Search Results (195)

This study offers a comprehensive overview of the scientific landscape surrounding computer-aided drug design (CADD) for triplex-forming oligonucleotides (TFOs) within antisense and antigene therapeutic strategies. A dual-method approach was used, combining bibliometric mapping of 6154 Scopus-indexed articles (1980–2023) to identify publication trends and intellectual networks, with a PRISMA 2020-guided systematic review of 62 experimental studies (2015–2024) from Scopus and Web of Science, after removing duplicates using AteneaSIRES. Results show the strong dominance and clinical maturity of antisense strategies, supported by 18 FDA/EMA/MHLW-approved drugs, whereas antigene approaches remain technically limited and underdeveloped. Antigene research has focused on triplex stability modeling and biophysical feasibility but faces challenges with poor biochemical stability, limited in vivo validation, and outdated methods. Meanwhile, antisense design benefits advanced CADD pipelines, including molecular dynamics and docking modeling. Based on these insights, we propose a practical, narrative roadmap as a methodological guide: integrating proven antisense design practices and providing actionable strategies to enhance antigene research, ultimately increasing the translational potential of therapeutic TFOs with solid mechanistic and translational support. Full article

Lyophilization (freeze-drying) has become a cornerstone pharmaceutical technology for stabilizing biopharmaceuticals, overcoming the inherent instability of biologics, vaccines, and complex drug formulations in aqueous environments. The appropriate literature for this review was identified through a structured search of several databases (such as PubMed, Scopus) covering publications from late 1990s till date, with inclusion limited to peer-reviewed studies on lyophilization processes, formulation development, and process analytical technologies. This succinct review examines both fundamental principles and cutting-edge advancements in lyophilization technology, with particular emphasis on Quality by Design (QbD) frameworks for optimizing formulation development and manufacturing processes. The work systematically analyzes the critical three-stage lyophilization cycle—freezing, primary drying, and secondary drying—while detailing how key parameters (shelf temperature, chamber pressure, annealing) influence critical quality attributes (CQAs) including cake morphology, residual moisture content, and reconstitution behavior. Special attention is given to formulation strategies employing synthetic surfactants, cryoprotectants, and stabilizers for complex delivery systems such as liposomes, nanoparticles, and biologics. The review highlights transformative technological innovations, including artificial intelligence (AI)-driven cycle optimization, digital twin simulations, and automated visual inspection systems, which are revolutionizing process control and quality assurance. Practical case studies demonstrate successful applications across diverse therapeutic categories, from small molecules to monoclonal antibodies and vaccines, showcasing improved stability profiles and manufacturing efficiency. Finally, the discussion addresses current regulatory expectations (FDA/ICH) and compliance considerations, particularly regarding cGMP implementation and the evolving landscape of AI/ML (machine learning) validation in pharmaceutical manufacturing. By integrating QbD-driven process design with AI-enabled modeling, process analytical technology (PAT) implementation, and regulatory alignment, this review provides both a strategic roadmap and practical insights for advancing lyophilized drug product development to meet contemporary challenges in biopharmaceutical stabilization and global distribution. Despite several publications addressing individual aspects of lyophilization, there is currently no comprehensive synthesis that integrates formulation science, QbD principles, and emerging digital technologies such as AI/ML and digital twins within a unified framework for process optimization. Future work should integrate advanced technologies, AI/ML standardization, and global access initiatives within a QbD framework to enable next-generation lyophilized products with improved stability and patient focus. Full article

Natural disasters have various adverse effects on human lives, making it challenging for authorities to manage post-disaster situations with limited resources. Due to the extreme extent of the damage, the huge amount of resources needed to restore life to normality makes such a situation challenging. For this purpose, different methodologies have been proposed to effectively handle these types of situations. All these methodologies consider different aspects of the post-earthquake context, taking into account core parameters such as the time and cost required for reconstruction, as well as the people directly affected by the earthquake. In this paper, we conduct a Systematic Literature Review (SLR) of various state-of-the-art techniques proposed for different phases of post-earthquake situations, specifically for reconstruction planning with sustainability considerations. All these proposed solutions are differentiated on the basis of input data, parameters, and type of solutions (data sciences, civil engineering, socio-economics, and modelling). The time range chosen to filter out relevant studies is between 2000 and 2025. Eventually, we reviewed 55 related articles out of 47,539 analysed from seven different digital libraries. The findings of this SLR reveal that optimization and simulation-based approaches dominate the current research landscape, with a growing trend toward data-driven and AI-assisted reconstruction planning. However, only a few studies focus on integrating socio-economic, environmental, and physical infrastructure aspects, which represents a major research gap. These findings provide insights that can guide future researchers in designing more comprehensive frameworks to improve post-earthquake reconstruction in a sustainable manner by prioritising economic, social, and environmental infrastructures, as well as facilities for affected individuals, thereby utilising available resources more effectively. Full article

Artificial intelligence (AI) is reforming pharmaceutical sciences by renovating traditional drug formulation and dosage calculation approaches. This review provides a comprehensive overview of how AI technologies, such as machine learning (ML), deep learning (DL), and natural language processing (NLP), are currently being used in pharmaceutical calculations to improve accuracy, efficiency, and personalization. We have explored the role of AI in predicting drug properties, excipient optimization, and formulation design, as well as its applications in pharmacokinetic/pharmacodynamic (PK/PD) modeling, real-time dose adjustment, and precision medicine. Despite significant progress, data quality, interpretability, regulatory acceptance, and ethical considerations persist. Therefore, this review examines the impact of AI on automated decision-making, quality control, and regulatory compliance in pharmaceutical formulation development. The article also highlights the emerging trends in pharmaceuticals, including AI-assisted 3D printing, integration with wearable technologies, and emphasizing AI’s transformative potential in reforming the landscape of pharmaceuticals and personalized therapeutics. Full article

The increasing adoption of smart home technologies introduces significant cybersecurity and forensic challenges. This necessitates a shift from traditional reactive digital forensics to a more proactive approach to safeguarding these environments. This research is situated within Saudi Arabia’s ambitious digital transformation, as outlined in Vision 2030, which promotes the development of smart cities and homes. The unique technological landscape and national initiatives in Saudi Arabia require tailored cybersecurity solutions. Existing models are often too theoretical, generic, or overly specialized, lacking practical validation and comprehensive integration for modern IoT ecosystems. There is a pronounced lack of a scalable, validated framework designed explicitly for proactive digital forensic readiness in smart homes. The study employs a mixed-methodology approach, combining a PRISMA systematic literature review with Design Science Research (DSR) to develop and validate the Smart Proactive Forensic Metamodel for Smart Homes (SPFMSH). The developed SPFMSH was tested against realistic cyberattack scenarios, including unauthorized access and intrusion, data exfiltration, and device hijacking by ransomware. In each scenario, the model demonstrated its capability to proactively detect threats, automatically preserve forensic evidence, and provide structured investigative timelines. This validation proved its effectiveness in transforming security incidents into forensically sound investigations within the Saudi smart home context. SPFMSH delivers a practical, holistic framework that addresses the limitations of previous models, moving beyond theory to offer an implementable solution. Its development is a significant step towards enhancing national cybersecurity resilience and supporting the secure adoption of smart home technologies in alignment with Saudi Vision 2030. Full article

Background: Periodontitis is a multifactorial inflammatory disease with a complex interplay between microbial, environmental, and host-related factors. Among host factors, genetic susceptibility plays a significant role in influencing both disease onset and progression. Over the past two decades, a wide range of genetic tests, ranging from single-nucleotide polymorphism (SNP) analysis to genome-wide association studies (GWAS), have been explored to assess individual risk profiles and potential treatment responses. However, despite initial enthusiasm, the clinical integration of genetic testing in periodontics remains limited. This narrative review aims to critically examine the current landscape of genetic testing in periodontitis, including commercially available tests, their scientific validity, and their clinical utility. Methods: Most relevant studies which were published in recent years were identified by using the major scientific search engines, including PubMed, Scopus, and Web of Science. Articles discussing genetic susceptibility, key gene polymorphisms, and emerging technologies were included in this narrative review. Results: Polymorphisms in genes coding for IL-1, IL-6, TNF-α, and in others involved in immune modulation and bone metabolism, are associated with periodontitis. Nevertheless, there are limitations related to heterogeneity in study design, population stratification, and gene–environment interactions. Moreover, emerging technologies, including polygenic risk scoring and machine learning approaches, may enhance the predictive value of genetic tools in periodontology. Conclusions: A deeper understanding of genetic susceptibility could pave the way for precision dentistry and personalized periodontal care, but significant hurdles remain before genetic testing can become a routine component of periodontal diagnostics. Full article

We introduce an IoT architecture that addresses critical mobility challenges in Morocco’s urban transportation ecosystem. Using Design Science Research methodology, we developed a complete system integrating smart infrastructure, edge computing, and accessible interfaces to enhance service quality while prioritizing inclusivity for vulnerable populations. Our five-layer architecture targets institutional capacity limitations, inadequate service levels, and accessibility barriers present in Morocco’s transportation landscape. An evaluation of our proposed solution shows how technology integration can advance eco-friendly transport goals while accommodating limited resources in developing contexts. The research contributes novel insights into IoT architectural models for inclusive design alongside practical recommendations for transportation authorities seeking to leverage digital transformation for more equitable urban mobility. Full article

Single-molecule detection (SMD) has reformed analytical science by enabling the direct observation of individual molecular events, thus overcoming the limitations of ensemble-averaged measurements. This review presents a comprehensive analysis of the principles, devices, and emerging materials that have shaped the current landscape of SMD. We explore a wide range of sensing mechanisms, including surface plasmon resonance, mechanochemical transduction, transistor-based sensing, optical microfiber platforms, fluorescence-based techniques, Raman scattering, and recognition tunneling, which offer distinct advantages in terms of label-free operation, ultrasensitivity, and real-time responsiveness. Each technique is critically examined through representative case studies, revealing how innovations in device architecture and signal amplification strategies have collectively pushed the detection limits into the femtomolar to attomolar range. Beyond the sensing principles, this review highlights the transformative role of advanced nanomaterials such as graphene, carbon nanotubes, quantum dots, MnO₂ nanosheets, upconversion nanocrystals, and magnetic nanoparticles. These materials enable new transduction pathways and augment the signal strength, specificity, and integration into compact and wearable biosensing platforms. We also detail the multifaceted applications of SMD across biomedical diagnostics, environmental monitoring, food safety, neuroscience, materials science, and quantum technologies, underscoring its relevance to global health, safety, and sustainability. Despite significant progress, the field faces several critical challenges, including signal reproducibility, biocompatibility, fabrication scalability, and data interpretation complexity. To address these barriers, we propose future research directions involving multimodal transduction, AI-assisted signal analytics, surface passivation techniques, and modular system design for field-deployable diagnostics. By providing a cross-disciplinary synthesis of device physics, materials science, and real-world applications, this review offers a comprehensive roadmap for the next generation of SMD technologies, poised to impact both fundamental research and translational healthcare. Full article

Due to its unique benefits over conventional subtractive manufacturing, additive manufacturing methods continue to attract interest in both academia and industry. One such method is called Cold Spray Additive Manufacturing (CSAM), a solid-state coating deposition technology to manufacture repair metallic components using a gas jet and powder particles. CSAM offers low heat input, stable phases, suitability for heat-sensitive substrates, and high deposition rates. However, persistent challenges include porosity control, geometric accuracy near edges and concavities, anisotropy, and cost sensitivities linked to gas selection and nozzle wear. Interdisciplinary research across manufacturing science, materials characterisation, robotics, control, artificial intelligence (AI), and machine learning (ML) is deployed to overcome these issues. ML supports quality prediction, inverse parameter design, in situ monitoring, and surrogate models that couple process physics with data. To demonstrate the impact of AI and ML on CSAM, this study presents a systematic literature review to identify, evaluate, and analyse published studies in this domain. The most relevant studies in the literature are analysed using keyword co-occurrence and clustering. Four themes were identified: design for CSAM, material analytics, real-time monitoring and defect analytics, and deposition and AI-enabled optimisation. Based on this synthesis, core challenges are identified as small and varied datasets, transfer and identifiability limits, and fragmented sensing. Main opportunities are outlined as physics-based surrogates, active learning, uncertainty-aware inversion, and cloud-edge control for reliable and adaptable ML use in CSAM. By systematically mapping the current landscape, this work provides a critical roadmap for researchers to target the most significant challenges and opportunities in applying AI/ML to industrialise CSAM. Full article

In response to escalating global concerns about waste generation throughout the product life cycle, the Circular Economy (CE) has emerged as a central alternative to the dominant linear economic model. The integration of CE principles into supply chain management is manifested in Circular Supply Chain Management (CSCM), offering a novel perspective on supply chain sustainability. Despite the growing research interest in developing CSCM to enhance supply chain sustainability, assessment approaches of this concept are notably absent in the literature. This study addresses this gap by focusing on the assessment and performance measurement of circular practices in the context of supply chains. At first, the research presents a bibliometric analysis to delve into the performance and science mapping of CSCM assessment, providing a comprehensive view of the scientific landscape. Subsequently, a content analysis is then used to identify current assessment approaches, focusing on frameworks, methodologies, barriers, enablers, and CE strategies. The study proposes a conceptual model based on the SCOR framework, including core categories such as enablers (business model, technology, collaboration, design) and results (material, water, energy flows) represented by the Rs strategies. This model contributes to bridging theoretical gaps and guiding practitioners and policymakers in the design, evaluation, and implementation of circular supply chains. Full article

Densely integrated microarchitectures spanning three-dimensional integrated circuits (3D-ICs), chiplet-based designs, and system-in-package (SiP) assemblies make heat a first-order security concern rather than a mere reliability issue. This review consolidates the landscape of thermal side-channel attacks (TSCAs) on densely integrated microarchitectures: we systematize observation vectors and threat models, clarify core concepts and assumptions, compare the most credible evidence from the past decade, and distill the main classes of defenses across the hardware–software stack. We also explain why hardening against thermal leakage is integral to cyber–physical system (CPS) security and outline the most promising research directions for the field. The strategic relevance of this agenda is reflected in current policy and funding momentum, including initiatives by the United States Department of Homeland Security and the Cybersecurity and Infrastructure Security Agency (DHS/CISA) on operational technology (OT) security, programs by the National Science Foundation (NSF) on CPS, and Canada’s Regional Artificial Intelligence Initiative and Cyber-Physical Resilience Program (RAII, >CAD 35 million), to bridge advanced microelectronics with next-generation cybersecurity. This survey offers a clear, high-level map of the problem space and a focused baseline for future work. Full article

The fashion industry, despite its global economic importance, is a major contributor to environmental degradation and social inequality. In response, sustainable fashion has emerged as a growing movement advocating ethical, ecological, and socially responsible practices. This study presents a comprehensive bibliometric analysis of 1134 peer-reviewed journal articles on sustainable fashion indexed in Scopus from 1986 to 2025. Results show an exponential rise in research output after 2015, with interdisciplinary contributions from social sciences, business, environmental science, and engineering. By applying performance analysis and science mapping techniques, the study identifies five major research themes: “Consumer Behavior,” “Design Ethics,” “Circular Economy,” “Innovation,” and “Digital Media.” The geographic distribution reveals strong outputs from both developed and emerging economies. This study provides an integrative overview of the intellectual landscape of sustainable fashion and serves as a roadmap for researchers, policymakers, and practitioners who are interested in the development of sustainable fashion. Full article

Background: The incorporation of biosensors into orthopedic implants, particularly tibial components in total knee arthroplasty (TKA), marks a new era in personalized joint replacement. These smart systems aim to provide real-time physiological and mechanical data, enabling dynamic postoperative monitoring and enhanced surgical precision. Objective: This narrative review synthesizes the current landscape of electrochemical biosensor-embedded tibial implants in TKA, exploring technical mechanisms, clinical applications, challenges, and future directions for translation into clinical practice. Methods: A comprehensive literature review was conducted across PubMed and Google Scholar. Articles were thematically categorized into technology design, integration strategies, preclinical and clinical evidence, regulatory frameworks, ethical considerations, and strategic recommendations. Findings were synthesized narratively and organized to support forward-looking system design. Results: Smart tibial implants have demonstrated feasibility in both bench and early clinical settings. Key advances include pressure-sensing intraoperative tools, inertial measurement units for remote gait tracking, and chemical biosensors for infection surveillance. However, the field remains limited by biological encapsulation, signal degradation, regulatory uncertainty, and data privacy challenges. Interdisciplinary design, standardized testing, translational funding, and ethical oversight are essential to scaling these innovations. Conclusions: Biosensor-enabled tibial components represent a promising convergence of orthopedics, electronics, and data science. By addressing the technological, biological, regulatory, and ethical gaps outlined herein, this field can transition from prototype to widespread clinical reality—offering new precision in arthroplasty care. Full article

Fire has had a profound impact on Australia’s landscapes and biodiversity since the late Tertiary. Indigenous (Aboriginal) people have lived in Australia for at least 65,000 years and fire is an integral part of their culture and cosmology. In 2015, an Indigenous cultural burn was undertaken by Banbai rangers at Wattleridge Indigenous Protected Area, New England Tablelands, NSW. We compared the impact of this burn on the composition, cover, abundance, and species richness of dry sclerophyll vegetation and fuel hazard, with a hazard reduction burn at nearby Warra National Park, using a Before-After-Control-Impact experimental design. Our study found that the low-severity cultural burn and moderate-severity hazard reduction burn reduced fuel loads but did not have a significant impact on the composition of the vegetation overall or the herb layer. The hazard reduction burn had a significant impact on shrub and juvenile tree (woody species) cover, while the abundance of woody species was significantly affected by both fires, with a mass germination of ‘seeder’ species, particularly after the cultural burn. The long unburnt fire regime at Wattleridge may have made the vegetation more responsive to fire than the more frequently burnt vegetation at Warra, through accumulation of seed in the seed bank, so that the patchy cultural burn had a greater impact on woody species abundance. In terms of ecological and bushfire management outcomes, this study provides evidence to support claims that Indigenous cultural burning decreases fuel loads, stimulates regeneration of shrubs and trees, and manages at a local, place-based scale. We recommend cultural burning as a key management tool across Indigenous Protected Areas and other land tenures, with its implementation monitored and adaptively managed through two-way science, to foster fire regimes that are both culturally and ecologically beneficial. This is a vital element of our resilience in the Pyrocene and a significant step toward decolonizing science and land management. Full article

The growing frequency of cybersecurity incidents, coupled with the increasing significance of blockchain technology in today’s digital landscape, highlights the urgent need for enriched, hands-on educational programs within Computer Science and Engineering curricula. While core blockchain curricula typically cover consensus protocols, smart contracts, and cryptographic foundations, more advanced topics like InterPlanetary File System (IPFS) and oracles pose teaching challenges due to their complexity and reliance on broader system knowledge. Despite this, their critical role in decentralized applications (dApps) justifies their inclusion at least through practical use cases. The integration of the IPFS protocol with Distributed Ledger Technologies (DLTs) can enable pure decentralized storage subsystems for dApps, avoiding single points of failure and ensuring data integrity and security. At the same time, as an external source of information, oracles are required to ensure data correctness while managing IPFS data. Despite the potential use of such components in real use cases, the current literature lacks detailed oracle implementations designed to interact with the IPFS protocol. To tackle such an issue, this article presents two open-source use cases that integrate smart contracts, an oracle and an IPFS-based storage subsystem that will allow future professors, students, researchers and developers to learn and experiment with advanced dApps and DLTs. Full article