Artificial Intelligence Detection and Treatment of Biological, Chemical and Radiation Exposure
Methods, systems, and compositions of matter for detection, analysis and treatment of biological, chemical and radiation exposure using artificial intelligence-based systems including fuzzy logical and/or machine learning. A system capable of compiling physical, biochemical, hematological, psychological and neural datapoints, analyzing said data points and creating actionable therapeutic interventions in a graded manner based on invasiveness and probability of success. Optimized biological intervention to chemical, radiation and biochemical threats using stem cells, T regulatory cells and induced pluripotent stem cells.
1 . A method of identifying the potential of developing pathogenic radiological, chemical, or biological reactions using a machine learning system programmed to observe, analyze, and predict based on patient-derived datapoints.
2 . The method of claim 1 , wherein said patient-derived datapoint is perturbation in homeostatic functions.
3 . The method of claim 1 , wherein said patient-derived datapoint is perturbation in immunological functions.
4 . The method of claim 3 , wherein said immunological function alteration is assessed by levels of immune modulatory proteins in one or more biological matrices.
5 . The method of claim 4 , wherein said immune modulatory protein is an aggregation of proteins.
6 . The method of claim 5 , wherein said aggregation of proteins are exosomes.
7 . The method of claim 1 , wherein said detection of potential reactions is accomplished by obtaining a biological fluid and irradiating said fluid, exciting components to emit IR radiation in a wavelength range 8-12 μM; providing a detection system with a detector and analyzer to compare IR radiation patterns with a reference database for identifying threats.
8 . The method of claim 1 , wherein said machine learning system utilizes a knowledge acquisition input platform connected with a database comprising a correlation algorithm to score pathological outcomes based on biological parameters.
9 . The method of claim 8 , wherein elevation of C-reactive protein more than 25% above baseline is utilized to alert need for increased intensity of monitoring.
10 . A method of utilizing one or more artificial intelligence algorithms to suggest therapeutic doses of hematopoiesis-protective therapeutics after exposure to radiation.
11 . The method of claim 10 , wherein historical data is combined with genotypic, phenotypic, and physiological data in respect to the patient being analyzed.
12 . The method of claim 11 , wherein gene polymorphisms for Bcl-2Xs are evaluated and incorporated into said calculation, wherein enhanced Bcl-2Xs is associated with enhanced need for mesenchymal stem cell administration.
13 . The method of claims 12 , wherein said mesenchymal stem cell is derived from a pluripotent stem cell source.
14 . The method of claim 13 , wherein said mesenchymal stem cell is pretreated with one or more agents capable of augmenting expression of interleukin-10.
15 . The method of claim 13 , wherein said agent capable of increasing expression of interleukin-10 is brain-derived neurotrophic growth factor.
16 . A computer-implemented method for identifying risk of biological, chemical, or radiological threats, categorizing potential risks, and providing treatment recommendations through the utilization of an artificial intelligence system.
17 . The method of claim 16 , which creates a score for each of the one or more pathologies, reflecting the likelihood and urgency of treatment needed.
18 . The method of claim 16 , further comprising indicating whether the one or more molecular pathways have synergy in the treatment of the patient.
19 . The method of claim 16 , wherein said molecular pathways include toll-like receptor-associated pathways.
20 . The method of claim 19 , wherein said pluripotency-associated gene is the octamer-binding transcription factor 4 (OCT4).