Accurate Health Assessment and Nutritional Needs Analysis
By using synthetic biosensors to collect health data such as gene testing, metabolomics, dietary habits, exercise records, etc., and utilizing machine learning models to analyze, potential health risks can be understood and precise nutritional demand plans can be generated
Research and development of functional ingredients
Analyze a large database of bioactive ingredients, screen candidate ingredients with specific functions, simulate the metabolic pathways of ingredients in the human body, develop microbial or enzyme catalytic systems for producing efficient functional ingredients, synthesize natural substances that are difficult to produce in large quantities through traditional methods, and improve effectiveness and stability
Personalized Nutritional Supplement Program
Based on real-time user feedback and changes in health data, through algorithm analysis, design synergistic combinations between different components, develop nutritional components that can target specific metabolic pathways, and improve absorption efficiency and targeting
Application in Aging Intervention:
By deeply analyzing the metabolic pathways of the body, we can better understand the key biological reactions in the aging process. With the help of biosynthesis and organic synthesis technologies, we can accurately synthesize aging intervention substances targeting specific targets, effectively regulating the aging process of the body. At the same time, new enzymes were discovered through high-throughput screening technology, and advanced enzyme protein structure prediction technology was further adopted. Combined with semi rational design methods, the target enzyme was designed and modified to optimize its activity and selectivity, and improve its conversion efficiency for specific compounds during the synthesis process.
Intelligent skin analysis
Development of efficient ingredients
Personalized skincare plan design
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Develop synthetic biosensors for detecting specific molecules in body fluids such as blood, sweat, and urine
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Using deep learning models to integrate multi-source data (genome, proteome, metabolome, etc.) to identify health status
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Develop specific biomarkers for precise detection
Application in Aging Intervention:
By deeply analyzing the metabolic pathways of the body, we can better understand the key biological reactions in the aging process. With the help of biosynthesis and organic synthesis technologies, we can accurately synthesize aging intervention substances targeting specific targets, effectively regulating the aging process of the body. At the same time, new enzymes were discovered through high-throughput screening technology, and advanced enzyme protein structure prediction technology was further adopted. Combined with semi rational design methods, the target enzyme was designed and modified to optimize its activity and selectivity, and improve its conversion efficiency for specific compounds during the synthesis process.
With the help of these data, personalized health management plans can be developed, lifestyle and dietary habits can be optimized, and even targeted intervention measures can be explored to effectively delay the aging process. Through scientific means, we will not only passively respond to aging, but also take the initiative to help everyone achieve their health and longevity goals.
Data collection and health status modeling
- Provide multidimensional genomic, microbiome, metabolome, physiological indicators, and other health data through genome sequencing and metabolic testing
- Using deep learning algorithms to integrate and clean data, establish personalized health digital twin models, and identify health status deviations through the models, such as microbial imbalances, potential disease risks, or nutritional deficiencies
- Based on AI analysis results, design data-driven synthetic biology strategies, such as optimizing probiotic functions and synthesizing key nutritional molecules
Health risk prediction and plan formulation
- Analyze individual and population data to predict potential health risks such as obesity, diabetes or accelerated aging
- Simulate the effects of different intervention strategies, such as nutritional adjustment, medication intervention, or metabolic optimization
- Provide decision recommendations, such as recommending specific antioxidants or probiotic combinations that regulate metabolism
- Design and produce intervention products, such as nutritional supplements, probiotics, functional molecules, etc
Real time monitoring and dynamic intervention
- Accurate monitoring and warning: Real time monitoring of physiological indicators, triggering warning mechanisms when abnormal indicators are detected
- Health data analysis: using AI algorithms to analyze health data, identify individual health trends and potential risks
- Personalized advice: Based on the results of data analysis, provide personalized health advice such as dietary adjustments and exercise plans
Personalized optimization and long-term management
- Long term collection of health data, tracking of intervention effects, and dynamic adjustment of health management plans
- Simulate the effects of different intervention strategies, such as nutritional adjustment, medication intervention, or metabolic optimization
- Optimize products based on long-term needs