| "Diplococcus magnus" Prevot 1933| ATCC 15794| CCUG 17636| DSM 20470| Finegoldia magna| Finegoldia magna (Prevot 1933) Murdoch and Shah 2000| GIFU 7629| GIFU:7629| JCM 1766| NCTC 11804| Peptococcus magnus| Peptococcus magnus (Prevot 1933) Holdeman and Moore 1972 (Approved Lists 1980)| Peptostreptococcus magnus| Peptostreptococcus magnus (Prevot 1933) Ezaki et al. 1983
Skin and Soft Tissue Infections: Finegoldia magna has been found in various skin and soft tissue infections, including abscesses and cellulitis.
Joint and Bone Infections: The bacterium has been isolated from cases of joint and bone infections, particularly in the context of polymicrobial infections.
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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Other Labs | 1.94 | 10 | 17780 | 0 | 15380 | 2867.4 | 80 | 6383.9 | 0 | 610 | 0 %ile | 100 %ile | ||
biomesight | 38.77 | 0 | 20 | 10 | 280 | 0 | 6398 | 515.7 | 40 | 3001 | 0 | 260 | 0 %ile | 90.2 %ile |
thorne | 100 | 8 | 61222 | 0 | 26117 | 3666.4 | 547 | 11454.4 | 0 | 2237 | 0 %ile | 100 %ile | ||
thryve | 71.21 | 0 | 99 | 18 | 509 | 0 | 13796 | 1114 | 70 | 6470.3 | 0 | 464 | 8 %ile | 90.2 %ile |
ubiome | 47.78 | 0 | 208 | 58 | 203705 | 0 | 38474 | 5295.2 | 448 | 16928.1 | 0 | 4816 | 8 %ile | 100 %ile |
Source of Ranges | Low Boundary | High Boundary | Low Boundary %age | High Boundary %age |
---|---|---|---|---|
XenoGene | 0 | 1000 | 0 | 0.1 |
Thorne (20/80%ile) | 2.16 | 6.11 | 0.0002 | 0.0006 |
Lab | Frequency Seen | Average | Standard Deviation | Sample Count | Lab Samples |
---|---|---|---|---|---|
BiomeSight | 40.552 % | 0.049 % | 0.274 % | 1161.0 | 2863 |
BiomeSightRdp | 25 % | 0.014 % | 0.028 % | 8.0 | 32 |
CerbaLab | 66.667 % | 0.005 % | 0.003 % | 2.0 | 3 |
CosmosId | 9.375 % | 0.002 % | 0.002 % | 3.0 | 32 |
custom | 35.593 % | 0.945 % | 0.895 % | 21.0 | 59 |
es-xenogene | 89.655 % | 0.013 % | 0.012 % | 26.0 | 29 |
Medivere | 14.286 % | 0.001 % | % | 1.0 | 7 |
SequentiaBiotech | 8.333 % | 0.507 % | 0.833 % | 3.0 | 36 |
Thorne | 84.091 % | 0.26 % | 0.772 % | 74.0 | 88 |
Thryve | 70.86 % | 0.118 % | 0.625 % | 980.0 | 1383 |
uBiome | 47.727 % | 0.528 % | 1.691 % | 378.0 | 792 |
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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