| Anaerocolumna aminovalerica| Anaerocolumna aminovalerica (Hardman and Stadtman 1960) Ueki et al. 2016| ATCC 13725| CIP 104304| Clostridium aminovalericum| Clostridium aminovalericum Hardman and Stadtman 1960 (Approved Lists 1980)| DSM 1283| JCM 11016| JCM 1421
Commensal organism: Anaerocolumna aminovalerica may be part of the normal microbial community (microbiota) in the human gastrointestinal tract, particularly in anaerobic environments such as the colon. In this context, it may contribute to various metabolic processes, nutrient metabolism, and microbial interactions within the gut ecosystem.
Metabolic activities: Anaerocolumna aminovalerica is known for its ability to metabolize amino acids, including valeric acid, which can have implications for the production of short-chain fatty acids (SCFAs) and other metabolites in the gut. SCFAs, such as butyrate, acetate, and propionate, are important energy sources for colonocytes and have been associated with various health benefits, including maintenance of gut barrier function, modulation of immune responses, and regulation of inflammation.
Ecological interactions: Anaerocolumna aminovalerica may interact with other microorganisms and host cells in the gut environment, influencing factors such as microbial community structure, fermentation processes, and host-microbiota interactions. These interactions can have implications for gut health, metabolic disorders, and immune function.
Research gaps: Due to limited research on Anaerocolumna aminovalerica, its specific role in human health and disease is not well-understood. Further studies are needed to elucidate its metabolic activities, ecological interactions, and potential contributions to gut health and disease states.
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 | Frequency | UD-Low | UD-High | KM Low | KM High | Lab Low | Lab High | Mean | Median | Standard Deviation | Box Plot Low | Box Plot High | KM Percentile Low | KM Percentile High |
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Other Labs | 0.44 | 1 | 1660 | 0 | 1354 | 342.7 | 110 | 515.7 | 1 | 1660 | 8.3 %ile | 83.3 %ile | ||
thryve | 81.41 | 0 | 176 | 28 | 336 | 0 | 1514 | 274.9 | 88 | 632.2 | 0 | 354 | 12.1 %ile | 90.7 %ile |
Source of Ranges | Low Boundary | High Boundary | Low Boundary %age | High Boundary %age |
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Lab | Frequency Seen | Average | Standard Deviation | Sample Count | Lab Samples |
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BiomeSightRdp | 18.75 % | 0.017 % | 0.019 % | 6.0 | 32 |
custom | 11.864 % | 0.017 % | 0.024 % | 7.0 | 59 |
Thryve | 80.55 % | 0.029 % | 0.064 % | 1114.0 | 1383 |
Click on Impact for information if high or low levels are causing the impact
Magnitude | Impact | Symptom |
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And display level must be raised above public.
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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