Search Results (416)

Cancer continues to pose a major global burden because of the high incidence and mortality, underscoring the urgent need for innovative and highly sensitive diagnostic technologies. Conducting polymer–nanomaterial (CP–NM) hybrid biosensors have become promising platforms for cancer biomarker detection, integrating the redox-active and biocompatible nature of conducting polymers such as polyaniline (PANI), polypyrrole (PPy), and poly(3,4-ethylenedioxythiophene) (PEDOT) with the high surface area and charge transport properties of nanomaterials, including metallic nanoparticles, metal oxides, carbon-based nanostructures, and two-dimensional materials. The synergistic interfaces in these hybrids enable efficient electron transfer, signal amplification, and stable biomolecular immobilization, facilitating ultrasensitive and multiplexed detection of proteins, nucleic acids, and metabolites associated with tumor progression. This review highlights recent advances in CP–NM hybrid biosensors for cancer diagnostics, focusing on material design strategies, sensing mechanisms, and representative applications across electrochemical, optical, and mechanical modalities. Finally, key challenges and future perspectives are discussed, emphasizing the potential of CP–NM hybrid platforms to drive next-generation approaches for early cancer detection, therapeutic monitoring, and personalized healthcare. Full article

The rapid digitalisation of healthcare has accelerated the adoption of telemedicine, Electronic Health Records (EHRs), and the Internet of Medical Things (IoMT), transforming healthcare delivery into a highly interconnected and patient-centric ecosystem. In response to growing concerns about data security, privacy, and interoperability, blockchain technology has emerged as a promising solution for its decentralization, immutability, auditability, and secure access control. However, many existing blockchain infrastructures rely on classical cryptographic primitives, including RSA- or elliptic-curve-based public-key mechanisms and cryptographic hash functions such as SHA-256, whose relevant security properties may be affected by sufficiently powerful quantum attacks. This review investigates the convergence of blockchain and quantum technologies to address emerging security threats in e-health systems. A structured literature review was conducted in accordance with the PRISMA 2020 guidelines using the IEEE Xplore, PubMed, ACM Digital Library, Google Scholar, and Crossref databases, covering studies published between January 2018 and June 2025. Following a systematic screening and eligibility-verification process, 57 relevant studies were selected and analyzed. The review evaluates quantum-resilient security mechanisms, including Quantum Key Distribution (QKD), Quantum Random Number Generation (QRNG), and NIST-standardized Post-Quantum Cryptography (PQC) algorithms specified in FIPS 203, FIPS 204, and FIPS 205. Based on the identified research gaps in the state of the art, this study also proposes a novel four-layer Quantum-Blockchain Security Architecture (QBSA) designed for secure healthcare environments. The analysis further reveals significant challenges associated with lightweight PQC deployment for IoMT devices, interoperability standardization, quantum hardware limitations, and regulatory compliance in cross-institutional healthcare systems. The findings highlight the necessity of integrating quantum-resilient cryptographic frameworks with blockchain infrastructures to support the development of secure, scalable, and patient-centric next-generation e-health ecosystems. Full article

Traditional assisted reproductive technologies (ART) remain constrained by subjective, descriptive diagnostics and empirical, one-size-fits-all preservation strategies that expose gametes to nonphysiological stressors, risking disruptions to cellular homeostasis and epigenetic programming. This review explores the technological convergence of artificial intelligence (AI), reproductive organ-on-chip bioengineering, multiomics, and translational cryobiology and proposes an integrated, systems-level paradigm for next-generation precision reproductive medicine. By evaluating the clinical readiness, mechanistic insights, and translational trajectories of these emerging platforms, we show how AI architectures transition fertility diagnostics from descriptive metrics to predictive computational phenotyping by integrating high-dimensional imaging, multiomics, and sperm functional datasets. Concurrently, microphysiological platforms—such as testis-, ovary-, and endometrium-on-a-chip systems—recapitulate complex multicellular architecture and endocrine dynamics. When embedded with miniaturized biosensors and machine learning loops, these “smart” closed-loop microfluidic devices enable real-time biological monitoring and adaptive culture regulation. Furthermore, integrating AI analytics into cryobiology optimizes nonlinear thermodynamic variables, shifting the field from basic postthaw morphologic survival toward safeguarding macromolecular fidelity, mitochondrial competence, and long-term epigenetic safety across lifespans and generations. Ultimately, this computational–bioengineering roadmap transitions reproductive healthcare from a reactive discipline into a predictive, personalized, and adaptive framework. Overcoming persistent challenges in biological complexity, data interoperability, and multicenter clinical validation will lead to the establishment of safe, scalable, and ethically governed healthcare infrastructures capable of protecting developmental integrity. Full article

Artificial intelligence (AI)-based digital twins (DTs) are emerging as a groundbreaking paradigm in rehabilitation and physiotherapy, enabling the creation of dynamic virtual representations of patients for continuous monitoring, prognostic assessment and personalised therapeutic interventions. This article presents a global, interdisciplinary review of AI-based DT technologies in rehabilitation settings utilising the Internet of Medical Things (IoMT), with particular emphasis on the integration of wearable and implantable sensor systems in next-generation wireless healthcare applications. The article analyses how multimodal wearable sensors, implantable devices and smart wireless communication networks can support the acquisition of real-time biomechanical and physiological data for adaptive rehabilitation. By combining perspectives from biomedical engineering, physiotherapy, computational intelligence and wireless healthcare systems, this article highlights the emerging opportunities and challenges associated with the creation of scalable digital twin ecosystems for precision rehabilitation. The proposed vision contributes to the development of smart, connected and personalized rehabilitation infrastructures, in line with future paradigms of healthcare and wireless communication. Full article

Gender bias in generative artificial intelligence (GenAI) is both a technical and a social phenomenon: it emerges from historically patterned data, model design, and interactions in institutional use, and it cannot be understood by engineering or by social critique alone. This critical integrative review develops a more differentiated account. It connects feminist epistemology, Science and Technology Studies, critical AI scholarship, natural language processing, and governance research to examine five levels: historical knowledge production, technical representation and generation, benchmark evaluation, institutional deployment, and accountability. The review explains tokenization, next-token prediction, transformers, and the transition from static embeddings to contemporary language models before assessing evidence from standard fairness tests—coreference tests (WinoBias), sentence-pair tests (CrowS-Pairs), and stereotype tests (StereoSet)—as well as open-ended generation, multilingual testing, and text-to-image systems. It shows that measured bias varies with task, prompt, language, model version, and metric. What a test records and what that record means are therefore distinct questions: measurements are situated and depend on the instrument, and their interpretation draws on theory rather than following from the numbers alone. Evidence from employment, education, healthcare, and translation further indicates that the relevant unit of analysis is the model-in-context—the model together with the institution and workflow in which its outputs are used. Technical mitigation can reduce specific harms but does not repair unequal criteria, incomplete evidence bases, or weak institutional accountability. The review proposes a multilevel governance approach combining technical evaluation, documentation, professional and community oversight, appeals, remedies, and public-interest knowledge infrastructure. Its distinctive contribution is to connect three observations usually kept apart—how bias is measured, how generative systems concentrate epistemic authority, and how statistical learning is oriented toward past data—and to show why democratic and feminist governance can keep alternative technological futures open. Full article

Artificial intelligence has emerged as a promising paradigm for advancing the screening, diagnosis support, and monitoring of autism spectrum disorder (ASD) through scalable and data-driven clinical augmentation. Recent advances in machine learning, multimodal sensing, and digital phenotyping have enabled AI systems to analyze behavioral, neurophysiological, speech, and clinical data to identify early markers of ASD. Despite encouraging experimental results, major barriers to clinical translation remain, including limited generalizability, fragmented datasets, insufficient evaluation rigor, lack of semantic interoperability, and unresolved ethical and regulatory concerns. This systematic review provides a comprehensive technical review of AI for ASD, covering data modalities, feature engineering, learning paradigms, evaluation protocols, deployment architectures, and knowledge representation frameworks. Particular emphasis is placed on system-level and translational considerations, including cloud–edge infrastructures, explainable clinical decision-support systems, privacy-aware deployment, and ontology-driven reasoning. Beyond summarizing existing work, this paper critically analyzes challenges related to reproducibility, dataset bias, interpretability, and clinical integration and derives design requirements for next-generation trustworthy ASD AI systems. We argue that meaningful clinical impact will require the integration of multimodal learning, semantic knowledge representation, explainable reasoning, and human-in-the-loop decision processes to support safe, interpretable, and clinically deployable AI systems in pediatric healthcare environments. Full article

Pectin, a natural biopolymer, is a cost-effective, biocompatible, non-toxic, abundant, and flexible material, making it suitable for recording high-quality bioelectric signals from the dynamic surface of the human body. In this work, pectin-based flexible bioelectrodes were developed for the non-invasive monitoring of biopotentials. The bioelectrodes are composed of pectin, polyaniline emeraldine salt (PANI-ES), glycerol, and polydimethylsiloxane (PDMS) and therefore abbreviated as PPGP. The PPGP electrodes demonstrated a bulk electrical conductivity of (7.54 ± 0.81) × 10⁻³ S/cm, a very low impedance of 34 Ω, and a high charge storage capacity of 4.63 ± 2.70 mC/cm². The surface morphology of the PPGP electrode plays a crucial role in enhancing biopotential signal detection by improving adhesion to skin contours. PPGP electrodes have been successfully used for high-fidelity electromyographic (EMG) bioelectric signal measurements. The developed PPGP bioelectrodes have the potential to advance next-generation human–machine interface (HMI) technologies and wearable healthcare systems, including prosthetic control, rehabilitation monitoring, and assistive communication devices. Full article

The Internet of Medical Things (IoMT) is transforming healthcare through continuous patient monitoring, telemedicine, cloud–edge services, and Healthcare 5.0. However, the rapid growth of interconnected medical devices has expanded the healthcare cyberattack surface, making intelligent intrusion detection essential for protecting sensitive medical data and ensuring resilient clinical operations. Existing reviews examine specific aspects of AI-powered intrusion detection but rarely provide a deployment-oriented synthesis linking technical performance with operational and clinical requirements. This review critically examines Artificial Intelligence (AI)-powered Intrusion Detection Systems (IDSs) for IoMT across six analytical dimensions: detection performance, explainability, privacy preservation, computational efficiency, benchmarking practices, and cross-dataset generalization. This structured narrative review adopted the PRISMA 2020 framework to ensure transparent record identification, screening, and reporting, with evidence synthesized qualitatively rather than through quantitative meta-analysis. A total of 5127 records published between 2021 and 2026 were screened, resulting in 24 primary studies supported by 115 complementary studies. The findings show that machine learning, deep learning, hybrid AI, Explainable Artificial Intelligence (XAI), Federated Learning (FL), blockchain-assisted security, and edge intelligence have significantly advanced IoMT intrusion detection. However, despite benchmark accuracies often exceeding 95%, deployment remains constrained by dataset dependency, weak cross-dataset generalization, computational overhead, limited explainability, fragmented benchmarking, and insufficient operational validation. This review identifies deployment readiness, rather than predictive accuracy alone, as the principal challenge for next-generation healthcare cybersecurity and provides a practical framework for developing trustworthy, interoperable, privacy-preserving, and deployment-ready IoMT cybersecurity architectures supported by standardized evaluation protocols. Full article

The proliferation of Edge-AI in personalized healthcare has driven significant demand for energy-efficient Systems-on-Chip (SoCs) capable of executing high-accuracy, real-time disease detection. Conventional accelerator architectures struggle to simultaneously accommodate disparate AI models. Specifically, memory-intensive Convolutional Neural Networks (CNNs) for high-fidelity feature extraction and event-driven Spiking Neural Networks (SNNs) for ultra-low-power, brain-inspired computation. To address this bottleneck, this paper presents a comprehensive survey of Reconfigurable Hybrid Neural Networks (RHNNs), an emerging paradigm that dynamically merges the strengths of CNNs and SNNs to meet the stringent resource constraints of biomedical edge devices. We establish a comprehensive taxonomy of existing RHNN architectures, categorizing them by hardware interconnection topologies, dataflow orchestration strategies, and internal structural adaptation mechanisms. Furthermore, we examine the integration of these hybrid accelerators within the open-source RISC-V processor ecosystem, evaluating how custom instruction set extensions optimize control efficiency and minimize energy overhead. The survey also analyzes commonly used datasets based on three major biomedical signal modalities, including electroencephalography (EEG), electrocardiography (ECG), and electromyography (EMG), in the context of processing systems for hardware accelerators. Finally, we highlight the open research challenges and outline future research directions to guide the development of next-generation biomedical intelligent systems. Full article

Hemodialysis (HD) remains the most widely adopted renal replacement therapy for patients with end-stage kidney disease; however, its delivery entails a substantial environmental burden due to high water and energy consumption and extensive reliance on single-use synthetic polymeric membranes. As the global prevalence of chronic kidney disease increases, the ecological footprint of dialysis systems has become a critical challenge for sustainable healthcare. Conventional HD membranes, based on petroleum-derived polymers, provide controlled permeability but are inherently non-renewable, non-biodegradable, and susceptible to fouling and bio-incompatibility, underscoring the need for alternative, more sustainable materials. Chitosan has emerged as a promising biopolymer owing to its biodegradability, intrinsic antimicrobial activity, chemical versatility, and favorable hemocompatibility. This review presents a comprehensive analysis of chitosan-based hybrid membranes for HD, with emphasis on sustainability-driven material innovation. The structural chemistry and functional properties of chitosan are discussed in relation to molecular weight, degree of deacetylation, and supramolecular organization, followed by a comparative assessment of chitosan derived from crustacean, insect, fungal, and cephalopod sources. Attention is given to fungal chitosan as a naturally deacetylated, high-purity, and reproducible biomaterial aligned with circular bioeconomy principles. Eco-innovative extraction and purification strategies, including enzymatic and low-energy processes, are critically examined alongside membrane fabrication approaches such as polymer blending, electrospinning of hollow fibers, and functionalization strategies aimed at improving hemocompatibility, antimicrobial performance, and fouling resistance. Key challenges related to membrane reuse, scale-up, regulatory compliance, and clinical translation are also addressed. Overall, this review highlights fungal-derived chitosan as a sustainable platform for next-generation HD membranes. Full article

Biosensors have become important analytical platforms that enable rapid, selective, sensitive and portable analysis for early disease diagnosis, biomarker monitoring and point-of-care diagnostic applications. Their analytical performance depends on the coordinated function of molecular recognition elements, surface chemistry, transduction mechanisms and signal-processing strategies. Nevertheless, the analysis of real biological samples remains challenging because of low target concentrations, matrix effects, interfering species, signal noise, sensor drift and device-to-device variability. Therefore, artificial intelligence and machine learning are gaining increasing importance as data-driven tools for signal preprocessing, calibration, feature extraction, pattern recognition, quantitative prediction and diagnostic decision support. These approaches are particularly valuable for interpreting complex datasets generated by electrochemical, optical, wearable and microfluidic biosensors. This review presents an overview of healthcare-oriented biosensor systems beginning with molecular recognition principles, bioreceptor design, and transduction technologies, and extending to applications in clinical diagnosis and health monitoring. It also examines the roles of supervised, unsupervised and deep learning approaches in biosensor data analysis, while critically discussing model validation, generalizability, interpretability and clinical translation. By linking molecular-level recognition with computational signal interpretation, this review highlights the advantages and limitations of artificial intelligence-integrated biosensors for next-generation point-of-care diagnostics, continuous health monitoring, and personalized healthcare applications. Full article

The rapid emergence and dissemination of antimicrobial resistance (AMR) among bacterial pathogens, particularly the ESKAPE group (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp.), represents one of the most pressing global public health challenges, contributing to increased morbidity, mortality, and healthcare costs. The limited development of new antibiotic classes over the past two decades has intensified the search for structurally novel antimicrobial agents and adjuvants capable of overcoming multidrug resistance. Natural products continue to serve as an invaluable source of anti-infective drug leads owing to their remarkable structural diversity and broad spectrum of biological activities. Mangrove ecosystems, located at the interface of terrestrial and marine environments, harbor highly diverse microbial communities, including fungi that have evolved under extreme environmental conditions and produce a wide range of unique secondary metabolites. Beyond their ecological significance, mangrove-associated fungi have emerged as prolific producers of bioactive compounds with promising antibacterial activity against multidrug-resistant pathogens. This review comprehensively summarizes recent advances (2018–2026) in the discovery of antibacterial metabolites from mangrove-associated fungi active against ESKAPE pathogens, which discusses their structural diversity, reported antimicrobial activities, and emerging strategies for accelerating natural product discovery, including genome mining, metabolomics, OSMAC, adaptive laboratory evolution, and artificial intelligence-assisted approaches. A total of 139 chemically distinct metabolites (Compounds 1–139) isolated from mangrove-associated fungi are critically reviewed, highlighting their potential as promising leads for the development of next-generation antimicrobial agents against ESKAPE pathogens. Full article

Background: Although cervical cancer is preventable, Latinas are twice as likely to receive a late diagnosis compared with non-Hispanic Whites (NHW). We currently know very little about the experiences of cervical cancer among middle- to older-age Latinas in the U.S. This study aimed to document Latina cervical cancer survivors’ journeys with an emphasis on improving the cervical cancer system of care to address inequities among a rapidly aging Latina population. Methods: We interviewed participants aged ≥ 40 years with a diagnosis of cervical cancer of three months or greater who identify as Latina. Recruitment was multi-pronged, including electronic healthcare records search, community tabling events, and snowball sampling. We conducted a qualitative study using the Database of Individual Patient Experiences (DIPEx) methodology and identified themes using grounded theory constructivist analysis. Results: A total of 20 Latinas (43 to 85 years old) participated, including 16 Spanish-language interviews. We identified three themes: formative experiences driving screening and prevention behaviors, provider communication that was suboptimal throughout the cancer continuum, and survivorship impacts including isolation and stigma with implications for the next generation. Conclusions: A central takeaway was the pervasiveness of Latinas’ feelings of shame, lack of communication, and isolation. Healthcare systems must improve culturally responsive communication, survivorship support, and screening equity for aging Latina populations. Abuelas, tias, and/or primas (grandmothers, aunts, and cousins) could play a critical role in raising awareness and increasing timely and early screening among the wider Latina population. Full article

Nosocomial bronchopneumonia is a severe lung infection that develops more than 48 h after hospital admission and is frequently caused by antibiotic-resistant bacteria. It is often identified postmortem in forensic practice, particularly in patients with severe traumatic injuries requiring prolonged hospitalization and immobilization. Diagnosis is typically based on macroscopic findings and histopathological examination of lung tissue. This study aimed to evaluate the diagnostic value of postmortem microbiological testing by comparison with antemortem microbiological data. Ten patients with a clinical diagnosis of nosocomial bronchopneumonia were selected from forensic cases. During autopsy, tracheal swabs and lung tissue samples were collected and subjected to culture-based and molecular analyses. The results were compared with those obtained from antemortem microbiological investigations. Pathogens characteristic of nosocomial infections were identified; however, concordance with in-hospital microbiological data was highest when using next-generation sequencing (NGS) metagenomic analysis. Tracheal swab culture appears to have limited reliability for postmortem identification of bacterial agents in healthcare-associated bronchopneumonia. In contrast, metagenomic next-generation sequencing (mNGS) of lung tissue obtained at autopsy showed the highest concordance with antemortem microbiological findings and may provide valuable complementary diagnostic information, particularly in polymicrobial infections. Full article

The rapid proliferation of the Internet of Healthcare Things (IoHT) has significantly increased the reliance on Wireless Sensor Networks (WSNs) for real-time healthcare monitoring, data collection, and communication. However, ensuring secure, scalable, and efficient data transmission in resource-constrained IoHT environments remains a major challenge, as conventional cryptographic solutions often impose excessive computational and memory overhead. To address this issue, this study investigates the suitability of BitCube, a lightweight cryptosystem inspired by the structural transformations of a Rubik’s Cube, for securing IoHT applications. Designed to provide a balanced combination of confidentiality, integrity, authentication, and operational efficiency, BitCube aims to meet the stringent security and performance requirements of healthcare-oriented sensor networks while maintaining low resource consumption. To systematically evaluate its effectiveness, a Multi-Criteria Decision-Making (MCDM) framework integrating the Hesitant Fuzzy (HF) Analytical Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is employed. The proposed framework enables the incorporation of uncertainty and expert hesitation in the evaluation process, thereby providing a more realistic assessment of cryptographic alternatives. BitCube is compared with seven existing cryptosystems across multiple criteria, including execution efficiency, memory utilisation, scalability, authentication, and confidentiality. The results indicate that BitCube consistently achieves the highest overall ranking among the evaluated schemes, demonstrating superior suitability for resource-constrained IoHT environments, while the Lightweight Advanced Encryption Standard (AES) emerges as the second-best alternative. Furthermore, validation through comparison with other MCDM techniques reveals only minor variations in the ranking outcomes, confirming the robustness and reliability of the proposed evaluation framework. These findings highlight the potential of BitCube as a promising lightweight cryptographic solution for enhancing security in next-generation IoHT systems. Full article