Hyperledger Healthchain: Patient-Centric IPFS-Based Storage of Health Records

Monitoring oral health remotely: ethical considerations when using AI among vulnerable populations

Technological innovations in dentistry are revolutionizing the monitoring and management of oral health. This perspective article critically examines the rapid expansion of remote monitoring technologies-including artificial intelligence (AI)-driven diagnostics, electronic health records (EHR), wearable devices, mobile health applications, and chatbots-and discusses their ethical, legal, and social implications. The accelerated adoption of these digital tools, particularly in the wake of the COVID-19 pandemic, has enhanced accessibility to care while simultaneously raising significant concerns regarding patient consent, data privacy, and algorithmic biases. We review current applications ranging from AI-assisted detection of dental pathologies to blockchain-enabled data transfer within EHR systems, highlighting the potential for improved diagnostic accuracy and the risks associated with over-reliance on remote assessments. Furthermore, we underscore the challenges posed by the digital divide, where disparities in digital literacy and access may inadvertently exacerbate existing socio-economic and health inequalities. This article calls for the development and rigorous implementation of ethical frameworks and regulatory guidelines that ensure the reliability, transparency, and accountability of digital health innovations. By integrating multidisciplinary insights, our discussion aims to foster a balanced approach that maximizes the clinical benefits of emerging technologies while safeguarding patient autonomy and promoting equitable healthcare delivery.

Building an Ethical and Trustworthy Biomedical AI Ecosystem for the Translational and Clinical Integration of Foundation Models

Foundation Models (FMs) are gaining increasing attention in the biomedical artificial intelligence (AI) ecosystem due to their ability to represent and contextualize multimodal biomedical data. These capabilities make FMs a valuable tool for a variety of tasks, including biomedical reasoning, hypothesis generation, and interpreting complex imaging data. In this review paper, we address the unique challenges associated with establishing an ethical and trustworthy biomedical AI ecosystem, with a particular focus on the development of FMs and their downstream applications. We explore strategies that can be implemented throughout the biomedical AI pipeline to effectively tackle these challenges, ensuring that these FMs are translated responsibly into clinical and translational settings. Additionally, we emphasize the importance of key stewardship and co-design principles that not only ensure robust regulation but also guarantee that the interests of all stakeholders-especially those involved in or affected by these clinical and translational applications-are adequately represented. We aim to empower the biomedical AI community to harness these models responsibly and effectively. As we navigate this exciting frontier, our collective commitment to ethical stewardship, co-design, and responsible translation will be instrumental in ensuring that the evolution of FMs truly enhances patient care and medical decision-making, ultimately leading to a more equitable and trustworthy biomedical AI ecosystem.

Scalable Lightweight Protocol for Interoperable Public Blockchain-Based Supply Chain Ownership Management

Scalability prevents public blockchains from being widely adopted for Internet of Things (IoT) applications such as supply chain management. Several existing solutions focus on increasing the transaction count, but none of them address scalability challenges introduced by resource-constrained IoT device integration with these blockchains, especially for the purpose of supply chain ownership management. Thus, this paper solves the issue by proposing a scalable public blockchain-based protocol for the interoperable ownership transfer of tagged goods, suitable for use with resource-constrained IoT devices such as widely used Radio Frequency Identification (RFID) tags. The use of a public blockchain is crucial for the proposed solution as it is essential to enable transparent ownership data transfer, guarantee data integrity, and provide on-chain data required for the protocol. A decentralized web application developed using the Ethereum blockchain and an InterPlanetary File System is used to prove the validity of the proposed lightweight protocol. A detailed security analysis is conducted to verify that the proposed lightweight protocol is secure from key disclosure, replay, man-in-the-middle, de-synchronization, and tracking attacks. The proposed scalable protocol is proven to support secure data transfer among resource-constrained RFID tags while being cost-effective at the same time.

Cloud-based blockchain technology to identify counterfeits

Multi-stakeholder and organizational involvement is an integral part of the medicine supply chain. Keeping track of the activities associated with medical products is difficult when the system is complex. Their complexity limits transparency and data provenance. Deficiencies within existing supply chains result in the counterfeiting of drugs, illegal imports, and inefficient operations. Due to these limitations, product integrity is compromised, resulting in product wastage. Visibility of the entire product supply chain is crucial for the pharmaceutical industry in terms of product safety and reduction of manufacturing costs. The Cloud-based Blockchain-powered architecture of the system provides a platform for addressing the need of pharma-material traceability, data storage, privacy of data, and quality assurance. This framework comprises of the identification of activities through tagging, information sharing in a secure environment; cloud-based storage using an off-chain Interplanetary File System (IPFS) and an on-chain couch DB; and access to this information that is controlled by the system's regulator. Electronic drug records will be accessed via a smart contract in Hyperledger Blockchain. The system assists in identifying false and cross-border products through the manufacturer and country of origin. A scan will identify counterfeit medications, showing that they are unauthorized products which may pose a risk to patients. Our experiments demonstrated the efficiency and usability of the design platform. Finally, we benchmarked the system using Hyperledger Caliper.

IoT Adoption and Application for Smart Healthcare: A Systematic Review

In general, the adoption of IoT applications among end users in healthcare is very low. Healthcare professionals present major challenges to the successful implementation of IoT for providing healthcare services. Many studies have offered important insights into IoT adoption in healthcare. Nevertheless, there is still a need to thoroughly review the effective factors of IoT adoption in a systematic manner. The purpose of this study is to accumulate existing knowledge about the factors that influence medical professionals to adopt IoT applications in the healthcare sector. This study reviews, compiles, analyzes, and systematically synthesizes the relevant data. This review employs both automatic and manual search methods to collect relevant studies from 2015 to 2021. A systematic search of the articles was carried out on nine major scientific databases: Google Scholar, Science Direct, Emerald, Wiley, PubMed, Springer, MDPI, IEEE, and Scopus. A total of 22 articles were selected as per the inclusion criteria. The findings show that TAM, TPB, TRA, and UTAUT theories are the most widely used adoption theories in these studies. Furthermore, the main perceived adoption factors of IoT applications in healthcare at the individual level are: social influence, attitude, and personal inattentiveness. The IoT adoption factors at the technology level are perceived usefulness, perceived ease of use, performance expectancy, and effort expectations. In addition, the main factor at the security level is perceived privacy risk. Furthermore, at the health level, the main factors are perceived severity and perceived health risk, respectively. Moreover, financial cost, and facilitating conditions are considered as the main factors at the environmental level. Physicians, patients, and health workers were among the participants who were involved in the included publications. Various types of IoT applications in existing studies are as follows: a wearable device, monitoring devices, rehabilitation devices, telehealth, behavior modification, smart city, and smart home. Most of the studies about IoT adoption were conducted in France and Pakistan in the year 2020. This systematic review identifies the essential factors that enable an understanding of the barriers and possibilities for healthcare providers to implement IoT applications. Finally, the expected influence of COVID-19 on IoT adoption in healthcare was evaluated in this study.

Design of Smart and Secured Healthcare Service Using Deep Learning with Modified SHA-256 Algorithm

Background: The modern era of human society has seen the rise of a different variety of diseases. The mortality rate, therefore, increases without adequate care which consequently causes wealth loss. It has become a priority of humans to take care of health and wealth in a genuine way. Methods: In this article, the authors endeavored to design a hospital management system with secured data processing. The proposed approach consists of three different phases. In the first phase, a smart healthcare system is proposed for providing an effective health service, especially to patients with a brain tumor. An application is developed that is compatible with Android and Microsoft-based operating systems. Through this application, a patient can enter the system either in person or from a remote place. As a result, the patient data are secured with the hospital and the patient only. It consists of patient registration, diagnosis, pathology, admission, and an insurance service module. Secondly, deep-learning-based tumor detection from brain MRI and EEG signals is proposed. Lastly, a modified SHA-256 encryption algorithm is proposed for secured medical insurance data processing which will help detect the fraud happening in healthcare insurance services. Standard SHA-256 is an algorithm which is secured for short data. In this case, the security issue is enhanced with a long data encryption scheme. The algorithm is modified for the generation of a long key and its combination. This can be applicable for insurance data, and medical data for secured financial and disease-related data. Results: The deep-learning models provide highly accurate results that help in deciding whether the patient will be admitted or not. The details of the patient entered at the designed portal are encrypted in the form of a 256-bit hash value for secured data management.

Data Analysis for Modeling the Effect of Acupuncture on Postchemotherapy Cancer Fatigue in Gynecologic Oncology Patients

Now cancer-related fatigue is gradually being emphasized, which is a common symptom in cancer patients. During long-term radiotherapy, the emotion of patients will be affected directly, and inevitably produce cancer-caused fatigue needle symptoms. Moreover, the weakness and fatigue are always produced simultaneously, which are harmful to patients' prognosis level of their overall survival quality. The acupuncture has a helpful effect on improving the Chinese medical evidence of side effects caused by radiotherapy and chemotherapy in tumor patients. In this paper, we model the effect of acupuncture on cancer fatigue after chemotherapy in gynecologic oncology patients through data analysis, so as to effectively analyze the degree of cancer fatigue after chemotherapy in patients.

Treatment of Cancer Gene Changes in Chronic Myeloid Leukemia by Big Data Analysis Platform-Based Dasatinib

Based on data mining, an innovative big data analysis platform was utilized to discuss the treatment of cancer in chronic myeloid leukemia (CML) by dasatinib, aiming to offer help to the diagnosis and treatment of cancer. An integrated gene expression analysis system (IEAS) was firstly constructed to automatically classify data in the online human Mendelian genetic database using clustering algorithms. At the same time, the gene expression profile was analyzed by principal component analysis (PCA) in the analysis system. In addition, the efficacy of dasatinib in the treatment of patients with advanced CML was then retrospectively analyzed. The results showed that the IEAS system could incorporate the gene expression analysis vectors it contained by JAVA-related technologies, and the generated clustering genes showed similar functions. The clustering algorithm could homogenize data and generate visual clustering heat maps. The analysis results of major elements were diverse under different experimental conditions. The characteristic value of the first major element was the largest. Messenger ribonucleic acid (mRNA) datasets of CML patients were selected from cancer genomic map, including 120 samples and 20,614 mRNA in total. In micro-RNA (miRNA) datasets, there were 202 samples including 1,406 miRNAs. Data were screened by miRNA-mRNA regulation template, and 20 differentially expressed mRNAs were obtained. In conclusion, the proposed IEAS system could mine and analyze the gene expression data. Dasatinib showed good efficacy in the treatment of patients with advanced CML. Besides, it could improve visual queries, and data mining had a broad application prospect in clinical application. Dasatinib was considered to be a good option for patients with advanced CML.

Addressing the Challenges of Electronic Health Records Using Blockchain and IPFS

Electronic Health Records (EHR) are the healthcare sector's core digital strategy meant to improve the quality of care provided to patients. Despite the benefits afforded by this digital transformation initiative, adoption among healthcare organizations has been slower than desired. The sheer volume and sensitive nature of patient records compel these organizations to exercise a healthy amount of caution in implementing EHR. Cyberattacks have also increased the risks associated with non-optimal EHR implementations. An influx of high-profile data breaches has plagued the sector during the COVID-19 pandemic, which put the spotlight on EHR cybersecurity. One objective of this research project is to aid the acceleration of EHR adoption. Another objective is to ensure the robustness of the system to resist malicious attacks. For the former, a systematic review was used to unearth all the possible causes why the adoption of EHR has been anemic. In this paper, sixty-five existing proposed EHR solutions were analyzed and it was found that there are fourteen major challenges that need to be addressed to reduce friction and risk for health organizations. These were privacy, security, confidentiality, interoperability, access control, scalability, authentication, accessibility, availability, data storage, data ownership, data validity, data integrity, and ease of use. We propose EHRChain, a new framework that tackles all the listed challenges simultaneously to address the first objective while also being designed to achieve the second objective. It is enabled by dual-blockchains based on Hyperledger Sawtooth to allow patient data decentralization via a consortium blockchain and IPFS for distributed data storage.

Microscopic Interface and Multiscale Failure Analysis of Proposed Molecular Chain Polymers Based on Aifantis Strain Gradient Theory

A study on the microscopic morphology of real-world polymer blends and its mechanism of change showed that the microscopic morphology of equiproportional mixtures gradually changed from a dense body structure to a network structure with the addition of the total polymer concentration up to 20%; the microscopic morphology of mixtures with different proportions was characterized by the most uniform network structure of equiproportional mixtures when the total polymer concentration was 20%. The polymer acts as a defoamer in the mixed system. In this paper, the relationship between the microscopic morphology of each mixture and the physicochemical behavior of the two polymer chains in the mixed system was investigated on the basis of the Aifantis strain gradient theory. Molecular polymer microscopic interface and multiscale failure analysis are proposed. It is shown that for the dihedral angle distribution of four consecutive coarse-grained particles, the peaks obtained from all atomic-scale simulation data are reproduced in the coarse-grained model simulations. The deviation is within 2.5% in most places, except for the local area where the deviation exceeds 5%. Therefore, we have achieved good results for large-scale failures.

Integrated Blockchain-Deep Learning Approach for Analyzing the Electronic Health Records Recommender System

Blockchain is a recent revolutionary technology primarily associated with cryptocurrencies. It has many unique features including its acting as a decentralized, immutable, shared, and distributed ledger. Blockchain can store all types of data with better security. It avoids third-party intervention to ensure better security of the data. Deep learning is another booming field that is mostly used in computer applications. This work proposes an integrated environment of a blockchain-deep learning environment for analyzing the Electronic Health Records (EHR). The EHR is the medical documentation of a patient which can be shared among hospitals and other public health organizations. The proposed work enables a deep learning algorithm act as an agent to analyze the EHR data which is stored in the blockchain. This proposed integrated environment can alert the patients by means of a reminder for consultation, diet chart, etc. This work utilizes the deep learning approach to analyze the EHR, after which an alert will be sent to the patient's registered mobile number.

Prevention and Treatment of Sports Injuries and Rehabilitative Physical Training of Wushu Athletes

This paper is aimed at exploring the characteristics of research on prevention and treatment of sports injuries and rehabilitation physical training of Wushu athletes. It also considers the application of rehabilitation physical training in the rehabilitation of Wushu athletes. By searching literature, conducting questionnaires, and combining mathematical statistics, it studies the injury prevention and rehabilitation training of Wushu athletes. This paper chooses the level of first class and above of sports, and a total of 50 elite male and female Wushu athletes were systematically trained as subjects of study. Athletes, aged 15 to 20 years, were trained for 2 to 5 years, 35 male athletes and 15 female athletes. Different from traditional rehabilitation therapy, athletes' physical rehabilitation training is also different from traditional sports rehabilitation treatment. By evaluating the physical condition of athletes, the causes of sports injuries were analyzed, to formulate special rehabilitation training programs and carry out athletes' rehabilitation training targeted and purposeful. Record the experimental data and analyze the experimental results. The experimental results show that physical rehabilitation training can make athletes avoid the influence of unsafe factors of sports injury, improve the safety of training, and effectively prevent sports injury. The experimental results show that physical rehabilitation training combined with rehabilitation medicine has obvious advantages, which can make Wushu athletes recover quickly without sequelae.

Drop Height Impacts the Lower Limb Elastic Energy's Utilization for Male High Jumpers: A Experimental Research from Biomechanics

The study's objective is to clarify the influence of drop height on elastic energy's utilization of the lower extremity, to indicate the correlations between elastic energy's utilization and personal best, and to determine the optimal loading height for elastic energy's utilization for male high jumpers. Ten male athletes who belong to high jump events work out the drop jump at different drop heights (0.3 m, 0.45 m, 0.6 m, and 0.75 m). Two AMTI force platforms were used to capture the dynamics data for the lower extremity. Drop height has obvious influence on utilization ratio for elastic energy (P < 0.01). The utilization ratio of elastic energy has no note correlation with personal best (r = 0.149, P > 0.05). In this study, the optimal loading height for utilization ratio of elastic energy was 0.75 m. The optimal loading height can be determined in terms of the elastic energy utilization ratio for each high jumper to enhance their training effects.

Application Value of Health Education Combined with Aerobic Exercise in Nursing of Patients with Mastitis Found in Physical Examination

Objective

To explore the application value of health education combined with aerobic exercise in patients with mastitis found in physical examination.

Methods

The clinical data of 100 patients with mastitis who underwent physical examination in the physical examination center of our hospital from October 2020 to October 2021 were retrospectively analyzed. According to the order of physical examination, they were equally split into experimental group and control group. The control group received the routine clinical intervention, while the experimental group received health education combined with aerobic exercise to evaluate the clinical effects of different intervention modes on patients with mastitis.

Results

Compared with the control group, the experimental group after intervention achieved notably higher scores of CD-RISC, self-management ability, and mastitis-related knowledge (P < 0.001), lower scores of breast pain, skin color, and local mass diameter (P < 0.001), and a higher SF-36 score (P < 0.001).

Conclusion

The clinical intervention combining health education with aerobic exercise in patients with mastitis found in the physical examination is an effective method to improve their mood state and self-management ability, and further research will help provide a good solution for such patients.

Blockchain-Enabled Vehicular Ad Hoc Networks: A Systematic Literature Review

This systematic literature review provides an extensive categorization of the blockchain-enabled applications across the domain of vehicular ad hoc networks (VANETs). Within the paradigm of distributed ledger technology (DLT), the communication models and practices for VANETs have been revolutionized. An analytical review and a survey were conducted to explore the advancements of blockchain and VANETs. The techniques, limitations, and advantages of blockchain deployment in VANETs are discussed for the effective implementation of a decentralized network. To this end, 68 studies were selected on the basis of the procedural steps to provide a comprehensive overview of blockchain and the smart contracts in VANETs. In particular, a decentralized communication model is also proposed for the advanced implementation of blockchain in VANETs. Researchers and practitioners are being attracted to these technologies for applications for various industrial sectors. Therefore, this study also emphasizes the identification of any blockchain-related open issues for future prospects. The comprehension of blockchain applications for the Internet of Vehicles (IoV) is also explored in order to fill the research gap on advanced communication networks across the Internet of Things.

A Recommendation System Based on AI for Storing Block Data in the Electronic Health Repository

The proliferation of wearable sensors that record physiological signals has resulted in an exponential growth of data on digital health. To select the appropriate repository for the increasing amount of collected data, intelligent procedures are becoming increasingly necessary. However, allocating storage space is a nuanced process. Generally, patients have some input in choosing which repository to use, although they are not always responsible for this decision. Patients are likely to have idiosyncratic storage preferences based on their unique circumstances. The purpose of the current study is to develop a new predictive model of health data storage to meet the needs of patients while ensuring rapid storage decisions, even when data is streaming from wearable devices. To create the machine learning classifier, we used a training set synthesized from small samples of experts who exhibited correlations between health data and storage features. The results confirm the validity of the machine learning methodology.