Background: The endoplasmic reticulum (ER) is a critical organelle responsible for the folding, modification, and transport of proteins. Perturbations to this process lead to an accumulation of misfolded proteins, a condition known as ER stress. The unfolded protein response (UPR) is a complex signaling network that is activated to restore protein homeostasis. While the UPR is an essential adaptive mechanism for cellular survival, its chronic activation is implicated in the progression of various pathologies.

Main Body: This review synthesizes current knowledge on the ER stress response, detailing its molecular mechanisms and its dual role in maintaining cellular health and contributing to disease states. Initially, the UPR orchestrates a series of adaptive measures, including the downregulation of global protein synthesis and the upregulation of chaperone proteins to alleviate the protein folding burden. However, sustained or unresolved ER stress triggers pro-apoptotic pathways, leading to cellular dysfunction and death. This chronic state is a key factor in the pathogenesis of a range of diseases, including neurodegenerative disorders, metabolic syndromes, and cancer. The interplay between ER stress, inflammation, and cellular homeostasis is also explored, highlighting the intricate regulatory networks that govern cellular fate.

Conclusion: A comprehensive understanding of the ER stress response is crucial for developing therapeutic strategies. By targeting specific components of the UPR, it may be possible to mitigate disease progression and restore cellular function. This article concludes by identifying key areas for future research and emphasizing the potential of UPR-modulating agents as novel therapeutic interventions.

The aim of this study was to analyze risk factors contributing to the development of gestational diabetes in a group of pregnant women. It is crucial for healthcare professionals to assess the prevalence and consequences of gestational diabetes for its early detection, prevention, and treatment. The obtained results indicate that overweight, obesity, previous gestational diabetes, and high blood pressure before pregnancy may be associated with the risk of developing gestational diabetes, although the average BMI values did not differ significantly between the groups. Early introduction of a vegetarian diet rich in dietary fiber to pregnant women helps reduce inflammation, oxidative stress, endothelial dysfunction, and insulin resistance.

Observational studies have shown that levels are inversely associated with disease activity, but population-based studies remain inconsistent

Methods: Information was collected from studies measuring visual analog scale (VAS) pain, disease activity score 28 (DAS28), serum vitamin D levels, tender joint count (TJC), swollen joint count (SJC), erythrocyte sedimentation rate (ESR), C-reactive protein (CRP).

Results: Six studies (n = 120) were included in the meta-analysis. Vitamin D supplementation resulted in significant improvements in DAS28 (95% weighted mean difference (p < 0.001), ESR 95% (p = 0.04), and painful joint count 95% (p = 0.03), but not in other outcomes. Subgroup analysis showed that VAS and serum vitamin D were improved across subgroups. TJC and serum vitamin D levels were improved in subgroups with treatment duration ≤ 12 days, while VAS and DAS28 in the subgroup with treatment duration > 12 days differed from those in the control group. At a vitamin D dose ≤50,000 IU, only serum vitamin D and TJC improved, while at a vitamin D dose > 50,000 IU, VAS and DAS28 improved.

Background: Contemporary supply chains face unprecedented levels of volatility driven by demand uncertainty, supplier disruptions, geopolitical shifts, and sudden shocks such as pandemics. Theoretical and empirical work across operations research, logistics, and management science has explored facets of resilience, inventory policy under disruption, forecasting during crises, and strategic decisions such as reshoring. However, fragmented treatments limit a comprehensive understanding of integrative strategies that span forecasting, inventory design, network configuration, and organizational memory.

Purpose: This article synthesizes seminal and recent contributions to provide an integrated, publication-ready theoretical framework for resilient supply chain design. The aim is to unify modelling approaches for inventory under disruption, strategic facility location and reshoring decisions, forecasting and planning under pandemic-like conditions, and learning mechanisms that enhance resilience. The framework emphasizes interactions among stochastic supply interruptions, demand dynamism, strategic relocation, and data-driven detection of hidden supply chain linkages.

Methods: Drawing strictly from the provided literature, the study undertakes an exhaustive conceptual synthesis, weaving robust optimisation and stochastic inventory modelling (including EOQ extensions and partial backordering), facility location robustness, forecasting under pandemic dynamics, graph-based detection of hidden links, and cognitive mapping of domino effects. The methods section elaborates purely text-based methodological constructs: model families, solution philosophies, evaluation metrics, and comparative analytical approaches, all explained without equations or visual constructs.

Results: The synthesis produces a multi-layered resilience design architecture: (1) demand-adaptive inventory policies that combine safety-stock rethinking with partial backordering accommodation and supplier diversification; (2) robust facility-location strategies integrating disruption probabilities and demand uncertainty; (3) forecasting and planning processes that adapt growth-rate estimation under rapid regime shifts; (4) machine-learning-driven network inference to reveal hidden dependencies; and (5) organizational memory and experiential learning as a core resilience capability. Each element is elaborated with theoretical implications, practical trade-offs, and counterfactual analyses based on the literature.

Conclusions: Integrative resilience requires coordinated strategies across forecasting, inventory rules, network design, and organizational learning. Firms should treat reshoring or onshoring as strategic levers to be weighed against diversification and flexibility investments rather than as singular solutions. The framework highlights open research avenues: empirical calibration of integrated models, psychological and organizational barriers to memory adoption, and scalable machine learning tools for link prediction in large, fragmented supply networks.

Non-GAAP financial reporting has evolved from a marginal disclosure practice into a central element of modern corporate communication, shaping how investors, analysts, and regulators interpret firm performance. While non-GAAP measures are often justified as tools for improving the informativeness and relevance of earnings, their increasing prevalence has also generated persistent controversy regarding managerial opportunism, behavioral bias, and comparability across firms and jurisdictions. Drawing strictly on foundational and contemporary accounting literature, this study develops an integrated conceptual analysis of non-GAAP reporting through three interrelated lenses: managerial behavior, corporate governance, and global accounting harmonization. The article synthesizes insights from early capital markets research on earnings informativeness, behavioral studies on executive influence and narcissism, governance-based debates on voluntary disclosure credibility, and international accounting research on comparability under differing GAAP and IFRS regimes. By weaving these streams together, the study demonstrates that non-GAAP reporting cannot be understood solely as a technical adjustment to GAAP earnings but must be interpreted as a behavioral and institutional phenomenon shaped by executive incentives, board oversight, analyst mediation, and regulatory architecture. The analysis further incorporates emerging conceptual perspectives on multi-GAAP reconciliation and technological facilitation, positioning these developments within long-standing debates on relevance versus reliability. The study contributes a comprehensive theoretical framework that explains why non-GAAP reporting persists despite regulatory scrutiny, how managerial traits influence disclosure style, and why comparability remains an unresolved challenge even in harmonized accounting environments. The findings offer implications for standard setters, regulators, and market participants seeking to balance flexibility with credibility in financial reporting.

The contemporary data economy is characterized by unprecedented volumes of heterogeneous data generated across cloud platforms, enterprise applications, Internet of Things infrastructures, and increasingly, edge-based computing environments. This transformation has fundamentally altered the architectural and governance requirements of data warehousing systems, which must now operate not merely as repositories of structured records but as complex, distributed, and security-critical knowledge infrastructures. Traditional data warehouse paradigms, which emerged in the era of centralized on-premises databases and batch-oriented extract-transform-load processes, are increasingly inadequate for supporting real-time analytics, large-scale unstructured data ingestion, regulatory compliance, and continuous security assurance. Cloud-native platforms such as Amazon Redshift have therefore become central to modern analytical ecosystems, providing elastic scalability, high-performance query processing, and deep integration with data lakes and streaming frameworks, as extensively elaborated in Worlikar, Patel, and Challa’s authoritative treatment of Redshift-based architectures (Worlikar, Patel, & Challa, 2025).

Legacy software systems continue to underpin mission-critical operations across public and private sectors, yet they simultaneously constrain organizational agility, scalability, and innovation. Over multiple decades, scholars and practitioners have examined modernization as both a technical and socio-organizational phenomenon, emphasizing that legacy systems persist not because of technical excellence but because of their embeddedness in institutional processes, accumulated business logic, and historical success (Bennett, 1995). In recent years, modernization has become inseparable from cloud computing, microservices, DevOps, and digital transformation agendas, reframing legacy renewal as a continuous evolutionary process rather than a one-time intervention (Leon and Horita, 2021). Within this broader modernization discourse, the evolution of Microsoft’s web development ecosystem from ASP.NET to ASP.NET Core represents a particularly illustrative case of platform-level modernization that intersects tooling innovation, architectural paradigms, and migration strategy design (Valiveti, 2025).

This research article presents an extensive, theory-driven, and literature-grounded examination of legacy software modernization, with particular emphasis on architectural transformation, cloud migration, and framework evolution. Drawing exclusively on established scholarly references, the study synthesizes insights from software evolution theory, modernization frameworks, empirical case studies, and architectural migration research to construct an integrated analytical narrative. The transition from monolithic architectures to service-oriented and microservices-based systems is explored not as a deterministic trajectory but as a contested and context-sensitive process, shaped by organizational capability, risk tolerance, and regulatory constraints (Knoche and Hasselbring, 2018; Mendonca et al., 2021). The article critically evaluates claims regarding the promised benefits of modernization, including cost reduction, maintainability, and business alignment, juxtaposing them against empirical findings that reveal mixed outcomes and unintended consequences (Khadka et al., 2015).

Methodologically, the study adopts a qualitative, interpretive research design grounded in systematic literature analysis. Rather than proposing new empirical data, it reinterprets existing studies through a comparative and theoretical lens, allowing for deep conceptual elaboration and cross-study synthesis. Particular attention is devoted to the ASP.NET to ASP.NET Core evolution as a paradigmatic example of framework-level modernization, illustrating how tooling, runtime design, cross-platform support, and cloud-native readiness reshape modernization strategies at the application level (Valiveti, 2025). By situating this evolution within broader modernization taxonomies and software evolution laws, the article demonstrates how platform transitions both enable and constrain legacy renewal efforts.

The findings highlight that modernization success is contingent upon aligning architectural decisions with organizational objectives, legacy system characteristics, and long-term evolutionary capacity. Modernization is shown to be neither inherently beneficial nor universally applicable; rather, it is a negotiated process involving trade-offs between stability and change, innovation and continuity. The discussion advances a nuanced understanding of modernization as an ongoing socio-technical transformation, offering theoretical implications for software engineering research and practical insights for organizations navigating complex legacy landscapes.

The convergence of microservice architectures and ecological modeling represents a transformative approach in both software engineering and environmental sciences. Recent advances in .NET Core microservices have enabled zero-downtime migration strategies, significantly improving system reliability and continuity ( .NET Core Microservices for Zero-Downtime AuthHub Migrations, 2025). Simultaneously, ecological research has increasingly leveraged high-resolution remote sensing and advanced statistical modeling to understand the spread of invasive species and the dynamics of tropical montane cloud forests (Bradley & Mustard, 2006; Ah-Peng et al., 2017). This research examines the integration of these paradigms, proposing a framework wherein distributed software services support large-scale ecological simulations with minimal operational interruptions. Methodologically, the study employs a comprehensive literature synthesis, critical evaluation of microservice deployment strategies, and ecological model adaptation to cloud-based platforms. Findings indicate that the deployment of modular service architectures can enhance the scalability of ecological simulations, allow real-time data integration, and provide robust frameworks for handling uncertainty in species distribution models (Britton-Simmons & Abbott, 2008; Gotsch et al., 2015). Moreover, the interoperability between microservices and ecological databases facilitates advanced predictive modeling of plant invasions, epiphytic community dynamics, and forest structure gradients (Burton et al., 2005; Bohlman et al., 1995). The discussion addresses the theoretical underpinnings of software modularity in ecological contexts, examines the historical evolution of both fields, and critically evaluates the limitations of current integration strategies. The study further identifies key areas for future research, emphasizing multi-scale ecological modeling, automated service orchestration, and resilience in computational frameworks. This work contributes to an emerging interdisciplinary dialogue, highlighting the potential of computational engineering innovations to enhance ecological understanding while informing sustainable management strategies.