Health insurance has become a crucial component of people’s lives as the occurrence of health problems rises. Unaffordable healthcare problems for individuals with little income might be a problem. In the case of a medical emergency, health insurance assists individuals in affording the costs of healthcare services and protects them financially against the possibility of debt. Security, privacy, and fraud risks may impact the numerous benefits of health insurance. In recent years, health insurance fraud has been a contentious topic due to the substantial losses it causes for individuals, commercial enterprises, and governments. Therefore, there is a need to develop mechanisms for identifying health insurance fraud incidents. Furthermore, a large quantity of highly sensitive electronic health insurance data are generated on a daily basis, which attracts fraudulent users. Motivated by these facts, we propose a smart healthcare insurance framework for fraud detection and prevention (SHINFDP) that leverages the capabilities of cutting-edge technologies including blockchain, 5G, cloud, and machine learning (ML) to enhance the health insurance process. The proposed framework is evaluated using mathematical modeling and an industrial focus group. In addition, a case study was demonstrated to illustrate the SHINFDP’s applicability in enhancing the security and effectiveness of health insurance. The findings indicate that the SHINFDP aids in the detection of healthcare fraud at early stages. Furthermore, the results of the focus group show that SHINFDP is adaptable and simple to comprehend. The case study further strengthens the findings and also describes the implications of the proposed solution in a real setting. In recent years, the number of individuals with health insurance (HIN) has expanded considerably around the globe. In industrialized nations such as the US, more than ninety percent of inhabitants have health insurance. HIN promotes access to care and is connected with decreased mortality rates, improved health outcomes, and enhanced productivity ,2. ThosePreviously, the health insurance claim (HIC) procedure was semi-automated, with several drawbacks, such as wasted time and money and the HINSs’ need to frequently visit the HIN office to initiate the premium request and to know about HIC status. Furthermore, auditing of HIC requires more human resources and is more prone to mistakes as handling paper-based HIC data is arduous. In such a system, HIC documents are modifiable and freely available. Consequently, the HIP, healthcare service provider (HSP), and HINS are vulnerable to fraud owing to a lack of privacy and transparency. Nowadays, information is collected in a digital format, revolutionizing HIC globally. The advantages of digitization include convenience to the parties engaged in HIC, no need for manual visits, fast communication between HINS and HIP, audit and checking, lack of fraudulent activities, low cost, easy claim verification, and many more ,10,11. TDespite several advantages, the digitized HIN market has to face numerous challenges, and one of the critical challenges faced globally is HIN fraud. Vast sums of money are lost worldwide due to HIN fraud , raisingSince the last decade, cutting-edge technologies, especially AI and blockchain, have been widely used in the healthcare sector for fraud detection and prevention ,16,17. BOn the other hand, 5G facilitates the fast transmission of insurance data between different entities. At the same time, the cloud overcomes the limited capacity of the blockchain digital ledger (DTL) and provides a secure storage mechanism for healthcare data. Considering the importance of HIN globally, this research leverages the potential of the above-mentioned cutting-edge technologies to propose a smart solution for HIN fraud detection and prevention. The problem statement and the explicit contribution of the study are mentioned in the following subsectionsPolicyholders frequently give false or deceptive information to health insurance companies in an effort to obtain reimbursement for unapproved benefits, which results in a significant loss to the HINP. A transparent end-to-end processing method for HIN claims is required in order to help identify and stop HIN scams.We explore the existing literature to identify possible HIN fraud and the entities involved in it;We provide a review of the various current usages of cutting-edge technologies for healthcare applications, especially in the HIN process;We propose a layered framework based on cutting-edge technologies to detect and mitigate HIN frauds;The proposed framework is evaluated using mathematical modeling, and an industrial focus group;The practical implications of the proposed framework are elaborated with the help of a case study.The explicit contributions of this study are listed below:Before proceeding to the background and overview of existing studies, Acronyms part in Abbreviations lists the study’s acronyms and their complete forms.This section will provide an overview of HIN, the challenges faced by HIN, and the role of cutting-edge technologies in HIN.Having HIN improves access to care, which leads to fewer deaths and improved health outcomes. HIN may represent the difference between disease and health, or life and death. One of the primary causes of mortality, particularly in emerging and underdeveloped nations, is a lack of HIN. Healthy individuals and young adults may believe they do not need HIN since they are seldom ill. Accidents and diseases, on the other hand, may happen to anybody at any moment. Without HIN, we are responsible for funding our own medical expenditures, placing us in severe financial jeopardy ,27,28. TFinancial benefitsThe following financial benefits are associated with HIN:HIN aids in the payment of medical costs and prescription medication costs.It assists you in avoiding significant medical debt.It limits how much you may spend on healthcare each year.Health benefitsPeople usually do not visit a healthcare center regularly without HIN. Individuals with HIN have the following benefits:HIN helps you live a longer, healthier life.It connects you to a consistent source of care.It aids in the diagnosis and treatment of ailments and disorders.Peace of mindAn essential thing in an individual’s life is peace of mind; however, the chances of illness are always there. Most people are not only afraid of disease but also more fearful of the cost they have to bear. HIN provides peace of mind in the following ways:Reducing financial worries regarding medical expenses.Reducing health worries regarding a lack of access to care.Removing the stigma associated with being uninsured.The HIC processing cycle needs to be understood to better understand HIN fraud. Various stakeholders are involved in processing HIC, such as doctors, paramedical staff, hospital administration, HIN subscribers, and HIP ,30,31. THIN fraud impacts people and companies equally, costing tens of billions of dollars annually. It may result in higher HIN rates, more invasive medical treatments, and more taxes. HSP, HINS, HIP, and other parties that purposefully mislead the healthcare system to obtain illegal benefits or payments may engage in HIN fraud ,22,23,24HSPs are the professionals involved in providing and managing healthcare services. These professionals include doctors, hospital administration, ambulances, and laboratories. HSP involvement is a prime factor that facilitates most HIN frauds, even those that HINSs commit. Some common HIN frauds committed by HSPs include: (1) Double-billing—In a double-billing scam, an HSP submits numerous claims for the same set of services. It is common for HSPs to double-bill the same entity (such as the government) for the same set of services. They may obfuscate their activities by changing the service’s date or description, or the patient’s or provider’s identity. (2) Phantom billing—Phantom billing is invoicing the HIN or the government for services that were never provided. (3) Unbundling—This is a kind of fraudulent activity in which doctors charge for each stage of a multi-part process instead of lumping them together under one code. By separately billing each phase, the HSP may earn more money while still providing the same level of care. Unbundling, or “fragmentation,” is breaking up a single medical operation into many codes to increase individual payments. The medical coding industry often engages in unbundling. Instead of utilizing the normal billing code, a surgeon who participates in unbundling could input different codes for incision and suturing during a regular surgical operation. As a result, a doctor will receive more money. (4) Upcoding—Using a more expensive billing code for the same medical service is called “upcoding” or up-charging. This means that the HSP is charging patients for a greater quality of service than they are providing. The HSP falsely increases their remuneration by upcoding the severity or complexity of the patient’s condition or treatment. For financial gain, an HSP may sometimes misdiagnose their patients’ conditions, such as diagnosing with chronic bronchitis when they have acute bronchitis or with a minor skin lesion when they actually had a bigger, more complex excision. (5) Unnecessary treatment/drug repetitions—In this fraud, the doctor continues the treatment/drugs for a long time without needing them. (6) Unnecessary procedures/diagnostics—Sometimes, doctors recommend costly procedures/diagnostics that are not required . (7) Creating forged prescriptions—HSPs counterfeit or fraudulently modify or create a prescription or other document in this scam. Two examples are adding a medicine or changing the date, amount, or dispensing instructions. (8) Kickback—This is a term used to describe a misuse of money that benefits a person of authority to use his position or influence to benefit another individual, organization, or enterprise. It is a prevalent deception in the HIN area, where HSPs such as pharmaceutical corporations incentivize other HSPs to suggest certain drugs. (9) Billing for non-entitled services as entitled—This fraud usually happens when an HINS is not entitled to a particular service by the HIP and the doctor provides him this service, putting this treatment in another category entitled to him.The HINS is the one who purchases HIN policy from the private- or public-sector organization. Most HIN fraud comes from HINSs seeking to obtain financial benefits. HINSs perform HIN fraud in different ways: (1) The HINS may change prescriptions by himself or with the help of an HSP. (2) Sometimes, HINSs make false claims and try to receive money for a treatment they never received. (3) Another method of HIN fraud is claim fixation, in which both the HINS and HSP are involved, modifying the claim amount to obtain mutual financial benefits. (4) One of the most commonly occurring types of fraud that significantly affect HIPs is identity swapping, in which an HINS’s insurance card/policy is used by someone else to access healthcare facilities. (5) HINSs sometimes receive unnecessary services due to the availability of HIN . (6) Another fraud committed by HINSs is medical equipment fraud, in which the HINS tries to obtain something more than what is required . (7) Age/gender inconsistency is another form of fraud, in which an HINS tries to hide his age/gender to obtain additional benefits for him or someone else. These are the possible methods of fraud initiated by the HINS. (8) Last but not least is the misuse of a healthcare card, in which a person uses his healthcare card for his family members or friends with the consent of the HSP and obtains the treatment cost from the HIP.HIPs conduct HIN fraud by misleading, hiding, or distorting information, although not to the level that HSPs and HINSs are engaged in healthcare fraud. The reason for not being involved in HIN fraud is to maintain the reputation of the HIP’s company and customer attraction. Still, some HIP fraud occurs when an impostor HIN agent attempts to sell you a false HIN plan or when a fraudster contacts an HINS to verify personal information for his plan and then steals the HINS’s identity. HIN firms may sometimes perpetrate fraud by fabricating medical equipment claims, particularly for older people or persons with disabilities, who often need a large amount of durable medical equipment. Sometimes, an HIP deliberately delays the claim processing and expects some incentive for the quick processing of HIC.The above explanation demonstrates that HIN fraud may be conducted by HSPs, HIPs, HINSs, and others who purposefully defraud the HIN system to earn illegal benefits or payments. Hence, there is a need to investigate HIN fraud and provide a solution by using cutting-edge technologies. In the subsequent sections, we explore the role of cutting-edge technologies in healthcare fraud detection.AI approaches have shown to be an effective tool for detecting HIN fraud. The HIN fraud detection system may be automated using AI. According to recent research, AI has primarily been utilized to identify HIN fraud using various techniques and models ,33. AnomA dataset of previously known fraudulent and valid records is used in the SL approach in HIN fraud detection. These data are used to capture fraud patterns and construct the model. The primary advantage of the SL approach is that the classification results produced by this technique are simple to understand. Many classifications and SL regression analysis methods are applied in HIN fraud detection. SL techniques used to detect HIN fraud include neural networks, decision trees, statistical analysis, Bayesian approaches, SVM, graph analysis, and rule-based systems ,37. AparUSL algorithms detect fraudulent HIN activities in unlabeled HIN datasets. The absence of tagged data is an advantage of HIN fraud detection via USL ,39. WhenA BLC is a network of interconnected blocks that serves as a digital ledger (DTL) of transactions . Each blThe working and roles of cutting-edge technologies in HIC processing and fraud detection are discussed in the above sub-sections; now, we explore the existing studies on using cutting-edge technologies for HIC fraud detection and prevention in the following subsection.Fifth generation (5G) is a term referring to wireless network technology, and its use in the healthcare industry has created new opportunities for medical innovation and increased access to treatment. It is an air interface that is unified and powerful and was developed with an improved capacity to enable the user experiences and services of the future generation. The technology of 5G networks is one of the important technologies for the digital transformation of society ,48,49. IThis section will provide an overview of recent studies that have used AI or BLC or both to identify and mitigate HIN fraud to provide a picture of the state of the art.A rigorous assessment of AI- and BLC-enabled secure HIN fraud detection is presented by Kapadiya et al. . This stAccording to Saldamli et al. , HIN firAccording to Ismail and Zeadally, HIN fraud detection is a big challenge for healthcare . HIN fraAmponsah et al. suggest a method for detecting and preventing fraud in HIC processing utilizing ML methods and BLC technology . The oriAccording to Gera et al., several concerns are linked to the HIN sector, such as fake HICs and claims unlawfully influenced by competent authorities . This arAccording to Goyal et al., having health insurance is vital for everyone, especially with rising medical expenditures, since medical crises may have a significant financial and emotional effect . HoweverAmponsah et al. suggest a BLC-based method to improve the financial sustainability of HIN systems . Poor daThe above discussion shows that cutting-edge technologies play vital roles in e-health and HIC processing. However, existing studies mainly focus on improving HIN acceptability and the security of HIN systems. Furthermore, existing studies mainly focus on a particular technology instead of leveraging the potentials of combination of technologies. We did not find any study that explicitly addresses fraud detection and mitigation in the end-to-end HIC processing cycle. To address this gap, we propose an end-to-end security model for HIN fraud detection using cutting-edge technologies in the next section.To address fraud detection and mitigation for HICs, we proposed an end-to-end layered security framework named SHINFDP, as shown in Algorithm 1: Working of user layerBegin  1.  2.  3.  4.  5.  6.  7.  8.  9.Add HIC Blockchain  10.  11.  12.EndThree main users are involved in the proposed SHINFDP, including the HIP, HSP, and HINS. The HINS is the insured patient who initiated the system by approaching the HC for treatment. The insured patient can visit the HC in person or may contact the HSP using a smart healthcare system (SHS). The HSP concerned with handling patients assesses the HINS, checks his insurance policy, and sends him to the relevant HSP for receiving treatment. The HSP includes various healthcare professionals . The HSP performs the treatment according to the patient’s situation and enters the treatment details in the SHS, from which the HSP concerned with HIC processing fetches the data and prepares the HIC. The prepared HIC is approved by the relevant authorities and is stored in the healthcare cloud using a proper encryption mechanism. The prediction layer responsible for detecting legitimate HICs and fraudulent HICs provides classification of the HIC and stores the legitimate HIC in the BLC after the execution of the BLC consensus mechanism. A smart contract is created for all the legitimate HICs. The HIP accesses the legitimate HIC and processes it according to the patient’s HIN policy. The claim record is also stored in the BLC’s digital ledger (DTL), from which each party can access it. The user layer uses the services of various other layers; this layer uses 5G services for fast data transmission between multiple entities. Furthermore, this layer is connected to a storage layer, where the HIC is stored for further processing. This layer is also associated with the security layer, as all user records are stored in the BLC. The details of the tasks performed by all three categories of users are outlined in Algorithm 1.Algorithm 2: Working of communication layer1:2:3:4:a.b.c.d.e.f.g.h.This layer leverages the potential of modern 5G technologies to provide fast data transmission between multiple layers. As proposed, SHINFDP provides end-to-end security for HIC processing; therefore, 5G services are vital as they will provide massive capacity, low latency, and high speeds. The HINS will use 5G to communicate with the HSP and HIP and to track the HIC. The HSP will use 5G to transmit and store patients’ data and the corresponding HIN policy. Furthermore, 5G will be involved in data transmission between the SHS and the cloud. The prediction layer will also use 5G services to fetch and analyze data. The security layer will use 5G’s potential to execute consensus mechanisms, generate smart contracts, and manage the BLC network. The HIC processing layer will use 5G potentials to fetch legitimate HIC for processing and dispatching the HIC amount to the concerned authority. Algorithm 2 elaborate the working of communication layer.Algorithm 3: Working of storage layer2: and Blockchain3: 1: This layer of SHINFDP leverages the potential of cloud computing to store HIC data on the cloud securely. As the proposed SHINFDP is smart, the data collection mode will be online only. All the data generated by the HSP will be stored on the SHS. The HSP responsible for HIC generation will fetch data from SHS, prepare the HIC, and store it on the cloud. The data generated by this layer include payment-related sensitive information of the HINS and HIP so that it will be shared and controlled through smart contract conditions for security considerations. The prediction layer will fetch data from this layer for classifying legitimate and fraudulent HICs. The HIP will fetch the data of legitimate HICs from this layer for further processing. Working of storage layer is elaborated in Algorithm 3.Algorithm 4: Working of prediction layer2: 3: 1: This layer of the proposed SHINFDP will leverage AI’s potential to classify HICs into legitimate and fraudulent HICs. This layer will fetch HIC data from the cloud and will clean the data for redundancy, missing values, and incorrect information. The process of preparing data for analysis by deleting or changing data that is erroneous, incomplete, irrelevant, redundant, or badly formatted is known as data cleaning. In order to clean the data, suitable data cleaning technique of ML will be used. Data cleaning is one of the important steps in ML; this process will detect missing or null values, outliers, and erroneous data. The claim amount range for each treatment will be determined and saved in the healthcare cloud repository. To identify outliers, the data’s lower and upper bounds will be computed. Outliers are values that are both below and above the lower and upper bounds. This will be accomplished by employing the appropriate ML algorithms to find outliers in the data. The cleaning process will reduce the data size; after that, data from different sources will be integrated and transformed into a uniform format for preprocessing. Once the data is transformed, ML algorithms will be applied to this data to classify it into legitimate and fraudulent data. The techniques used at this layer can be SL, USL, or SSL, depending on the nature of the data. AI data analytics is the key to minimizing the frequency of HIN fraud detection and the expense of false claims filed against HIN policies. The working of prediction layer is elaborated in Algorithm 4.Algorithm 5: Working of security layerBegin Step 1.                     Step 2.                                 Step 3.                                              EndThe security layer is one of the critical layers of SHINFDP, which leverages the potential of BLC to prevent improper manipulation of the HIC. BLC is a series of blocks, each of which is nothing more than an encrypted real-world transaction. It is a transactional DTL that is open-source and community based. It is a peer-to-peer mechanism that guarantees openness and accountability. Each transaction is stored as a block in the DTL and may be programmatically validated by numerous users connected with the application. Every node has access to a copy of the BLC. It is built such that no one entity or node may conduct fraud. Every node utilizes the same BLC, and no conflicting versions are possible. There is a continual verification process with mining, and the consensus intends to provide evidence for every transaction. This technology is unusual in that it can keep data immutable and does not rely on any central authority. As a result, it maintains transaction integrity and may effectively prevent malpractice or hacking. It can also replace conventional organizations while providing a greater degree of security. The HIC procedure involves many parties, including HIP, HINS, and HSP. Each participant will have a peer node in the BLC’s peer-to-peer network. The HIP utilizes the HSP peer, which is responsible for HIC processing. The prediction layer requires a peer to complete the verification procedure. An HSP peer may help with claim development and verification. All of these peers will be allowed to participate in HIC processing, and the transactions will be stored in a DTL. Each transaction is digitally signed and recorded on the BLC. In a distributed context, an irreversible collection of transactions is therefore preserved. Furthermore, smart contracts are generated for each transaction and are stored on the BLC. A smart contract allows each layer of SHINFDP to interface with the BLC layer. Smart contracts are programs for the authentication and payment of HIN coverage and are automatically executed when all the conditions are met. Consensus algorithms are used in smart contracts to authenticate every transaction and input. A smart contract is connected with the IPFS to reduce total data storage and quick data retrieval costs. The benefits of using a smart contract for HIN fraud detection include transparency, task automation, time saving, protection of HIN policy documents, and risk assessment. The detailed working of the security layer is outlined in Algorithm 5.The key stakeholders of this layer are HIPs, each of which can be a private or a governmental firm responsible for paying health services directly to the HINS or the HC. The HIN processing layer is connected with all the other layers of the proposed framework. It is associated with a storage layer to fetch HIC information from the healthcare cloud. It communicates with the prediction layer to obtain information about claim authenticity. It uses security layer features to ensure HIC authenticity through smart contracts. This layer uses 5G services of the communication layer for data fetching and transmission. This layer is also connected with the HC and HINS to inform them about the results of HICs.In this section, we will show the details of framework evaluation using various methods. We first evaluated the proposed framework using mathematical modeling. The feasibility of the framework was assessed with the help of a focus group by evaluating various parameters using concept mapping and Likert scale evaluation, and finally, a case study of wearable devices was used to further elaborate and evaluate the proposed framework. Below, we discuss these evaluation results in detail.Below, we explain the evaluation and step-wise working of SHINFDP. Notations part in Abbreviations provides the list of mathematical notations used in the model.Let the smart HIN process Under the constraints of According to Equation (2), patient data input According to Equation (2), in order to make According to Equation (3), for smart and secure For efficient The stored claim data According to Equation (7), the blockchain digital ledger The insurance claim will be validated by Corollary. The Security of the can be compromised if is generated and not detected by . Furthermore, may be involved in HIN fraud. The is immediately detected, and any manipulation in is prohibited using the SHINFDP.Proof. Assume that a 1.2.3.The above discussion shows that properly implementing SHINFDP makes the HIN process fair and transparent. Furthermore, the proposed SHINFDP provides flexibility to the stakeholders in selecting cloud services and ML algorithms according to their choice. The limited and expensive storage of the blockchain DTL can be overcome by storing HIN data on the cloud and just saving the hash of the data on DTL. □A focus group technique was used to evaluate the ease of use, security, understanding, and structure of the proposed SHINFDP. Numerous factors, such as cost-effectiveness, observation of body language, and enhanced consumer engagement, contribute to the selection of the focus group technique for evaluation. Focus groups are comparatively inexpensive to conduct, and the moderator is able to observe the interviewee and modify the conversation accordingly. It is also more engaging than impersonal research methods such as questionnaires and surveys. However, sizable samples are difficult to target when using focus groups. To measure the ease of use, security, and structure of the SHINFDP, closed-end questions were designed and asked to the focus group respondents. In contrast, the concept mapping technique was used to evaluate the understanding of the SHINFDP. Seven professionals were included in the focus group, from which four belonged to a well-known HIN provider company in Saudi Arabia, while three of them were healthcare professionals dealing with insurance claim handling in a well-known public sector hospital. Below, we describe the details of each measure used to evaluate SHINFDP.A desktop meeting was arranged with respondents of the focus group in which the author presented the workings of the SHINFDP to the respondents via PowerPoint presentation. At the end, the respondents were asked five closed-ended questions to evaluate the feasibility of the SHINFDP. Using the same pattern mentioned in “ease of use” evaluation, respondents of the focus group were asked five close-ended questions for the security evaluation of SHINFDP. The questions and responses are given in The security evaluation results are depicted in In order to evaluate the understanding level of respondents regarding SHINFDP, a concept mapping technique was used. Concept maps are diagrams that express information visually. Charts, graphic organizers, tables, flowcharts, Venn diagrams, timelines, and T-charts are a few examples of how they might be presented. Concept maps are particularly helpful for assessing how well a phenomenon is understood. In order to evaluate the focus group respondents’ understanding of SHINFDP, we extracted eight relationships between concepts and asked the respondents to evaluate whether this relationship should exist or not. SHINFDP is a layered framework consisting of six layers, and different cutting-edge technologies are proposed at various layers. Three questions were used to evaluate the structure of SHINFDP which are given in Because of the COVID-19 epidemic, individuals have become more sensitive to positive living choices. They have started adopting healthier lifestyles using cutting-edge technologies such as wearable devices (WRD) and smartphone healthcare applications. Wearable technology can be any electronic device worn on the user’s body ,58,59. SHIN fraudsters become smarter daily and develop new methods to commit fraud. As a result, every HIN company must discover a new and enhanced future strategy to prevent fraud utilizing a WRD’s data. As a result, we provide a case study to meet the criteria mentioned above for HIN companies. The use of WRDs in a HIC has various advantages. It enhances the HINS’s involvement with the HIP regularly and in real time. The WRD will be used to track the HINS’s whereabouts. HINS location data and real-time health status may be used to determine what occurred before, during, and after a hospitalization emergency. HIN corporations may use this data to identify pain levels, prevent medication-seeking behavior, and uncover inconsistent conduct with a HIC. Furthermore, BLC technology provides new avenues for improving inefficiencies in the HIN market. It may benefit HIPs by improving security, lowering costs, and increasing confidence. BLC in the HIN business also offers real-time data that may be utilized for risk evaluation, claims settlement, and other purposes.Based on the case study and existing state-of-the-art review, it can be concluded that a combination of cutting-edge technologies, including BLC, 5G, cloud, AI, and wearable devices, can improve the HIC process and make it more transparent. When HIC fraud reduces, HIP firms will benefit from it, and HINSs will also benefit in the form of lower HIN premium amounts. Academia and practitioners should provide easy solutions for implementing these solutions in reality so that more people around the globe could benefit from healthcare facilities.A reduced mortality rate, better health outcomes, and increased productivity are all related to having health insurance. Health insurance also makes it easier to receive treatment. Even though it has many advantages, many people still do not have health insurance, which puts their physical, emotional, and financial health in danger. One of the challenges that the HIN procedure must contend with is fraud with the HIN. In this sort of fraud, a health insurance company is given information that is either false or deceptive to trick them into paying unauthorized benefits to the policyholder, another party, or the organization delivering services. It is possible for the insured person or the supplier of health services to commit the offence. The majority of fraud is conducted by HINSs and HSPs; however, HIPs are also guilty of conducting fraud on occasion, although their involvement in fraud is often limited to intentionally delaying the processing of claims or artificially understating their value. We presented a six-layered framework that utilizes the potentials of cutting-edge technology for safeguarding healthcare data and combating HIN fraud in order to make the HIN process smart and secure. An assessment of the suggested framework was carried out with the assistance of mathematical modelling, an evaluation of the industry that made use of the focus group approach, and a case study. The outcomes indicate that the suggested framework is straightforward, useful, secure, and adaptable to various circumstances.Existing solutions mainly focus on a particular technology and try to determine its potential in improving the HIN process;Existing solutions discuss the benefits of using BLC in the HIN process, but they do not evaluate the solution due to the limitation of there being less use of BLC in healthcare;The existing studies used a single method of evaluation, while the proposed solution is evaluated using hybrid approach , which shows the generalizability of research findings;Existing studies mainly emphasize the adaptability of the HIN process and provide various techniques for it. Furthermore, there exist no solutions that specifically address the end-to-end HIN process cycle security.The proposed approach is different from the existing solutions ,54,55,56Below are some limitations of this research work.We evaluated the proposed framework SHINFDP using a focus group technique in which only seven professionals were involved. However, we tried to choose a representative sample.The use of mentioned technologies will add to the cost for HINP and HSP, but the enormous costs associated with insurance fraud will be avoided. The use of cutting-edge technology requires expenses in terms of hardware and training, but it is a one-time investment on the part of healthcare organizations and HINP, with long-term rewards. Other advantages of this automation will include convenience for the parties engaged in the HIN process, no need for manual visits, fast communication between health insurance subscribers and health insurance providers, audit and checking, lack of fraudulent activities, easy claim verification, and many more that will also indirectly save on costs.ML and cloud computing are widely used in healthcare for prediction and storage. However, blockchain technology is somewhat new and is not commonly adopted in the HIN process. Therefore, we could not implement the proposed framework in a real setting.We presented a comprehensive summary of HIN fraud detection and related security vulnerabilities in HIC. First, we discussed the background of HIN, including the HIN revolution, varieties of HIN fraud, and the HIC process. Then, we discussed the many parties in the HIC process and the accompanying fraudulent acts that lead to HIN fraud. We discussed the role of BLC and AI in HIN. Given the significance of HIN fraud, we presented SHINFDP, an end-to-end solution for safe HIC processing to prevent HIN fraud. SHINFDP is made up of six layers: the user layer, the communication layer, the storage layer, the security layer, the prediction layer, and the HIP layer. Each layer contributes to the improvement of the HIC process. The proposed framework was refined with the assistance of a case study. The results indicate that the proposed method may assist in identifying and mitigating HIN fraud and make the HIC process more transparent. In the future, we want to verify the suggested framework by deploying it in a real-world healthcare context or through simulation. The proposed framework might be improved further by using more cutting-edge tools and technologies to optimize the HIC process.


Aging of the vascular system is associated with deep changes of the structural proprieties of the arterial wall. Arterial hypertension, diabetes mellitus, and chronic kidney disease are the major determinants for the loss of elasticity and reduced compliance of vascular wall. Arterial stiffness is a key parameter for assessing the elasticity of the arterial wall and can be easily evaluated with non-invasive methods, such as pulse wave velocity. Early assessment of vessel stiffness is critical because its alteration can precede clinical manifestation of cardiovascular disease. Although there is no specific pharmacological target for arterial stiffness, the treatment of its risk factors helps to improve the elasticity of the arterial wall. Cardiovascular disease is the leading cause of death worldwide. Several risk factors have been identified and, among these, increased arterial stiffness has gained attention due to its correlation with many cardiovascular endpoints . StudiesThe propagative model based on the visco-elastic hypothesis is one of the most reliable models used to describe and understand the hemodynamic of the circulatory system. Here, the elasticity of the tube generates a forward pressure wave that travels along the tube. However, the intrinsic characteristics of the tube (branch points and high resistance) generate retrograde wave reflection. This mechanism is critical for the diastole phase and promotes coronary perfusion.During systole, the stroke volume is forwarded directly to the peripheral tissues, but part of it involves the stretching of the aorta and central arteries walls. Thus, part of the energy is stored in the vessel walls, which, during diastole, recoils the aorta, squeezes the accumulated blood forward into the peripheral tissues, and ensures a continuous flow. This ability mainly depends on the stiffness of the artery, whose loss impairs the protective mechanisms and somewhat facilitates damage of the microvascularization of organs, in particular brain and kidney . Indeed,In this review, we summarize the pathophysiological events that contribute to the development of arterial stiffness  strategiThe ratio between collagen and elastin in the vascular wall ensures the optimal arterial compliance and is guaranteed by a balance between the production and degradation processes. Collagen limits the stiffness due to its stiff properties, whereas elastin provides the flexibility and extensibility of the artery wall.Vascular aging is characterized by deep changes in arterial structures, with specific regional differences. In the central artery, collagen fibers increase , whereasPeripheral arteries, compared to central arteries, are more muscular and stiffer. However, they are less prone to vascular aging. For example, the stiffness of the femoral artery does not change with aging in men or women, but a marked increase of its stiffness was documented in women over 60 years of age . The carA clear factor of reduced wall distensibility of the arterial wall during aging is the calcium deposition within the arterial wall . AlthougVascular tone is a process of alternating vasoconstriction and vasodilation. Nitric oxide regulates vasodilation, and any alteration of its production leads to endothelial dysfunction. This, in turn, causes an increase of oxidative stress, damages vessels, and promotes arterial stiffening . Recent The thickening of the intima-media tunica layer is a marker of subclinical atherosclerosis. Large arteries are characterized by a 15–40% increase of intima-media thickness. Tunica media at the aorta level does not substantially change with aging, whereas tunica intima shows a thickening that is contributing factor for the increase in the aortic wall diameter. Atherosclerosis is characterized by the presence of vascular injury, which causes the migration of smooth muscle cells from the tunica to the intima. The presence of a thickened intima tunica causes a reduction in its elasticity. Intima media thickness at the carotid level shows an age-specific pattern of increased rates: from 0.02 mm/decade at the age of 50 to 0.05 mm/decade at age of 80 years. Furthermore, the rates are higher in men than in women . HoweverStudies focused on the heritability of arterial stiffness found that genes explain 23–50% of the variability in arterial stiffness and act independently of traditional cardiovascular risk factors. However, for most of them, the underlying mechanism has not been fully elucidated at the molecular level.CUL3 gene codes for cullin-3, which is a protein that belongs to the cullins family and is a component of the cullin-RING E3 ubiquitin ligases complex [CUL3 gene can cause pseudohypoaldosteronism type 2, which is characterized by hypertension, hyperkalemia, and increased arterial stiffness. The reduction of the degradation of cullin-3 substrate RhoA, secondary to mutations of cullin-3, causes an increased Rhoa/ROCK signaling. An additional proposed mechanism is the reduced expression of subunits of soluble guanylyl cyclase (sGCα1 and sGCβ1), which leads to the impaired production and activity of cGMP [TEX41, FOXO1, MRVI1, and C1orf21. The first locus has also been associated with coronary artery disease [FOXO1 gene, located within the O class of the forkhead family of transcription factors, regulates mindin, which has a role in vascular smooth cell proliferation. The MRVI1 locus encodes the MRVI1/IRAG protein that is involved in smooth muscle contractility. The function of C1orf21 is not currently known. Another study reported an association adrenomedullin gene expression and indexes of arterial stiffness (reflection index and stiffness index) [BCL11B gene [BCL11B gene codes for a promoter transcription factor (COUP-TF) that modulates the angiopoietin-1 and vascular endothelial growth factor pathway. BCL11B may also regulate T-cell production because its specific deletion causes an increase in proinflammatory T-cells that could potentially promote inflammation, fibrosis, and stiffening [ complex . Mutatio of cGMP . A recen of cGMP : TEX41,  disease . The FOXs index) . Adrenom11B gene , which h11B gene . The BCLiffening .It should be noted that the association between genetic polymorphisms and arterial stiffness is not univocal, and several reason can be found. The most important source of inconsistent results is the different methods used to assess the arterial stiffness. Regional differences  and selection of the study sample can be considered as additional confounding factors.Pulse wave velocity (PWV) is a surrogate of arterial stiffness and represents the velocity at which the blood pressure pulse moves down the vessels. The Bramwell-Hill equation can theoretically explain the PWV, providing the relationship between arterial distensibility and PWV. In clinical practice, PWV is calculated as the ratio between distance between two measuring sites and the ratio of time taken by the pulse to travel between the measuring sites. Several methods have been created to assess PWV.Carotid-femoral PWV (cf-PWV) is the most widely used method to assess the arterial stiffness. Signals are collected from the carotid and femoral artery, which are both close to the aorta, thought different commercially available systems. The collection of data can be done simultaneously or sequentially guided by ECG-gated. The appropriateness of its measurement relies on the assessment of the carotid-femoral path length, which can be simply calculated using 0.8 times the distance between the two measuring sites . AlthougMagnetic Resonance Imaging (MRI) is probably the only method that can provide accurate anatomical imaging and transit time and, in addition, allows the assessment of the segments of aorta not evaluated by the cf-PWV . HoweverBrachial-ankle PWV (ba-PWV) is an alternative method to assess PWV that is frequently used in Asia . ba-PWV The cardio-ankle vascular index (CAVI) is derived from the arterial stiffness index β and was introduced by Japanese researcher to obtain pressure-independent values of arterial stiffness. Although it displays a good reproducibility among different vascular diseases , there aPulse wave can be also assessed indirectly through an analysis of wave reflection, which is quantified by augmentation index (Aix) at the carotid or at radial artery. Aix is determined by the amplitude of the reflection wave, by the distance to the reflected site, and by the cardiac cycle. Due to its intrinsic limits, Aix also needs additional parameters (pulse pressure and systolic blood pressure) and is often assessed together with the measurement of PWV.Stiffening of the arterial wall is a physiological process strictly connected with aging . SeveralArterial hypertension has a high frequency in the general population and is deeply connected with arterial stiffness . AlthougPrevious reports suggest that increased pulse pressure accelerates elastin degradation through an increase in pulsatile wall stress . This me2.7/mL greater arterial stiffness index.The effect of inappropriate glycemic control on arterial stiffness is well acknowledged and is multifactorial. Advanced glycation end products (AGE) are highly oxidizing products of nonenzymatic reactions between reducing sugar and biological proteins  or lipids . They maThe association between atherosclerosis and arterial stiffness is not clear. Although many studies reported that aorta stiffening increases with plaque burden , it shouThe cross-talk between the kidney and heart has been known about for decades, and the functional abnormalities of one reflect on the other. The reduction in aortic stiffness causes a loss of buffering of systolic volume, which could damage the kidney vasculature and, therefore, its function. Patients with chronic kidney disease are prone to developing vascular calcification . CalciumChronic kidney disease may also impair collagen cross-linking and matrix remodeling. In this context, lysyl oxidase has recently been proposed by Sharma et al., as a cofactor for the development of increased arterial stiffness in mice with early impaired kidney function . AuthorsArterial stiffness is associated with different behavior risk factors, some of which are strongly related, whereas for others, the association is less clear due to issues correlated to their quantification.Smoke is one of the most important risk factors for cardiovascular disease. Studies showed that smoking is a strong risk factor for increased arterial stiffness  and direThe effect of alcohol on arterial stiffness is complex. Indeed, it has been demonstrated that a chronic, low dose of alcohol can decrease arterial stiffness  and probThe beneficial effect of physical activity is well acknowledged and not limited to arterial stiffness. Studies reported that physical activity can lower PWV in some cases and reduce the physiological age-related increase in others . RegularAtrial fibrillation is one of the most prevalent arrhythmias in the general population, and its incidence dramatically increases with aging . SeveralStroke is the second leading cause of death and is correlated to loss of ability. Studies found that increased arterial stiffness is associated with the onset of acute stroke, even fatal , indepenCognitive decline has become a major health issue due to the increased life expectancy around the world. Several risk factors for its development have been identified so far, and arterial stiffness has gained a major role as a contributing factor to dementia . The assSeveral studies have been published aiming to test which therapy is the best option to treat arterial stiffness. However, no specific drug has been synthetized to specifically modulate the arterial stiffness, and almost all previous studies reported the effect on arterial stiffness during the treatment of other specific diseases . This meThe different classes of drug used to treat arterial hypertension showed different effects of arterial stiffness. Among these, angiotensin receptor blockers and ACE inhibitors showed the most prominent effect in reducing stiffness . Other tDiabetes mellitus is an independent risk factor of arterial stiffness, and its treatment intrinsically modulates the stiffening in the artery wall by reducing AGE and its interaction with RAGE. Among the drugs used to treat diabetes, glitazones are the most studied and can decrease aortic stiffness . Araki eStatins are commonly used to treat dyslipidemia and act by inhibiting HMG-CoA reductase, a key enzyme of the mevalonate pathway. Beyond the direct effect of reducing cholesterol, statins could improve arterial stiffness. Indeed, although some previous studies reported an improvement of aortic as well as carotid stiffness, others failed ,97,98. TMany researchers are currently testing different treatment approaches to improve age-related vascular dysfunction in humans. Among these, Murray et al., tested the effect of mitochondrial-targeted antioxidant supplementation on 45 healthy older men and women aged at least 60 years. The study, which was a randomized, placebo-controlled, double blind, phase IIa clinical trial, showed that its use in older adults without clinical disease improves endothelial function and reduces arterial stiffness . Similar


Additional experiments were carried out on samples that were further loaded with glycerol (5% w/w). Gel formation was assessed by analysing the gelatinisation extent, structuring level, and swelling power of the samples. At the processing conditions utilised, stable hydrogels were obtained even in the presence of the extract and/or the glycerol in the starch suspension. As expected, the colour of the hydrogels formed was affected by the addition of green tea extract in the starch solution. HPP starch hydrogels were characterised by Fourier transform infrared spectroscopy (FT-IR) to determine the interactions between the different compounds utilised in the formulation. Moreover, the release kinetics of bioactive compounds from HPP rice starch hydrogels was evaluated using a vertical Franz diffusion cells system, simulating a transdermal pattern. The diffusion of bioactive compounds was measured spectrophotometrically and via HPLC analysis. A controlled release of bioactive compounds from the hydrogel structure was detected, suggesting that small molecules, such as polyphenols, positively interacted with the rice starch HPP hydrogel network that allowed a smooth and constant release of these bioactive compounds over time.This work aimed to carry out a preliminary study on the release of bioactive compounds loaded into starch-based hydrogels produced by high-pressure processing (HPP). As a study case, the experiments were carried out on rice starch HPP hydrogels. Rice starch (20%  The increasing attention of consumers on keeping their living standards as high as possible, particularly their health, as well as the occurrence of pandemic and climate changes has promoted, especially in young adults, more consciousness in their purchase decisions and habits .For this reason, in recent years the demand for products based on natural ingredients, including foods, cosmetics, and pharmaceuticals, is constantly increasing and their health-related benefits are widely recognised . MoreoveThe so-called cosmeceuticals, containing biologically active ingredients have shown a rising interest not exclusively to counteract skin aging but, more generally, to protect the skin, nails, and hair, and to provide defence against harmful environmental factors, including ultraviolet radiation and free radicals ,5,6,7. AIn this framework, high-pressure processing (HPP) has been proven as a suitable technology to prepare stable starch-based hydrogels overcoming the main limitations related to the traditional preparation methods. High-pressure processing (HPP) is a non-thermal technology able to modify the properties or gelatinize starches or proteins, which was also proposed for the production of highly structured and functional hydrogels based on aqueous starch suspensions ,16,17,18Depending on the type of starch used, HPP hydrogels may show creamy or gummy structures with good stability, which can be easily functionalised. Natural extracts can be added in the formulation of such biopolymers, which can be considered smart carriers for the encapsulation of the bioactive compounds to design “green” cosmeceutical, pharmaceutical, or food products . Agri-foHowever, to the best of our knowledge, no studies have been carried out to prove the capacity of starch-based HPP hydrogel to incorporate and release bioactive compounds, whose determination is of utmost importance to formulate new products for topical use based on these novel structures. This work aimed to investigate the gel formation and physical properties of rice starch hydrogels when green tea extract was added to the formulation as well as the release kinetics of these bioactive compounds encapsulated into starch-based hydrogels produced by high-pressure processing (HPP). To this purpose, a Franz diffusion cells system was used to determine the in vitro dermal release of bioactives.The starch gelatinisation under pressure was evaluated by macroscopic assessment and determination of the efficiency index and swelling power. It is well known that the production of starch-based hydrogels under pressure is influenced by various factors such as formulation and HPP processing conditions ,28,29. RAs shown in The swelling power, which is a property related to the length distribution of the amylopectin chain, the branching pattern, and the molecular weight ,36 and aThe results showed that rice starch-based hydrogels can be used as smart carriers of biocompounds to manufacture products where these cream-like structures are required.ab* were calculated. The human eye perceives the colour differences if ΔEab* values are >3 [ab* was high.To better understand if the colour differences among the hydrogels obtained, which are affected both by the loaded extract and starch source, could be detected by the human eye, the values of the parameter ΔΕs are >3 . It shou−1. These bands mainly account for the C-O stretching of the ring and the linkage of C-O-C and COH groups, and their position is similar in all carbohydrates because the corresponding vibrational modes are unlikely to be significantly affected by polymer formation [FT-IR was used to determine the interactions between starch and water, as well as the additional compounds in the formulation, namely green tea extract and glycerol. Indeed, FT-IR is an important technique that can be used to determine organic compounds , including the chemical bonds between them . In addiormation ,54.−1. The intense bands at 1156 cm−1 are assigned to the asymmetrical stretching vibrations C-O-C and the bands at 1080 and 1023 cm−1 are assigned to the stretching vibrations C-O [−1 indicates amorphous structures [−1, accounting for a highly structured profile. As already discussed by other authors [In this study, the changes in the intensity of the peaks in the above-mentioned sensitive starch region were observed. Based on the data reported in ions C-O ,56. Moreions C-O . The absions C-O . Thus, tions C-O , among wructures . Hydroge authors ,60, high authors . Thus, iThe design and development of the in vitro study of the transdermal release of bioactive compounds loaded into starch-based hydrogels was achieved through the optimisation of the diffusion conditions of the pure green tea extract and the results obtained were more fitted by applying different kinetic release models.In a preliminary phase, several parameters were evaluated to determine the optimal diffusion conditions that are influenced by the solubility of the bioactive compounds present in the green tea extract. In particular, the sink conditions should be reached and maintained, meaning that the backflow from the acceptor phase can be considered equal to zero . In thisMore specifically, according to the studies found in the literature ,63,64, fThe appropriate receptor fluid was selected according to the cumulative amount of polyphenols and antioxidants released at the end of the test, and the possible presence of foam and residues in the cell, as summarised in The in vitro dermal release of bioactive compounds loaded into rice starch-based hydrogels was investigated utilising a Franz diffusion cells system for 8 h with artificial human sweat as receptor fluid. 2 = 0.9873, n = 0.61; antioxidants: R2 = 0.9955, n = 0.50). Considering the n-values, it was possible to conclude that the mechanism of biocompound release from HPP rice starch-based hydrogels was diffusion and swelling; however, the diffusion was predominant due to the polymer chain relaxation process [A controlled and constant release of bioactive compounds from the hydrogel structure over time was recorded, as shown in  process . This is2 = 0.9782; antioxidants: R2 = 0.9783), assessing that the release mechanism of biocompounds was driven by diffusion. Rice starch HPP hydrogels showed a noticeably greater release of biocompounds compared to the cream probably due to the strong network characterising the hydrogel structure and its swelling capacity acting as permeation enhancers. In addition, w/w). The release kinetics, in this case, are better described utilising the Korsmeyer–Peppas model , with diffusion and polymer chain relaxation process rates comparable. However, a slower release of the bioactives from this structure was observed due to the combined presence of glycerol and green tea extract that hindered the swelling capacity of the hydrogel, as discussed in 2/h, 29.24 μg/cm2/h, and 63.46 μg/cm2/h from hydrogels loaded with the green tea extract, hydrogels loaded with the green tea extract and glycerol, and cream, respectively. The flux value was higher for hydrogels loaded with the extract, followed by the hydrogels loaded with both the extract and glycerol as well as the cream.To better understand the release behaviour of gels, green tea extract was added to the commercial cream  at the same concentration (2%) used in rice-starch hydrogels. The sample was loaded in the Franz cells system, as also depicted in These results further highlighted that rice starch-based hydrogels loaded with bioactive compounds such as green tea extract represent suitable and effective carriers for their controlled release in topical applications.Although the presence of glycerol did not affect the gel formation, its addition needs to be optimised to individuate the optimal glycerol percentage enabling it to obtain the desired performances of the hydrogel structure. Indeed, glycerol has been included for many years in topical preparations thanks to its hydrating effect ; therefow/w), which is the maximum allowed concentration of this extract in topical products. A controlled release over time of the bioactive compounds from the structure of the hydrogels was detected, highlighting their effective encapsulation inside these structured materials. The addition of glycerol at a percentage of 5% translated into an improvement of the structural profile of the hydrogel, as confirmed by FT-IR spectra, while a decrease in biocompound release was observed. More work is needed to better understand the relationship between the strength of the hydrogels and their behaviour during release over time of the biocompounds included in their formulation. Moreover, the physical and microbiological stability of these structured materials during shelf life should be also assessed.This work demonstrated that the utilisation of HPP starch-based hydrogels as smart carriers of bioactive compounds can be envisaged, which is a good starting point for the design of topical products. The results obtained highlighted that the addition of green tea extract in the formulation of rice starch suspensions did not hinder the gelation process under high pressure and did not affect the structural integrity, homogeneity, and cream-like appearance of the hydrogels. A highly structured hydrogel was formed when the green tea extract was incorporated at a concentration of 2% (Rice starch powder (S7260) (17.7% amylose content) was supplied by Sigma Aldrich . Distilled water was used to prepare all starch solutions. Green tea extract was obtained from industrial agri-food residues using extraction processes assisted by non-thermal technologies. The commercial cream , which represented the control, was purchased from AIESI Hospital Service . All other chemicals and reagents were of analytical grade or superior.w/w) was used to prepare the hydrogels, based on the results of previous studies [w/w). The latter, according to [w/w), which is able to promote hydrogel formation by HPP and allows the reinforcement of the rheological and textural properties of the structures formed [A suspension of rice starch powder and distilled water at a concentration of 20% . The suspensions were agitated until full homogenisation to prevent particle sinking and then treated under pressure in a laboratory-scale high-pressure unit  previously described elsewhere . BrieflyBased on the results of previous studies , the preGel formation was assessed by determining the gelatinisation extent, structuring level, and swelling power of the samples.The degree of gelatinisation was evaluated by measuring the loss of birefringence of the starch granules using an optical inverted microscope  with a polarisation filter and a 20× objective coupled to a DS Camera Control Unit  for image acquisition and analysis. Before observation, a small amount of the sample was spotted on a microscope slide and covered with cover glass.The gelatinisation level of samples under HPP conditions was assessed by evaluating using Equation (1) the efficiency index (EI), as proposed by Larrea-Wachtendorff . The EI g for 10 min and the pellet was weighed before and after drying for 6 h at 105 °C. The swelling power ratio, evaluated by Equation (2), is defined as the weight of the wet pellet over the dry weight of the starch in the hydrogel samples:The swelling power was determined by modifying the method described by Kusymayanti  accordinThe lightness (L*), redness (a*), and yellowness (b*) of the HPP hydrogels were measured against a white background using a colorimeter CR-400 .ab*) was determined using Equation (3):The colour difference (ΔΕThe whiteness index (WI) was evaluated using Equation (4), as reported by Kaur .(4)WI=1−1. The FT-IR spectra of the samples were recorded at wavelengths ranging from 1200 to 950 cm−1. The resulting starch-averaged spectrum was smoothed to remove any potential noise using a fifteen-point adaptive smoothing function. Subsequently, the baseline modification and a normalised function were applied.Fourier Transform Infrared Spectroscopy (FT-IR) was used to characterise the presence of specific chemical groups in the hydrogels. Each sample (control and loaded hydrogels) was analysed using an FT/IR-400 spectrometer . A total of 128 scans were obtained for each measurement at a resolution of 4 cmn = 5) were previously soaked in the receptor fluid for 24 h and then placed between the donor and the receptor compartments. A volume of 5 mL of solution was used as the acceptor phase and the diffusion area was 0.64 cm2. The receptor phase was kept at 32.5 ± 0.5 °C in thermostatic conditions with an external jacket and agitated with a magnetic stirrer. Then, 1 mL of the hydrogel containing the bioactive compound was loaded on the membrane. At given time intervals aliquots of 0.2 mL were collected from each cell simultaneously and replaced with fresh receptor fluid [The determination of the release of bioactive compounds from hydrogels was performed on a system of five vertical Franz diffusion cells operating in parallel  using dialysis tubing cellulose membranes . The membranes (or fluid . To addror fluid . BioactiAA) according to Equation (5):593 is the absorbance measured at 593 nm.The antioxidant activity (AA) of the samples was determined using the ferric reducing antioxidant power (FRAP) assay in accordance with the modified variant of Benzie and Strain’s method . In briev/v) Folin–Ciocalteau reagent, and 2 mL of sodium carbonate  were mixed and incubated in the dark for 60 min. The absorbance of the reacting mixture was then measured at 765 nm against the blank . The total polyphenols content (TPC), expressed as the concentration of gallic acid in ppm (CGA), was evaluated according to Equation (6).765 is the absorbance measured at 765 nm.The concentration of polyphenols was measured by the method of Folin–Ciocalteau as described by Bobinaitė , with slThe loaded hydrogel samples were subjected to HPLC-PDA analysis using a Waters 1525 Separation Module linked to a Waters 2996 photodiode array detector , in accordance with a modified method reported by Carpentieri . The det2 = 0.999). The results were represented in terms of mg of the target components  per L of the withdrawn sample.The mobile phase consisted of phosphoric acid  and methanol . Analytical separation was carried out by the following gradient elution: 0–30 min from 5% B to 80% B, 30–33 min 80% B, and 33–35 min from 80% B to 5% B. The injection volume and the flow rate of the mobile phase were 5 µL and 0.8 mL/min, respectively. The signal was provided for the quantification of each compound at the wavelength of maximum absorbance. All commercial standards were dissolved into distilled water to generate 6-point standard calibration curves ), first-order (Equation (8)), Higuchi’s model (Equation (9)), and Korsmeyer–Peppas model (Equation (10)) equations ,67.(7)Qp < 0.05).All the experiments and the analyses of the produced hydrogels were carried out in triplicate, and the results were presented as means ± standard deviations. One-way analysis of variance (ANOVA) was used to assess differences between mean values using the statistical software SPSS 20 . Tukey test was performed to identify statistically significant differences (


This study explores the impact of being “green” as a response to variability in the business environment. We examine the financial resilience of green Small and Medium-sized Enterprises (SMEs) in Italy compared to non-green during the COVID-19 pandemic. We verify whether green SMEs are more able to attract external funding than non-green and whether green SMEs rely more heavily on trade credit than non-green ones. We carry out an analysis with 215,564 observations, of which 6844 refer to “green” firms, over the period 2017–2020 and we find that before and during the pandemic, Italian green SMEs do not attract more external funding than other SMEs, but they rely more on trade credit than non-green SMEs. Our results partially confirm the traditional substitution effect, and we suggest that the reasons for this relationship are also supplied in the literature which sees trade credit as a component of a long-term portfolio management strategy, i.e., as a tool for consolidating relationships with clients, for price discrimination and/or for increasing firm profitability in facing variable demand conditions. Our paper contributes to the literature in two ways. First, it investigates the relationship between the “green” characteristics of a firm and its level of economic and financial resilience during the pandemic. Second, it verifies whether, during a complex economic shock, green orientation increases or decreases the importance of trade credit relative to bank credit in financing the firm. The impact of the COVID-19 pandemic was particularly strong, and on Small and Medium Enterprises (SMEs) stronger than that of the global financial crisis in 2008. In normal credit conditions, banks tend to consider SMEs as high-risk borrowers, because they are subject to liquidity shortfalls and bankruptcies reflecting conditions such as asymmetric information and agency problems, liquidity and profitability conditions and their ownership structure. Although relationship banking prevents errors in SME lending and in evaluating the creditworthiness of firms  policy has placed great emphasis on a green economic growth  compared to the EU average. We focus on manufacturing firms for two reasons. First, firms in manufacturing sectors have a greater concentration of tangible assets  and have better access to debt financing (both trade and bank credit). Secondly we wish to avoid industry-specific firms, such as farms, in agriculture, and industries which may not be sufficiently clearly defined, such as services. Our results if, from one side, appear to exclude the existence, during the COVID-19 pandemic, of a better access to financial credit for green SMEs than for non-green ones, from the other side, confirm a higher importance of trade credit for green SMEs than for non-green ones. The reasons of this relationship are probably complex and can only partially be explained by poor access to bank credit for marginal firms, since on average green SMEs seem to be more profitable than non-green ones. On the contrary, this relationship could also be explained according to the literature which sees trade credit as a component of a long-term portfolio management strategy, i.e., as a tool for consolidating relationships with clients, for price discrimination and/or for increasing firm profitability in variable demand conditions.This study makes a significant contribution to the literature in two ways. First, it investigates the relationship between the “green” component and the degree of economic and financial resilience of Italian manufacturing SMEs during the COVID-19 pandemic. Second, it aims to verify whether an orientation to the green economy during a complex economic shock like COVID-19 increases or decreases the importance of trade credit relative to bank/financial credit in financing the firm.The rest of the paper is structured as follows. Section The impact of the COVID-19 pandemic has been investigated in many countries .There is overwhelming evidence to support the positive effect of environmental performance on firm performance, but there is as yet little evidence on the impact of a financial crisis on the relationship between social performance and firm risk  and carry out a panel analysis and OLS regressions with 215,564 observations, of which 6844 refer to firms belonging to the “green” sector. The concept of “green economy” Barbier  is percet-1”), which is the ratio of trade payable to total assets of the previous year; “Account receivable” . In order to verify whether a potential substitution effect between trade and bank credit exists, we consider “Short-term bank credit”  and “Long-term bank credit” . Table In order to study differences between green and non-green SMEs in terms of economic and financial performance, the degree of financial resilience and the size, age of the firm and the orientation towards trade or bank credit before or during the COVID-19, we collect a set of information that includes age, sector and financial ratio for each SME. Our data source is the AIDA Bureau van Dijk database. Consistently with much existing literature , with clustered standard errors at firm level. The Hausman test is performed to ascertain whether the individual effects are fixed or random. If the null hypothesis is rejected, correlation between the independent variables and the individual unobservable effects does not exist and the random effects model is considered a good estimator. The Hausman test results suggest that fixed effect models are appropriate for our dataset. Tables reported in Sect. To answer our research questions, we use an OLS regression to compare 2019 with 2020 and a panel data approach. We also compare the three-year period 2017–2019 with the year 2020 (see Appendix). We next apply the diff-in-diff methodology. The equation models include the variables described in Sect. e of Eq. , 2019, “Y is the outcome variables of interest  of firm i at time t, observed in periods 1 and 2. “Green” is a dummy variable which takes the value of 1 for "treated" subjects (green SME) and 0 otherwise (non-green SME). In other words, the variable “Green” captures the possible a priori differences between the treated group and the control group. “COVID” is a dummy variable which takes value 1 in 2020 (the year of COVID-19 pandemic) and 0 otherwise. The coefficient of this variable tells us how much the external credit  of the control group changes on average after treatment. The variable “Green*COVID” represents the interaction between “Green” and “COVID” and takes a value of 1 for subjects treated in the second period. The coefficient of the variable (Green*COVID) is the parameter of interest because it expresses the effect of the treatment, or the differential impact of COVID-19 on green SMEs. The diff-in-diff estimator is the difference between the mean differences: it takes the difference in the treatment group before and after the treatment (the treatment effect) and subtracts the difference in the control group before and after the treatment (the trend over time), as in the following formula:Z” in Eq.  on subjects. It uses a “treatement group or treated” (green SMEs) relative to a control group not exposed to the treatment (non-green SMEs) (e.g., Bertrand et al. In order to investigate the relationship between the “green” component and the degree of economic and financial resilience during the COVID-19 pandemic, and to verify whether orientation to the green economy tends to increase or decrease the importance of trade credit relative to bank/financial credit in financing the firm, we carry out our analysis on green and non-green SME sub-samples. Table Table Table In order to deepen the analysis on changes in levels of trade credit as a component of full credit, as well as short-term bank credit and long-term bank credit, we also apply diff-in-diff methodology. Table Results presented in Table Tables a negative relationship with age, liquidity, and particularly profitability and short-term bank credit for both green and non-green SMEs;t-1, and with account receivable, which confirms the matching hypotheses, for both green and non-green SMEs.a positive relationship with trade crediFor trade credit, Table The year-over-year comparison reported in Table This paper examines the financial resilience of green manufacturing compared to non-green manufacturing SMEs in Italy during the COVID-19 pandemic. Since the AIDA Bureau van Dijk database contains no specific data on green or ESG ratings, we consider as “green” those SMEs in industries characterized by higher green component than other industries, following the study by the Politecnico di Milano and Camera di Commercio di Milano .We verify the three hypotheses formulated in Sect. At the same time, descriptive statistics on the entire period as well as statistics for the years up to 2020, before the pandemic, show that, on average, green SMEs are younger and smaller but more profitable, and in better financial condition, than non-green SMEs. They also take up and offer more trade credit than non-green SMEs.To conclude, the importance of trade credit for green SMEs could be explained by two complementary trends. A substitution effect, demonstrated by previous literature and recent research on Italian firms [Arcuri and Pisani  on Itali


In this study, porous starch was modified using pulsed electric field (PEF) pretreatment and alcoholic–alkaline treatment to prepare porous granular cold-water-soluble starch (P-GCWSS). The soluble porous starch has high adsorption capability and high cold water solubility, allowing effective encapsulation of zeaxanthin and improving zeaxanthin’s water solubility, stability, and bioavailability. The physical and chemical properties of GCWSS and complex were investigated using scanning electron microscopy, Fourier transform infrared spectroscopy, and X-ray diffraction. The results showed that the cold water solubility of the pulsed electric field-treated porous granular cold-water-soluble starch (PEF-P-GCWSS) increased by 12.81% compared to granular cold-water-soluble starch (GCWSS). The pulsed electric field treatment also increased the oil absorption of PEF-P-GCWSS was improved by 15.32% compared to porous granular cold-water-soluble starch (P-GCWSS). PEF-P-GCWSS was effective in encapsulating zeaxanthin, which provided a good protection for zeaxanthin. The zeaxanthin-saturated solubility in water of PPG–Z was increased by 56.72% compared with free zeaxanthin. The zeaxanthin embedded in PEF-P-GCWSS was able to be released slowly during gastric digestion and released rapidly during intestinal digestion. Modified starch with pore structures on the surface or throughout the entire starch particulate is called porous starch. The physicochemical properties of porous starch are closely related to its pore structures. Porous starch has excellent biodegradability, biocompatibility, and safety . In addiThe mechanism of pulsed electric field (PEF) on starch  is usualAs starch is naturally not easily soluble in water, modification methods are needed to increase its cold water solubility . A modif40H56O2) is a fat-soluble yellow powder or oil. Zeaxanthin has good antioxidant activity, which is important for human physiological health. However, zeaxanthin can only be obtained by the body through the diet as it cannot be synthesized by the body. Zeaxanthin protects visual function [Zeaxanthin  was purchased from Shanghai Macklin Biochemical Co., Ltd. . Analytical-grade potassium chloride, glacial acetic acid, sodium hydroxide, sodium chloride, potassium dihydrogen phosphate, DMSO, and anhydrous sodium acetate were purchased from Shanghai Macklin Biochemical Co., Ltd. Glucose amylase was purchased from Sigma Aldrich CO. . α-amylase, pancreatin, and pepsin were purchased from Shanghai Macklin Biochemical Co., Ltd. Analytical-grade anhydrous ethanol purchased from Tianjin Fuyu Fine Chemical Co., Ltd. . Hydrochloric acid was purchased from Guangdong Guangshi Reagent Technology Co., Ltd. Soybean oil was purchased from COFCO Fulinmen Food Marketing Co., Ltd. .(1)m/m). Potassium chloride solution (0.5 mol/L) was added to the starch dispersion to increase the conductivity of the starch dispersion to 150 ± 5 μS/cm. The starch dispersion was pumped into the PEF treatment equipment [Pulsed electric field-treated corn starch (PEF-CS): Corn starch (CS) was dissolved in deionized water with a concentration of 15% (quipment  at a con(2)m/m). Starch dispersion was stirred and activated for 30 min under water bath conditions at 50 °C. The enzymatic hydrolysis method was slightly modified based on the method proposed by Chen et al. [Porous starch (PS): Corn starch was dissolved in acetic acid sodium acetate  buffer solution with a concentration of 30% Pulsed electric field-treated porous starch (PEF-PS): Similar to the preparation method of PS, porous starch assisted by pulsed electric fields (PEF-PS) was obtained by replacing corn starch with PEF-CS.(4)Granular cold-water-soluble starch (GCWSS): Corn starch (CS) was modified with the alcoholic–alkaline method ,28. Corn(5)Pulsed electric field-treated granular cold-water-soluble starch (PEF-GCWSS): Similar to the preparation method of GCWSS, pulsed electric fields-pretreated granule cold-water-soluble starch (PEF-GCWSS) was obtained by replacing corn starch with PEF-CS.(6)Porous granular cold-water-soluble starch (P-GCWSS): Similar to the preparation method of GCWSS, granule cold-water-soluble porous starch (P-GCWSS)  was obta(7)Pulsed electric field-treated porous granular cold-water-soluble starch (PEF-P-GCWSS): Similar to the preparation method of GCWSS, granular cold-water-soluble porous starch assisted by pulsed electric fields (PEF-P-GCWSS) was obtained by replacing corn starch with PEF-PS.A simple procedure for sample preparation is shown in The different samples  were dispersed in anhydrous ethanol with a concentration of 10% (m/m). Zeaxanthin solution (20 mg zeaxanthin in 10 mL anhydrous ethanol) was added to the starch dispersion and incubated for 2 h under magnetic stirring at 600 r/min ,30. The m/v) was stirred in a water bath at 25 °C for 20 min. After centrifugation for 20 min (3000 rpm/min), the supernatant was transferred into weighed flat bottles and dried in an oven at 105 °C for 6 h to constant weight. Cold water solubility was determined according to Chen  with somCWS) is as follows:m0 (g) is the mass of starch sample; and m1 is the mass of dried sample.The equation for cold water solubility  is as follows:m2 (g) is the mass of the centrifuge tube containing the starch sample (1.0 g); and m3 is the total mass of the precipitated sample and the centrifuge tube.Oil-adsorption capacity was measured according to Fang  with somThe apparent morphology of the samples was observed by scanning electron microscope   was used to characterize the short-range crystal structure of starch samples . All samX-ray diffraction  was used to characterize the long-range crystalline structure of the sample at 40 kV and 40 mA . The difAC, mg/g) was calculated with the following equation:X (μg/mL) is the zeaxanthin concentration calculated corresponding to the absorbance-concentration zeaxanthin standard curve; and V (mL) is the volume of supernatant.The zeaxanthin content and encapsulation efficiency were determined according to the previous methods  with minER, %) was calculated with the following equation:SM (g) is the weight of the given composite obtained; AC (mg/g) is the zeaxanthin content of the sample; and ZM (mg) is the total weight of zeaxanthin added.The encapsulation efficiency  was calculated with the following equation:SM (μg) is the content of zeaxanthin in the saturated solution; and V (mL) is the volume of the saturated solution.Excessive samples were added to deionized water (5 mL) and incubated at 37 °C for 48 h. The supernatant was collected and diluted after centrifugation at 5000 r/min for 20 min. The absorbance of the solution was measured using a UV spectrophotometer at the 446 nm wavelength. The saturated solubility   of the zeaxanthin was calculated as follows:tA (mg) is the content of zeaxanthin after t day; and A0 (mg) is the initial zeaxanthin content of the samples.The storage stability (light and temperature) of the sample was determined according to the method reported in  with minZR, %) was calculated as follows:tC (mg) is the content of zeaxanthin released at t min, and C0 (mg) is the zeaxanthin content of the sample.Simulated gastric fluid (SGF) and simulated intestinal fluid (SIF) were prepared following the methods of McClements  and Marep < 0.05) using the SPSS 17.0 statistical software program .All the experiments were performed at least in triplicate. Data were analyzed by Duncan’s multiple range test , PEF-assisted preparation of granular cold-water-soluble starch (PEF-GCWSS), porous granular cold-water-soluble starch (P-GCWSS), and PEF-assisted preparation of granular cold-water-soluble porous starch (PEF-P-GCWSS), respectively. The oil absorption rates of different samples are shown in Scanning electron microscopy of the different samples is shown in −1, 1149 cm–1 and 996 cm−1 peaks, were weakened to various degrees, indicating that the folding structure inside the starch molecules was destroyed.The Fourier transform infrared spectra of the samples are shown in The X-ray diffraction analyses of different samples and corn starch are shown in Both the hollow single helix of GCWSS and the pore structure of porous starch can be used to accommodate small guest molecules. In general, hydrophobic interactions and concentration gradients (diffusion) are the main driving forces . The zeaScanning electron microscopy of the various encapsulate zeaxanthin samples is shown in In summary, zeaxanthin was successfully encapsulated and adsorbed into the interior of the starch granules through the cracks, pores, and other channels. The results of the scanning electron micrographs were consistent with those of −1 represent the O-H telescopic vibration. Values of 3039 cm−1 and 1569 cm−1 represent the C–H telescopic vibration in C=C. Around 2923 cm−1 is the C-H telescopic vibration in -CH3. The value of 2853 cm−1 is the C–H telescopic vibration in -CH2. Around 1441 cm−1 and 1361 cm−1 are the C–H in-plane bending vibration peaks in -CH2, and 962 cm−1 is the C–H out-of-plane bending vibration in C=C [−1 indicated the presence of zeaxanthin [The Fourier transform infrared (FTIR) spectra of various encapsulated samples, original starch, and zeaxanthin are shown in n in C=C ,44. The axanthin  in the cThe X-ray diffractions of the various encapsulated zeaxanthin samples are shown in The saturated solubility of the various encapsulated zeaxanthin samples was shown in The water-soluble states of the various encapsulated zeaxanthin samples are shown in the inset of Zeaxanthin has limited industry application because it is susceptible to losses caused by external environmental influences during transportation, storage, and processing . The effZeaxanthin release from different composites under simulated stomach/intestinal conditions is shown in Free zeaxanthin had the highest cumulative zeaxanthin release rate under simulated gastric/intestinal conditions, and the release rate was significantly higher than that of the complex under simulated gastric conditions, which indicates that starch is an effective carrier for slowing down the release of zeaxanthin in the stomach. The higher rate of zeaxanthin release from CS–Z under simulated gastric/intestinal conditions could be attributed to the weaker adsorption of zeaxanthin by CS. PPS–Z, PG–Z and PPG–Z all showed lower zeaxanthin release rates under simulated stomach/intestinal conditions. The release of zeaxanthin was related to the amount of starch adsorbed and the water solubility of the complexes. The faster release of zeaxanthin from P-GCWSS and PEF-P-GCWSS could be attributed to its ability to dissolve rapidly in cold water. The slower release of zeaxanthin from PEF-PS may be due to its lower solubility in cold water. The rate of zeaxanthin release from PG–Z and PPG–Z was significantly higher in the mock intestine than in the stomach, which may be attributed to the rapid solubility and high digestive sensitivity to enzymes of P-GCWSS and PEF-P-GCWSS. This makes them susceptible to hydrolysis, causing faster release of zeaxanthin. In summary, free zeaxanthin cannot be released slowly under gastric/intestinal conditions, and PEF-P-GCWSS-encapsulated zeaxanthin is an effective strategy for slow release of zeaxanthin in gastric digestion followed by a large amount of zeaxanthin release in intestinal digestion, which improves zeaxanthin absorption and utilization by the human body.In the present study, it was shown that PEF-assisted preparation of granular cold-water-soluble porous starch (PEF-P-GCWSS) is an effective encapsulation matrix for zeaxanthin, which improves the solubility, stability, and slow release of zeaxanthin. The PEF-assisted enzymatic method and the alcoholic-alkaline method were used to enhance the adsorption properties and water solubility of porous starch, respectively. The cold water solubility and oil absorption of PEF-P-GCWSS were enhanced. PEF-P-GCWSS was the most effective in encapsulating zeaxanthin, which provided a good protection for zeaxanthin. Zeaxanthin encapsulated in PEF-P-GCWSS was slowly released during gastric digestion followed by rapid release during intestinal digestion. Therefore, this work prepared porous granular cold-water-soluble starch using the pulsed electric field-assisted enzymatic hydrolysis method and alcohol–alkali method, which enhanced the efficiency and cold water solubility of porous starch preparation. The PEF-P-GCWSS was a carrier with efficient zeaxanthin-loading capacity. In summary, this work expands the modification methods and applications of porous starch and provides beneficial information for further studies on the use of PEF-P-GCWSS as a lipid-soluble active substance encapsulation carrier.(1)The similarities and differences in the preparation of PEF-P-GCWSS from starch granules with different branched chains, different crystalline forms, and different plant sources;(2)Structural and physicochemical property changes of PEF-PS under other methods of preparing granular cold-water-soluble starch;(3)Adsorptive encapsulation of different structural guests by PEF-P-GCWSS.In the future, more in-depth exploration is needed in the following areas: