Healthcare Applications Research Articles

Specification, validation and verification of social, legal, ethical, empathetic and cultural requirements for autonomous agents

Autonomous agents are increasingly being proposed for use in healthcare, assistive care, education, and other applications governed by complex human-centric norms. To ensure compliance with these norms, the rules they induce need to be unambiguously defined, checked for consistency, and used to verify the agent. In this paper, we introduce a framework for formal specification, validation and verification of social, legal, ethical, empathetic and cultural (SLEEC) rules for autonomous agents. Our framework comprises: (i) a language for specifying SLEEC rules and rule defeaters (that is, circumstances in which a rule does not apply or an alternative form of the rule is required); (ii) a formal semantics (defined in the process algebra tock-CSP) for the language; and (iii) methods for detecting conflicts and redundancy within a set of rules, and for verifying the compliance of an autonomous agent with such rules. We show the applicability of our framework for two autonomous agents from different domains: a firefighter UAV, and an assistive-dressing robot.

Exploring Long-Term Determinants and Attitudes Toward Smartphone-Based Commercial Health Care Applications Among Patients With Cancer.

Our study explores how attitudes of patients with cancer toward smartphone-based commercial health care apps affect their use and identifies the influencing factors. Of the 960 patients with cancer who participated in a randomized controlled trial for a smartphone-based commercial health care app, only 264 participants, who completed a survey on app usage experiences conducted between May and August 2022, were included in this study. Participants were categorized into three groups: Positive Persistence (PP), Negative Nonpersistence (NN), and Neutral (NE) on the basis of their attitude and willingness to use smartphone-based commercial health care apps. The Health-Related Quality of Life (QOL) Instrument (8 Items), European QOL (5 Dimensions; 5 Levels), The Human Interaction and Motivation questionnaire, and open-ended questionnaires were used to examine factors potentially influencing extended utilization of digital interventions. Despite demographic similarities among the three groups, only the PP and NE groups showed similar app usage compared with the NN group. The combined group (positive persistence and neutral) exhibited significant improvement in depression (P = .02), anxiety (P = .03), and visual analog scale scores (P = .02) compared with the NN group. In addition, patient interaction (P < .01) and the presence of a chatbot/information feature on the app (P < .01) demonstrated a significant difference across the three groups, with the most favorable response observed among the PP group. Patients were primarily motivated to use the app owing to its health management functions, while the personal challenges they encountered during app usage acted as deterrents. These findings suggest that maintaining a non-negative attitude toward smartphone-based commercial health care apps could lead to an improvement in psychological distress. In addition, the social aspect of apps could contribute to extending patient's utilization of digital interventions.

A traceable and revocable decentralized attribute-based encryption scheme with fully hidden access policy for cloud-based smart healthcare

Smart healthcare is an emerging technology for enabling interaction between patients and medical personnel, medical institutions, and medical devices utilizing advanced Internet of Things (IoT) technologies. It has attracted significant attention from researchers because of the convenience of storing and sharing electronic medical records (EMRs) in the cloud. Given that a patient’s EMR contains sensitive individual information, it must be encrypted before uploading it to the cloud. As a countermeasure for data confidentiality and fine-grained access control, the Ciphertext Policy Attribute-Based Encryption (CP-ABE) technique is proposed, which helps manipulate private personal data without explicit authorization. However, most CP-ABE schemes use a centralized mechanism which may lead to performance bottlenecks and single-point-of-failure issues. They will also be at risk of key abuse and privacy breaches in smart healthcare applications. To this end, in this paper, we investigate a traceable and revocable decentralized attribute-based encryption scheme with a fully hidden access policy (TR-HP-DABE). Firstly, to overcome the issues of user privacy leakage and single-point-of-failure, a fully hidden access policy is established for multiple attribute authorities. Secondly, to prevent key abuse, the proposed TR-HP-DABE can achieve the tracking and revocation of malicious users by using Key Encryption Key (KEK) trees and updating the partial ciphertext. Furthermore, the online/offline encryption and verifiable outsourced decryption are applied to improve its efficiency in practical smart healthcare. According to our analysis, the security and traceability of TR-HP-DABE can be proved. Finally, the performance evaluation of TR-HP-DABE is more effective than some existing typical ones.

Enhanced air filtration and ammonia sensing with cellulose nanofibers-based triboelectric nanogenerators under harsh environments

Fire accidents often occur in environments characterized by severe air pollution, toxic gas emissions, and microbial growth, creating significant challenges for developing multifunctional portable healthcare devices. These devices must achieve high filtration efficiency, minimal pressure drop, and the capability to provide early warnings of toxic gas leaks. Triboelectric nanogenerators present a sustainable solution as self-powered energy converters for advanced healthcare applications. In this study, we grafted Ti3C2Tx/MoS2 nanohybrid materials, which form Schottky heterojunctions, onto cellulose diacetate using tetraethyl orthosilicate, followed by electrospinning to produce nanofibrous films. When paired with a negatively charged material, the resulting device achieved an optimal balance with a low-pressure drop (52 Pa) and high filtration efficiency (98.72 % for PM0.3). It also demonstrated exceptional stability under high-temperature and high-humidity conditions. Notably, the device maintained rapid sterilization within 15 min and consistent filtration performance even after multiple washes. Furthermore, the device exhibited precise detection of trace amounts of NH3 (0.1 ppm) and demonstrated a rapid response, achieving an 86 % response rate within 2 s for 100 ppm NH3, providing an early warning 28 s faster than commercial NH₃ monitors. This study introduces a novel approach to the development of multifunctional, self-powered, wearable medical devices that offer high-efficiency air filtration and sterilization, breath monitoring, and rapid toxic gas leakage detection in harsh environments.

ZIF-8-enhanced multifunctional, high-performance nanocomposite hydrogel–based wearable strain sensor for healthcare applications

ZIF-8-enhanced multifunctional, high-performance nanocomposite hydrogel–based wearable strain sensor for healthcare applications

Exploring the Landscape of Polymerase Chain Reaction in Indian Medical Research: A Scientometric Analysis

Background and Aims: The polymerase chain reaction (PCR) has revolutionised medical research in India by offering a rapid and precise method for amplifying deoxyribonucleic acid (DNA) sequences crucial for various applications in healthcare. It aims to investigate publication growth, leading authors, productive journals, collaborating countries, keywords, institutional affiliations and funding agencies involved in PCR-related research within India. Methodology: A scientometric analysis assessed India’s PCR research performance using 10,509 articles from Scopus (2013-2022). Biblioshiny and VOSviewer aided data analysis, while Microsoft Excel facilitated computations. Results: The finding reveals the continuous growth in PCR publications, with significant contributions from researchers like Aman Sharma from the Post Graduate Institute of Medical Education and Research (Chandigarh) and journals like the Indian Journal of Medical Microbiology. Notable contributors hail from the All India Institute of Medical Sciences (AIIMS), New Delhi. International collaborations, especially with the United States, underscore the global outreach of PCR research in India. Keyword analysis reveals emerging trends and research themes in PCR, reflecting its evolving role in addressing contemporary health challenges. Funding agencies such as the Indian Council of Medical Research and the Ministry of Science and Technology, India (DBT) are crucial in supporting PCR research endeavours. Conclusions: This study provides valuable insights into the dynamics of PCR research in India, highlighting areas for future investigation and collaboration to further advance medical research and healthcare outcomes.

Machine Learning Applied to Edge Computing and Wearable Devices for Healthcare: Systematic Mapping of the Literature

The integration of machine learning (ML) with edge computing and wearable devices is rapidly advancing healthcare applications. This study systematically maps the literature in this emerging field, analyzing 171 studies and focusing on 28 key articles after rigorous selection. The research explores the key concepts, techniques, and architectures used in healthcare applications involving ML, edge computing, and wearable devices. The analysis reveals a significant increase in research over the past six years, particularly in the last three years, covering applications such as fall detection, cardiovascular monitoring, and disease prediction. The findings highlight a strong focus on neural network models, especially Convolutional Neural Networks (CNNs) and Long Short-Term Memory Networks (LSTMs), and diverse edge computing platforms like Raspberry Pi and smartphones. Despite the diversity in approaches, the field is still nascent, indicating considerable opportunities for future research. The study emphasizes the need for standardized architectures and the further exploration of both hardware and software to enhance the effectiveness of ML-driven healthcare solutions. The authors conclude by identifying potential research directions that could contribute to continued innovation in healthcare technologies.

A Systematic Investigation Based on BCI and EEG Implemented using Machine Learning Algorithms

BCI is a strong tool that improves human-system communication. It improves the brain's ability to interact with its surroundings. Recent decades have seen substantial advances in neuroscience and computer science. This has made BCI a leader in computational neuroscience and intelligence research. Recent technological advances including wearable sensing devices, real-time data streaming, machine learning, and deep learning have raised the need for electroencephalographic (EEG)-based brain-computer interface (BCI) in clinical and translational applications. EEG-based Brain-Computer Interfaces (BCIs) detect cognitive state variations throughout laborious tasks, making them advantageous for individuals. To fill in the gaps in the wide overview of the past five years (2019-2024), we surveyed the newest research on EEG signal detection and computational intelligence in brain-computer interfaces. To provide a more accurate account, we will begin by reviewing Brain-Computer Interface (BCI) technology and its main challenges. Modern signal detection and enhancement techniques for EEG signal collection and refinement follow. We also provide advanced computational intelligence methods for tracking, maintaining, and monitoring human cognitive and operational performance in everyday applications. Combinations, interpretable fuzzy models, transfer learning, and deep learning are used. We conclude with a sample of cutting-edge BCI-driven healthcare applications and explore future EEG-based BCI research.

CRISPR-Cas Innovative Strategies for Combating Viral Infections and Enhancing Diagnostic Technologies

Background: CRISPR-Cas technology has transformed molecular diagnostics and therapeutic strategies for viral infections, particularly COVID-19. Its ability to precisely edit viral genomes and detect viral RNA/DNA offers a novel approach to combating persistent viral infections.Objective: This study aimed to evaluate the diagnostic accuracy and therapeutic potential of CRISPR-Cas systems in viral infections, with a focus on COVID-19.Methods: A systematic review and meta-analysis of 25 peer-reviewed studies were conducted, including clinical trials and experimental research. Data collection involved searching PubMed, Scopus, and Google Scholar using keywords such as "CRISPR-Cas," "viral diagnostics," and "COVID-19." Statistical analysis was performed using SPSS version 25, with pooled sensitivity and specificity estimates calculated for CRISPR-based diagnostics.Results: CRISPR-based diagnostics, including SHERLOCK and DETECTR, achieved pooled sensitivity of 94% (95% CI: 92%-96%) and specificity of 97% (95% CI: 95%-99%) for SARS-CoV-2 detection. Therapeutic interventions using CRISPR-Cas9 showed an 84% reduction in viral replication across HIV and Hepatitis B studies (95% CI: 80%-88%).Conclusion: CRISPR-Cas technologies demonstrate high diagnostic accuracy and therapeutic potential, particularly in resource-limited settings. Further clinical validation is needed to enhance global healthcare applications.

Piezoelectric nanogenerators from sustainable biowaste source: Power harvesting and respiratory monitoring with electrospun crab shell powder-poly(vinylidene fluoride) composite nanofibers

Wearable piezoelectric nanogenerators (PENGs) are increasingly significant in healthcare and energy harvesting applications due to their ability to convert mechanical energy into electrical signals. In this study, we developed PENGs by incorporating crab shell powder (CS-NFs) into electrospun polyvinylidene fluoride (PVDF) nanofibers to enhance their piezoelectric properties. The PVDF-CS-NFs (PC-NFs) composites were evaluated for structural, thermal, and piezoelectric performance. The 1.5 wt% CS-NFs composite exhibited a notable improvement, with a maximum output voltage of 19 V under mechanical deformation, significantly higher than pristine PVDF NFs. Furthermore, the device demonstrated excellent sensitivity in real-time respiratory monitoring when applied to various body locations, including the chest, throat, and mask. Additionally, the PC-NFs-based PENGs were capable of charging a 2.2 µF capacitor to 2 V within 180 s and powering 56 LEDs. These results underscore the potential of using sustainable crab shell waste in biocompatible, eco-friendly piezoelectric devices for wearable sensors and energy harvesting applications.

Mapping World: A Comparative Analysis of Functionality and Usability between QGIS and ArcGIS

The current review conducts a comprehensive comparison of Quantum GIS (QGIS) and ArcGIS in the context of healthcare applications, aiming to assist users in selecting the most appropriate GIS platform for their specific needs. Geographic Information Systems (GIS) are essential tools in healthcare, providing capabilities for spatial analysis, disease mapping, and resource allocation. Driven by the need to improve healthcare delivery and public health strategies, this study compares the functionalities, costs, and support systems of QGIS and ArcGIS. The major objective is to determine which GIS platform offers the best balance of cost-efficiency and performance across various healthcare scenarios. The methodology encompasses a detailed comparative analysis, incorporating quantitative data from user satisfaction surveys, performance evaluations, and real-world case studies. Findings reveal that QGIS, as an open-source GIS software, is highly cost-effective and customizable, making it particularly advantageous for budget-limited projects and smaller organizations requiring flexibility. Its extensibility via plugins and robust user community fosters continuous innovation and improvement. QGIS proves exceptionally effective in healthcare applications such as disease mapping and accessibility analysis, where customization and cost-efficiency are critical. Conversely, ArcGIS is distinguished by its advanced analytical capabilities and strong performance in handling complex, large-scale studies. Its sophisticated tools and comprehensive spatial analysis functionalities are essential for thorough healthcare research and planning. Although ArcGIS incurs higher costs, its extensive feature set, professional support, and exhaustive documentation justify the investment for large organizations and research institutions engaged in detailed GIS projects. In conclusion, both QGIS and ArcGIS exhibit significant strengths in key healthcare applications. QGIS excels in affordability and flexibility, while ArcGIS provides superior analytical power and support for extensive projects. Future research should focus on integrating real-time data and enhancing user experience to further optimize GIS applications in healthcare.

A SUPERVISED ONLINE CROWD ANALYSIS NETWORK WITH DUAL-TASK DEEP LEARNING IDEA IN HEALTHCARE APPLICATION

A SUPERVISED ONLINE CROWD ANALYSIS NETWORK WITH DUAL-TASK DEEP LEARNING IDEA IN HEALTHCARE APPLICATION

Enhancement of broadband terahertz trace absorption spectrum based on angular multiplexing of trapezoidal dielectric grating

Terahertz spectroscopy provide a fast, label-free, and non-destructive method for detecting bio-medical molecules, with wide applications in national security, pharmacy, and healthcare. However, detecting terahertz absorption spectra of trace analytes still faces significant challenges. In recent years, metamaterial parameter multiplexing technology has been used to detect the terahertz fingerprint spectra of trace substances. This paper proposes a new structure based on the angle-multiplexing enhancement principle of a trapezoidal dielectric grating to enhance the terahertz absorption spectra of thin-film analytes. By scanning the incident wave angle, a series of resonance absorption peaks can be obtained. The enhanced absorption spectrum formed by the envelope of absorption spectrum peaks increases by 141 times compared to directly measuring the absorption spectrum of the same film. This structure provides a new and effective means for terahertz trace detection using metamaterial parameter multiplexing technology.

Attapulgite-Reinforced Cellulose Hydrogels with High Conductivity and Antifreezing Property for Flexible Sensors.

Ionic conductive cellulose hydrogels are some of the most promising candidates for flexible sensors. However, it is difficult to simultaneously prepare cellulose hydrogels with high mechanical strength, good ionic conductivity, and antifreeze performance. In this work, a natural clay (attapulgite)-reinforced cellulose hydrogel was fabricated. Through a one-pot method, cellulose and attapulgite were dispersed in a concentrated ZnCl2 solution. The obtained hydrogel exhibited a dual network of hydrogen bonds and Zn2+-induced ionic interactions. Attapulgite serves as an inorganic filler that can regulate the hydrogen-bonding density among cellulose molecules and provides abundant channels for fast ion transport. By optimizing the attapulgite loading, a mechanically strong (compressive strength up to 1.10 MPa), tough (fracture energy up to 0.36 MJ m-3), highly ionic conductive (4.15 S m-1), and freezing-tolerant hydrogel was prepared. These hydrogels can be used for sensitive and stable human motion sensing, demonstrating their great potential for healthcare applications.

Synergistic antimicrobial action of chlorogenic acid and ultraviolet-A (365 nm) irradiation; mechanisms and effects on DNA integrity

Chlorogenic acid (CGA) is abundant in various plants and notably in coffee beans. This study investigated the bactericidal activity of CGA combined with ultraviolet-A light (UVA, 365 nm) (CGA + UVA) against Escherichia coli DH5α, with the aim of developing novel strategies for food preservation and healthcare. CGA + UVA treatment was superiorin reducing bacterial survival than either treatment alone. At 20 J/cm2 and pH 7, CGA (0.3%) + UVA treatment resulted in only about a 3-log reduction in bacterial survival, whereas at 15 J/cm2 and pH 3, no surviving bacteria could be detected, demostrating that the treatment was more effective at acidic pH. CGA + UVA treatment was also bactericidal in green plum juice, confirming that its low pH-dependent property could be effective in acidic food products. To elucidate the bactericidal mechanism of CGA + UVA treatment, its effects on reactive oxygen species (ROS) generation, membrane integrity, and enzyme activity were measured. ROS generated via the type-1 reaction, such as hydrogen peroxide (H2O2) and hydroxyl radicals (·OH), were mainly detected. CGA + UVA disrupted the bacterial cell membrane, causing the leakage of cellular components, particularly proteins. CGA + UVA treatment also led to deoxyribonucleic acid (DNA) degradation and reduced succinate-coenzyme Q reductase activity by approximately 72 %. Furthermore, CGA + UVA treatment decreased β-lactamase activity and plasmid transforming efficacy with maximal reductions of 68 % and 98 %, respectively, highlighting its potential for increasing antibiotic susceptibility and preventing the spread of antimicrobial resistance. The results demonstrate that CGA + UVA treatment could be used to effectively combat antibiotic-resistant bacteria and prevent the spoilage of preserved foods or food poisoning.

Development of Secure Infrastructure for Advancing Generative AI Research in Healthcare at an Academic Medical Center.

The increasing interest in leveraging generative AI models in healthcare necessitates secure infrastructure at academic medical centers. Without an all-encompassing secure system, researchers may create their own insecure microprocesses, risking the exposure of protected health information (PHI) to the public internet or its inadvertent incorporation into AI model training. To address these challenges, our institution implemented a secure pathway to the Azure OpenAI Service using our own private OpenAI instance which we fully control to facilitate high-throughput, secure LLM queries. This pathway ensures data privacy while allowing researchers to harness the capabilities of LLMs for diverse healthcare applications. Our approach supports compliant, efficient, and innovative AI research in healthcare. This paper discusses the implementation, advantages, and use cases of this secure infrastructure, underscoring the critical need for centralized, secure AI solutions in academic medical environments.

AI and Human-Centric Approach in Smart Cities Management: Case Studies from Silesian and Lesser Poland Voivodships

The presented paper examines the integration of Artificial Intelligence (AI) in the management of smart cities, focusing on the Silesian and Lesser Poland Voivodships in Poland. This research addresses a notable gap in the analysis of regional AI strategies within urban management, providing a comparative analysis of AI implementation in these two distinct regions. The Silesian Voivodship, with its emphasis on traditional industries such as manufacturing and energy, contrasts with the broader approach of the Lesser Poland Voivodship, which includes applications in life sciences and ICT. The paper explores how AI technologies enhance urban efficiency, sustainability, and livability through practical applications in traffic management, healthcare, energy efficiency, and environmental management. It highlights the importance of a human-centric approach in smart city development, emphasizing inclusivity, transparency, and ethical considerations. The paper also delves into the socio-technical dynamics of AI deployment, illustrating how these technologies can transform urban environments while ensuring that the benefits are equitably distributed and that urban developments are sustainable and resilient. By analyzing specific case studies, the authors aim to provide empirical evidence and insights that contribute to the academic and practical understanding of AI’s role in smart cities, ultimately advocating for the design of AI applications that prioritize human well-being and environmental health.

Biosynthesis and Extraction of Chlorophyll, Carotenoids, Anthocyanins, and Betalaine In Vivo and In Vitro.

As natural bioactive compounds, plant pigments play crucial roles not only in plant phenotype, growth, development, and adaptation to stress but also hold unique value in biotechnology, healthcare, and industrial applications. There is growing interest in the biosynthesis and acquisition of plant pigments. Thus, this paper explores emerging extraction methods of natural pigments and elucidates the biosynthesis pathways of four key plant pigments, chlorophylls, carotenoids, anthocyanins, and betalaine in vivo and in vitro. We comprehensively discuss the application of solvent, supercritical fluid [extraction], ultrasonic, and microwave-assisted extraction techniques, as well as introducing key enzymes, precursors, and synthetic pathways involved in pigment synthesis. δ-Aminolevulinic acid represents a pivotal initiating enzyme for chlorophyll synthesis, whereas isopentenylpyrophosphate, (IPP) and dimethylallyl pyrophosphate, (DMAPP) are closely associated with carotenoid biosynthesis. Phenylalanine and tyrosine are critical substances for anthocyanin and betalaine synthesis, respectively. Hence, crucial genes such as chlI, crtB, PGT8, CYP76AD1, and BvDODA can be employed for heterologous biosynthesis in vitro to meet the demand for increased plant pigment amount. As a pivotal determinant of plant coloration, an in-depth exploration into the high-quality acquisition of plant pigments can provide a basis for developing superior pigments and offer new insights into increasing pigment yield.

Competition between cross-linking and force-induced local conformational changes determines the structure and mechanics of labile protein networks

Folded protein hydrogels are emerging as promising new materials for medicine and healthcare applications. Folded globular proteins can be modelled as colloids which exhibit site specific cross-linking for controlled network formation. However, folded proteins have inherent mechanical stability and unfolded in response to an applied force. It is not yet understood how colloidal network theory maps onto folded protein hydrogels and whether it models the impact of protein unfolding on network properties. To address this, we study a hybrid system which contains folded proteins (patchy colloids) and unfolded proteins (biopolymers). We use a model protein, bovine serum albumin (BSA), to explore network architecture and mechanics in folded protein hydrogels. We alter both the photo-chemical cross-linking reaction rate and the mechanical properties of the protein building block, via illumination intensity and redox removal of robust intra-protein covalent bonds, respectively. This dual approach, in conjunction with rheological and structural techniques, allows us to show that while reaction rate can ‘fine-tune’ the mechanical and structural properties of protein hydrogels, it is the force-lability of the protein which has the greatest impact on network architecture and rigidity. To understand these results, we consider a colloidal model which successfully describes the behaviour of the folded protein hydrogels but cannot account for the behaviour observed in force-labile hydrogels containing unfolded protein. Alternative models are needed which combine the properties of colloids (folded proteins) and biopolymers (unfolded proteins) in cross-linked networks. This work provides important insights into the accessible design space of folded protein hydrogels without the need for complex and costly protein engineering, aiding the development of protein-based biomaterials.

Flower-like nickel oxide nanostructures: Superior ammonia gas sensing and efficient dye removal behavior under UV–visible light illumination

In this study, we fabricated the NiO nanostructures using a straightforward solvothermal technique. The NiO nanoparticles were characterized using various analytical techniques. The FESEM images clearly show the flower-like structure of NiO nanoparticles. These nanostructures were then employed as a sensor to detect the presence of highly toxic ammonia (NH3) gas. The flower-shaped nanostructures of NiO played a vital role in improving sensing performance. Moreover, the NiO sensor displayed rapid response to 100 ppm ammonia at room temperature, with responses/recovery times of 40 s/44 s. This research holds promise as a viable approach for effectively sensing and detecting NH3 gas. The NiO exhibited excellent photocatalytic activity and degradation rates of 80% and 84.75% for Methyl orange (MO) and methylene blue dye (MB) respectively under UV-visible light irradiation. The NiO nanoflower remains unaffected after recycling MB dye degradation. This effective photocatalytic performance might be due to the formation of flower-like morphology. This underscores the potential of NiO as a promising candidate for applications in environmental and healthcare safety applications.