· Take a long time for diagnosis
· Risk of misdiagnosis
· Generation of additional costs
- · Construction of database through pattern analysis about diseases
- · Rapid diagnosis through pattern analysis
- · Matching with small amount of sample
As mass spectrometers with fast analysis speed and high sensitivity are introduced in the market, mass spectrometry application is quickly extended from cutting-edge research to medical analysis.
In the field of pathogen diagnosis, in particular, it is replacing existing systems for microbial identification.
MicroIDSys® diagnosis method
- · The database-based diagnostic method matches the biodata obtained from disease and control samples with the database built with the statistical algorithm developed by ASTA to identify to which disease group the unknown sample belongs.
- · The analysis results become more accurate and precise as the database expands, leading to improved diagnosis.
- · It took 8-48 hours per sample at the earliest with the conventional microbial identification took 8-48 hours per sample at the earliest 2-4 days with the molecular genetics method. ASTA’s method drastically reduced it to less than 3 minutes to save cost and quickly diagnose the causative bacteria.
- · Fast and accurate identification of pathogens enables increasing treatment efficiency by quickly prescribing the optimal drug in the medical field. In addition to the medical field, the system can be widely used for research on agriculture/livestock/fishery, soil, fermented food, processed food, water resources, and quarantine.
NosIDSys® that ASTA is developing is a new disease diagnosis system and can diagnose cancer and brain disease, such as Alzheimer’s disease, by analyzing biomarkers.
Statistical analysis of database
It optimizes the distance between Inter(species)/Intra(strain) so that the microbes can be clustered in the species level during a statistical analysis for disease diagnosis.
Moreover, it can confirm the flow of linkage between species with a phylogenetic tree structure. ASTA’s future-oriented, cutting-edge data science statistical analysis technology based on machine learning, goes one step further from other statistical analysis methods, such as heatmap, ROC, PCA, Cut-Off Method, and Random Forest.
The NOSQUEST database uses the AI machine learning algorithm to transform a variety of causative bacteria spectral profiles obtained from the MALDI-TOP mass spectrometer into patterns to derive the accurate and reliable diagnosis results through the real-time matching and analysis with the reference signature from ASTA’s database.
ASTA’s database has accumulated the signatures, representing the algorithms of biological markers generated differently according to microbes and infection or disease progress level in the blood, in the cloud-based big data to enable scalable and efficient diagnosis.
General microbe database
Mycobacterium tuberculosis database
Filamentous fungi database
Animal infectious disease database