Search Results (311)

The global swine industry suffers persistent economic losses and health challenges due to major viral pathogens such as African swine fever virus (ASFV), porcine reproductive and respiratory syndrome virus (PRRSV), classical swine fever virus (CSFV), and porcine circovirus (PCV). Traditional diagnostic methods, including virus isolation, serology, and quantitative PCR (qPCR), are limited by time, equipment requirements, and field applicability. Recent advances in CRISPR-based diagnostics, particularly those leveraging the collateral cleavage activity of Cas12a and Cas13a, have enabled rapid, sensitive, and field-deployable nucleic acid detection. This review outlines the principles of CRISPR-Cas12a/13a systems, their integration with isothermal amplification techniques, and their application in detecting major swine viruses. Cas12a-based platforms (e.g., DETECTR) and Cas13a-based systems (e.g., SHERLOCK) achieve detection limits as low as single-copy/μL within 25–60 min at 37 °C, offering high specificity and compatibility with visual readouts. Applications include ASFV, PRRSV, CSFV, PCV, foot-and-mouth disease virus (FMDV), porcine rotavirus (PoRV), and porcine parvovirus 7 (PPV7). Despite significant advances, challenges remain, notably the reliance on nucleic acid extraction and the need for fully integrated “sample-in, result-out” systems. Ongoing innovations in extraction-free methods, lyophilized reagents, and multiplex detection will strengthen the role of CRISPR diagnostics in swine disease surveillance and control. From an application standpoint, the technology offers a low-capital, field-adaptable alternative to qPCR, with its value proposition rooted in early outbreak containment and loss prevention. Its adoption pathway is expected to vary across production systems—serving as a sentinel tool in intensive settings, a leapfrogging solution in rapidly intensifying regions, and through shared-service models in resource-limited contexts. However, translation to routine use still requires overcoming standardization hurdles, regulatory validation, and workflow integration. Full article

Social security systems constitute a structurally significant component of public finance in developing economies and often generate persistent fiscal pressures through budgetary transfers. Demographic transformation, widespread informality in labor markets, and weaknesses in contribution-based financing increase the dependence of social security systems on public resources. The objective of this study is to examine whether budget transfers to the social security system affect fiscal sustainability in Turkey by analyzing their relationship with the budget deficit and the public sector borrowing requirement. The analysis employs annual data for Turkey covering the period of 1984–2024. A comprehensive time-series econometric framework is adopted, incorporating conventional and structural-break unit root tests, the ARDL bounds testing approach with error correction modeling, and the Toda–Yamamoto causality method. The empirical findings provide evidence of a stable long-run relationship among the variables. The results indicate that social security budget transfers exert a statistically significant and persistent effect on the public sector borrowing requirement, while no direct long-run effect on the headline budget deficit is detected. Causality results further confirm that fiscal pressures associated with social security financing materialize primarily through borrowing dynamics rather than short-term budgetary imbalances. By explicitly modelling social security budget transfers as an independent fiscal channel over a long historical horizon, this study contributes to the literature by offering new empirical insights into the fiscal sustainability implications of social security financing in Turkey. The findings also provide policy-relevant evidence for developing economies facing similar institutional, demographic, and fiscal challenges. Full article

Although Micro, Small and Medium Enterprises (MSMEs) are the backbone of economic activity and inclusive growth in Indonesia, and recent data from Jambi Province reveal a disconnect between robust post-pandemic recovery and meaningful poverty reduction. While regional GDP climbed from 0.99% to 6% between 2020 and 2024, poverty declined only slightly, highlighting persistent inequality. This study addresses this gap by examining, for the first time in the context of Jambi Province, how e-commerce adoption mediates the link between Micro, Small and Medium Enterprises’ (MSMEs’) quality and the achievement of economic growth, innovation, and Sustainable Development Goals (SDGs) 1 and 9. Using Structural Equation Modeling–Partial Least Squares (SEM-PLS) on data from 250 Micro, Small and Medium Enterprises (MSMEs), the findings reveal that improvements in Micro, Small and Medium Enterprises’ (MSMEs’) quality alone do not drive growth or reduce poverty unless they are accompanied by the effective adoption of e-commerce. This integrated approach, combining Micro, Small and Medium Enterprises’ (MSMEs’) capacity, digital transformation and regional Sustainable Development Goal outcomes, offers new empirical evidence and practical recommendations for emerging economies. Despite a sectoral and regional focus, the framework and results are generalizable to similar contexts. Future research should expand into additional sectors and regions, and adopt longitudinal analysis to validate and enrich these findings. Full article

The agricultural sector, a vital industry for human survival and a primary source of food and raw materials, faces increasing pressure due to global population growth and environmental strains. Productivity, efficiency, and sustainability constraints are preventing traditional farming methods from adequately meeting the growing demand for food. Precision farming has emerged as a transformative paradigm to address these issues. It integrates advanced technologies to improve decision making, optimize yield, and conserve resources. This approach leverages technologies such as wireless sensor networks, the Internet of Things (IoT), robotics, drones, artificial intelligence (AI), and cloud computing to provide effective and cost-efficient agricultural services. Smart sensor technologies are foundational to precision farming. They offer crucial information regarding soil conditions, plant growth, and environmental factors in real time. This review explores the status, challenges, and prospects of smart sensor technologies in precision farming. The integration of smart sensors with the IoT and AI has significantly transformed how agricultural data is collected, analyzed, and utilized to optimize yield, conserve resources, and enhance overall farm efficiency. The review delves into various types of smart sensors used, their applications, and emerging technologies that promise to further innovate data acquisition and decision making in agriculture. Despite progress, challenges persist. They include sensor calibration, data privacy, interoperability, and adoption barriers. To fully realize the potential of smart sensors in ensuring global food security and promoting sustainable farming, the challenges need to be addressed. Full article

Background: Hypoxic–ischemic (HI) brain injury triggers a dynamic, multi-phase response involving early microglial efferocytosis followed by extracellular matrix (ECM) deposition and scar formation. Arginase-1 (ARG1), a key enzyme in tissue repair, is implicated in both processes, yet its role in neonatal microglia remains poorly defined. We characterize ARG1-linked pathways in neonatal microglia, identifying distinct efferocytic and fibrotic phases post-HI. Methods: HI was induced in P9 mice using the Vannucci model, and brains were collected at 24 h (D1) and 5 days (D5). Spatially resolved single-cell transcriptomics (seqFISH) was performed using a targeted panel enriched for microglial, ARG1-pathway, efferocytosis, and profibrotic genes. Cell segmentation, clustering, and spatial mapping were conducted using Navigator and Seurat. Differential expression, GSEA, and enrichment analyses were used to identify time- and injury-dependent pathways. Results: Spatial transcriptomics identified 12 transcriptionally distinct cell populations with preserved neuroanatomical organization. HI caused the expansion of microglia and astrocytes and the loss of glutamatergic neurons by D5. Microglia rapidly activated regenerative and profibrotic programs—including TGF-β, PI3K–Akt, cytoskeletal remodeling, and migration—driven by early DEGs such as Cd44, Reln, TGF-β1, and Col1a2. By D5, microglia adopted a collagen-rich fibrotic state with an upregulation of Bgn, Col11a1, Anxa5, and Npy. Conclusion: Neonatal microglia transition from early efferocytic responses to later fibrotic remodeling after HI, driven by the persistent activation of PI3K–Akt, TGF-β, and Wnt/FZD4 pathways. These findings identify microglia as central regulators of neonatal scar formation and highlight therapeutic targets within ARG1-linked signaling. Full article

The construction industry is undergoing a rapid digital transformation, with Digital Twins (DTs) and Extended Reality (XR) as two emerging technologies with great potential. Despite their potential, there are several challenges regarding DT and XR use in construction projects, including implementation barriers, interoperability issues, system complexity, and a lack of standardized frameworks. This study presents a systematic literature review (SLR) of DT and XR technologies—including Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR)—in the construction industry. The study analyzes 52 peer-reviewed articles identified using the Web of Science database to explore thematic findings. Key findings highlight DT and XR applications for safety training, real-time monitoring, predictive maintenance, lifecycle management, renovation or demolition, scenario risk assessment, and education. The SLR also identifies core enabling technologies such as Building Information Modeling (BIM), Internet of Things (IoT), Big Data, and XR devices, while uncovering persistent challenges including interoperability, high implementation costs, and lack of standardization. The study highlights how integrating DTs and XR can improve construction by making it smarter, safer, and more efficient. It also suggests areas for future research to overcome current challenges and help increase the use of these technologies. The primary contribution of this study lies in deepening the understanding of DT and XR technologies by examining them through the lenses of their benefits as well as drivers for and challenges to their adoption. This enhanced understanding provides a foundation for exploring integrated DT and XR applications to advance innovation and efficiency in the construction sector. Full article

Neuromuscular diseases are biologically diverse, clinically heterogeneous, and often difficult to diagnose and treat, highlighting the need for computational tools that can help resolve overlapping phenotypes and support timely, mechanism-informed interventions. This narrative review synthesizes recent advances in artificial intelligence (AI) and machine learning applied to neuromuscular diseases across diagnosis, outcome modeling, biomarker development, and therapeutics. AI-based approaches may assist clinical and genetic diagnosis from phenotypic data; however, early phenotype-driven tools have seen limited clinician adoption due to modest accuracy, usability challenges, and poor workflow integration. Electrophysiological studies remain central to diagnosing neuromuscular diseases, and AI shows promise for accurate classification of electrophysiological signals. Predictive models for disease outcome and progression—particularly in amyotrophic lateral sclerosis—are under active investigation, but most remain at an early stage of development and are not yet ready for routine clinical use. Digital biomarkers derived from imaging, gait, voice, and wearable sensors are emerging, with MRI-based quantification of muscle fat replacement representing the most mature and widely accepted application to date. Efforts to apply AI to therapeutic discovery, including drug repurposing and optimization of gene-based therapies, are ongoing but have thus far yielded limited clinical translation. Persistent barriers to broader adoption include disease rarity, data scarcity, heterogeneous acquisition protocols, inconsistent terminology, limited external validation, insufficient model explainability, and lack of seamless integration into clinical workflows. Addressing these challenges is essential to moving AI tools from the laboratory into clinical practice. We conclude with a practical checklist of considerations intended to guide the development and adoption of AI tools in neuromuscular disease care. Full article

Spray-induced gene silencing (SIGS) represents a transformative paradigm in sustainable pest management, utilizing the exogenous application of double-stranded RNA (dsRNA) to achieve sequence-specific silencing of essential genes in arthropod pests. Unlike transgenic approaches, sprayable RNA interference (RNAi) biopesticides offer superior versatility across crop systems, flexible application timing, and a more favorable regulatory and public acceptance profile. The 2023 U.S. EPA registration of Ledprona, the first sprayable dsRNA biopesticide targeting Leptinotarsa decemlineata, marks a significant milestone toward the commercialization of non-transformative RNAi technologies. Despite the milestone, large-scale field deployment faces critical bottlenecks, primarily environmental instability, enzymatic degradation by nucleases, and variable cellular uptake across pest taxa. This review critically analyzes the mechanistic basis of spray-applied RNAi and synthesizes the recent technological breakthroughs designed to overcome physiological and environmental barriers. We highlight advanced delivery strategies, including nuclease inhibitor co-application, liposome encapsulation, and nanomaterial-based formulations that enhance persistence on plant foliage and uptake efficiency. Furthermore, we discuss how innovations in microbial fermentation have drastically reduced synthesis costs, rendering industrial-scale production economically viable. Finally, we outline the roadmap for broad adoption, addressing essential factors such as biosafety assessment, environmental fate, resistance management protocols, and the path toward cost-effective manufacturing. Full article

(1) Background: The convergence of Big Data and the Internet of Things (IoT) is transforming digital accounting from retrospective documentation into real-time operational intelligence. This systematic review examines how Industry 4.0 technologies—artificial intelligence (AI), blockchain, edge computing, and digital twins—transform accounting practices through intelligent automation, continuous compliance, and predictive decision support. (2) Methods: The study synthesizes 176 peer-reviewed sources (2015–2025) selected using explicit inclusion criteria emphasizing empirical evidence. Thematic analysis across seven domains—conceptual foundations, system evolution, financial reporting, fraud detection, audit transformation, implementation challenges, and emerging technologies—employs systematic bias-reduction mechanisms to develop evidence-based theoretical propositions. (3) Results: Key findings document fraud detection accuracy improvements from 65–75% (rule-based) to 85–92% (machine learning), audit cycle reductions of 40–60% with coverage expansion from 5–10% sampling to 100% population analysis, and reconciliation effort decreases of 70–80% through triple-entry blockchain systems. Edge computing reduces processing latency by 40–75%, enabling compliance response within hours versus 24–72 h. Four propositions are established with empirical support: IoT-enabled reporting superiority (15–25% error reduction), AI-blockchain fraud detection advantage (60–70% loss reduction), edge computing compliance responsiveness (55–75% improvement), and GDPR-blockchain adoption barriers (67% of European institutions affected). Persistent challenges include cybersecurity threats (300% incident increase, $5.9 million average breach cost), workforce deficits (70–80% insufficient training), and implementation costs ($100,000–$1,000,000). (4) Conclusions: The research contributes a four-layer technology architecture and challenge-mitigation framework bridging technical capabilities with regulatory requirements. Future research must address quantum computing applications (5–10 years), decentralized finance accounting standards (2–5 years), digital twins with 30–40% forecast improvement potential (3–7 years), and ESG analytics frameworks (1–3 years). The findings demonstrate accounting’s fundamental transformation from historical record-keeping to predictive decision support. Full article

Point-of-care ultrasound (POCUS) is an essential component of emergency medicine, enabling rapid bedside assessment across a wide spectrum of acute conditions. Its effectiveness, however, remains constrained by operator dependency, variable image quality, and time-critical decision-making. Recent advances in artificial intelligence (AI) offer opportunities to augment POCUS by supporting image acquisition, interpretation, and quantitative analysis. This narrative review synthesizes current evidence on AI-enhanced POCUS applications in emergency care, encompassing trauma, non-traumatic emergencies, integrated workflows, resource-limited settings, and education and training. Across trauma settings, AI-assisted POCUS has demonstrated promising performance for automated detection of pneumothorax, hemothorax, and free intraperitoneal fluid, supporting standardized eFAST examinations and rapid triage. In non-traumatic emergencies, AI-enabled cardiovascular, pulmonary, and abdominal applications provide automated measurements and pattern recognition that can approach expert-level performance when image quality is adequate. Integrated AI–POCUS systems and educational tools further highlight the potential to expand ultrasound access, support non-expert users, and standardize training. Nevertheless, important limitations persist, including limited generalizability, dataset bias, device heterogeneity, and uncertain impact on clinical decision-making and patient outcomes. In conclusion, AI-enhanced POCUS is transitioning from proof-of-concept toward early clinical integration in emergency medicine. While current evidence supports its role as a decision-support tool that may enhance consistency and efficiency, widespread adoption will require prospective multicentre validation, development of representative POCUS-specific datasets, vendor-agnostic solutions, and alignment with clinical, ethical, and regulatory frameworks. Full article

For more than a century, pathology has served as a cornerstone of modern medicine, relying primarily on static microscopic assessment of tissue morphology—such as H&E staining—which remains the “gold standard” for disease diagnosis. However, this conventional paradigm provides only a snapshot of disease states and often fails to capture their dynamic evolution and complex functional mechanisms. Moreover, animal models are constrained by marked interspecies differences, creating a persistent gap in translational research. To overcome these limitations, we propose the concept of New Pathophysiology, a research framework that transcends purely morphological descriptions and aims to resolve functional dynamics in real time. This approach integrates Organ-on-a-Chip (OOC) technology, multi-omics analyses, and artificial intelligence to reconstruct the entire course of disease initiation and to enable personalized medicine. In this review, we first outline the foundations and limitations of traditional pathology and animal models. We then systematically summarize more than one hundred existing OOC disease models across multiple organs—including the kidney, liver, and brain. Finally, we elaborate on how OOC technologies are reshaping the study of key pathological processes such as inflammation, metabolic dysregulation, and fibrosis by converting them into dynamic, mechanistic disease models, and we propose future perspectives in the field. This review adopts a relatively uncommon classification strategy based on pathological mechanisms (mechanism-based), rather than organ-based categorization, allowing readers to recognize shared principles underlying different diseases. Moreover, the focus of this work is not on emphasizing iteration or replacement of existing approaches, but on preserving past achievements from a historical perspective, with an emphasis on overcoming current limitations and enabling new advances. Full article

Newborn screening (NBS) for sickle cell disease (SCD) has been performed in the United States (US) for decades, significantly reducing infant morbidity and mortality. A landmark clinical trial demonstrated that early identification of SCD enabled timely and life-saving prophylactic penicillin; this led to recommendations for universal NBS across the US. Early use of hydroxyurea as a safe and effective treatment for SCD further improved clinical outcomes by preventing acute and chronic disease complications. These advances add to the importance of early diagnosis through NBS, providing an opportunity for early treatment intervention. In recent years, high-resource countries—including those in Europe, the UK, and Canada—have adopted NBS for SCD using diverse strategies. Simultaneously, pilot programs in lower-resource settings such as Africa, Brazil, and India have demonstrated local feasibility and impact through implementation efforts. An overarching equity gap for achieving global NBS for SCD is the variable access to simple, accurate, and affordable testing. Other challenges include timing of NBS testing, targeted populations, laboratory methods, and parental education with genetic counseling. Questions remain about the equitable enrollment of affected infants worldwide into comprehensive care to ensure early treatment. These challenges raise concerns about sustainability, underscore the need for long-term funding and a strategic plan, and highlight persistent inequities from the lack of global NBS standards. Full article

Frontline workers across health, emergency, and social care sectors are repeatedly exposed to distressing events and chronic stressors as part of their occupational roles. Unlike single-event trauma, these cumulative exposures accrue over time, generating persistent psychological and physiological strain. Traditional diagnostic frameworks, particularly post-traumatic stress disorder (PTSD), were not designed to capture the layered and ongoing nature of this occupational trauma. This commentary introduces the concept of Persistent Traumatic Stress Exposure (PTSE), a framework that reframes trauma among frontline workers as an exposure arising from organisational and systemic conditions rather than solely an individual disorder. It aims to reorient understanding, responsibility, and intervention from a purely clinical lens toward systems, cultures, and organisational duties of care. PTSE is presented as an integrative paradigm informed by contemporary theory and evidence on trauma, moral injury, organisational stress, and trauma-informed systems. The framework synthesises findings from health, emergency, and social care settings, illustrating how repeated exposure, ethical conflict, and institutional pressures contribute to cumulative psychological harm. PTSE highlights that psychological injury may build across shifts, careers, and lifetimes, requiring preventive, real-time, and sustained responses. The framework emphasises that effective support is dependent on both organisational readiness, the structural conditions that enable trauma-informed work, and organisational preparedness, the practical capability to enact safe, predictable, and stigma-free responses to trauma exposure. PTSE challenges prevailing stigma by framing trauma as a predictable occupational hazard rather than a personal weakness. It aligns with modern occupational health perspectives by advocating for systems that strengthen psychological safety, leadership capability and access to support. By adopting PTSE, organisations can shift from reactive treatment models toward proactive cultural and structural protection, honouring the lived realities of frontline workers and promoting long-term wellbeing and resilience. Full article

Agricultural marketing increasingly integrates Agriculture 4.0 technologies—Blockchain, AI/ML, IoT, and recommendation systems—yet systematic evaluations of computational maturity and deployment readiness remain limited. This Systematic Literature Review (SLR) examined 99 peer-reviewed studies (2019–2025) from Scopus, Web of Science, and IEEE Xplore following PRISMA protocols to assess algorithmic performance, evaluation methods, and Technology Readiness Levels (TRLs) for agricultural marketing applications. Hybrid recommendation systems dominate current research (28.3%), achieving accuracies of 80–92%, while blockchain implementations (15.2%) show fast transaction times (<2 s) but limited real-world adoption. Machine learning models using Random Forest, Gradient Boosting, and CNNs reach 85–95% predictive accuracy, and IoT systems report >95% data transmission reliability. However, 77.8% of technologies remain at validation stages (TRL ≤ 5), and only 3% demonstrate operational deployment beyond one year. The findings reveal an “efficiency paradox”: strong technical performance (75–97/100) contrasts with weak economic validation (≤20% include cost–benefit analysis). Most studies overlook temporal, geographic, and economic generalization, prioritizing computational metrics over implementation viability. This review highlights the persistent validation–deployment gap in digital agriculture, urging a shift toward multi-tier evaluation frameworks that include contextual, adoption, and impact validation under real deployment conditions. Full article

Background and Objectives: Payment parity laws require commercial health plans to pay for telehealth on the same basis as in-person care. We systematically reviewed open-access empirical studies to identify and synthesize empirical U.S. studies that explicitly evaluated state telehealth payment parity (distinct from coverage-only parity) and to summarize reported effects on telehealth utilization, modality mix, quality/adherence, equity/access, and expenditures. Methods: Following PRISMA 2020, we searched PubMed/MEDLINE, Scopus, and Web of Science for U.S. studies that explicitly modeled state payment parity or stratified results by payment parity vs. coverage-only vs. no parity. We included original quantitative or qualitative studies with a time or geographic comparator and free full-text availability. The primary outcome was telehealth utilization (share or odds of telehealth use); secondary outcomes were modality mix, quality and adherence, equity and access, and spending. Because designs were heterogeneous (interrupted time series [ITS], difference-in-differences [DiD], regression, qualitative), we used structured narrative synthesis. Results: Nine studies met inclusion criteria. In community health centers (CHCs), payment parity was associated with higher telehealth use (42% of visits in parity states vs. 29% without; Δ = +13.0 percentage points; adjusted odds ratio 1.74, 95% CI 1.49–2.03). Among patients with newly diagnosed cancer, adjusted telehealth rates were 23.3% in coverage + payment parity states vs. 19.1% in states without parity, while cross-state practice limits reduced telehealth use (14.9% vs. 17.8%). At the health-system level, parity mandates were linked to a +2.5-percentage-point telemedicine share in 2023, with mental-health (29%) and substance use disorder (SUD) care (21%) showing the highest telemedicine shares. A Medicaid coverage policy bundle increased live-video use by 6.0 points and the proportion “always able to access needed care” by 11.1 points. For hypertension, payment parity improved medication adherence, whereas early emergency department and hospital adoption studies found null associations. Direct spending evidence from open-access sources remained sparse. Conclusions: Across ambulatory settings—especially behavioral health and chronic disease management—state payment parity laws are consistently associated with modest but meaningful increases in telehealth use and some improvements in adherence and perceived access. Effects vary by specialty and are attenuated where cross-state practice limits persist, and the impact of payment parity on overall spending remains understudied. Full article