| Barnesiella intestinihominis| Barnesiella intestinihominis Morotomi et al. 2008 emend. Hahnke et al. 2016| Barnesiella sp. YIT 11860| DSM 21032| JCM 15079| YIT 11860
Gut Microbiota: Barnesiella intestinihominis is one of many bacterial species that inhabit the human gastrointestinal tract. The gut microbiota is a complex ecosystem that plays a crucial role in various aspects of human health, including digestion, nutrient metabolism, and immune system regulation.
Short-Chain Fatty Acid Production: Like many bacteria in the gut, Barnesiella intestinihominis is involved in the fermentation of dietary fibers, leading to the production of short-chain fatty acids (SCFAs) such as acetate and butyrate. SCFAs have been associated with various health benefits, including the maintenance of gut barrier function and immune regulation.
Association with Host Health: While specific information on the health impacts of Barnesiella intestinihominis is limited, its presence in the gut microbiota is generally considered beneficial. Changes in the composition of the gut microbiota, including alterations in the abundance of Barnesiella species, have been associated with certain health conditions, and researchers are actively exploring these associations.
Immunomodulatory Effects: Some studies suggest that the presence of Barnesiella intestinihominis may have immunomodulatory effects, potentially influencing the host's immune response. The interplay between the gut microbiota and the immune system is an area of ongoing research.
Context-Dependent Impact: The impact of Barnesiella intestinihominis on health is likely context-dependent and influenced by factors such as diet, lifestyle, and individual host characteristics. Researchers continue to investigate the specific roles of different bacterial species in the gut and their contributions to host health.
A lot more information is available when you are logged in and raise the display level
Other Sources for more information:
Statistics | NCBI | Data Punk | End Products Produced |
Different labs use different software to read the sample. See this post for more details.
One lab may say you have none, another may say you have a lot! - This may be solely due to the software they are using to estimate.
We deem lab specific values using values from the KM method for each specific lab to be the most reliable.
Lab Low and High are calculated using the formula that most labs use: Mean - 2 Standard Deviation to Mean + 2 Standard Deviation
Lab | KM Low | KM Percentile Low | KM High | KM Percentile High | Lab Low | Lab High | Mean | Median | Standard Deviation | Box Plot Low | Box Plot High |
---|---|---|---|---|---|---|---|---|---|---|---|
* | 1 | 0 %ile | 39760 | 100 %ile | 0 | 21329 | 6323.6 | 4180 | 7655.7 | 0 | 15240 |
thorne | 3 | 8.3 %ile | 70 | 83.3 %ile | 0 | 53 | 13.1 | 8 | 20.3 | 3 | 70 |
thryve | 27 | 12.5 %ile | 7168 | 90.8 %ile | 0 | 15348 | 3633.3 | 933 | 5976.8 | 0 | 9119 |
ubiome | 1160 | 8 %ile | 68345 | 100 %ile | 0 | 28915 | 9586.7 | 6596 | 9861.6 | 0 | 20609 |
Low Boundary | High Boundary | Low Boundary %age | High Boundary %age | Lab Samples |
---|---|---|---|---|
0 | 11300 | 0 | 1.13 | Microba Co-Biome |
100 | 15000 | 0.01 | 1.5 | XenoGene |
Lab | Frequency Seen | Average | Standard Deviation | Sample Count | Lab Samples |
---|---|---|---|---|---|
BiomeSightRdp | 25 % | 1.247 % | 1.27 % | 8.0 | 32 |
bugspeak | 100 % | 0.059 % | % | 1.0 | 1 |
CosmosId | 37.5 % | 0.651 % | 0.636 % | 12.0 | 32 |
custom | 16.949 % | 0.461 % | 0.614 % | 10.0 | 59 |
es-xenogene | 24.138 % | 0.002 % | 0.001 % | 7.0 | 29 |
Microba | 60.714 % | 0.713 % | 0.603 % | 17.0 | 28 |
Microba1 | 100 % | 0.38 % | % | 1.0 | 1 |
SequentiaBiotech | 2.778 % | 0.12 % | % | 1.0 | 36 |
Thorne | 22.353 % | 0.001 % | 0.001 % | 19.0 | 85 |
Thryve | 66.667 % | 0.361 % | 0.587 % | 920.0 | 1380 |
uBiome | 46.97 % | 0.961 % | 0.985 % | 372.0 | 792 |
Click on Impact for information if high or low levels are causing the impact
Magnitude | Impact | Symptom |
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Data comes from FoodMicrobionet. For the meaning of weight, see that site. The bacteria does not need to be alive to have an effect.
This is an Academic site. It generates theoretical models of what may benefit a specific microbiome results.
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