an AI, it suggested “L” may be a contributor. I always took this at bed time, and symptoms became much worse at night. I stopped taking it, the result was significant improvement every day since. My wife has told me that I am looking more normal every day, but I still have some distance to go.
A hint of one possible cause?
Although many many symptoms matched my wife’s MCAS symptom, it appears that histamines were not the issue.
“L” breaks down bradykinin, so lisinopril can increase bradykinin levels; this helps vasodilation but is also linked to cough and angioedema risk.
“Bradykinin and mast cell activation can overlap because mast cells may help trigger the kallikrein-kinin system, which can increase bradykinin production. Bradykinin can also increase vascular leak and swelling, so some symptoms can look similar to MCAS flares.” Mast cell degranulation and bradykinin-induced angioedema – searching for the missing link, 2024
Bradykinin can be measured but not usually as a routine clinical test. In practice, doctors usually test for the cause of bradykinin-related swelling rather than measuring bradykinin itself, because bradykinin is very unstable and hard to measure directly in blood. The most direct method is a specialized blood test using LC-MS/MS or a similar lab technique that measures bradykinin and its breakdown products. Some research methods use special sample handling, like drawing blood into chilled tubes and processing it very quickly, because bradykinin can change fast after the blood is drawn, Bradykinin measurement by liquid chromatography tandem mass spectrometry in subjects with hereditary angioedema enhanced by cold activation ,2025
Excessive bradykinin can be treated. In my case, the treatment was easy, stopping L.
Also note: that all of the papers being cited are 2024 and later.
Microbiome Role?
Just before these events I did a microbiome test. In two more weeks I am planning to do a second test. I will attempt to identify possible changes and how such changes could have cause these change.
Stay tune!
Random Notes:
“L” half life is report to be around 50 hrs. using 5 half lives to fully clear “L”, that is 250 hours or 10 days. I am assuming even a small amount of this “toxin” is sufficient for triggering
Where can I find a practioner well versed on gut microbiome, mold, and possibly gastrology? I’ve been getting worse the last few years with my functional provider that I’m now dealing with low ferritin, oxalates, mold, Sibo, Candida, and malnutrition. This has thyroid and hormones out of whack along with increasing TM.
Response:The harsh reality is many promises, very few deliver. A Colleague is spending many hours a week trying to instruct medical professionals on the gut microbiome… he says that it is painful…
Why is this so?
Looking at studies on the microbiome on PubMed we see an explosion of knowledge. 228,197 studies cited for “microbiome”. The consequences are simple:
Any medical professional that graduated before 2017 likely has had near-zero training on the microbiome.
Medical professional tend to use “established cook-book recipes” for treatment. One key reason is medical liability insurance.
For a microbiome issue like Ulcers, it took almost 50 years for the treatment cookbook to be widely adapted.
Antibiotics working was reported in the 1950’s
Barry Marshall and Robin Warren in 1979 identified the bacteria
In the 1990’s the FDA approved the use of antibiotics.
My father suffered (nearly dying) from a bleeding ulcer in the 1960’s. That reality contributed to my not wanting to wait until treatments are approved by medical insurance companies.
Solutions
Conventional
There are a small number of people with the appearance of skills. I have a list here. The people have not been evaluated (buyer beware), but they are at least interested. I know that Kristina Mitts actively uses microbiome prescription and is also a writer for BiomeSight ,example of one of her posts.
Self-Serve
I suffered from ME/CFS (Chronic Fatigue Syndrome) multiple times and have a lot of contacts that still have it. Most of those people cannot afford to go conventional. The same people also suffer from brain challenges, so I started up a blog site for them: CFS Remission. That site spawned my Microbiome Prescription site.
The intent of the Prescription site is to be an adjunct resource for patients. It will generate lists of suggestions based on 14,425,455 facts extracted from 21,279+ studies on PubMed. It may also prepare a detailed list of suggestions with evidence and logic for a MD to review. An example for depression.
Medical professionals do not have time to review an average of 2000 new studies a year. A professional is lucky to review 2 studies a week, not 40. There is a need to alter how they learn.
The site does no use “gathering of hearsay” a.k.a. LLM or ChatGPT, but an older fuzzy logic expert system AI. This is very different from the “hot new AI’s”
I do not know the “right/best way” of determining suggestions. I compute suggestions that mathematics suggests having a greater chance of helping instead of hurting. From feedback and from 2nd sample analysis — it seems to improve people (Analysis Posts on Long COVID and ME/CFS).
The intent is for users to review the suggestions with their medical professionals and get their approval for the plan before starting. Most medical professional will identify any risky items (for example, “Round-Up” once showed up!!) and then say “Whatever, no concerns”
The site is free for individual use. Donations covers operating costs. I have no need to generate revenue from it (50 years in information technology paid well).
Postscript – and Reminder
I am not a licensed medical professional, and the laws where I live prohibit any activity that could be interpreted as practicing medicine or giving personal medical advice. My work is limited to academic and analytical models, and I restrict myself to the language of science and statistics rather than clinical recommendations.
I cannot tell anyone what they should or should not take. Instead, I can present information about items that, based on numerical and statistical analysis, appear to have better odds of improving microbiome-related measures. I am a trained, experienced statistician with appropriate degrees and professional affiliations, and my role is to interpret data—not to treat patients.
All information I provide is for educational and informational purposes only and is not a substitute for professional medical advice, diagnosis, or treatment. Any ideas, rankings, or “suggestions” derived from my analyses must be reviewed and approved by your qualified medical professional before you decide to act on them.
The answers and explanations I provide describe my reasoning and methodology. They are not intended as medical advice for you or for anyone else, and they do not create a doctor–patient or provider–patient relationship. Always consult a knowledgeable licensed healthcare professional before starting, changing, or stopping any treatment, supplement, or health-related regimen.
I wonder if you would be willing to write a blog post looking at my recent test results in comparison to last year? Confirmed diagnosis of ME/CFS. UK NHS only helps with pacing advice.
First BiomeSight test: 2025-04-17
Following this, 3 self-directed cycles of antibiotics, probiotics, prebiotics, and diet changes based on MicrobiomePrescription results. First 2 cycles increased my baseline and reduced symptoms dramatically, third cycle set me back slightly. Overall very positive.
Unfortunately then was hospitalised later in 2025 with a perforated and infected gallbladder, sepsis. They rotated through quite a few different harsh antibiotics trying to find one which worked. Then in December 2025 went in for surgery to remove the gallbladder, more antibiotics.
Second BiomeSight test: 2026-03-16
My baseline now is worse again, many symptoms returned. I am loathe to use more antibiotics while some of my bacteria are so low (Akkermansia at 0.006) even though my positive scores are dominated by antibiotic suggestions. Would like to focus on probiotics, prebiotics, herbs, supplements, diet changes for now.
Any insight would be most appreciated.
Confirming the Worst
Going over to the symptom compare tool, we see that you are now worse than a year ago. 140 of 141 symptom forecasts are significantly worse! Seeing number this much worse is unusual but consistent with his events and perception.
Going Forward
My last two post has been evaluating the alternative path — instead of attacking the bacteria causing symptoms, push the person to a statistically significant healthy microbiome. The following links may be worth a reading:
This approach matches “I am loathe to use more antibiotics” because antibiotics typically are on the avoid lists with the healthy approach and high on the to take list attacking symptoms. It is sitting on the Simple UI page.
Basic Results:
52 bacteria were identified — every single one was too high.
I have broken suggestions into classes below. In general, I have kept them to items with an impact of at least 1.
Items listed are order by largest impact first.
Herbs
The top herbs are below. I was delighted {Bofutsushosan} was listed because it is well known increases Akkermansia which he is concerned about.
Food
Flavonoids
Vitamins
Common and OTC Supplements
Probiotics x PubMed
This list is done using PubMed studies.
Probiotics x R2 Model
I prefer the R2 Model because we have a lot more data to use than with PubMed. On the flip side, this does not have clinical studies supporting the choices.
The top probiotic Bacillus thuringiensis suitable for human consumption may be a challenge. Most retail products are formulated to control caterpillars, worms, or mosquito larvae in gardens and standing water, not for ingestion or probiotic use.
DoMyOwn sells a dedicated Bt category and says it’s available through their store rather than big-box shelves.
FBN lists Bt ingredient-based products, including Bacillus thuringiensis subspecies tenebrionis.
DIY Pest Control lists Bt products and notes common trade names like Thuricide and Mosquito Dunks.
Summary
I look forward to see how well this alternative approach performs. It does not focus on the bacteria associated with his 141 symptoms — instead, we focus on shifting to a healthy microbiome profile (with very high statistical significance, p < 0.0001,) I would suggest retesting every 3-4 months to track progress.
Questions And Answers
Q: It’s interesting to see how some Odds Ratio based suggestions match with the Consensus Suggestions, and some vary wildly.
A: Suggestions are based on bacteria target and available literature. Literature is sparse and often without replication of results
The safest path would be to start with items that are in agreement.
Q: I had one question with regard to whole milk, dairy, and lactose. The Odds Ratio analysis suggested these were positives – this makes my life a lot easier as I was using milk to help ferment and increase the CFU of the probiotics I used last year and hoped to again, and I eat a fair amount of dairy in general (I mostly eat a vegetarian diet with occasional fish, and dairy helps with my protein intake).
However when I ran the Consensus Suggestions earlier this week I got scores of -294.9 for bovine milk products, -120 for whole cow milk, and -158.6 for lactose.
A: I favor the Odds Ratio. On this point as you have no issues with dairy, keep to your current usage.
Q: Does this mean I likely need to make a choice between the Consensus Suggestions route (which I followed last year) and the new Odds Ratio route?
A: No, you could start doing a consensus of the consensus and odds ratio. Then add in items that disagree. I would suggest using an ratio evaluation:
Consensus: -120 with min of -960, so -(120/960) = -12.5%
I am not a licensed medical professional, and the laws where I live prohibit any activity that could be interpreted as practicing medicine or giving personal medical advice. My work is limited to academic and analytical models, and I restrict myself to the language of science and statistics rather than clinical recommendations.
I cannot tell anyone what they should or should not take. Instead, I can present information about items that, based on numerical and statistical analysis, appear to have better odds of improving microbiome-related measures. I am a trained, experienced statistician with appropriate degrees and professional affiliations, and my role is to interpret data—not to treat patients.
All information I provide is for educational and informational purposes only and is not a substitute for professional medical advice, diagnosis, or treatment. Any ideas, rankings, or “suggestions” derived from my analyses must be reviewed and approved by your qualified medical professional before you decide to act on them.
The answers and explanations I provide describe my reasoning and methodology. They are not intended as medical advice for you or for anyone else, and they do not create a doctor–patient or provider–patient relationship. Always consult a knowledgeable licensed healthcare professional before starting, changing, or stopping any treatment, supplement, or health-related regimen.
This is the second review I have done since working through the implications of Mathematically Derived Healthy Microbiome. It highlights two very different strategies for improving gut health.
The earlier approach focuses on targeting bacteria that show statistical associations with symptoms. This approach often places many antibiotics near the top of the recommendations. When antibiotics are involved, I tend to favor the Cecile Jadin Protocol for ME/CFS.
The newer approach uses the revised model to target bacteria associated with a healthy, asymptomatic gut. In this approach, antibiotics often appear among the major items to avoid.
Both approaches are based on statistics, but the newer one has a much stronger statistical association.
The earlier approach has a track record of significantly improving the microbiome during the first few cycles. For some people, however, those improvements eventually stall. It also requires a friendly MD to prescribe the antibiotics, which is often a challenge.
If you apply the earlier approach one symptom at a time, the recommendations often contradict each other. “No man may serve two masters” becomes “No recommendations may heal two symptoms.”
The newer approach has no track record yet. It has only recently become available, and it is now being tried by someone whose progress has stalled.
So which one should you use? If you have a friendly MD, I would go with the earlier approach. If you do not, I would go with the newer approach.
Back Story
My symptoms have been somewhat confusing, but for many years ,like over 10 years ago I was constantly bloated with excessive wind/gas but also a lot of belching too. I would eat lots of wheat and sugar and processed foods. 2011 ended up on Proton-pump inhibitors (PPIs) on and off for over 10 years. 2016 appendix burst & got severe peritonitis and ended up very poorly, had 2 weeks of intravenous antibiotics. Slowly recovered.
Years of migraines and brain fog – but yet very active and social.
Then in 2022 I developed throat irritation that was exacerbated (i now believe by certain foods/Ingredients, alcohol, occasional smoking Definately fatty foods but I still cant quite put my finger on what made my throat irritation/ hoarse voice worse). I then developed Biliary Gastritis in October 2024 (Stomach lining erosion) likely from a possible intolerance just like the throat irritation. I became very constipated and still struggle with that.
Foods that make me worse maybe
Overly fatty foods
Possibly milk
Wheat, bread
Possibly some fruit
Big blood sugar spikes off things like carrots and oats, sweet potatoes, very sensitive to carbs and sugar (i wore a glucose monitor out of curiosity) I am not diabetic.
Initial Review
There are two distinct paths, or algorithms, available in Microbiome Prescription.
The prior approach, which I call Traditional, begins with a few straightforward questions:
Are you prepared to risk a severe Jarisch-Herxheimer reaction? It happens often.
If you are working, can you afford to miss a few weeks?
Do you have a friendly MD who is willing to prescribe a single course of each antibiotic listed below and become familiar with Jadin’s protocol?
The newer approach, Healthy Target, was recently added based on an odds-ratio model derived from healthy people. Instead of chasing symptoms, it shifts the goal toward a healthy gut. It does not address individual symptoms directly.
Building Suggestions
Probiotics
We actually have three ways of getting probiotics suggestions:
Traditional Approach
Healthy Target using Clinical Studies from PubMed
Data is very sparse on impacts
Healthy Target using the “R2 Model” (a statistical model, not clinical studies)
Data is rich on impacts
We are going to compare only the positive probiotics from the R2 model that are easily available and the top 3 of the other models.
Apart from Lactobacillus fermentum we have disagreement on positive or negative impact. A similar result is often seen when doing symptom by symptom with the traditional approach. We lack sufficient data to have certainty. Being a statistician, I favor the approach with the highest statistical significance — i.e. the novel or Healthy Target approach.
My Current Preference
There is nothing stopping a person trying one approach for 6-12 weeks and then retest; then switch to the other for 6-12 weeks; retest. Make sure that you keep detail notes on responses.
Sample Comparison Tool (example below)
comparing the Healthy Microbiome Estimate from the two sample
My impression is that the novel algorithm agrees better with their reactions. This shifts me further towards advocating for the novel algorithm.
Diet Plan
Often people want to simplify suggestions to one specific type of popular diet. This approach often defeat suggestions. Where diet are mentioned, they are secondary or tertiary guidance. Some generic diet studies appears in the suggestions, for example:
This is intended as supplemental information to refine other suggestions where there is not sufficient information. The diets are what is cited in the literature. Most diets tend to be poorly defined. The classic example is Mediterranean diet. Often the “US Version” fails on the seafood or lamb aspects.
The exact foods vary by region: Greek-style diets may include more yogurt, feta, olives, and seafood, while other Mediterranean areas may use more pasta, beans, lamb, or different local vegetables and herbs. Even the meal pattern can differ, but the overall theme remains plant-forward, minimally processed, and olive-oil based.
Another example is the low-fiber diet. It is usually defined to be under 10 to 15 grams of fiber per day (about 1/2 of the recommended amount of fiber). However, studies show that the US population averages 15-16 grams per day!! so most Americans are already on a low-fiber diet
From the recommendations given I would build a general food diet from:
1 cup of blackberries each day, some lingonberry if available at a reasonable price
Chicken as proteins source, no fish, little meat, no rare beef
I am not a licensed medical professional, and the laws where I live prohibit any activity that could be interpreted as practicing medicine or giving personal medical advice. My work is limited to academic and analytical models, and I restrict myself to the language of science and statistics rather than clinical recommendations.
I cannot tell anyone what they should or should not take. Instead, I can present information about items that, based on numerical and statistical analysis, appear to have better odds of improving microbiome-related measures. I am a trained, experienced statistician with appropriate degrees and professional affiliations, and my role is to interpret data—not to treat patients.
All information I provide is for educational and informational purposes only and is not a substitute for professional medical advice, diagnosis, or treatment. Any ideas, rankings, or “suggestions” derived from my analyses must be reviewed and approved by your qualified medical professional before you decide to act on them.
The answers and explanations I provide describe my reasoning and methodology. They are not intended as medical advice for you or for anyone else, and they do not create a doctor–patient or provider–patient relationship. Always consult a knowledgeable licensed healthcare professional before starting, changing, or stopping any treatment, supplement, or health-related regimen.
Hello, could you tell me which antimicrobials are okay to use without killing the good bacteria? I have hydrogen SIBO, methane SIBO, and hydrogen sulfide SIBO. I don’t want to make things worse because I no longer have bifidobacteria, lactobacilli, and Oxalobacter in particular. And I don’t want to take something broad-spectrum.
I was especially wondering about clove and thyme. I also have fungal issues and yeast in my body, possibly related to mold. Could you explain how to tell whether an antimicrobial is harmful to the good bacteria? Thank you
What is defined as good or bad?
The issue is not that simple as “good” or “bad”. Too much of a “good” bacteria is associated with a variety of conditions. Let us look at the research for two commonly believed “good” bacteria:
Lactobacillus is reported HIGH (from 119 studies) with
The Human Need for Simplicity versus Biological Reality
I am a high functioning autistic spectrum individual. Others in the spectrum include those with photographic memory and complete memory recall. I lack those, but where I excel is my tolerance for complexity and uncertainty.
Across my 50-year career in software development, I’ve noticed that code I find straightforward often overwhelms other developers. One once remarked, “Any JavaScript file over 200 lines is black magic to me,” while reviewing what I considered a simple application. That experience reflects something broader: people naturally seek simplicity, even when reality is irreducibly complex.
In the same way, many approach microbiome science by labeling bacteria as “good” or “bad.” This reduction helps those who feel saturated by excessive detail—but the truth is far more nuanced.
The Evolution of Microbiome Prescription
For more than a decade, my goal with the Microbiome Prescription project has been simple in principle:
Accept scientific evidence—a microbiome test.
Compute suggestions aimed at correcting dysbiosis.
The biggest challenge lies in determining which bacteria should shift, and in what direction. My early approach relied on lab-provided ranges: if a value was above range, reduce it; if below, increase it. But this method failed. Lab ranges are based on naïve averages and assume normal distributions. After teaching Ph.D.-level statistics, I knew better—bacterial populations follow heavily skewed distributions, not bell curves.
The next phase was to use symptom-annotated samples to mathematically model bacterial associations. When a new sample arrived, the system forecasted likely symptoms. Users checked which symptoms applied, improving both the model and predictive power.
Subsequent tests validated these forecasts: 53 predictions improved, while 19 worsened. It became clear that “gut health” cannot be captured by any single number. The ecosystem is too complex.
“No Protocol Can Serve Two Symptoms”
This phrase is an adaptation of Matthew 6:24: “No man can serve two masters.” When multiple symptoms are modeled independently, the results often conflict—what helps one symptom can worsen another. The earlier data illustrates this problem: 53 improvements, 19 regressions.
Rather than fighting symptoms individually, I began shifting focus toward the overall trajectory of health.
From Symptom Fighting to Health Trekking
A turning point came during an experiment using odds ratios derived from annotated microbiome samples—this time ranking bacteria by percentiles instead of percentages. Different labs report percentages inconsistently; percentiles normalize those variations (as discussed in this review).
Using 1,000 healthy individuals’ shotgun results from PrecisionBiome.EU, I noticed a striking pattern: “Asymptomatic: No Health Issues” consistently ranked as the top prediction.
That insight simplified everything. Instead of juggling countless symptom-specific models (10, 20, or even 200 symptoms), we can statistically track a single target—how far a sample deviates from “asymptomatic.” See definition here.
Now we’re just juggling one ball.
Reality vs. Model
The refined model depends on detailed microbiome tests—at least 16s sequencing, shotgun preferred—and percentile rankings for each bacterium. Unfortunately, most labs don’t provide percentile data. From Biomesight and Ombre, I can derive percentiles accurately from their percentage data. Some others attempt to estimate percentiles by assuming a bell curve—again, incorrect.
Recommendations for Individuals
Before ordering a microbiome test, confirm that it allows downloadable data with:
Percentile and percentage values.
Bacteria identified by NCBI Taxon numbers.
Recommended providers: Ombre or Biomesight (for better percentile reliability).
After testing, upload your results to Microbiome Prescription and simply click to start analysis.
Older analytical methods remain available and effective for many users, though progress may plateau for some. See Another ME/CFS Microbiome Update for details.
This is part of this continuing saga with this person. Prior posts and the labs shown below. Repairing the microbiome is not a single test, take a pill, and you are done. It may be like a long journey by sail through the fjords of Norway: a lot of course corrections!
I would say that there is no improvement since the last test. So this is still applicable:
I have not been feeling so well lately (since the last year). I would say that my symptoms has become worse. Earlier it has always felt as I have done some progress but the last 18 months it has been the opposite. Earlier I got rid of my muscle and joint pain but it has come back and I have much bigger issues with my red nose and my body feels very stressed.
Also feel very bloated.
A summary of my biggest issues:
Get the red nose (some form of rosacea).
Feel fatigued (both physically and mentally).
Feeling stressed.
Brain fog.
Bloated.
Lots of gas – I fart and burps a lot.
Issues with allergies
Muscle and joint pain
For the last 4-5 years I’ve been eating large amounts of rye and oats.
Around 150-200 gram of rye bread every day.
Around 70 gram of oats every day.
Been eating low fat, low protein and high carb (specially from rye, oats, apple juice and potatoes) because this diet seem to reduce my symptoms. As soon as I start to eat high meat and high fat my symptoms get worse.
In this analysis, I am going to look at:
Changes since the last sample
Review a new approach that is being incorporated
Looking at suggestions and the difference between the new approach and the traditional approach
At the end, I suggest following the new approach with the motivation that the traditional approach has appeared to have stalled. The microbiome adapts to antibiotics and diet changes; rotation to alternatives often seems to be needed to keep destabilizing the microbiome dysbiosis.
Changes Between Samples
Going to Old UI/Multiple Samples we compare symptom matching values. We see that just 1 of 42 showed improvement.
Looking at the new Odds Ratio data, we see the number of bacteria identified as critical in different samples below / Odds Estimate. I am not clear on the meaning and significance…
Odds Estimate: The higher the number, the more likely that the person is healthy
Number of Bacteria: Not reliable because different bacteria contribute differently to health.
2026-03-06: 59 / 1632
2025-11-17: 36 / 1588
2025-03-30: 20 / 1671
2024-12-03: 21 / 1561
2024-09-02: 36 / 1611
2024-01-22: 58 / 1586
2023-09-12: 38 / 678
2023-02-22: 52 / 1707
2022-08-11: 30 / 886
2022-03-25: 24 / 1037
2021-12-03: 15 / 1287
2021-08-31: 49 / 757
My general reading is that from 2021-2024 there was improvement and now the person is in a new stable healthier state but with still dysbiosis. I am hoping that the switch to an alternative view of solving his health may result in further improvement. In other words, rotation of approaches.
Another View on the Same Issue
In my recent post, Turning Fixing the Microbiome Upside Down!, I introduced a different way to think about repairing the microbiome. A human–society analogy might make it clearer.
Imagine your city is struggling with homelessness, vagrancy, and petty crime. The usual response—especially in the U.S.—is to send in the police. Round up those panhandling on the streets! In microbiome terms, that’s like identifying “bad bacteria” and launching an attack.
But there’s another approach: offer housing, mental health care, and job training. You don’t punish people—you help them heal and reintegrate.
Traditionally, Microbiome Prescription has focused on detecting problematic bacteria and trying to suppress or adjust them. The challenge is that most people have many interconnected symptoms. Research often shows that substance X improves one symptom but worsens another. You end up chasing symptoms—fixing one only to see another emerge or intensify.
A more holistic alternative, which has only recently become possible, is to guide the person’s microbiome toward a naturally healthy state instead. See this post: Mathematically Derived Healthy Microbiome.
Recently I asked the head of a microbiome testing company, what statistical evidence do you have for what is a healthy or desired microbiome profile. How do you obtain the importance of each bacteria? He knew that using means and standard deviation were invalid because of the high skew with the data. His response was requesting his staff to remedy this situation, looking at odds-ratio as a starting point.
Evaluation
I am a modeler, not a medical professional. Modelers try putting together mathematics using available data and use that to generate predictions. Once the predictions are made, they are evaluated against any available facts.
Above we have some observations from the person, the model does not know this information — so we can evaluate predictions against this data.
Been eating low fat, low protein and high carb (specially from rye, oats, apple juice and potatoes) because this diet seem to reduce my symptoms. As soon as I start to eat high meat and high fat my symptoms get worse.
Each of the above depends heavily on the bacteria selected and the threshold used. It is interesting to see that the new “Make Healthy” is a clear winner against his observations.
What is particularly interesting with the “Make Healthy” is that values were computed for 1,632 substances. Looking at the list os suggestions, we do not have a mass of antibiotics seen on the other lists. We are not focused on reducing bad bacteria, rather on improving the good bacteria, and letting those address the bad bacteria. The top items are below.
N-acetylneuraminic acid {Sialic acid} : a bioactive sugar involved in cell signaling, glycoproteins, glycolipids, and is abundant in the brain; it is linked to cognitive function, memory, and immune function in general mechanistic descriptions
My impression is that this is a much friendlier set of suggestions. In fact, the bottom of the list (to avoid) are pages of antibiotics and prescription drugs.
Probiotics Exploration
There are two ways of getting probiotics:
Using published studies on their impact. In general, each study describes one or two bacteria impacted. This results in low data
Using the R2 Associations: This is a modelling of their impact with hundreds of bacteria impact estimated.
The new Healthy Algorithm includes R2 recommendations
We got the following suggestions
We will explore how different algorithms evaluate these.
Bacteria
Healthy With R2
Healthy With Studies
Symptoms With Studies
Novice with Studies
Streptococcus thermophilus
1988
-1 to 4
-132 to -139
-132 to -139
Enterococcus faecalis
1291
.6
-579
-579
Bifidobacterium infantis
1124
-2
76
76
Bifidobacterium breve
377
1
213
213
Bifidobacterium longum
365
1
-1423
-1423
Bacillus thuringiensis
96
n/a
n/a
n/a
Pichia kudriavzevii
38
n/a
n/a
n/a
Acidipropionibacterium acidipropionici
22
n/a
n/a
n/a
Aspergillus oryzae
15
.1 – 3
-192 to 8
-192 to 8
Lactobacillus acidophilus
4
0
-574
-574
Keeping to the “When in disagreement, leave it out” a.k.a. Minimal Risk a.k.a. “Do not harm”, we have
Top choice is Bifidobacterium breve
Reasonable choice is Bifidobacterium infantis
Alternatively, Streptococcus thermophilus high value, cheap, and easy availability — it is a good candidate to try a 2-4 week experiment.
Personally, I would be tempted to try the following pattern (starting at a low dosage and increasing):
3 weeks of Streptococcus thermophilus (up to 10 BCFU)
2 weeks of Bifidobacterium breve (up to 20 BCFU)
2 weeks of Bifidobacterium infantis (up to 20 BCFU)
Bottom Line
My personal choice would be to go with the “Healthy Algorithm” for the following reasons:
The traditional approach has appeared to stall, time for a change
It is heavily based on very statistical significance over the entire scope of bacteria involved (i.e. dense data) but it has not been validated by clinical studies.
I have always been unhappy about clinical studies because the data is:
Messy (typically in the context of one or another medical condition)
Small sample sizes
Low resolution to bacteria
I am also curious to see how well the “Healthy Algorithm” performs.
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 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.
A reader asked why different models to generate suggestions for their microbiome disagreed for a probiotic.
Two primary data sources exist regarding the effects of Bifidobacterium longum subsp. infantis: clinical studies and computational modeling data. I pulled some statistics from my databases.
Two primary data sources exist regarding the effects of Bifidobacterium longum subsp. infantis: clinical studies and computational modeling data.
1. Clinical studies (PubMed):
A total of 34 clinical studies were identified, many of which involved participants with existing medical conditions.
Across these studies, an impact was reported on only 24 bacterial taxa.
Among these, six taxa showed consistent results (replication)
The remaining 18 taxa were each reported as impacted in only a single study, indicating a lack of replication.
Statistical significance in these studies was typically defined as (P < 0.05).
Moreover, each study employed distinct microbiome testing methodologies, contributing to substantial variability—a phenomenon sometimes referred to as the “taxonomy nightmare.”
2. Computational model (R2 Model):
Analysis using the R2 Model (link) identified statistically strong associations between B. longum subsp. infantis and 73 bacterial taxa among healthy individuals.
The statistical significance threshold in this dataset was generally (P < 0.00001),
All samples were processed using a standardized microbiome testing pipeline, eliminating cross-platform variability.
Interpretation: Medical professionals predominantly rely on clinical trial data when evaluating probiotic efficacy, often without critically assessing methodological consistency or statistical robustness. Consequently, computational models such as R2—despite their reproducibility and rigor—are often perceived as opaque or “black box” approaches.
Example: Bacteroides
The following probiotics report different results (i.e. one study report increases, a different study report decreases)
bacillus subtilis {B.Subtilis }
Bifidobacterium animalis {B. animalis}
bifidobacterium longum {B.Longum }
Lacticaseibacillus casei {L. casei}
Lacticaseibacillus paracasei {L.paracasei}
Lacticaseibacillus rhamnosus {l. rhamnosus}
lactobacillus acidophilus {L. acidophilus}
Lactobacillus plantarum {L. plantarum}
Limosilactobacillus fermentum {L. fermentum}
Limosilactobacillus reuteri {L. Reuteri}
Example: Clostridium
The following probiotics report different results (i.e. one study report increases, a different study report decreases)
bacillus subtilis {B.Subtilis }
Bifidobacterium animalis {B. animalis}
bifidobacterium longum {B.Longum }
Heyndrickxia coagulans {B. coagulans}
Lactobacillus plantarum {L. plantarum}
Limosilactobacillus reuteri {L. Reuteri}
Example: Lachnospiraceae
The following probiotics report different results (i.e. one study report increases, a different study report decreases)
One famous story of operations research success during the war involved an analysis of Allied bombers returning from bombing missions over Europe. The military analyzed the location of shrapnel damage and bullet holes in returning bombers, to identify where to place additional armor on aircraft. Operations researchers were brought in at the last minute to do a “confirmatory” analysis, but they recommended that additional armor be placed on bombers everywhere except the places with damage or bullet holes! The operations researchers realized that analyzing damage to returning bombers involved a sampling error. It was the bombers that did not return that needed extra protection—and they needed it in the most vulnerable places (the places not damaged on the returning bombers).
In the past, most medical work has focused on bacterial shifts statistically linked to individual symptoms. Think of the “bullet holes” problem: the literature usually looks at one symptom at a time, while real patients often present with dozens.
Recently, I started using odds ratios instead. Most people know odds ratios from smoking and cancer risk, but they can be applied broadly—for example, the odds of working at Microsoft if you graduated from a particular university.
Using about 5,500 samples and roughly 350 symptoms, I built odds tables and then tested those odds ratios against a reference set of healthy individuals. To my surprise, sample after sample showed the highest odds for being asymptomatic, far more often than I expected.
On reflection, this implies we now have a well-defined, statistically grounded model of a healthy (asymptomatic) microbiome. That was the “lightbulb” moment.
Instead of hunting for individual “holes” and trying to patch them, we should look at all the shifts away from this asymptomatic model. Once those shifts and their contributions (odds ratios) are identified, we can use published research to determine what is most likely to normalize the microbiome. A long list of symptoms (bullet holes) stops being the target; the real target becomes making the microbiome asymptomatic.
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