The year 2020 has witnessed unprecedented levels of demand for COVID-19 medical equipment and supplies. However, most of today’s systems, methods, and technologies leveraged for handling the forward supply chain of COVID-19 medical equipment and the waste that results from them after usage are inefficient. They fall short in providing traceability, reliability, operational transparency, security, and trust features. Also, they are centralized that can cause a single point of failure problem. In this paper, we propose a decentralized blockchain-based solution to automate forward supply chain processes for the COVID-19 medical equipment and enable information exchange among all the stakeholders involved in their waste management in a manner that is fully secure, transparent, traceable, and trustworthy. We integrate the Ethereum blockchain with decentralized storage of interplanetary file systems (IPFS) to securely fetch, store, and share the data related to the forward supply chain of COVID-19 medical equipment and their waste management. We develop algorithms to define interaction rules regarding COVID-19 waste handling and penalties to be imposed on the stakeholders in case of violations. We present system design along with its full implementation details. We evaluate the performance of the proposed solution using cost analysis to show its affordability. We present the security analysis to verify the reliability of the smart contracts, and discuss our solution from the generalization and applicability point of view. Furthermore, we outline the limitations of our solution in form of open challenges that can act as future research directions. We make our smart contracts code publicly available on GitHub. I.The world has experienced unprecedented crises in 2020 due to the COVID-19 pandemic. For example, worldwide trade disruptions, slower economic growth, shortage of medical supplies, equipment, and hospitals, and an overwhelmed medical waste generation in hospitals. During the COVID-19 pandemic, a severe shortage of ventilators and personal safety equipment for medical staff has been reported in many hospitals in the USA The forward supply chain involves the electronic ordering of medical equipment and supplies from manufacturing to consumption in the hospitals/COVID-19 testing centers. On the other hand, waste management of medical equipment and supplies refers to those processes that are involved in waste generation, collection, transportation, segregation, and disposal and recycling •We showcase an Ethereum blockchain-based approach to trace and track the COVID-19 medical equipment and supplies and their waste after usage in a decentralized, secure, transparent, auditable, and trustful manner.•We develop smart contracts along with algorithms to automate and implement the services for the forward supply chain management of COVID-19 related medical equipment and supplies, their waste shipment, and assurance of compliance with safety measures to minimize the spread of COVID-19. We employ decentralized storage of interplanetary file systems (IPFS) to overcome the blockchain storage limitations.•We test and validate the proposed approach against many functionalities/services such as medical equipment order handling, ownership transformation, inventory management, waste shipment management, real-time waste monitoring, issuance of penalties to stakeholders to assure safety compliant operations, to assess their correctness, practicality, and feasibility. We report the logs on executing the transactions on the blockchain for each functionality.•We present transaction execution and computational cost and security analysis of each function in the implemented smart contracts to demonstrate the affordability and reliability of the proposed solution. We outline several important challenges and limitations acting as future directions for new researchers.•We propose a generic prototype solution that can be implemented on both public and private blockchains with minimal modifications based on the requirements and preferences of existing healthcare industries. Our implementation code can be publicly accessed via GitHub repository.1https://github.com/AhmadKhalifaUniversity/Code/blob/main/RegistrationMost of the current technologies leveraged for managing forward supply chain and waste disposal are mainly based on cloud computing and the Internet of things (IoT) to cope with domestic, electronic, and biodegradable waste problems. For instance, the studies conducted in The remainder of the paper is organized as follows. II.This section briefly discusses the recent developments and proposals related to the forward supply chain of medical equipment and management of domestic and electronic waste.The forward supply chain processes of medical products  aim to deliver medical products timely and reliably to end users from the source. The technology-driven approaches including RFID tags, bar codes, near field communication (NFC), and serialization can assist to track the medical products throughout their forward supply chain phase in the healthcare industry Traditional waste management systems do not offer incentives to people to produce less amount of waste than what they are producing. In In In In In The lack of operational transparency can cause frauds that occur during the waste collection stage. The study conducted in In summary, none of the aforementioned studies has highlighted the need for improved transparency, efficiency, and trust in the forward supply chain and waste management of COVID-19 medical equipment. In contrast, this research study proposes a blockchain-based solution that can assist the stakeholders to trace and track the medical equipment such as swab sticks and swab sample test tubes to verify their disposal in a secure, transparent, trustful, decentralized, and auditable way.III.In this section, we present a high-level design of the proposed blockchain-based solution to manage COVID-19 medical equipment supplies and their waste after usage. A.Herein, we briefly explain the responsibilities of each participant involved in the forward supply chain of COVID-19 medical equipment and medical waste generation, handling, monitoring, and shipment. It also discusses the key components of the system that facilitates the secure sharing of data among participating stakeholders.1)The role of a medical manufacturer is to store details about the newly manufactured medical equipment and supplies on the blockchain. The most important data about the manufactured medical equipment and supplies include product name, type, cost, credentials of the manufacturer, manufacturing date, design, composition, and certifying agency. A manufacturer normally researches the current need and requirements of the market to develop medical equipment. It surveys the market to find the trusted raw material supplier and places an order for the supply of raw material to manufacture medical equipment and equipment. It assures that all necessary documents such as specifications and certification records regarding all manufactured medical equipment are available. Also, the manufacturer should be genuine and all the details about the valid license should be publicly made available.2)The responsibility of the distributor in the forward supply chain of medical equipment and supplies used in COVID-19 screening centers is to revisit the market demand to purchase equipment from the registered and highly reputed manufacturers. Thus, it acts as an intermediary between medical equipment consumers such as COVID-19 screening centers or hospitals and medical equipment manufacturers. A distributor aims that the order placed by the hospitals for the medical equipment and supplies should be immediately fulfilled. It is also responsible to create and prepare the lot of medical equipment and ship them to consignees. It needs to be ensured that the distributor should have a valid license to purchase and sell medical equipment and supplies.3)COVID-19 testing centers/Hospitals are one of the consumers of medical equipment and supplies (COVID-19 swab sticks). Medical equipment and supplies are normally purchased from the distributor or they are directly shipped from the manufacture. COVID-19 testing centers/Hospitals generate medical waste on testing the public for COVID-19 using medical equipment and supplies such as nasal swab sticks. The hospital staff temporarily stores medical waste in a safe storage room. The waste handlers and nursing staff makes sure that the waste is managed in a safe way to comply with standard operating procedures to deal with the COVID-19 pandemic. Later on, this medical waste is transported to the waste treatment facility for processing and disposal.4)The medical waste generated in hospitals/COVID-19 testing centers is shipped to the waste treatment facility via third-party shipping services for treatment and disposal. To comply with the COVID-19 standard operating procedures, medical staff, and medical waste shippers having COVID-19 symptoms are not allowed to handle medical waste. However, those staff and shippers who have received COVID-19 vaccination can handle medical waste. Finally, it should be assured that the bags carrying COVID-19 medical waste are sealed properly, not mixed up with general waste, and secure enough against human interaction during its shipment.5)The main responsibility of the manager in a medical waste treatment facility is to ensure safe interaction with medical waste during its treatment and disposal stage. At the medical waste treatment facility, COVID-19 medical waste is handled carefully especially during the waste segregation and processing phase to comply with the standard operating procedures devised to interact with COVID-19 waste. The medical equipment including nasal swab sticks and swab tubes are disposed of at the medical waste treatment facility since they are not recyclable, thereby minimizing the chances of spreading COVID-19 disease.6)The responsibility of the regularity authority such as the Food and Drug Administration (FDA) is to register (on-chain) each stakeholder (license) along with their role in the forward supply chain of COVID-19 waste delivery. It also assures that stakeholders perform actions in compliance with defined rules and regulations. Moreover, it imposes penalties on the stakeholder when an action that is non-complying with the stated rules is noticed. For instance, it can suspend the license of a participant such as a shipper if the COVID-19 waste is not properly handled during its disposal or shipment.7)The participants of the medical waste management framework can store large-sized files on the distributed storage systems, such as IPFS, to overcome the scalability issues of blockchain 8)Smart contracts replace the intermediaries and assure the governance of rules as agreed upon by the participating entities B.This subsection explains sequence diagrams showing the function calls and blockchain-based events to automate the processes involved in the traceability of COVID-19 related medical equipment and their associated waste disposal.PalceOrderRequest function that as a result successfully places an order for the manufacturer to arrange medical equipment and supplies such as COVID-19 swab sticks. As a response, the manufacturer calls ConfirmOrder function to accept/reject the medical equipment order request by the distributor after analyzing the available medical equipment and supplies stock. Manufacturer prepares a lot of medical equipment and supplies and stores the details such as lot ID, quantity, name, type, certification number, and quality of the equipment on the blockchain platform. It also uses the IPFS system to store large-sized medical equipment certification documents and images of the shipping packages. It stores the hash of these documents on the blockchain to assure the authenticity of medical equipment. On successfully receiving the medical equipment lot, the distributor notifies the manufacturer by calling Confirm-ReceivingOf-RequestedOrder function. Finally, the distributor is promoted as the new owner of the medical equipment lot by the manufacturer.The sequence diagram shown in PlaceOrderRequest function in order manager smart contract by the COVID-19 testing center places an order to the distributor to prepare and ship the medical equipment. In response, the distributor calls ConfirmOrder function to notify the COVID-19 testing center about the availability of requested medical equipment. An event is triggered by the smart contract to notify all related stakeholders about the response of the distributor. The distributor entity creates the lot and assigns new labels to the existing packages to ship it to the COVID-19 testing center. It also triggers Transfer-OwnershipRequest function to make COVID-19 testing center a new owner of the medical equipment lot. In contrast to ShipmentRequest function to place a request to the certified shippers to ship the medical waste to the medical waste treatment facility. The shipper, in response, confirms its availability by calling ConfirmShipment function. Also, the shipment management smart contract notifies all the related entities about the response of the shipper. To comply with precautionary measures taken to mitigate the spread of COVID-19, waste bags are required to not be opened during its shipment. The shipper continuously stores state and location data on the edge server. In response, it calls Share-SensorsData function to store the location and state of the medical bags on the blockchain. The state of the waste bag indicates whether the bag was opened during the shipment or not. The smart contract notifies the relevant entities such as the medical waste treatment facility and COVID-19 testing center about the updated data to track the location and state of the medical waste during its shipment. This data is used by the FDA to verify that the COVID-19 medical waste was handled properly during its transportation to the medical waste treatment facility to minimize the spread of COVID-19. It cancels the license of the shipper if the COVID-19 medical waste is not handled properly.IV.This section presents the implementation details of the proposed blockchain-based approach. We use an Ethereum platform to implement smart contracts to govern the business activities of stakeholders. We use Remix IDE to develop, deploy, and test the smart contracts. The smart contracts developed to digitize the services of participating stakeholders include registration, lot and ownership management, order management, and shipment management.Abi.EncodedPacked function is used to generate a padded byte string of the input data including caller EA, time, equipment EA, equipment type, and name. The Keccak256 hash function uses this byte string to create a unique reference for each medical equipment and supplies order. On successful execution, The pending medical supplies and equipment requests are accepted or rejected by the distributor/manufacturer. Abi.EncodedPacked function to produce the encoded byte string. This unique identifier serves as a tool to trace and track the medical equipment and supplies waste to assure that it is handled in compliance with COVID-19 related rules to restrict the virus from spreading. The shipment order request to the waste shipment company includes a set of parameters including waste pickup time, waste pickup location, order status, shipper address, the weight of the shipment package, a hash of the medical waste images, sensors state, and unique identifier of the medical equipment waste. The data collected using sensors attached to the waste bags are referred as sensors state data in Medical equipment is used for screening, swab checking, and providing treatment to the people suffering from COVID-19. The medical waste is generated once such medical supplies or equipment  are used in hospitals. This medical waste requires an immediate service to ship it to the medical waste treatment centers for disinfection and disposal. The shipper confirms the shipment request of the COVID-19 screening center or hospital and starts the shipment. The rules to overcome COVID-19 from spreading have already been formulated to provide guidelines to the business personnel. Strict compliance with such rules by issuing penalties to the business stakeholders can significantly minimize the COVID-19 from spreading. V.In this section, we present system testing and validation details. We explain all the functions and their outputs when they are executed in the Remix IDE environment.The proposed blockchain-based system is developed and rigorously tested to verify that the rules implemented in smart contracts are not violated during the business processes execution among system users. More specifically, it validates the behavior of functions implemented in smart contracts based on a set of inputs and scenarios. The modifiers are used for the function to assure that only authorized users can call a particular function. It generates an error when it recognizes unauthorized access to call a function. Many events are implemented in smart contracts that transparently record the logs of the transactions when they are executed. Finally, the entities of the system including smart contracts, system users, and medical equipment and supplies are uniquely identified using their Ethereum addresses. This system can be used to trace the medical equipment and supplies from their manufacturing till the waste disposing stage. The use of penalties for breaching the rules during COVID-19 waste handling can significantly control the spread of COVID-19.Each entity in the system is represented by a unique Ethereum address. A.This section presents a few results about the forward supply chain management of medical equipment and supplies to highlight the correct working of our proposed system.PlaceOrderRequest function of the OrderManager smart contract in remix IDE. PlaceOrderRequest function enables the participants such as medical equipment distributor and COVID-19 testing center to register a request for system participants responsible to arrange and supply medical supplies and equipment such as COVID-19 swab testing kit. In the presented result in PlaceOrderRequest function is triggered by the distributor stakeholder of the proposed system. The system validates the identity of the distributor to verify that it is a registered user and holds a valid license number. Since the distributor gets the medical supplies from the manufacturer, hence system verifies that the receiver of this function call should be the manufacturer stakeholder. On successful execution of this function, an event named Orderplaced is emitted that stores the transaction log on the blockchain. The transaction log includes medical equipment EA, OrderID, Equipment Name, requested LotSize, and address of the order receiver.PlaceOrderRequest function, the system sets the status of the transaction to pending, thereby enabling the users to view the current status of the order for medical equipment. The receiver of the order can examine the needs and requirements of the order request (in offline mode), and it notifies the orderer about its response using on-chain resources. ConfirmOrder function of the OrderManager smart contract. The manufacture uses this function to publish its response by either accepting or rejecting the distributor request. This function is triggered by the manufacturer stakeholder only since the orderer entity in ConfirmOrder function, the manufacturer response is recorded on the blockchain by emitting an event called OrderReceived. This event stores the updated status of the order along with the OrderID that was generated by the PlaceOrderRequest function. The presented result in On successfully executing the UpdateInventory function is responsible to assure that the stock available in the store can fulfill the requirements of the distributor. Further, it updates the remaining medical supplies stock by subtracting the requested quantity from the available stock. On successful execution, as can be seen in EquipmentSoldtoOrderer event to record the logs on blockchain to show that medical supplies are sold out to the distributor. It records the address of the distributor, quantity sold, available quantity, Ethereum address of the medical supplies, and the time when this action is performed. After updating the inventory, the ownership is transferred and the distributor is updated as the new owner. TransferOwnership function of the orderManager smart contract. The system verifies that only the shipper of the medical supplies can call the TransferOwnership function. In our case, this function was called by the manufacturer. On successful execution of this function, an event is emitted and it stored the type and Ethereum address of the new owner, e.g., distributor.After accepting the order request, the manufacturer updates the record of medical supplies inventory based on the lot size requested by the distributor. In the orderManager smart contract, the TransferOwnership, ConfirmReceived function is called by the medical equipment orderer . Before ConfirmReceived function execution, shipment order status must be previously set as accepted. On successful execution of this function, an event called OrderReceived is emitted that logs the OrderID.After successful execution of PlaceOrderRequest function in RemixIDE. In contrast to PlaceOrderRequest function, the medical supplies distributor calls ConfirmOrder function to confirm the medical equipment supplies order.UpdateInventory function of the Ordermanager smart contract is called. On successful execution, the log of the transaction is generated and it transparently stores COVID-19 testing center as the receiver of the order in addition to many other important parameters such as quantity sold and remaining quantity, and the orderID. On successful execution of TransferOwnerhip function, the Ethereum address of the new owner of medical supplies is immutably recorded on the blockchain as can be seen in After successful order confirmation, the B.PlaceShipmentRequest function.Many medical equipment and supplies such as personal protective equipment and swab testing tubes, are specially designed to be used for one time only. Thus, these equipment require immediate disposal after their usage. Considering the contagious nature of coronavirus, the COVID-19 related medical waste demands to be carefully handled by the stakeholders. Our proposed system has implemented a shipment handling smart contract to assure that the medical waste shipper should have a valid COVID-19 result to ship the medical waste. The system is programmed such that it triggers an error on selecting a medical waste shipper who does not have a valid COVID-19 result. The error occurs in response to the COVID-19 testing center manager’s interaction with the PlaceShipmentRequest function by the COVID-19 testing center. This function accepts the shipper’s Ethereum address along with the weight of COVID-19’s waste, earliest pick-up time, sensors data attached to the waste bags, and the hashes of the waste certificates and 3D images. It proceeds further only if the shipper has a valid COVID-19 result. It sets the transaction status to pending to notify the shipper that the shipment services for medical waste are required (for traceability purposes). On successfully executing this function, it emitted an event and records the Ethereum address of the shipper and waste generator, shipment ID, and other necessary details on the blockchain. The shipment request is examined by the shipper to either accept or reject it based on the function’s input parameters. After accepting the medical waste shipment request, the shipper collects the waste and routes it to the medical waste treatment facility.UpdateShipmentStatus function. This function can be called only if the medical waste shipper has already accepted the waste shipment request. It continuously records the updated data  related to the current location, state, and status of the shipment. This data is periodically stored on the blockchain. It emits the event called shipmentstatusandlocationupdated to record the current location and state of sensors to notify the users about the current location and state of the shipment. The state of the shipment enables the users to know that at which time and place the sealed bags of COVID-19 waste were unsealed.PenaltyCalculationResult function. This function can be called by the owner of the smart contracts. As can be seen in The COVID-19 related waste is usually packed in multiple boxes that are carefully sealed to minimize the chances of spreading COVID-19 during its shipment stage. Many sensors are mounted inside such boxes to identify any human attempt made to open and stealing them (if any). The deployed sensors can sense, monitor, and record this data on to the blockchain ledger. PenaltyCalculationResult function. It also shows that four violations are reported during the medical waste shipment. It is to note that we have set the threshold value to three to trigger the license cancellation event for the shipper.In contrast to VI.This section presents the cost and security analysis of the proposed solution. It also outlines the future research challenges and generalization.A.The Ethereum blockchain platform meters the transaction computations in the unit of gas. The user is required to set a maximum amount of gas for each transaction to execute it. Each Ethereum virtual machine (EVM) operation consumes a certain amount of gas. Also, in smart contracts, storage variables, loops, nested decision making, mappings, and manipulations share the major portion of gas consumption. During transaction execution, if the required amount of gas is higher than the set Gas-limits then EVM halts the operations and charges the users to make sure that non-terminating computations cannot be performed to slow down the network. Because of the high flexibility, Ethereum allows the users to specify the cost of gas consumption based on their requirements (slow or fast execution). Hence, users can offer more gas price for the miners to prioritize their transactions. The Remix IDE calculates and stores the transaction and computation cost as part of the transaction log Ethereum Gas Station estimates the gas price for the Ethereum-based transactions. It offers different speeds of executing the transactions that are largely affected by the gas price offered by the user PlaceShipmentRequest and PlaceOrderRequest need the highest number of tokens for their successful execution since these functions have involved various complex operations. For instance, placeShipmentRequest function has implemented several structures, mappings, manipulations, and modifiers to assure the correct business operations among participating stakeholders. It also has implemented many modifiers to assure that, (1) the potential medical waste shipper has no COVID-19 symptoms, (2) the caller of the function is a business representative of COVID-19 testing hospital, and (3) the shipper has a registered license number. It also implements structures to record the details about the medical waste material and shipper. The GetOrderID function requires the least number of Ethers for its execution among all functions.B.Herein, we present a detailed security analysis of the proposed solution that offers high security, robustness, and resilience features. Below is given a brief discussion on the key security and privacy issues that are addressed in our system. It also discusses the security analysis of the implemented smart contracts.1)Blockchain technology has gained tremendous popularity in the healthcare sector because of its inherited security features. Unlike centralized-based systems, the decentralized feature makes it highly acceptable and dependable to healthcare organizations. Furthermore, the decentralization makes the system highly fault-tolerant and available despite the occurrence of malicious codes such as the denial of service (DoS) attacks. Therefore, such high availability makes it acceptable to the industries. Since our system has been implemented on the Ethereum blockchain platform, the services of our system will always remain available to the participating stakeholders.2)Data manipulation is one of the serious threats to the integrity of the data. Among many, approaches such as append-only, data certification, and sophisticated access control can enable organizations to protect their data. In the blockchain, through cryptographic functions, data integrity is preserved. The hashing algorithms make the blockchain transactions immutable. In our proposed waste management system, the transactions of the participants such as the manufacturer and COVID-19 testing center are digitally signed before they are written on the blockchain. Thus, the immutability feature of blockchain assures that these transactions cannot be edited, modified, or deleted. Hence, the proposed solution preserves data integrity.3)Digital signatures are based on mathematical schemes and are often used to verify the authenticity of a transaction. Also, digital signatures provide non-repudiation service to the existing systems, since the signer cannot deny that he/she did not sign the transaction. In the Ethereum blockchain platform, each transaction of the user is secured by the digital signature. For instance, a waste treatment facility might have sent a waste shipment confirmation transaction to the hospital. The blockchain assure that the transaction is signed with the private key of the waste treatment facility. This transaction cannot be modified or deleted because of immutable blockchain technology. At any time, this transaction cannot be denied by the waste treatment facility since the originator of the transaction can be verified by the signature.4)In a Man-in-middle attack, a user intercepts communication between two parties to secretly eavesdrop or modify the exchanging data. Blockchain technology assures that no man-in-middle attack can be successfully launched. It requires the users to sign digitally the encrypted transactions to immutably store it on the blockchain platform. The launch of the Man-in-middle attack is successful only if the malicious actor has successfully retrieved the private key of the communicating parties. The miners of the Ethereum blockchain platform cannot approve those transactions wherein signatures do not match with the signatures of the legitimate users. Our system is comprised of several stakeholders who are often located in geographically different locations. Thus there is a high probability of Man-in-middle attacks. Since our system is implemented on the Ethereum blockchain platform, hence it is safe against Man-in-middle attacks.5)An ill-designed or poorly written smart contract can exhibit low performance, limited security, and high transaction execution fee. It is highly recommended to write smart contracts carefully to avoid any exploitation of smart contracts for malicious activities. There are many tools available that statically analyze the smart contracts  and provide guidelines to improve the cost, performance, and security of smart contracts. The guidelines state that smart contracts should be checked for infinite loops, floating-point operations, reentrancy errors, and common bugs that can make the smart contract highly vulnerable to attacks. We improved our smart contracts by analyzing them using the SmartCheck C.In this subsection, we enumerate and discuss several important challenges remaining to be addressed.1)Blockchain accomplishes data security through transparency and storing data on multiple nodes. However, the transparency and distributed storage of data can greatly affect data privacy. The public blockchain platforms face data privacy assurance challenges since all transactions and data are publicly available on the ledger, thus affecting the privacy of user data. The EU General Data Protection Regulation (GDPR) laws protect the data related to the organizations that participate in business processes in the forward supply chain and waste management of medical equipment and supplies. Our study has used a registration smart contract and function modifiers to control data access on the chain. Our proposed smart contracts can be modified and implemented on private permissioned blockchain platforms such as Hyperledger Fabric, Hyperledger Besu, and Quorum, 2)Blockchain technology offers many advantages to the stakeholders involved in the forward supply chain of medical equipment and their disposal in terms of lower transaction cost, streamlined operations, improved system efficiency, data traceability, audit, and operational transparency. Blockchain is an emerging technology that lacks in providing certain specifications and surety required to the business organizations to adopt the new technology. For instance, integrating blockchain-based systems with legacy systems is extremely challenging. Also, the lack of regulation, awareness, understanding, governance, and developers’ support are some of the imperative challenges that should be addressed in the future.3)Bugs and vulnerabilities can affect a smart contract’s normal functioning and behavior. Vulnerabilities in the code of smart contracts are often exploited by hackers to perform harmful activities. The examples of vulnerabilities in the code of the smart contract include reentrancy, overflow and underflow, unsecured balance, unchecked and failed send, non-validated arguments, short address attack, default visibilities, timestamp dependence, call stack depth limitation, multi-stage attack, prodigal contracts, transaction ordering dependency, and block gas limit 4)Blockchain nodes use adequate time and energy to arrange and validate the transactions, store them, and create and broadcast each block to other mining nodes for verification and updating. The throughput of the Ethereum 1.0 platform is 16 to 20 transactions per second. Nevertheless, Ethereum 2.0 promises a throughput of 100,000 transactions per second 5)The stakeholders involved in the forward supply chain and waste management of medical equipment and supplies could be practicing multiple platforms to do their business activities. The interoperability feature in existing blockchain-based solutions provides cross-platform communication ability to increase system productivity and throughput 6)The notable increase in the transaction rate of blockchain platforms can influence the average response time and transaction fees. Consequently, the increased transaction rate can make the blockchain remarkably slow and unstable 7)Blockchain is governed and run by several communities including, core developers, node operators, token holders, and blockchain teams. Being an evolving technology, mature strategies that can efficiently govern blockchain are lacking. Broadly, governance models are classified into two types such as on-chain and off-chain blockchain governance. The Ethereum platform follows an off-chain governance model. The on-chain governance model can leverage the voting mechanism to achieve direct democracy in blockchain affairs. Tezos and hard fork are on-chain and off-chain governance strategies, respectively D.E.We present a working prototype of a blockchain-based system along with its security and cost analysis to investigate the feasibility of employing blockchain technology in the forward supply chain and waste management of COVID-19 related medical equipment and supplies. The proposed system is designed and tested on a public Ethereum blockchain platform to meet the high visibility, transparency, and security requirements of users of supply chain and waste management industries. Also, such users do not have fast and real-time transaction processing requirements. Since data and transactions stored on public blockchain platforms are encrypted, thereby enforcing trust which makes it more appealing to business organizations to store their transactions. The proposed system can be customized to meet the growing demands and requirements of various organizations. To provide high transaction execution speed, privacy, transparency, and security, one can modify the proposed smart contracts with few efforts to deploy them on private blockchain platforms. Even, our system can be integrated with external oracles and proxy encryption schemes to further improve the privacy-related issues.The proposed system can efficiently trace and track the forward supply chain of medical equipment supplies related to COVID-19. It also assists in tracing and tracking the medical waste caused by medical equipment and supplies and assures that the medical shipped waste complies with COVID-19 safety rules. It employs sensors to monitor and ensure that sealed medical waste bags cannot be exposed during shipment to minimize the COVID-19 from spreading. It cancels the shipper license for violating the rules specifically designed to decrease the chances of COVID-19 from spreading during waste transportation. It also assures that medical waste found missing is immediately reported by the medical waste treatment facility.COVID-19 inherits many similar characteristics of many other viruses or diseases that belong to a similar category . Since the proposed solution has successfully traced and tracked the forward supply chain and waste related to medical equipment and supplies; therefore, this research is equally valid to manage the forward supply chain of medical equipment for other diseases too. For example, the authorities can use this research to trace and track the medical supplies for Influenza (Flu). In addition to the forward supply chain and waste management of medical equipment and devices, this research is valid to trace and track any vaccine for COVID-19 treatment. The examples of other beneficiaries of this work include drug dealers, pharmacists, telehealth specialists, telemedicine, regularities, health authorities, and health professionals.The smart contracts are implemented in solidity for the Ethereum-blockchain platform. Since our solution is generic; therefore, one can easily transform it with minimal efforts for other blockchain platforms. As our solution can successfully minimize the spread of COVID-19 during the forward supply chain and waste handling, hence healthcare authorities can extend it by introducing new laws to further minimize the spreading of COVID-19.VII.In this paper, we have designed, developed, and evaluated a blockchain-based solution to automate forward supply chain processes and establish data provenance about the COVID-19 medical equipment and their waste disposal. We developed four smart contracts and proposed five algorithms to implement all functionalities and trigger events and notifications. We integrated the Ethereum blockchain with decentralized storage of IPFS to provide a secure, transparent, auditable, reliable, traceable, and trustworthy solution for the forward supply chain and waste management of COVID-19 medical equipment. The proposed approach can assist authorities in assuring that the COVID-19 medical waste is disposed of properly, and COVID-19 testing centers are using genuine medical equipment to treat COVID-19 patients. We presented a detailed cost analysis to show the affordability of the proposed approach. We also evaluated the proposed approach against known vulnerabilities using the SmartCheck software. The proposed approach is generic and applicable to various use case scenarios with minimal modifications. Our implementation code is made publicly available on GitHub. In the future, we aim to design and implement an incentive system for the COVID-19 treatment hospitals to dispose of medical waste.


Objective: Although guidelines have recommended standardized drug treatment for heart failure (HF), there are still many challenges in making the correct clinical decisions due to the complicated clinical situations of HF patients. Each patient would satisfy several recommendations, meaning the decision tree of HF treatment should be nonmutually exclusive, and the same patient would be allocated to several leaf nodes in the decision tree. In the current study, we aim to propose a way to ensemble a nonmutually exclusive decision tree for recommendation system for complicated diseases, such as HF.Methods: The nonmutually exclusive decision tree was constructed via knowledge rules summarized from the HF clinical guidelines. Then similar patients were defined as those who followed the same pattern of leaf node allocation according to the decision tree. The frequent medication patterns for each similar patient were mined using the Apriori algorithms, and we also carried out the outcome prognosis analyses to show the capability for the evidence-based medication recommendations of our nonmutually exclusive decision tree.Results: Based on a large database that included 29,689 patients with 84,705 admissions, we tested the framework for HF treatment recommendation. In the constructed decision tree, the HF treatment recommendations were grouped into two independent parts. The first part was recommendations for new cases, and the second part was recommendations when patients had different historical medication. There are 14 leaf nodes in our decision tree, and most of the leaf nodes had a guideline adherence of around 90%. We reported the top 10 popular similar patients, which accounted for 32.84% of the whole population. In addition, the multiple outcome prognosis analyses were carried out to assess the medications for one of the subgroups of similar patients. Our results showed even for the subgroup of the same similar patients that no one medication pattern would benefit all outcomes.Conclusion: In the present study, the methodology to construct a nonmutually exclusive decision tree for medication recommendations for HF and its application in CDSS was proposed. Our framework is universal for most diseases and could be generally applied in developing the CDSS for treatment. Heart failure (HF) is a clinical syndrome that is a result of the abnormalities in the structure and function of the myocardium impairing cardiac output or decreasing the filling of the ventricles . The treCDSSs provide evidence for physicians in making clinical decisions, such as differential diagnosis and recommending medications . The keyTo maintain the clinical correctness and interpretability of the model used in CDSS, knowledge-based decision trees should be constructed. A decision tree is a model consisting of consecutive decisions, starting from the root node, and each sample would be allocated to different branches based on the condition it satisfied. Nodes without downstream branches are called the leaf node, and all samples would be classified into different leaf nodes according to the decision tree. In general, the construction of such a decision tree is based on the summary of the clinical rules for a specific disease . The cliThe core challenge in the construction of the decision tree relies on the integration of clinical rules . For somIt is challenging to convert clinical guidelines into a normal decision tree since those trees, where one patient can only be allocated in a unique leaf node, are designed to be mutually exclusive. To tackle this problem, we proposed a novel way to construct and leverage a nonmutually exclusive decision tree in CDSSs. The construction process was just listing all the clinical rules horizontally if they were independent. Therefore, each branch was not mutually exclusive with others in the decision tree. One patient could enter multiple branches and be allocated to several different leaf nodes simultaneously.In the current study, we proposed a framework to construct the nonmutually exclusive decision tree and applied it in the HF treatment recommendation as shown in This study was approved by the ethics committee of the Chinese PLA General Hospital.The dataset we used was extracted from a heart failure database in the Chinese PLA General Hospital in Beijing, China. This database was initially constructed for all patients diagnosed with heart failure from 2008 to 2018. The database included 29,689 patients with 84,705 admissions in total. According to the classification and diagnostic criteria of heart failure in the guidelines , all samIt should be noted that the quality of the data was critical in providing real-world evidence. We used the data of inpatients instead of those of outpatients, considering that the coverages of many features of the data of inpatients were much higher than the data of the outpatients. Such substitution hypothesized that the medication for inpatients and outpatients of HfrEF was similar in terms of medication categories, such as ACEIs (angiotensin-converting enzyme inhibitors) or β-receptor blocker, and our CDSS also provided real-world evidence in the granularity of medication categories. The medication considered in the current study is listed in The preprocessing of the data of inpatients contained three steps: data standardization, data segmentation, and missing value imputations.First, the medications used in the current study were standardized by the ATC  five-digit code as listed in n samples. For each sample, the history was referred to as the activities that happened in the previous day. It should be noted that by multivariable logistic regression to the prescriptions, the day indexes in the segmented daily records were independent of the medications in each day. A total of 132,158 samples were obtained after data fragmentation.Second, the data of the inpatients were fragmented by the day. As shown in Third, a two-step strategy of missing value imputation was utilized in the current study. First, the missing values were imputed forward or backward for the same admission. Then the rest of the missing values were imputed with means for continuous variables and medoids for discrete variables. The coverage of each variable before and after the first step imputation is listed in To construct the knowledge model, clinical recommendations were first summarized and extracted from the clinical guidelines. Then the clinical recommendations were organized in the form of a decision tree with each branch representing one recommendation in the clinical guidelines. The decision-tree-like knowledge model was constructed and reviewed by multiple clinical experts.As shown in To construct the decision tree, the extracted clinical rules were integrated in the following ways. If the clinical conditions were independent, the clinical rules were organized horizontally; otherwise, they were integrated following the logical hierarchy. The constructed decision tree for the new cases and the cases after initial treatment were shown, respectively, in It should be noted that in After the knowledge tree was constructed, the clinical records would be allocated to a set of leaf nodes. In the current study, we proposed defining similar patients as those who followed the same pattern of leaf node allocation. For example, patients allocated to leaf node 4 and leaf node 6 were treated as the same patient group. Note that only the exact leaf allocated patterns were counted in the fine grouping of the patients.kth frequent itemsets, the k+1st frequent itemsets were generated. Then those generated candidates were excluded once they did not satisfy the frequency threshold. The remaining k+1st frequent itemsets were used for the next round of generation.To provide real-world clinical evidence, for each patient subgroup, the frequent drug patterns were mined using the Apriori algorithms . The AprIt should be noted that the nonmutually exclusive feature led to the independence of the frequent medication patterns between nonmutually exclusive parts in the decision tree. As shown in The real-world evidence for each subgroup was provided based on the prognosis analyses. The real-world evidence played a critical role for leaf nodes with variable choice of medications. Therefore, multiple outcomes were considered in the current study to assist the physician to make decisions even for the same patient subgroup.(1) Electrolyte disturbanceTo assess each treatment effect of HfrEF, six outcomes were considered in the current study. They are hyponatremia, hypernatremia, hypokalemia, hyperkalemia, acute kidney injury, and the reduction of B-type natriuretic peptide. They could be grouped into the following three categories:(2) Acute kidney injuryIn the treatment of HfrEF, electrolyte disturbances mainly referred to the blood potassium and the blood sodium. The high or low blood potassium and the blood sodium were all considered as electrolyte disturbances. Hyponatremia is defined as blood sodium <135 mmol/L, while hypernatremia is defined as blood sodium >145 mmol/L. The lower limit for blood potassium was 3.5 mmol/L, and the upper limit was 7.0 mmol/L. Any values out of the range were defined as hypokalemia or hyperkalemia accordingly.−1 h−1 (time >6 h).(3) B-type natriuretic peptideHeart failure and chronic kidney disease often coexist, and the existence of renal insufficiency could worsen the prognosis of HF . The fol17. The 20% increase in BNP was labeled as the deterioration of HF conditions.The B-type natriuretic peptide (BNP) was one of the most common heart failure biomarkers used in screening, diagnosis, severity assessment, and prognosis of heart failure. Also, it was an indicator of the risk of cardiovascular events in patients with heart failure after discharge. The lower the BNP values, the better the clinical conditions the patient had. We chose a 20% reduction of BNP as the indicator of improvement of clinical conditions for HF, denoted as BNP_ improvedn candidate medications, and there would be 6n prognosis analyses since there were six outcomes considered in the current study.As aforementioned, the treatment using diuretic and other HF drugs were independent; the prognosis analyses were also carried out separately for diuretic and other HF drugs. For each group of similar patients with each nonmutually exclusive part, suppose there were To calibrate the bias introduced by patients using the specific medication, the propensity score of the medications was included in the regression to the outcomes. To be specific, for the prognosis analysis with treatment T, other variables X, and the outcome Y, we first regressed T on X and obtained the propensity score of a patient using treatment T. Then the propensity score, as well as the variable X, was used to the regression of the outcome Y. The formula for the second regression was:The ramework , after bAccording to the HfrEF knowledge decision tree, the first decision point was the history of medication of the patient. To understand the distribution of medication history, we first mined the history medication using the Apriori algorithms  as introAmong all samples loaded for As shown in Since the existence of some subjective or hardly recorded clinical conditions, it may be difficult to run through the decision tree. Thus, we proposed their alternative estimations. For example, the decision condition 1/3/4 (the existence of congestion symptoms/the improvement of congestion symptoms/the improvement of heart failure symptoms) was difficult to assess directly by using our data. For the decision condition ¾, changes in the BNP values were used instead . More spAfter loading the records to the decision tree, we estimated the adherence of each clinical node to the clinical guideline. By adherence, we meant the medication patterns were not in conflict with the clinical guidelines. To estimate the adherence, the frequent medication patterns were mined for each leaf node, and the top ones were used to be compared with the guideline-recommended medications. The top five frequent medications and the corresponding guideline adherence for each leaf node are listed in Once the clinical records were loaded into the decision tree, the patients were clustered into different subgroups according to our definition of similar patients. The leaf node patterns and sample size of the top 10 subgroups are listed in Prognosis analyses, all combinations for the top five medications and the six outcomes were analyzed for each leaf node. As a running example, here we showed the prognosis analysis for subgroup d. For subgroup d, patients were allocated to five leaf nodes simultaneously. As the prescription of the diuretics and heart functional-related drugs was independent according to the clinical guidelines, the prognostic analyses were carried out separately. Shown in As introduced in As shown in In the present study, we proposed a framework to construct a nonmutually exclusive decision tree and to combine the tree with real-world data for a treatment recommendation. Based on this framework, we successfully built the CDSS for chronic heart failure treatment with a large real-world inpatient dataset. In addition, the multiple outcome prognosis analyses were carried out to assess the medications for the subgroup of each similar patient, which facilitated the physicians in making decisions in a patient-specific way.Although many achievements have been made in improving the model precision for medication recommendations , it is sThe decision tree is composed of the clinical conditions mentioned in the clinical guidelines and the candidate medications in the leaf nodes. In the mutually exclusive settings, following the clinical conditions on the decision trees, one patient is allocated to a unique leaf node. Those required a systematic partition of the whole population, which was infeasible for most cases, especially for complicated diseases such as chronic heart failure.As proposed in the current manuscript, the construction of the nonmutually exclusive decision tree simply organized the independent clinical rules horizontally. The similar patients were defined as the same patterns of leaf nodes allocated, and the preparation of the real-world evidence for each subgroup of similar patients relied on the two-step linear regression for the nonmutually exclusive parts separately. In summary, the three key components for nonmutually exclusive decision trees, the construction process, the similar patients, and the real-world evidence were nondisease specific; thus, the methodology would be a general solution for all diseases without a systemic partition of the whole population.The limitation of the current work was that we only used single-center data and had not yet tested the construct CDSS with an external dataset. Besides, the effect of the substitution of the data of outpatients for the data of the inpatients was not carefully evaluated in the application of CDSS.The novelty of our work relied on how to utilize such a nonmutually exclusive decision tree in CDSS. First, one key concept in CDSS was identifying similar patients, which were defined as patients assigned to the same set of leaf nodes. Second, to provide real-world evidence, we separated different types of medications and recommended independent medications for each similar patient group. To make precise medication recommendation for each patient, the prognoses evidence for each treatment should be calibrated by the propensity score of a particular treatment. In practice, the calibration included two steps. First, the propensity for patients in a particular patient group to choose one medication pattern were evaluated by regression to the medication patterns. Second, the effect of each medication pattern to the outcome should be calibrated by considering the propensity of medication chosen when regressed to the outcome. Therefore, our nonmutually exclusive decision tree would provide risks of different outcomes for each medication pattern of each patient group, which would assist physicians to make medication decisions for a specific patient.In conclusion, in the present study, the methodology to construct a nonmutually exclusive decision tree for medication recommendations for HFrEF and its application in CDSS was proposed. Our framework is universal for most diseases and could be generally applied for developing the CDSS for treatment. This provides a promising solution for diseases that are infeasible to obtain a mutually exclusive decision tree for treatment.


Even if the region of the binding has been identified in the N-terminal domain of Spike protein, so far no comprehensive analyses have been carried out on the spike-sialic acid conformations once in the complex. Here, we addressed this aspect performing an extensive molecular dynamics simulation of a system composed of the N-terminal domain of the spike protein and a sialic acid molecule. We observed several short-lived binding events, reconnecting to the avidic nature of the binding, interestingly occurring in the surface Spike region where several insertions are present with respect to the SARS-CoV sequence. Characterizing the bound configurations via a clustering analysis on the Principal Component of the motion, we identified different possible binding conformations and discussed their dynamic and structural properties. In particular, we analyze the correlated motion between the binding residues and the binding effect on the stability of atomic fluctuation, thus proposing regions with high binding propensity with sialic acid.Recent experimental evidence demonstrated the capability of SARS-CoV-2 Spike protein to bind sialic acid molecules, which was a trait not present in SARS-CoV and could shed light on the molecular mechanism used by the virus for the cell invasion. This peculiar feature has been successfully predicted by  OC43 and HKU1, are known to circulate in the human population for many years and cause a mild seasonal illness  receptor as primary entry channel  and a sialic acid molecule, in order to blindly highlight the spike region most prone to interact with glycans. We firstly perform a molecular dynamics simulation of the trimeric form of the spike protein, underlining how the inter-chain interactions influence very slightly the overall motion of each N-terminal domain. Thus, we run a molecular dynamics simulation with a sialic acid molecule, to blindly investigate the protein surface pocket preferentially contacted by the glycan. Our analyses assign to each spike residue a binding propensity score, based on the time that residue spends in interaction with sialic acid, identifying the sialic acid most preferred residues. Moreover, we executed a Principal Component Analysis on the coordinates of the interacting atoms belonging both to Spike protein and sialic acid molecule when these two molecules are bound, aiming to identify the principal conformations explored by the molecular complexes. We thus identified 5 binding modes occurring between these two molecules, involving five possible regions on the NTD, and we investigated if the presence of the glycan stabilize such regions.These results shed light on this important molecular interaction, that could provide an additional cell invasion mechanism to the virus possibly explaining the impressive rate of infection showed by SARS-CoV-2.To investigate protein-ligand interactions at the atomic level, molecular dynamics simulation (MD) with explicit solvent is a powerful computational approach because it allows us to take into account the exploration of the conformational space, as well as the solvent and entropic effects in a physically consistent manner , which aHowever, virus-glycan associations are typically characterized by low affinity and so they are inherently dynamic . MoreoveTo keep the computational costs low, increasing at the same time the simulation time to have a greater probability of observing the interaction between protein and ligand, we used only the N-terminal domain (NTD) of the spike protein. Indeed, since both theoretical prediction and early experimental validations indicate that the sialic acid-binding region is localized in the NTD of the spike protein, we firstly compare the free motion of the domain (residues 1–290) when inserted in the trimeric spike complex and alone in water using 140 ns long MD trajectories  and the root mean square fluctuation (RMSF) of the backbone carbons of the Spike N-terminal domain see . InteresRemarkably, both descriptors behave similarly when the domain is alone in water  or is inserted in the trimeric complex . In particular, the RMSD curves show the same trend, with about the same time to reach equilibrium, when both the single domain and the trimeric complex are considered. More specifically, the average RMSD value at equilibrium (after 30 ns of the simulation) of the N-terminal domain alone in solution is very similar to that of the same domain belonging to the chain A of the trimer, meaning that the interdomain interactions are not essential for the overall fold maintenance.On the other hand, the comparison between the residues RMSF obtained when the domain is simulated alone or in trimer shows that the most fluctuating and least fluctuating regions are conserved. Indeed, the average Pearson correlation coefficient between the RMSF values of the three domains of the trimer and the RMSF values of the domain alone is 0.63. Looking at the correlation of the motions between residues, we underlined how even the residues combined motions are conserved when the domain is simulated alone see .Taken together, these results indicate that the inter-chain interactions do not notably modify the motion of the Spike N-terminal domain. This allows us to simulate only the N-terminal domain, thus significantly reducing the computational cost of our simulations.We then performed a long molecular dynamics simulation of the N-terminal domain of SARS-CoV-2 in the presence of a sialic acid molecule (see section 4 for details), in order to observe a blind binding. To keep the computational cost of the simulation low and to avoid ligand-ligand interactions, we setup the system with a Spike N-terminal domain and a single sialic acid molecule.During the 1.75μs of the evolution, we observed various binding (and unbinding) events. In particular, each event consists of a progressive nearing of the sialic acid to the protein until a binding takes place. As testified by the various constant stretch in In The contact probability as a function of the residues shows well-defined peaks. This means that the binding with sialic acid involves specific regions. The sum of all the areas under the most pronounced peaks, whose residue ranges are reported in Asterisks in To characterize the different observed binding events, we investigated the structural conformations of the formed complexes. To do so, we performed a principal component analysis (PCA) on the spatial coordinate of the heavy atoms of both the 111 interacting residues of the spike domain and the sialic acid for uniform sampling of the molecular dynamics trajectory. Since we found not an unique binding region, we looked for a compact way to study and isolate the different binding regions on the spike protein. The PCA allows us to compactly represent each molecular dynamics frame as a point in essential plane, where the difference with the other possible bound conformation is maximum. In this way we are sure that the clustering procedure groups conformations that are similar focusing only on the position of the interacting atoms, considering both the protein and the ligand.In The groups obtained via the PCA analysis can be investigated following the time evolution of the simulation. Indeed, we show in The principal component analysis allows us to identify binding conformations between the N-terminal domain of the SARS-CoV-2 spike protein and the sialic acid molecule. Furthermore, selecting the most representative structure of each cluster and analyzing the protein-ligand inter-molecular contacts, we define 5 possible binding sites. Identified the interacting residues involved in the binding sites, we investigated their dynamic and structural properties, analyzing, on one hand, the correlation of motion between the residues during the unbound simulation time and on the other hand the effect of the binding in terms of structural stabilization.The correlation analysis between the residues of the N-terminal domain, shown in We also analyzed the variation of the mobility of the interacting residues before and after the binding with the ligand. For this purpose, we calculated the percentage difference of the atomic fluctuation between the bound and unbound state for each N-terminal domain residue . NotablyMore specifically, we here also analyze the stabilizing effect of binding with the ligand by considering only the residues of the 5 binding sites identified. Similarly to what was found with the correlation analysis, clusters 1, 2, and 5 also show a highly stabilizing effect upon binding with the sialic acid molecule . InteresThus, the analysis of the RMSD  shows thThe N-terminal domain of SARS-CoV-2 spike protein is characterized by some insertion with respect to the SARS-CoV homologous one, and the surface regions generated by these residues have acquired the capability to bind sialic acid molecule , 26, 27.Here, we address this issue through an extensive molecular dynamics simulation, where we were able to observe spontaneous binding events occurring between the spike protein of SARS-CoV-2 and the sialic acid molecule. In particular, we simulated the free motion of both the sialic acid molecule and the spike protein for times long enough to detect several binding between the two molecules. The main advantage of this approach is to consider exhaustively the physics of the system, taking into consideration electrostatic interactions as well as solvent and entropic effects.We identify 5 sialic acid-interacting regions belonging to the NTD of the spike protein.It has to be noted that physiologically the Spike protein is extensively glycosylated, a property that often escape the experimental characterization. Indeed, each Spike protomer has 22 glycosylation sites, which are supposed to affect which cells the virus can infect –35. ThisMoreover, since sialic acid is negatively charged, it is remarkable that the selected interacting regions are never characterized by negative net charge. The binding mode 2 is characterized by the highest positive charge that make it a suitable region to recognize the sialic acid molecule, and it is probably because of this that it is the most populated cluster among the five we proposed. Indeed, even looking at the sialic acid experimental binding region observed in MERS-CoV Spike protein, it emerges that the residues involved generate an overall charge of +1 (in unit of electron charge), similar to those characterizing our proposed binding modes.Dynamic and structural analyses on these binding sites allow us to characterize the regions most prone to binding with the molecule. In particular, we consider the binding region 4 unsuitable for binding to sialic acid since binding occurs several times but of short duration. In addition, the residues corresponding to this region have a lower degree of correlation of their motion and the binding of the sialic acid increases the atomic mobility. On the other hand, three regions  are particularly interesting because they have a high average correlation in terms of the motion of the residues that compose them and undergo an evident stabilizing effect after binding with sialic acid.Unbound SARS-CoV-2 spike protein: modeled by I-TASSER server . The TopN-Acetylneuraminic acid molecule: structure from PubChem database (id: 439197) (ase id: 4197 (39). 439197) .All simulations were performed using Gromacs 2019.3 . TopologLINCS algorithm  was used3, close to the experimental value. The system was simulated with a 2fs time-step for 140ns in periodic boundary conditions.Simulation of the SARS-CoV-2 spike trimeric complex was performed starting from the model structure proposed by the I-Tasser server . The add3. The simulation was carried out for 1.75μs with time steps of 2fs.We simulate also the N-terminal region of the SARS-CoV-2 spike in the presence of one molecule of sialic acid (neu5ac) in solution. We selected the domain ranging from residue 1 to 290 of the chain A of the trimeric complex. We added 2 Cl molecules to neutralize the system, while water density was 1, 008kg/mα of the residues belonging to the peaks reported in We performed a PCA on the covariances of the CThe original contributions presented in the study are included in the article/EM and GR conceived the research. LB performed molecular dynamics simulations. LB, MM, LD, and EM performed calculations and computational analysis. All authors analyzed the results, wrote, and revised the paper.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Fatigue of the ankle plantarflexors (dominant leg) was induced by a sustained posture (standing on the toes) until exhaustion. Traditional COP parameters  were obtained with a force plate, and time and frequency-domain EMG parameters were obtained by surface electrodes positioned on the tibialis anterior, soleus, lateral gastrocnemius, medial gastrocnemius, vastus lateralis, biceps femoris, spinal erector and rectus abdominis muscles. The main results showed that fatigue induced a significant increase in postural oscillations in the ML axis , with no significant effects in the AP axis. In terms of postural sway parameters , no superior balance stability was found for the GYN group as compared to CTRL, irrespective of the fatigue condition. On the other hand, the modulation of EMG parameters (in both time and frequency domains) indicated that expert acrobatic gymnastics athletes  used different neuromuscular control strategies to keep their postures on single-legged quiet standing after the fatiguing protocol. The present results improve our knowledge of the mechanisms behind the interplay between fatigue and postural performance associated with the neuromuscular adaptations induced by sport practice. The design of gymnastics training might consider strategies aimed at improving the performance of specific muscles  for which particular activation patterns were used by the acrobatic gymnastics to control single-legged quiet standing.This study investigated whether expert acrobatic gymnasts respond differentially than their non-trained counterparts during a single-legged stance task performed before and after a protocol designed to induce fatigue in the ankle plantarflexor muscles in terms of (a) postural steadiness and (b) electromyography (EMG) activation. We hypothesized that neuromuscular adaptation due to training would lead to different behavior of center of pressure (COP) and EMG quantifiers after fatigue. Twenty eight female volunteers (aged 11 to 24 years) formed two groups: expert acrobatic gymnastics athletes (GYN,  The control of body balance  is regulated by the postural control system, which needs to integrate information from visual, vestibular and somatosensory sources to generate adequate motor responses that keep the body still . AssessmSome studies have pointed out the practice of sports such as gymnastics promotes efficient stimuli in generating neuromuscular adaptations, which seems to be associated with intense and regular practice of complex motor skills, including high demand for production and torque control around the ankle joint. Specifically, ankle plantarflexors are traditionally considered the most important muscle group engaged in the maintenance and control of postural balance , and henThe performance of acrobatic gymnastics focuses on static and dynamic routines which demand high levels of strength, stability, flexibility and agility, and hence training programs are designed to enhance these features thereby improving overall performance. Additionally, practicing also focuses on the presentation of specific routines in which the athletes work together in an attempt to have their performance well ranked by referees. Consequently, the duration, frequency, and intensity of the training sessions are high, and often begin in early childhood. Therefore, one of the main issues coaches have to deal with concerns the deleterious effect of fatigue (that occurs during long and intense training sessions) on the athletes’ performance. In particular, the impact of fatigue on standing balance is of great importance as the overall stability during the static and dynamic routines are highly associated with the athletes’ performance . In this vein, fatigue has been considered of particular importance to sports-related injury risk and performance, and the negative effect of neuromuscular fatigue on postural control has been extensively reported in a wide variety of conditions and for different populations . TraditiSurface electromyography (sEMG) has been widely used to evaluate neuromuscular adaptations as a function of fatigue , and alsHowever, despite the large number of studies mentioned above, to the best of our knowledge, no study has addressed the putative effects of neuromuscular adaptations resulting from acrobatic gymnastics training on the behavior of the postural control system in the presence of muscle fatigue. Additionally, presumed neuromuscular mechanisms reflected by changes in time and frequency-domain parameters of the EMG signal recorded during postural tasks (and its fatigue-related effects) remain unexplored. Such an investigation has the potential to provide insights that might be useful for the design of gymnastics training strategies, besides improving our knowledge on the mechanisms behind the interplay between fatigue and postural performance associated with the neuromuscular adaptations induced by sport practice. More specifically, a deeper understanding of the long-term effects of gymnastics training on postural control, together with information on how the neuromuscular system of experienced athletes adapt to overcome the deleterious effect of fatigue on postural stability might help coaches to focus on specific training strategies (such as strength/endurance/control training of specific muscle groups) that can potentially benefit their athletes in improving performance and/or reducing injury risk.Therefore, the objective of this study was to investigate the effects of fatigue and acrobatic gymnastic expertise on the neuromuscular and behavioral performance of a unipedal quiet stance task. So, we assessed whether expert acrobatic gymnasts respond differentially than their non-trained counterparts during a single-legged stance task (performed before and after a protocol designed to induce fatigue in the ankle plantarflexors muscles) in terms of (a) postural steadiness and (b) EMG activation. The single-legged postural task was chosen because it is a posture commonly adopted in the practice of acrobatic gymnastics (present in the punctuation code considered by referees in competitions). The hypothesis addressed in the present study was that neuromuscular adaptation due to gymnastics training would lead to different behavior of postural sway and EMG quantifiers  in trained gymnasts submitted to neuromuscular fatigue of the plantarflexor muscles as compared to healthy untrained controls. More specifically, we expected that fatigue would reduce postural stability (as assessed by COP parameters) and change EMG activation patterns (increasing time-domain and decreasing frequency-domain parameters) in both expert acrobatic gymnasts and their non-trained counterparts; and that these changes would be more pronounced in the non-trained group, as the gymnasts would putatively have developed training-induced adaptation mechanisms that would help them in dealing with the deleterious effects of fatigue on postural control.n = 14]. Participants’ age, height, and body mass are depicted in The experiments were carried out in 28 female volunteers, aged between 11 and 24 years. Two groups were formed: acrobatic gymnastics athletes  and non-gymnasts , the positioning of the electrodes replicated protocols commonly used in the literature. Thus, RA electrodes were positioned 3 cm laterally to the navel  as recommended by SENIAM  in tibiahe navel ; and in he navel . Before For MIVC assessments, participants laid (or seated) on a customized stretcher equipped with Velcro tapes and adjustable mechanical levers that prevented undesirable movements of the participants. MIVC of the SO, GL, and GM were obtained with the participants in the prone position, with feet free off the stretcher and ankle at 90°, with the area of the heads of the metatarsal bones in contact with a fixed restraint. Participants were asked to exert as much force as possible to push against the restraint with the foot sole using only leg muscles. A mechanical support was positioned against the participants’ shoulders to prevent them to slip on the stretcher. TA MIVC was obtained with the participants in the supine position, the ankle at 90° and the dorsal part of the foot in contact with a fixed restraint. Participants were asked to pull the restraint with the dorsal part of the foot as hard as possible using only leg muscles. MIVCs of the VL and BF muscles were obtained with the participant seated on the edge of the stretcher with hip, knee, and ankle angles at 90°. A fixed restraint was positioned against the anterior and the posterior aspects of the legs, at ankle level. For the VL, participants were asked to push the anterior restraint forward, with maximum possible force using only thigh muscles, whereas for the BF they were asked to pull the posterior restraint backward. For the RA MIVC, the participants were in the supine position with a fixed restraint positioned over their chest. The participants were asked to exert as much force as possible with only the abdominal muscles in order to try to “lift the restraint.” For the MIVC of the ES, participants were placed in the prone position with a fixed restraint placed over their back (at the level of shoulder blades). Participants were asked to exert the maximum force possible to try to “lift the restraint” using only back muscles. During the MIVCs verbal stimuli were used to motivate participants to exert the maximum possible force.Participants were asked to maintain a single-leg stance , with the arms at the side, standing barefoot as quietly as possible over a force plate . The support leg was the participant’s preferred leg, defined by asking which leg they would use to kick a ball. The knee of the stance leg was in an extended position (but not hyperextended), whereas the hip of the contralateral leg was in a neutral position and the knee was flexed at 90°. Participants looked at a fixed point  at the wall ∼4 m away from them, at eyes level. The position of the subject’s feet on the platform was marked with adhesive tape to ensure that the same position was repeated on each trial. The postural tasks were performed with eyes opened. Each participant performed five trials, each lasting 60 s. A resting period of ∼2 min between trials was allowed (subject sat in a comfortable armchair placed next to the force plate). The single-legged postural task was chosen because it is a posture commonly adopted in the practice of acrobatic gymnastics (present in the punctuation code considered by referees in competitions).Then, plantar flexor muscles of the dominant leg were subjected to muscle fatigue by methodology previously described in the literature , which cPost-fatigue postural assessments were similar to those described initially (pre-fatigue). However, as a transient effect of fatigue-inducing protocols on postural control has been reported , the fatThe signals from the EMG and the force plate were acquired by an A/D board  at 2000 samples/s. Data were analyzed off-line using custom-written programs in Matlab .The EMG signals were pre-amplified and filtered with cutoff frequencies of 20 and 450 Hz. In order to obtain the EMG parameters during the postural tasks, the raw EMG signals were filtered by a 4th-order digital Butterworth bandpass filter (with cutoff frequencies at 20 and 450 Hz). The parameters in the time  and frequency  domains were then calculated, with Fmed computed in the frequency spectrum using the Discrete Fourier Transform. For aEMG, the RMS of the EMG signal was computed for the entire period over the postural tasks (60s), for every 250-ms epochs (time window), and averaged among the five trials performed for each condition  . Then, aThe forces and moments measured by the force plate were used to compute the two components of the center of pressure (COP): in the anterior-posterior axis (AP) and the media-lateral axis (ML), indicated as Copa and Comply, respectively. Before analysis, the COP data (acquired at 2 kHz) were low-pass filtered with a digital fourth-order Butterworth filter having an 8 Hz cutoff frequency and the mean was subtracted from each time series. The root-mean-square (RMS) and mean velocity (VM) of the COP data were computed for each axis . The area of the stabilogram (Area) was estimated from the COP data by fitting an ellipse to the AP x ML COP data that encompasses 95% of the data . No significant main effect of group was observed , neither significant interactions between group and time , which indicates that gymnasts showed similar performance during the fatiguing protocol (in terms of duration of the fatiguing contractions) as compared to the controls, along the 5 repetitions of the fatiguing contractions.vs GYN). Therefore, fatigue induced a significant increase in postural oscillations (including velocity and frequency components of the power spectrum), mainly in the ML axis. No significant main effect of fatigue was observed for the parameters that represent the COP exclusively in the AP axis. No significant group effects (CTRL vs GYN) were found for COP parameters, neither significant interactions between fatigue and group, which indicates that gymnasts showed similar postural sway as compared to the controls, during both pre and post-fatigue assessments. 2) obtained from the two-way ANOVA for the main effects and interactions observed for COP quantifiers.soleus (SO), lateral gastrocnemius (GL), and biceps femoris (BF). These results indicate that the aEMG recorded from these muscles were significantly increased after fatigue as compared to pre-fatigue, irrespective of the gymnastic expertise (CTRL vs GYN). No significant main effect of fatigue was observed for the remaining muscles. A significant group effect (CTRL vs GYN) was found for the aEMG of the TA muscle, indicating that the GYN group showed lower TA EMG activation as compared to the CTRL group. No significant main effect of group (CTRL vs GYN) was observed for aEMG of the remaining muscles, neither significant interactions between fatigue and group. 2) obtained from the two-way ANOVA for the main effects and interactions observed for aEMG.lateral gastrocnemius (GL), vastus lateralis (VL), biceps femoris (BF), rectus abdominis (RA) and spinal erector (ES). These results indicate that the Fmed of the EMG recorded from these muscles were decreased after fatigue as compared to pre-fatigue, irrespective of the gymnastic expertise (CTRL vs GYN). No significant main effect of fatigue was observed for the remaining muscles. A significant group effect (CTRL vs GYN) was found for the Fmed of the SOL, TA and BF muscles, indicating that the GYN group showed higher Fmed in SOL EMG signal, but lower Fmed in TA and BF EMG signals as compared to the CTRL group. No significant main effect of group (CTRL vs GYN) was observed for the remaining muscles. Interestingly, a significant interaction between fatigue and group was observed for Fmed of the ES muscle, indicating a less pronounced effect of fatigue  for the GYN group as compared to the CTRL group. No significant interactions between fatigue and group were found for Fmed of the remaining muscles. 2) obtained from the two-way ANOVA for the main effects and interactions observed for Fmed.The present study compared the performance of single-legged stance and the associated modulation of time and frequency-domain EMG parameters between trained gymnasts and healthy untrained controls before and after a protocol designed to induce muscle fatigue in the ankle plantarflexor muscles. As hypothesized, fatigue-induced increased postural sway, while decreasing Fmed and increasing aEMG, irrespective of the gymnastics expertise. We had also hypothesized that fatigue-induced effects on COP and EMG parameters would be more pronounced in the non-trained group as compared to GYN, as the trained gymnasts would have counteracted  the deleterious effects of fatigue by using neuromuscular adaptations developed as a result of their intense and regular practice. This last hypothesis was only partially confirmed , as no superior stability was found for the GYN group as compared to CTRL in terms of postural sway parameters , irrespective of the fatigue condition see . On the The fatigue protocol  was repeated before each trial associated with the post-fatigue condition to warrant that participants performed the postural assessments still under the effects of muscle fatigue . The durThe duration of the fatiguing contractions was not significantly different between groups, and no significant interactions between group and time were observed, which indicate that gymnasts showed similar performance during the fatiguing protocol (in terms of duration of the fatiguing contractions) as compared to CTRL, along with the 5 repetitions of the fatiguing contractions. Therefore, all putative mechanisms associated with the differential effects of fatigue on COP and EMG measurements (discussed below) between GYN and CTRL are very probably associated with the gymnastics expertise itself, rather than a mere consequence of a different fatigue protocol.Center of pressure (COP) analyses showed that fatigue induced a significant increase in postural oscillations in the ML axis, with no significant effects in the AP axis, whereas no superior stability was found for the GYN group as compared to CTRL in terms of postural sway parameters , irrespective of the fatigue condition see . These rAs expected, fatigue induced a significant increase in postural oscillations, irrespective of the gymnastics expertise. Interestingly, such an effect was only observed for the postural parameters computed for the ML axis (as no significant main effect of fatigue was observed for the parameters that represent the COP exclusively in the AP axis). This might be related to the nature of the unipodal balance task used in the present study, in which the body oscillations occur mainly along the ML axis. A previous study  showed tThe comparisons between the EMG parameters recorded before and after the fatigue protocol indicate that the effect of fatigue was not restricted to the target muscles (plantar flexors), considering that, except for SOL, GM, and TA, all Fmed values decreased significantly after the fatigue protocol (which includes tight and trunk muscles) and aEMG of the BF muscle significantly increased after the fatigue protocol (besides SOL and GM). These results reinforce the notion that the fatigue effect associated with a specific contraction might be extended beyond the target musculature . This hatibialis anterior, soleus, biceps femoris, and spinal erector muscles. The rationale relies on the preparation of specific muscle groups differentially used by the gymnasts to keep their postures on single-legged quiet standing before and after the fatiguing protocol. However, the potential benefit of such strategies in improving the athletes’ performance and/or reducing injury risk is made here in a speculative way, so that appropriated controlled trials might be undertaken to test this hypothesis.Acrobatic gymnastics showed lower TA aEMG as well as lower TA and BF (but higher SOL) Fmed as compared to the CTRL group. Finally, it is worth noting that the Fmed of the ES muscle decreased more sharply after the fatigue protocol for the CTRL group , to keep a balance steadiness during the single-legged stance performed before and after the fatigue protocol. Further studies are warranted to investigate whether these mechanisms are accompanied by differences in the angular variation of specific joints  that may not have been reflected on the EMG parameters, as well as any differences in the coordination patterns of the associated joint and segments with respect to each other.tibialis anterior, soleus, biceps femoris and spinal erector, for which particular activation patterns were used by the acrobatic gymnastics to control single-legged quiet standing) on gymnasts’ performance must be investigated by appropriate controlled trials.This study investigated whether expert acrobatic gymnasts respond differentially than their non-trained counterparts in controlling single-legged stance task performed before and after a protocol designed to induce fatigue in the ankle plantarflexors muscles . Fatigue induced a significant increase in postural oscillations in the ML axis, with no significant effects in the AP axis. In terms of postural sway parameters , no superior stability was found for the GYN group as compared to CTRL, irrespective of the fatigue condition. On the other hand, the modulation of EMG parameters (in both time and frequency domains) indicated that expert acrobatic gymnastics athletes  used different neuromuscular control strategies to keep their postures on single-legged quiet standing before and after the fatiguing protocol. Future studies are needed to investigate whether such mechanisms are associated with different movement patterns or, alternatively, are manifested only in the electrical patterns of muscle activation. The efficacy of strategies aimed at improving the performance of specific muscles . Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.FM and CS were equally involved in the conceptualization and design of the study. MS recruited participants. MS, FL, JG, and JL managed data collection. FM, FL, and CS completed data processing and analysis. FM drafted the first version of the manuscript and supervised data collection. All authors assisted with drafting, provided critical revision of the manuscript, and read and approved the final version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The stressors caused by the coronavirus disease 2019 (COVID-19) pandemic have influenced both the physical and the mental health of the elderly, increasing their vulnerability. Counselling by the mental healthcare nurse is a critical protective factor in mitigating the mental health effects of COVID-19. However, counselling is unintentionally interrupted by the effects of the mask as a barrier to non-verbal communication. This commentary aims at conscientisation of mental healthcare nursing practice interruptions to non-verbal communication, brought upon by the pandemic. Practice recommendations focus on mask-wearing becoming less of a stressor to offset the challenges experienced by both the elderly and the mental healthcare nurses.The authors conclude that a need exists to revisit the fundamentals of counselling, and show initiative to addressing the practice challenges created by the wearing of masks yet simultaneously contribute to #flatten_the_mental_illness_curve. As mental health care nurses (MHCNs), guided by Zubin and Spring’s  stress vMask wearing is familiar to nurses, and infection control is not a new practice (Karimi & Alavi Instead of being located as a protective factor, the mask’s presence shifted to a stressor creating challenges in our initial contact with the elderly. Our former welcoming smiles were hidden behind the mask. Smile intensity plays a significant role in conveying warmth, respect and competence (Padhy, Rina & Sarkar We found that we increased our voice volume in an attempt to be heard, which we realise posed a risk for the misimpression of inequitable power. Power discourses, synonymous with dominance and abuse, resonate with concern from studies targeting communication in the elderly (Kwame & Petrucka Apart from the more obvious thwarted counselling needs of the elderly, brought about by power and hearing impairments, we were less mindful of the mask’s negative influence on the elderly with neurocognitive decline. In the presence of neurocognitive decline, when the lower half of the MHCN’s face is concealed, the sense of disconnect and the complexity of counselling are increased, as dependence lies on additional facial cues to assist in interpreting conversations (Vaidhyanathan et al. Our recommendations are centred on the mask becoming less of a stressor and barrier to practise delivery in that non-verbal communication becomes less strained and offers its protective value. Reflecting on our actions and examining our restricted use of non-verbal communication skills with the vulnerable elderly, we considered how to deliver empathic, relevant and responsive mental healthcare, with half of our faces concealed. The MHCN needs to become more reliant on other forms of non-verbal communication, while remaining cognisant of factors such as the pandemic’s regulations, the ageing process, and cultural sensitivity.In response to no longer being able to use a welcoming smile to dissipate anxiety and communicate warmth, we shifted to increased use of the upper half of the face and hand movements. In considering the voice tone, pace, personal reflections, we asked: ‘Am I too loud? Am I starting to sound bossy?’ Vaidhyanathan et al. :2 have pThe strategies suggested by Vaidhyanathan et al. :2 includWe agree with Jena  in that After considering all of the above and re-looking at non-verbal communication concealed behind the masked face, we, as MHCNs, need to consider cultural sensitivity of the elderly accessing our services. The non-verbal expressions of empathy differs amongst cultures (Lorié et al. Another possible solution to overcome the challenges of the hidden non-verbal facing the MHCN and advance universal health coverage is digital health WHO . CounselThe MHCNs need to be reflective of their non-verbal communication styles and critically examine whether, as wearing a mask, there has been a shift in their interactions to a less effective communication style (Padhy et al. The use of non-verbal communication with the elderly in the current pandemic is complex. The wearing of a mask in a context synonymous with the challenges of physical and psychological vulnerability to COVID-19, physical constraints and restraints, and neurocognitive demands adds to the complexity of counselling, risking it becoming a stressor instead of a protective factor. Consequently, the pandemic’s influence on the landscape of non-verbal communication exposes the need for MHCNs to revisit the fundamentals of counselling showing initiative, whilse displaying cultural sensitivity. The adaptation to counselling with a mask is a process aimed at adjusting our practices such that the mask becomes ‘invisible’. The MHCN is responsible for responding with initiatives to the practice challenges created by wearing masks and contributing positively to #flatten_ the_ mental_illness_curve.