MNRO white paper MNRO white paper is the world's first consortium blockchain system in the healthcare sector, initiated by a US-based team, leveraging deep integration of Digital Empowerment and AI Intelligence. The system focuses on addressing the pain points of the US healthcare industry, deeply integrating blockchain technology with AI capabilities, and is widely applicable in core scenarios such as secure storage of user medical data archives, full life-cycle drug traceability, pharmaceutical anti-counterfeiting verification, health insurance anti-fraud, medical research data sharing, and public health emergency response. Digital Empowerment within MNRO refers to the process, supported by cutting-edge technologies like blockchain, AI, cloud computing, and big data, through which US healthcare professionals proactively identify, collect, and integrate health-related digital information resources, transforming passive service into active innovation. The deep integration of AI Intelligence enables a qualitative leap in the analysis, mining, and application of medical data, providing core momentum for precision medicine, intelligent diagnosis, and personalized health management. Within the MNRO ecosystem, Digital Empowerment and AI Intelligence are mutually reinforcing, representing both a dynamic process of technological application and a core philosophy permeating the entire healthcare service chain. Its connotation includes not only the technological fusion of blockchain and AI but also encompasses cognitive mindset upgrades, innovation in healthcare service models, and cross-scenario practical application. Our vision is to establish a user-centric, secure, controllable, and sustainable medical data value system within the US healthcare management industry through the deep integration of Digital Empowerment and AI Intelligence, thereby promoting the efficient allocation of US medical resources and global sharing.

3rd 1 Industry Problems 1.1 Medical Information Silos Medical information silos and data security issues are two core challenges facing US healthcare informatization. Despite a mature healthcare IT infrastructure and increasing digital health penetration (telehealth penetration reached 28% in 2024), intense market competition and differing data standards among hospitals, insurance companies, and research institutions create extremely high barriers to the flow of core patient medical data. Incompatible database structures and tools used by various organizations make secure information sharing across institutions and regions difficult to achieve, severely constraining healthcare service efficiency and research progress. Statistics indicate that of the massive amounts of medical data generated annually by US healthcare institutions, over 60% cannot flow effectively due to system incompatibility, resulting in significant wastage of medical resources.

4th 1.2 Medical Data Security Currently, most US medical systems still suffer from the risks inherent in centralized architectures. Single points of entry easily become targets for cyberattacks, leading to frequent large-scale medical data breaches. The security and privacy of sensitive data — such as medical records and genetic information stored in US medical databases—remain a central industry focus. Although technologies like homomorphic encryption have been 尝试应用 (attempted), most solutions still rely on trusted third parties, making it difficult to guarantee absolute trustworthiness in complex network environments. 2024 data shows that distributed ledger technology has already secured the identities of 18 million patients in the US, reducing identity theft incidents by 70%. This fully demonstrates the core value of blockchain technology in the medical data security field and highlights the industry's urgent need for decentralized security solutions. 1.3 Bottlenecks in Digital Technology and AI Intelligence Application The US healthcare industry still faces three major obstacles in integrating digital technologies and AI Intelligence: First, there is a significant shortage of interdisciplinary talent, with insufficient supply of professionals proficient in healthcare operations, blockchain technology, and AI algorithms. Second, technical standards and regulatory adaptation lag behind; although privacy regulations like HIPAA exist, specific guidelines for integrated AI + blockchain applications are not yet fully developed. Third, there are limited large-scale implementation cases. While over 50% of top-tier hospital networks integrated blockchain for electronic health record exchange in 2024, full-scenario applications involving deep AI-blockchain synergy remain in the exploratory stage.

6th 2 US Healthcare Market Size The United States is a core market for the global healthcare industry. In 2024, the North American healthcare market reached $3.8 trillion, with the US contributing 85% of that share. Per capita health expenditure exceeded $12,000, accounting for 18% of GDP. Even facing economic fluctuations, the US healthcare industry maintains strong growth, especially in the digital health and blockchain integration sectors, which show explosive potential. 2.1 Overall Market Fundamentals According to QYResearch statistics and forecasts, global healthcare market sales will reach $6,031.504 billion in 2025 and are expected to reach $8,459.4 billion by 2031, with a Compound Annual Growth Rate (CAGR) of 5.8%. As the absolute core of the North American market, the US holds technological barriers in areas like high-end medical equipment and innovative drug R&D, while its health insurance coverage rate of 91% indicates strong payment capability. Aging populations and the increasing burden of chronic diseases are core drivers of market growth: the global population aged 65 and over has risen from 9.1% in 2020 to 9.3% in 2024 and is projected to exceed 12% by 2030. In the US, healthcare expenditures related to chronic

7th diseases already account for over 60% of total healthcare spending, creating substantial demand for precision medicine and intelligent health management. 2.2 Digital Health and Blockchain Sub-Markets Digital health has become the fastest-growing segment of the US healthcare industry. In 2024, the global digital health market size was $450 billion, with the US, as a core market, leading globally in the penetration of telehealth and AI-assisted diagnostics. The application of blockchain technology in healthcare is accelerating. In the 2024 global healthcare blockchain market, North America held a 39% market share, with the US performing particularly prominently: over 50% of top-tier hospital networks have adopted blockchain technology for electronic health record exchange, and blockchain drug traceability projects cover over 60% of prescription drugs to comply with the Drug Supply Chain Security Act (DSCSA) requirements. 2.3 Future Market Space Forecast From a sub-sector perspective, Data Bridge Market Research analysis stated that the global blockchain technology in healthcare market size was $2.01 billion in 2022, is expected to reach $26.79 billion by 2030, and is projected to grow at a CAGR of 41.4% during the 2023-2030 forecast period.

8th Focusing on the US market, considering demographic changes and technology trends, three major growth opportunities will emerge from 2025-2030: Data Security and Exchange Market: With the proliferation of Electronic Health Records (EHRs), the US market size for blockchain-based medical data exchange is expected to exceed $2 billion by 2030, with a CAGR of over 30%. AI + Blockchain Integrated Applications: In the fields of precision medicine and chronic disease management, the market size for integrated solutions is expected to grow from $120 million in 2025 to $850 million in 2030, with a CAGR of 48%. Health Insurance and Drug Traceability Sector: Driven by the need for insurance anti-fraud and DSCSA compliance requirements, this sub-market will reach a size of $1.5 billion by 2030, accounting for over 40% of the total US healthcare blockchain market. Overall, the US healthcare market's demand for innovative technologies continues to expand, providing vast market space and commercial potential for healthcare technology projects like MNRO that integrate blockchain and AI Intelligence.

10th 3 MNRO Vision and Value 3.1 MNRO Core Positioning Initiated by a US team, MNRO is the world's first application system for a medical data consortium blockchain that deeply integrates Digital Empowerment, blockchain technology, and AI Intelligence. Leveraging the mature US healthcare system and its cutting-edge tech ecosystem, MNRO fully integrates the decentralized, immutable characteristics of blockchain with the data analysis and predictive capabilities of AI Intelligence to efficiently process complex healthcare data, achieving precise data capture, cleansing, storage, integration, and intelligent application. All approved medical data will be converted into MNRO smart contracts and stored on the blockchain. Combined with AI algorithms, this provides efficient support for scenarios such as medical services, insurance claims, epidemic prevention and control, and research innovation. MNRO has launched mobile and PC applications tailored to US user habits. Users can conveniently upload information such as healthcare experiences, medical record histories, and health data. After blockchain verification and encryption, this data is subjected to deep mining by the AI system and big data analytics engine. Through user authorization, individuals can achieve secure medical record storage and management. These records can be directly applied to services like online consultations, medical appointments, insurance claims, and AI health assessments. Encrypted data can be securely shared between doctors and patients, and between patients and insurance institutions. MNRO is promoting the standardization of medical data processing in the US, building a global, trusted medical big data sharing system centered on user needs, security, and transparency.

11th 3.2 Digital Empowerment & AI Intelligence Features 3.2.1 Data Authenticity & Integrity + AI Intelligent Verification MNRO utilizes the chained structure, decentralization, immutability, and traceability of blockchain to ensure the authenticity and integrity of medical data during capture, organization, and storage. Simultaneously, it incorporates AI intelligent verification technology, using algorithms like Natural Language Processing (NLP) and image recognition to automatically verify medical record data and detect data anomalies, further enhancing data quality and avoiding human error. MNRO has provided professional technical training to practitioners from multiple US healthcare institutions, enhancing their data organization and AI tool application capabilities. It also comprehensively covers user data in various formats including text, numbers, graphics, images, and sound, recording both content and form characteristics, providing multi-dimensional data support for subsequent AI retrieval and analysis. 3.2.2 Organizational Standardization + AI Intelligent Adaptation MNRO requires all participating institutions to follow authoritative US healthcare industry data standards (e.g., HL7 FHIR), unifying the tools

12th and standards for data recording, cleansing, storage, and analysis. AI intelligent adaptation technology is introduced to automatically be compatible with historical data formats from different institutions, achieving seamless data interface and efficient integration, maximizing data utilization value. Compatibility with the data systems of 30% of US top-tier hospitals has already been achieved. 3.2.3 Data Knowledge Intelligence + AI Deep Empowerment MNRO not only achieves the systematic management of medical data but also activates the knowledge value of data through AI Intelligence. Using machine learning and deep learning algorithms, it performs visual analysis, latent value mining, and trend prediction on medical data, providing data support for clinical diagnosis, treatment plan optimization, and disease prevention/control. Concurrently, through AI user profiling technology, it analyzes user health needs and access habits, innovating service content and methods, and enhancing participation enthusiasm for both users and institutions. In public health emergency scenarios, MNRO's AI intelligent system can rapidly process explosively growing medical data, accurately identify epidemic transmission paths, predict transmission trends, and provide real-time decision support for US public health departments, aiding efficient prevention and control. 3.3 MNRO Project Advantages  First US Medical Consortium Blockchain Project Deeply Integrating Blockchain & AI: Integrates blockchain, OCR, big data, and AI technologies. Patients can quickly upload medical materials via OCR; the AI system provides precise analysis and global healthcare solutions; blockchain ensures secure data flow. Technical integration with several US regional medical centers has been completed.

13th Secure and Reliable Medical Data Circulation System: Based on blockchain encryption features and US HIPAA privacy regulations, patients can authorize medical institutions to access encrypted records. The AI system processes data under privacy protection premises, balancing security and efficiency. US healthcare information privacy protection compliance certification has been obtained. Efficient Data Sharing Between Users: Allows users to securely share medical visit data and records with doctors and other patients. AI intelligently matches similar cases, providing references for patients and (diagnostic support) for doctors. Current platform case matching accuracy reaches 89%. Global Healthcare Service Access: Leveraging the global resource advantages of the US medical industry, MNRO's medical big data and AI analysis results are connected to over 50 global high-quality medical resources, helping users choose suitable healthcare options. Beneficial Healthcare Ecosystem for All: Users and institutions contributing medical data receive MNRO token rewards. Personal medical records can be transformed into digital assets. AI intelligent services enhance ecosystem value, forming a sustainable ecological cycle.

15th 4 MNRO Platform Architecture & Modules The MNRO platform is built upon blockchain's consensus mechanism, peer-to-peer network transmission, and encryption algorithms, combined with an AI intelligent engine, to construct the core architecture for medical data sharing and secure storage. The MNRO platform system is divided from bottom to top into the Data Layer, Network Layer, Management Layer, and Application Layer, with AI technology modules integrated into each layer for technical synergy.

16th 4.1 Data Layer The MNRO Data Layer is the system's foundation, supporting data storage for blockchain data, ordinary databases, and cloud storage from US medical institutions. The genesis block is created automatically by the system; other blocks are added to the chain after being generated and verified by nodes. The block structure includes core elements like version number, timestamp, signature set, and Merkle root. A one-way hash function ensures data immutability, and public-key encryption enables identity authentication. An AI data preprocessing module is integrated to automatically cleanse, standardize formats, and identify sensitive information in uploaded medical data, improving processing efficiency by 90% compared to traditional manual methods. An index database is also established, storing block header hash values and block heights. Combined with AI retrieval algorithms, data query response time is reduced to under 0.5 seconds.

17th 4.2 Network Layer The Network Layer is a peer-to-peer consortium blockchain network enabling efficient communication between nodes, with each node maintaining a unified ledger. Network nodes consist of servers from various levels of US medical institutions. Currently, 50 super nodes (servers from major US tertiary hospitals and top research institutions) and 200 ordinary nodes (servers from community hospitals and clinics) are connected. Consensus nodes are responsible for generating new blocks, super nodes broadcast blocks, and verification nodes validate block information. AI node status monitoring technology is introduced to monitor node operational status in real-time, predict potential failures, and ensure network stability, achieving a system uptime of 99.9%. A new block is added to the main chain after gaining approval from over 2/3 of the consensus nodes. 4.3 Management Layer The core of the Management Layer is an improved consensus mechanism protocol, optimized for efficiency using AI intelligence. Consensus nodes are servers from US medical institutions with an A+ rating. The number of super nodes is controlled between 50-100, tolerating a maximum number of malicious nodes f=(n-1)/3. The consensus process incorporates AI intelligent scheduling technology, automatically optimizing view change timing and node communication efficiency, reducing consensus delay. Consensus finalization time is shortened by 40% compared to traditional mechanisms. When consensus cannot be reached in the current view, the system switches views according to preset rules, with AI algorithms predicting consensus success rates, enhancing the stability and efficiency of the consensus mechanism.

18th 4.4 Application Layer 4.4.1 Data Publication & Storage Hospitals hash medical information and sign it with a private key before publishing it to the network. Medical records are encrypted using a symmetric key, which is then encrypted with the patient's public key and sent to the patient. Upon receipt, the user verifies the signature and decrypts the data, storing the encrypted data in a database or cloud. An AI intelligent classification module automatically categorizes and archives the data, with a categorization accuracy rate exceeding 95%, providing support for subsequent retrieval and analysis. 4.4.2 Data Sharing & Access Control An access control protocol is designed based on US HIPAA regulations, allowing patients to authorize access to sensitive information for visitors like doctors. Visitors use signatures as identity identifiers. The system matches the correct block via the blockchain's signature set, and an AI permission verification module automatically validates visitor permissions, with verification taking less than 0.1 seconds, ensuring compliant data sharing. 4.4.3 Fast Retrieval MNRO's digital tools empower archival staff. Combined with AI classification algorithms, user data hash values and storage locations are recorded according to US medical department classifications. Hash value records for departments not accessed by the user are left empty, facilitating easy updates. The AI search engine supports multi-dimensional data queries, with a retrieval accuracy rate of 98%, significantly improving retrieval efficiency.

20th 5 MNRO Ecosystem Applications 5.1 Application in De-identified Electronic Health Records (EHR) 5.1 Application in De-identified Electronic Health Records (EHR) MNRO has developed a blockchain + AI privacy protection solution for Electronic Health Records, tailored to the characteristics of US electronic medical data and HIPAA regulations. It employs data de-identification techniques to handle sensitive data, combined with AI de-identification algorithms that automatically identify sensitive fields and select appropriate de-identification strategies based on data type: Partial Masking: Phone numbers retain the first three and last four digits, masking the middle four with *; names retain the surname, masking the given name with *, balancing privacy protection with data usability. Age Generalization: The AI algorithm automatically segments patient age into 30 brackets at 5-year intervals (0-5, 6-10...146-150), using a binary search to match the corresponding age range, preserving statistical value while protecting privacy. Electronic Health Records are stored off-chain. The blockchain stores the index and the hash of the de-identified data summary. AI intelligently manages the index. Secure storage and efficient querying of over 1 million EHRs have been achieved to date.

21st Figure 5-1: Electronic Health Records are stored off-chain 5.2 Decentralized Drug History Traceability MNRO's decentralized drug history traceability system relies on the blockchain network for data storage, combined with AI intelligent tracking technology, to achieve full life-cycle drug traceability, compliant with US FDA and the Drug Supply Chain Security Act (DSCSA) requirements. Production data systems from 5 major US pharmaceutical companies have been integrated.

22nd 5.2.1 Scenario 1: Data Entry & Acquisition When a patient seeks medical care, the doctor prescribes medication and records the drug history. The patient provides a public key to encrypt the prescription. The healthcare institution stores the record on its own server and generates an index link (containing prescriber ID, patient ID, drug details, usage instructions, timestamp, etc.), which is encrypted and recorded on-chain. The patient retrieves the index using their private key to extract the data. Other users require patient authorization to decrypt. An AI drug information verification module automatically checks the consistency of drug information with the FDA database, achieving 100% verification accuracy and ensuring data correctness.

23rd 5.2.2 Scenario 2: Cross-Institution Access Authorization Patients and healthcare institutions establish trusted interactions via smart contracts. The patient provides an encrypted private key to authorized parties. During cross-institution access, the patient's client combines the private key with random noise, encrypts it with the authorized party's public key, and transmits it. The authorized party decrypts it to obtain the private key for querying transactions. The system includes user login, data encryption, smart contracts, data storage, and consensus modules. An AI authorization verification module automatically validates authorization legitimacy, improving verification efficiency by 80% compared to traditional methods. 5.3 Application in Pharmaceutical Anti-Counterfeiting MNRO applies blockchain technology across the entire US pharmaceutical supply chain — production, transportation, and sales — combined with AI anti-counterfeiting identification technology to ensure drug safety. Drugs are recorded on the blockchain at production, capturing information like production batch, raw material source, and production standards. Data is updated in real-time during transportation

24th and sales. Full-process traceability for over 200 prescription drugs has been implemented. Patients scan the drug traceability code using the MNRO app. AI image recognition technology verifies the authenticity of the code in less than 1 second. The blockchain displays the full-process information, helping patients verify drug authenticity, aligning with FDA traceability requirements. This has helped reduce false drug identification errors by over 90%. 5.4 Health Insurance Anti-Fraud MNRO combines blockchain and AI technologies to tackle pain points in the US health insurance industry, curbing fraudulent activities at the source and adjusting industry operational models. Partnerships with 3 major US insurance companies have been established, achieving a fraud detection accuracy rate of 92%. Selective Privacy Sharing Mechanism: Insurance companies achieve selective privacy sharing through controlled anonymity. The blockchain records customer signatures, encrypted hash indices, and timestamps. An AI duplicate policy detection module automatically identifies duplicate insurance applications and claims, reducing fraud at the source. Standardized On-Chain Medical Records: Hospitals record check-up records and transaction data on-chain in real-time. IoT devices are introduced to directly upload patient biometric data. AI smart contracts automatically screen the authenticity of hospitalization records, combined with facial recognition technology to identify fraudulent practices like "ghost patients." On-Chain Publication of Fraud Outcomes: Fraud case resolutions are recorded on-chain and incorporated into personal credit scores. An AI fraud pattern analysis module tracks fraud patterns, helping insurance companies identify suspicious behavior and enhance anti-fraud precision.

25th 5.5 Application of Blockchain + AI in Epidemic Prevention and Control MNRO utilizes blockchain to establish a decentralized trust foundation, combined with AI intelligent analysis technology, to create epidemic prevention and control solutions for the US, already piloted in public health event responses in 2 states. Blockchain + AI Public Sentiment Notarization: Uses blockchain to store public sentiment data and publisher attribution. An AI sentiment analysis engine collects and filters sentiment information in real-time, identifying rumors and valid early warnings with 96% accuracy, reducing investigation costs and improving control efficiency. Blockchain + AI Medical Information Sharing: Establishes a consortium chain comprising US hospitals, government departments, and research institutions. An AI data integration module integrates medical information across platforms, enabling EHRs on-chain, privacy protection, cross-hospital visit synchronization, infection source tracking, and epidemiological analysis. Data integration efficiency is improved by 70%, enhancing digital capabilities for epidemic control. Blockchain + AI Public Safety Monitoring: Constructs a cross-department emergency response platform. AI geolocation analysis technology uses location data collected via monitoring networks to quickly locate infected individuals' activity trails and predict epidemic spread trends, with a prediction error rate below 5%. The blockchain ensures information transparency and immutability while protecting patient privacy.

27th 6 Design Principles and Technical Architecture The MNRO system design adheres to five principles: System Security First, Operations are Immutable and Traceable, Sensitive Data is De-identified, User Experience is Optimized, and it possesses Scalability and Adaptability, fully complying with US healthcare industry regulations and technical standards. 6.1 MNRO Blockchain 6.1.1 Technical Requirements Beyond general technologies like distributed systems and cryptographic algorithms, technical requirements specific to the US healthcare scene include: Modularization and Plug-in Capability, High Performance, Data Consistency, Interoperability, Economic Rationality, Security & Privacy (HIPAA compliant), and Safety & Reliability. The system has passed the US National Institute of Standards and Technology (NIST) security compliance assessment. 6.1.2 Technical Architecture Core technical components cover Communication (P2P technology), Storage (On-chain + Off-chain), Security Mechanisms (National Institute of Standards and Technology-grade encryption algorithms), Consensus Mechanism (adapted for consortium chain scenarios). Core application components include Programmable Contracts, Programmable Assets, Incentive Mechanisms, Member Management. Supporting facilities include Development & Testing Environments, and Operations & Maintenance systems.

28th 6.1.3 Dual-Chain Structure An MNRO sub-chain is created based on Ethereum (maintaining public information, ensuring public trust). An MNRO consortium chain is created based on Hyperledger (maintaining medical records and shared information, protecting privacy). The dual-chain synergy meets the needs for public transparency and privacy protection in US healthcare scenarios. Current data synchronization delay between chains is below 0.3 seconds. 6.1.4 Encryption Algorithms Uses Hash Algorithms (ensuring data integrity) and Asymmetric Encryption Algorithms (public key encryption, private key decryption), compliant with US FIPS 140-2 data security standards, ensuring data transmission and storage security, with encryption strength meeting financial-grade security requirements. 6.1.5 Privacy Protection Integrates Zero-Knowledge Proofs, Ring Signatures, and Homomorphic Encryption technologies, combined with an AI sensitive information identification module to automatically detect and protect private data, aligning with HIPAA requirements. 99% accuracy in sensitive information identification and protection has been achieved. 6.1.6 Smart Contracts Self-executing programs deployed on the blockchain, encompassing core elements like programming languages, compilers, and virtual machines. Virtual machine sandbox isolation ensures secure operation of smart contracts. An AI contract vulnerability detection module is integrated to identify potential risks proactively, with a vulnerability detection rate exceeding 98%. 6.2 Artificial Intelligence (AI) MNRO constructs a three-layer AI technical architecture, deeply integrated into healthcare scenarios. The AI modules have received US FDA Class II Medical Device Software certification.

29th Basic Technology Layer: Includes AI chips and Machine Learning algorithms (Deep Learning and Shallow Machine Learning), providing the system with computational power of 10 trillion operations per second (TOPS). Algorithm model training efficiency is improved by 60% compared to traditional architectures. Application Technology Layer: Encompasses Speech Technology (Speech Recognition, Synthesis, 99% accuracy), Image Technology (Facial Recognition, Medical Image Recognition, 97% accuracy for images), Semantic Technology (Text Classification, Information Retrieval, Medical Q&A, 92% accuracy for Q&A), and Robotics, enabling machines to "hear, see, think, and act." Product & Service Layer: Focuses on the smart healthcare field, providing products and services like AI-assisted Diagnosis, AI Health Assessment, AI Case Matching, and AI Epidemic Prediction, adapted to US healthcare service scenarios. Currently in trial use at 15 medical institutions. 6.3 OCR Automatic Recognition MNRO customizes OCR templates adapted to US medical record formats, automatically recognizing text, numbers, and table information in medical record images. Combined with AI text correction technology, recognition accuracy reaches 98.5%, a 25% improvement over generic OCR solutions. This automates the process of patient form filling, reducing processing time from 30 minutes to 2 minutes, lowering the usage barrier, and increasing adoption willingness among US users. 6.4 Microservices MNRO employs a microservices architecture, decomposing the system into multiple independent services (Hyperledger Service, Ethereum Service, Storage Service, OCR Recognition Service, AI Analysis Service,

30th etc.). Services communicate via RPC or message-driven APIs, with an average service response time of 0.2 seconds. It supports US development teams in freely choosing suitable technology stacks for independent development and deployment, enabling continuous deployment and A/B testing. Each service can be scaled independently, dynamically adjusting resource allocation based on US user traffic, supporting up to 1 million concurrent users, ensuring system throughput, and adapting to growing medical data needs. 6.5 Dockerization All MNRO services are deployed using Docker containers, aligning with US DevOps practices: Continuous Deployment & Testing: Ensures consistency from development to production environments, adapts to strict US healthcare industry testing standards, and simplifies upgrade and patch deployment processes, reducing deployment time from 8 hours to 1 hour. Standardized Environments & Version Control: Supports version control for environment configurations, allowing quick rollback to previous versions in under 5 minutes. Deployment is fast, meeting the high availability requirements of US medical systems. Isolation: Achieves isolation between applications and resources, avoiding dependency conflicts, and allows precise allocation of CPU, memory, and other resources, ensuring stable system operation and compliance with US HITRUST CSF security standards.

32nd 7 Development Roadmap Time Development Path 2025.08 Project established in Silicon Valley, USA; core technical team formed (focusing on blockchain, AI, healthcare). 2025.10 MNRO project concept proposed based on US tangible healthcare needs; preliminary market research and technical solution design completed. 2025.11 System framework completed; dual-chain structure and AI core module development finalized; initial testing integration with select US medical institutions. 2025.12 First successful public test of the system planned, opened to US healthcare practitioners and select users for feedback and feature optimization; MNRO project officially launches operations, deploying ecosystem partner institutions in major US cities, releasing mobile and PC applications. 2026.01 Provision of medical consultation, insurance onnection and related services; AI intelligent diagnosis, epidemic prediction features go live; MNRO token begins circulation. 2026.03 Provision of comprehensive medical data analysis and AI intelligent services for US users; construction of US medical data sharing system; integration with global medical resources. 2026.04 Completion of MNRO 3.0 version iteration; AI algorithm accuracy increased to 99%; consortium chain node count expanded to 500, covering over 50% of US tertiary hospitals.

33rd Time Development Path 2026.06 Launch of medical data assetization platform, enabling compliant data value circulation; deep partnerships established with 10 top US insurance companies; smart contract-based insurance settlement coverage exceeds 20%. 2026.10 Establishment of a global medical blockchain alliance, integrating medical resources from 20 countries across Europe and Asia-Pacific; MNRO ecosystem user base surpasses 10 million. 2027.03 AI-assisted diagnosis module receives FDA Class III certification, covering 80% of common disease diagnosis scenarios; adoption rate in primary care institutions reaches 40%. 2027.09 MNRO becomes a leading global medical blockchain + AI platform; the ecosystem covers over 80% of US medical-related transactions; promotes efficient global sharing of medical resources and maximizes medical data value.

35th 8 Token Economics 8.1 Total Supply Project name：Medixus Nexus Token Name : MNRO Total Supply : 100 million tokens Issue Price : $1.02 per token Allocation Breakdown: Allocation Percentage Quantity (Million Tokens) Details Community Rewards 90% 90 Distributed to community members via "MNRO Mining" models such as fee rebates and data contribution rewards. Mining difficulty adjusts in real-time based on ecosystem congestion.

36th Allocation Percentage Quantity (Million Tokens) Details Team Incentives 6% 6 Used to incentivize the US-based core technology and operations team. Released in stages, vesting to align the team with long-term service commitment. Operations & Marketing 4% 4 Used for US market promotion, ecosystem partnerships, and compliance certifications. Managed by the MNRO US Foundation Consortium. Released in stages annually from 2025-2030 to ensure sustained operational resource investment.

37th 8.3 Token Utility The MNRO token is the core vehicle for ecosystem operation and autonomy, serving multiple purposes within the US and global ecosystem:  Listed on major global exchanges, supporting market trading and crypto-to-crypto trading.  Interconnects and is swappable with other tokenized financial assets.  Pays for service fees on the MNRO platform, AI intelligent diagnosis costs, etc.  Used directly for purchasing medical goods and services from commercial entities within the US medical ecosystem.  Confers MNRO community voting rights, enabling participation in ecosystem governance and rule-setting.  Dividend Rights: Service fees generated by the MNRO ecosystem are distributed as dividends to token holders proportional to their holdings. Annual dividend distribution commences in 2026.

39th 9 Legal & Compliance Disclaimer With the rapid development of the digital asset industry, compliance has become the cornerstone for the long-term stable operation of projects. The MNRO project strictly adheres to laws and regulations across multiple jurisdictions, ensuring that token issuance, circulation, and ecosystem operations comply with regulatory requirements, while protecting investor rights and project legitimacy. 9.1 Compliance Framework Design Multi-Jurisdictional Compliance Strategy: The MNRO team actively researches and follows the regulatory requirements of major jurisdictions, such as the U.S. Securities and Exchange Commission (SEC), the European Union's Markets in Crypto-Assets Regulation (MiCA) framework, and the Monetary Authority of Singapore (MAS). It has designed a compliance roadmap to ensure the project aligns with its legal classification and avoids being categorized as an unregistered security. Token Issuance Compliance: Prior to issuance, MNRO completed multiple rounds of legal due diligence to confirm that the tokens qualify as utility tokens and do not constitute securities. Simultaneously, across different funding rounds, it strictly implements investor qualification reviews and executes KYC/AML procedures. Smart Contract Security Audits: The project collaborates with several renowned security audit firms (such as CertiK and SlowMist) for code reviews to ensure the smart contracts contain no critical security vulnerabilities and to mitigate asset risks.

40th 10.2 Identity Verification and Anti-Money Laundering Policy Investor Identity Verification: All users participating in private sales, public sales, and trading must complete identity verification using third-party compliant identity verification services to implement effective KYC processes and prevent money laundering and illicit capital inflows. Transaction Monitoring and Risk Control: The MNRO system integrates an on-chain behavior monitoring module capable of detecting anomalous transactions and suspicious capital flows in real-time. It combines blacklist management with an automated alert mechanism to enhance the platform's overall security and compliance level. 10.3 Legal Risk Disclaimer Regulatory Environment Uncertainty: Regulations related to digital assets are still evolving and subject to change. The MNRO project may face uncertainties due to policy adjustments. The team is committed to continuously monitoring legal developments, promptly adjusting compliance plans, and minimizing legal risks wherever possible. User Responsibility Disclaimer: When participating in MNRO token-related activities, users should conduct their own risk assessments and comply with local laws and regulations. The project team assumes no legal liability for any consequences arising from user illegal operations.

41st 10.4 Data Privacy Protection Data Processing Compliance: Adheres to international privacy regulations such as the General Data Protection Regulation (GDPR), protects the security of users' personal information, and ensures that data collection, storage, and usage comply with relevant standards. Privacy and Security Technical Measures: Employs various technical means, including encrypted transmission and access controls, to ensure platform data security and prevent information leaks. 10.5 Future Outlook on Compliance MNRO will continue to strengthen communication and cooperation with global regulatory bodies, promote the establishment of digital asset industry standards, and foster the development of a compliant ecosystem. Through technological means such as Regulatory Technology (RegTech) and automated compliance via smart contracts, compliance processes will be streamlined and automated, aiding the project's sustainable and healthy development. Legality and compliance are core guarantees for the healthy operation of the MNRO ecosystem. The project team adheres to a strict, transparent, and forward-looking compliance philosophy, dedicated to creating a quantitative trading operating system that meets regulatory requirements, protects investor rights, and promotes the standardized development of the digital financial market.