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.
The organization Vitract.com recently referenced Jona Health during a conference call, noting that Jona’s platform reportedly incorporates approximately 200,000 studies. This claim prompted closer examination, particularly regarding how such figures are defined and communicated.
Public-facing descriptions of Jona’s methodology indicate that its system has “read” approximately 220,000 peer-reviewed studies, with an ongoing ingestion rate of roughly 2,000 new studies per month as microbiome research evolves. However, the distinction between studies that are “read” versus those that are critically evaluated and actively utilized is nontrivial. The use of the term “read” appears to function as a marketing construct, potentially conflating exposure to literature with meaningful incorporation into a validated analytical framework.
For comparative purposes, equivalent metrics from the Microbiome Prescription database demonstrate substantially greater scale and curation rigor. The system has processed a total of 2,953,169 studies—an order of magnitude greater than the figures cited above. Recent weekly ingestion rates further illustrate this difference:
May 29, 2026: 5,871 new studies
May 22, 2026: 4,984 new studies
May 15, 2026: 5,584 new studies
May 8, 2026: 5,582 new studies
Importantly, each study undergoes manual review prior to inclusion, reflecting the inherent complexity and nuance of microbiome literature that cannot be reliably interpreted through automated methods alone.
More critical than raw ingestion counts is the subset of studies that yield actionable, high-quality data. Within the Microbiome Prescription system, 21,391 studies have been identified as containing usable information and are actively incorporated into the knowledge base. These curated studies underpin approximately 14,518,553 PubMed-derived data points within the expert system. In addition, the platform includes approximately 71,000 experimentally derived bacterial interaction data points sourced from raw datasets.
These distinctions underscore the importance of evaluating not only the quantity of literature processed but also the depth of curation and the proportion of data that is methodologically sound and practically usable.
In conclusion, numerical claims regarding literature scale should be interpreted with caution, particularly when used in marketing contexts. The critical question is not how many studies are nominally “read,” but rather how many are rigorously evaluated and meaningfully integrated into a reliable analytical framework. Microbiome Prescription operates as a not-for-profit, citizen science initiative with the explicit goal of advancing microbiome-informed decision-making through careful curation and transparent methodology, rather than promotional positioning.
Bottom Line
As with all things marketing “Where’s the beef?” and not the hype. Microbiome Prescription is a not profit seeking citizen science endeavor seeking to improve the use of the microbiome.
During my Probability and Statistics studies in the early 1970s, I developed a strong interest in Markov chains. The core idea behind a Markov process is straightforward: the next state of a system depends only on its current state and a set of transition probabilities. Given that the microbiome is full of interactions, it seems the ideal model.
In practical terms, this can be represented as a matrix—similar to an Excel spreadsheet—where each column represents an intervention or event, and each row represents a state variable. When a given event occurs, its associated column of values describes how each variable is expected to shift.
To illustrate this concept in a microbiome context, consider a simplified model using R²-derived relationships between probiotics and bacterial taxa. In this matrix, each value represents the directional influence of a probiotic on a specific bacterium, where zero indicates no measurable effect.
Example interaction matrix:
Target Bacteria
Pro 1
Pro 2
Pro 3
Pro 4
A
-0.23
0.44
0.11
0.00
B
0.2
0.32
-0.22
0.14
C
0.18
-0.11
0.11
0.12
D
-0.31
0.13
0.22
-0.28
From this, we can evaluate each probiotic independently by applying its column to the current microbiome state and observing whether each bacterium moves toward or away from its target range.
A simplified qualitative interpretation might look like this:
Target Bacteria
Pro 1
Pro 2
Pro 3
Pro 4
A
n/a
worse
worse
need improvement
B
worse
worse
better
worse
C
n/a
worse
better
better
D
n/a
worse
worse
better
In this example, Probiotic 1 appears to be the best initial choice, as it minimizes negative outcomes relative to the others.
Once the first intervention is applied, we update the microbiome to its predicted new state. This updated state becomes the input for the next evaluation cycle. For instance, after adjusting bacterium B, we might find:
Target Bacteria
Pro 1
Pro 2
Pro 3
Pro 4
B
worse
worse
better
n/a
This suggests that Probiotic 3 is the most suitable follow-up intervention for B.
In practice, this process must be applied across all bacteria simultaneously—including those currently within the acceptable range—to generate a full predicted microbiome after each intervention. The goal is to evaluate all candidate substances and select the one that produces the greatest overall improvement.
By iterating this process, we can construct a sequence of interventions such as:
Herb 1
Probiotic 2
Diet Change 3
Once a candidate sequence is identified, it is important to test whether the order of interventions materially affects the outcome. This can be done by randomizing the sequence and comparing predicted results. If the sequence proves immaterial, then some interventions may be applied concurrently rather than sequentially.
That is the basic concept, the mathematics are a little more complex. How do you estimate the amount of shift?
A Rule of Thumb
My working assumptions are:
All bacterial abundances are converted to percentiles, with defined target percentile ranges.
For probiotics, assume a ±10 percentile shift scaled by the R² relationship for a given bacterium.
For other substances, estimate impact based on available studies:
One study: approximately 1 percentile shift.
Mixed evidence: net effect equals positive studies minus negative studies (e.g., 8 positive and 2 negative yields a 6 percentile shift).
Cap the maximum effect at 10 percentiles regardless of study volume.
These values are approximations and likely imperfect, but they provide a consistent framework given current data limitations.
Method Summary
Convert microbiome measurements into percentiles.
Identify bacteria that fall outside their target ranges.
Apply each candidate intervention to the current state and compute the predicted microbiome.
Select the intervention that produces the greatest reduction in out-of-range bacteria (or other chosen objective function).
Update the microbiome to this predicted state.
Repeat the process until all bacteria are within range or no further improvement can be achieved.
The result is an ordered sequence of interventions designed to progressively normalize the microbiome. Questions of dosage, duration, and clinical appropriateness are intentionally excluded from this model and should be addressed by qualified professionals.
This is a major transition from “Let us try this and see what happens” to an objective/numeric prioritization based on a reasonable mathematic model. Odds are, that the results will be better for the patient.
I am going to use microbiome data from the 1998 Alison Hunter Memorial Foundation conference on ME/CFS. The pattern reported is very common with sufferers of ME/CFS and Long COVID. I will use that report to phrase some questions. In theory, this is what could have been suggested by a progressive MD using AI in 1999. At the bottom are some comments from reviewing these responses. I have not mentioned the condition to the AI.
With a microbiome with decreased E.Coli (50% below normal), Bifidobacterium, Lactobacillus AND increased klebsiella/Enterobacter and Enterococcus how should a patient diet and supplements be changed?
With a microbiome with decreased E.Coli (50% below normal), Bifidobacterium, Lactobacillus AND increased klebsiella/Enterobacter and Enterococcus what prescription drugs be given?
Remember, with multiple answers from different experts:
Just go there and copy the above questions for full details including references.
A high-fiber, plant-rich diet is the most evidence-supported single intervention — it creates SCFA production, lowers colonic pH, and suppresses Enterobacteriaceae overgrowth
Prebiotics (inulin, FOS, GOS) selectively stimulate Bifidobacterium and Lactobacillus and attenuate Enterobacteriaceae blooms
Berberine shows antimicrobial activity against Klebsiella and Enterococcus at gut-relevant concentrations but caution is warranted as it may paradoxically increase Enterococcus in some contexts
Avoid refined carbohydrates, simple sugars, and ultra-processed foods — these preferentially fuel Enterobacteriaceae and Enterococcus growth
Importantly, note that the low E. coli finding is likely a relative reflection of the Enterobacteriaceae dysbiosis, not an independent deficiency requiring specific treatment
Simple sugars feed pathogenic bacteria and worsen dysbiosis
Feed Bifido & Lactobacillus
High diverse fiber from non-starchy veggies: leafy greens, broccoli, cabbage, zucchini, cucumbers, bell peppers, mushrooms, asparagus (if tolerated), artichokes
Fiber → short-chain fatty acids (SCFAs) that protect against pathogens and support beneficial bacteria
Low-FODMAP prebiotics if SIBO symptoms: start with psyllium husk, small amounts of oats, berries, citrus, carrots, green bananas
Supports good bacteria without excessive fermentation; high-FODMAP prebiotics (garlic/onion/jerusalem artichoke) can worsen SIBO initially
Use under professional guidance only; cycle (e.g., 2–4 weeks), then re-test. Avoid long-term use.
E. coli probiotic (Mutaflor/Symbioflor)
Contains specific E. coli Nissle strain to replenish low E. coli; used clinically for gut balance
Discuss with clinician; some protocols pulse then maintain
Prescription
No specific “best” prescription drug is indicated purely by this microbiome pattern; if treatment is needed, clinicians usually treat it as small intestinal/colonic bacterial overgrowth with short courses of narrowest‑possible antibiotics guided by symptoms, cultures, and resistance patterns, plus a prescription‐grade probiotic such as E. coli Nissle in some cases.
A Mediterranean-style dietary pattern rich in fruits, vegetables, whole grains, legumes, nuts, olive oil, and fish is the best-supported overall framework. This pattern is associated with favorable gut microbial profiles, increased fiber-degrading beneficial bacteria (including Faecalibacterium and Ruminococcus), and lower subclinical intestinal inflammation. [1-3] Ultraprocessed foods should be minimized, as they are linked to dysbiosis. [1]
Key dietary components to emphasize:
Prebiotic fibers — particularly fructans (inulin, FOS) and galacto-oligosaccharides (GOS) — have the strongest evidence for selectively increasing Bifidobacterium and Lactobacillus abundance. A meta-analysis of 64 RCTs (n=2099) demonstrated significantly higher Bifidobacterium (SMD 0.64, P<0.00001) and Lactobacillus (SMD 0.22, P=0.02) with dietary fiber interventions, with fructans and GOS showing the greatest effect. Food sources include chicory root, garlic, onions, leeks, asparagus, bananas, and Jerusalem artichokes. [4]
Resistant starch (found in cooked-and-cooled potatoes/rice, green bananas, legumes, and whole grains) acts as a fermentable substrate that promotes Bifidobacterium and Lactobacillus growth and increases butyrate production. [5-6]
Polyphenol-rich foods — berries, apples, grapes, green tea, dark chocolate, and extra-virgin olive oil — may help suppress Enterobacteriaceae (including Klebsiella). In vivo studies show resveratrol, phloretin (apples/strawberries), and other polyphenols can reduce Enterobacteriaceae levels while promoting beneficial bacteria, though most data are from animal models. [7-8]
Lactose restriction may be worth considering given the Enterococcus overgrowth. A recent study in a transplant unit found that implementing a lactose-free diet significantly reduced VRE carriage (16% → 3.6%), consistent with evidence that lactose can fuel Enterococcus growth. [9]
Fermented foods — yogurt, kefir, kimchi, sauerkraut, miso — provide live Lactobacillus and Bifidobacterium species that can transiently colonize the gut and modulate the microbiome. A high-fermented-food diet has been associated with increased microbial diversity and decreased inflammatory markers. [10-12]
Supplement Recommendations
Probiotic supplementation with multi-strain formulations containing Bifidobacterium (B. longum, B. lactis, B. bifidum) and Lactobacillus (L. rhamnosus, L. acidophilus, L. plantarum, L. paracasei) species is the most direct approach to replenishing depleted populations. L. paracasei CNCM I-3689 has specifically been shown to reduce Enterococcus persistence and promote Bacteroidetes recovery in a dysbiosis model. Probiotics suppress pathogens through competitive exclusion, bacteriocin production, pH lowering, and gut barrier enhancement. [13-17]
Prebiotic supplements — inulin-type fructans (ITF) and arabinoxylan-oligosaccharides (AXOS) — selectively stimulate Bifidobacterium growth and promote butyrate production through cross-feeding with butyrate-producing bacteria. A synbiotic approach (combining prebiotics with probiotics) may enhance probiotic colonization and provide synergistic benefits. [14][18-19]
Butyrate supplementation or butyrate-promoting strategies may help strengthen the gut barrier and create an environment less favorable to facultative anaerobes like Klebsiella and Enterococcus, as butyrate lowers colonic pH and supports epithelial integrity. [16][18]
Prescription
It is important to emphasize that no prescription drugs are FDA-approved specifically for treating gut dysbiosis as identified by stool microbiome testing. The clinical significance of specific bacterial abundance thresholds on commercial microbiome panels remains debated, and treatment should be guided by symptoms and clinical context rather than microbiome test results alone. That said, several prescription medications can be considered based on the specific dysbiotic pattern described:
Rifaximin (Xifaxan) — The Most Relevant Prescription Option
Rifaximin 550 mg TID for 14 days is the prescription drug with the strongest rationale for this dysbiotic profile. Unlike conventional antibiotics, rifaximin has unique “eubiotic” properties — it selectively increases Lactobacillus and Bifidobacterium abundance while suppressing pathobionts, without significantly disrupting overall microbial diversity. [1-3] Metagenomic analyses have confirmed a significant increase in Lactobacilli persisting in the short term after rifaximin treatment, independent of the underlying disease. [2] Rifaximin also reduces bacterial virulence and translocation, and has anti-inflammatory effects via PXR nuclear receptor activation. [4] The ACG recommends rifaximin as first-line therapy for SIBO at 550 mg TID for 14 days, with an overall ~70% eradication rate. [5-8] However, SIBO is not an FDA-approved indication, and insurance coverage may be challenging. [5]
E. coli Nissle 1917 (Mutaflor)
For the specific finding of depleted E. coli, E. coli Nissle 1917 (marketed as Mutaflor) is a prescription-grade probiotic available in some countries (Germany, Canada) but not FDA-approved in the United States. It is the only probiotic recommended in ECCO guidelines as an effective alternative to mesalazine for maintenance of remission in ulcerative colitis. [9] EcN stimulates human β-defensin 2 production, protects the mucosal barrier, and has demonstrated antagonistic effects against Salmonella, Shigella, and other invasive commensals. [10-11] It directly addresses the depleted commensal E. coli niche.
If the Klebsiella/Enterobacter overgrowth is causing clinical infection (not merely colonization), targeted antibiotics may be warranted:
For susceptible Enterobacterales: fluoroquinolones (ciprofloxacin), TMP-SMX, or amoxicillin-clavulanate based on susceptibility testing [5-6]
For AmpC-producing Enterobacter: cefepime is recommended by IDSA guidelines [12-13]
For ESBL-producing organisms: carbapenems (meropenem, imipenem) are preferred for serious infections [14-15]
However, broad-spectrum antibiotics for asymptomatic colonization are not recommended, as they would likely worsen the underlying dysbiosis. [6]
Antibiotics Targeting Enterococcus Overgrowth
Similarly, Enterococcus overgrowth in the absence of clinical infection does not typically warrant antibiotic treatment. If treatment is needed:
Ampicillin/amoxicillin is preferred when susceptible [16]
For VRE: linezolid or daptomycin have demonstrated clinical efficacy [16]
A novel decolonization approach combining bowel preparation with polyethylene glycol, oral linezolid + non-absorbable daptomycin, followed by Lactobacillus rhamnosus GG to maintain colonization resistance, has shown success in clearing VRE carriage in a small case series. [17]
Low-fermentation, low-starch approach initially: Klebsiella and Enterobacter thrive on resistant starch and certain fibers, producing gas, LPS, and histamine. A temporary low-FODMAP + low-starch phase (2–4 weeks) helps reduce their load.
Prebiotics with caution: Instead of high-FODMAP fibers (inulin, chicory, large amounts of legumes), use acacia fiber or PHGG (partially hydrolyzed guar gum) in small, increasing doses—these favor Bifidobacteria without overly feeding Klebsiella.
Polyphenol-rich foods: Berries, green tea, pomegranate, dark chocolate (>70%), and olive oil. Polyphenols inhibit Enterococcus and Klebsiella while promoting Lactobacillus.
Animal protein + low-carb vegetables: Grass-fed meat, eggs, fish, and leafy greens—limited fermentation in the colon.
Fermented foods if tolerated: Small amounts of sauerkraut, kimchi, or kefir (start with 1 tbsp) can reintroduce beneficial strains, but watch for histamine reactions due to possible gut permeability.
Colostrum or immunoglobulin G (IgG) – binds LPS from Enterobacter and helps clear pathobionts.
Butyrate (tributyrin or resistant starch only after pathogens reduced) – feeds colonocytes, suppresses Enterococcus.
3. Special considerations
Low E. coli means reduced production of colicins (natural antimicrobials against Klebsiella). In some contexts, supplementing a non-pathogenic E. coli strain (like E. coli Nissle 1917, Mutaflor) can be beneficial, but use only after pathogen load is reduced, and under medical guidance (risk of UTI in susceptible patients).
Low Bifidobacterium and Lactobacillus → consider human milk oligosaccharides (HMOs) like 2’FL (1–2 g/day) – very specific prebiotic for Bifidobacteria, minimally used by Klebsiella.
Biotin and B vitamins – overgrowth of Klebsiella/Enterococcus consumes biotin; supplementation may help host metabolism.
Prescription
💊 1. Treating the Overgrowth: A Targeted Approach
Prescription antibiotics are only indicated if the overgrowth of Klebsiella, Enterobacter, or Enterococcus is causing a confirmed active infection (e.g., urinary tract infection, pneumonia, bacteremia) or a diagnosed condition like Small Intestinal Bacterial Overgrowth (SIBO).
If treatment is necessary, the choice is not one-size-fits-all. Because these bacteria have high rates of antibiotic resistance, therapy must be guided by antibiotic susceptibility testing (culture and sensitivity) .
Overgrown Organism
Condition
Potential Prescription Approach
Klebsiella / Enterobacter
Confirmed SIBO or specific infection
Rifaximin is a non-absorbable antibiotic often used for SIBO, but studies show its effectiveness varies significantly. It inhibits only about 43.6% of Klebsiella and 34.8% of Enterobacter strains . For systemic infections, other antibiotics like third-generation cephalosporins, carbapenems, or fluoroquinolones may be used, but this depends entirely on resistance testing .
Enterococcus
Confirmed infection (e.g., UTI, bacteremia)
Ampicillin or amoxicillin are the agents of choice for susceptible strains . High-level resistance is common, especially with E. faecium, often requiring infectious disease consultation for drugs like linezolid or daptomycin .
Important note on antibiotics: Using systemic antibiotics to target the bacteria in the gut lumen is generally avoided. It will likely worsen the underlying dysbiosis by further reducing beneficial Bifidobacterium and Lactobacillus, which are already low .
🔄 2. Restoring the Microbiome: Fecal Microbiota Transplantation (FMT)
Rather than using a drug to kill the “bad” bacteria, a more logical approach to restoring the balance of E. coli, Bifidobacterium, and Lactobacillus is Fecal Microbiota Transplantation (FMT).
FMT involves infusing healthy donor stool into the patient’s intestine to directly restore a diverse and balanced microbiome. However, its use is currently restricted by clinical guidelines:
Approved Use: FMT is strongly recommended by the American Gastroenterological Association (AGA) for patients with recurrent Clostridioides difficile infection (CDI) .
Off-Label and Experimental: For the specific pattern of dysbiosis you described (without a CDI diagnosis), FMT is not recommended outside of clinical trials . While Chinese expert consensus suggests it may be explored for severe gut dysbiosis, this is not a standard or approved practice in most other regions .
🧫 A Note on E. coli Restoration
You noted that E. coli is 50% below normal. While E. coli is often thought of as a pathogen, certain strains are a normal part of a healthy gut microbiome. A specific probiotic called E. coli Nissle 1917 has been studied for its ability to support gut health. However, this is generally available as a medical food or supplement, not a standard prescription drug, and should only be used under the guidance of a physician. This is not a standard treatment in most clinical settings.
The most evidence-supported approach is usually to encourage beneficial bacteria through diet rather than trying to eliminate specific organisms.
Increase prebiotic fibers gradually
These preferentially support Bifidobacterium and other beneficial microbes:
Onions
Garlic
Leeks
Asparagus
Jerusalem artichokes
Oats
Barley
Legumes (if tolerated)
Green bananas
Cooked-and-cooled potatoes or rice (resistant starch)
If bloating is severe, introduce these slowly.
Increase diverse plant intake
Studies consistently associate greater microbial diversity with consuming a wide variety of plant foods.
A common target is:
25–40 g/day fiber (depending on tolerance and medical conditions)
20–30 different plant foods per week
Consider fermented foods
If tolerated:
Yogurt with live cultures
Kefir
Sauerkraut
Kimchi
Miso
These may help increase exposure to beneficial microbes, though they do not necessarily permanently colonize the gut.
Limit factors associated with Enterobacteriaceae overgrowth
Klebsiella and Enterobacter belong to the family Enterobacteriaceae.
Potentially helpful:
Reduce ultra-processed foods.
Reduce excessive refined sugars.
Moderate alcohol intake.
Ensure adequate fiber.
Evidence for aggressively restricting carbohydrates specifically to suppress these organisms is limited.
Supplements that may be discussed with a clinician
Bifidobacterium-containing probiotics
Given low Bifidobacterium, strains commonly studied include:
Bifidobacterium longum
Bifidobacterium lactis
Bifidobacterium bifidum
Lactobacillus-containing probiotics
Given low Lactobacillus, strains commonly studied include:
Lactobacillus rhamnosus GG
Lactobacillus plantarum
Lactobacillus reuteri
Prebiotics
Often more effective at increasing resident beneficial bacteria than probiotics alone:
Inulin
FOS (fructooligosaccharides)
GOS (galactooligosaccharides)
Resistant starch
GOS in particular is frequently associated with increases in Bifidobacterium.
Partially hydrolyzed guar gum (PHGG)
PHGG has some evidence for:
Increasing beneficial bacteria
Improving constipation and IBS symptoms in some people
Being better tolerated than some other fibers
Things to be cautious about
Antimicrobial supplements
Products marketed to “kill Klebsiella” or “eradicate Enterococcus” (oregano oil, berberine, etc.) can sometimes further disrupt the microbiome. Whether they are appropriate depends on the clinical situation and should not be based solely on a stool microbiome report.
Very restrictive diets
Long-term low-fiber or highly restrictive diets may further reduce Bifidobacterium and Lactobacillus populations.
Prescription
There is no standard prescription drug regimen that should be given solely because a microbiome report shows low E. coli, low Bifidobacterium, low Lactobacillus, and elevated Klebsiella/Enterobacter and Enterococcus.
In current clinical practice, doctors generally treat diseases and symptoms, not microbiome composition alone. A stool microbiome test cannot reliably determine which prescription drug is appropriate.
Summary
First unfortunate fact is that most retail and many clinical microbiome tests do not accurately measure E.Coli.In my own experience, the E.Coli probiotics cited several times above were very effective (with the warning that it can cause a major die off)
Concerning antibiotics, the Jadin approach of rotating with just one course per month is recommended.
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.
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