Foundation DB

In the context of abstracting the activities of the bacteria, their signaling, and the harm they do to the human host we identified a set of important elements that will help us cope with the balance and harmful understanding of each player in the ecosystem.

Therefore based on over 2000 medically approved and signed research papers we started to manually create an individual database of meta-characteristics for each bacteria with the purpose to use it as a foundational source of truth for a predictive algorithm

Among all the data that is relevant on each bacterial level some information is more relevant than others at this step in time of our technology system

1. Patogenity ( is it pathogen or not)

2. Normal levels without impact on health

3. The upper limit in a healthy version and the lower limit in a normal person

4. Multiplication rates

5. Consumption metabolites

6. Generated metabolites

7. Symbiosis with other bacteria strains

8. Signalling-damage-result ( what happens or could happen if this bacteria is present for too long in certain quantities)

9. Signaling-damage-result weight ( a leveling from 1 to 10 on the ratio between what presence in stool can create how big damage.

10. Effect on Intestinal Lining

11. Medical history in IBD

12. Relation to Ulcerations

13. Relation to bleeding

14. Relation to bloating

15. Relation to gut-blood axis

16. Relation to gut-liver-axis

17. Relation to gut-joints-axis

18. Relation to gut-skin-axis

19. Prevalence in other diseases

20. Sensitivity to antibiotics

21. The efficiency rate of antibiotics for each bacteria

Plenty more other attributes and taxonomic information are part of a bacteria body, but they are not relevant for the exercise of this document.

In essence, this database covering hundreds of bacteria on specific attributes was built by the NostraBiome team gradually by integrating data from multiple research papers, studies, and laboratories known how.

This database serves as a foundation for the algorithm to further make decisions and use mathematical data to drive predictions about how some drugs might alter the bacteria setup and eventually on what symptoms we should see results.