The Clinical and Psychosocial Phenotype of Fibromyalgia Syndrome in Female Patients: A Comprehensive Review of Etiology, Burden, and Management

2025-12-06T08:57:41+00:00

Background: Fibromyalgia Syndrome (FMS) is a chronic pain disorder characterized by widespread musculoskeletal pain, fatigue, and cognitive difficulties, predominantly affecting women. Given its complex etiology and significant comorbidity profile, understanding the specific clinical and psychosocial phenotype in female patients is essential for optimized management and resource allocation.

Methods: This comprehensive review synthesizes data from peer-reviewed literature (References 1-23) focusing on the demographics, core clinical symptoms, psychological distress (e.g., anxiety, depression), functional impairment, and socioeconomic burden of FMS in female cohorts. The analysis prioritizes studies utilizing standardized assessment tools (e.g., FIQ, WHOQOL) and current diagnostic criteria.

Results: Findings confirm the high prevalence of co-morbid psychological disorders, particularly anxiety and depression, which significantly correlate with higher pain severity and diminished health-related quality of life (HRQoL). Functional disability is substantial, leading to high direct and indirect healthcare costs. The female phenotype is associated with specific symptom clusters that often delay diagnosis and complicate treatment.

Conclusion: FMS in female patients represents a complex, multi-dimensional burden that extends beyond physical pain. A deeper understanding of this specific phenotype is critical to developing personalized, multidisciplinary interventions that simultaneously target central sensitization, pain amplification, and associated psychological distress. Further research is required to address current diagnostic heterogeneities.

ROBUST FRAMEWORK FOR ACCOUNTING INFORMATIZATION: INTEGRATING A CLOUD DATA INTEGRITY VERIFICATION MODEL

2025-12-09T05:50:27+00:00

Purpose: This study addresses the critical challenge of data integrity in cloud-based accounting informatization (AI) systems. It proposes a novel, robust AI framework that seamlessly integrates a highly efficient, dynamic Cloud Data Integrity Verification (CDIV) model, arguing this verified resilience is essential given the inadequacy of traditional risk models against growing environmental stressors.

Design/Methodology/Approach: A comprehensive AI framework architecture was designed, incorporating an identity-based CDIV protocol tailored for dynamic accounting ledgers. The model’s performance was validated in a simulated cloud environment, measuring computational and communication overhead against contemporary CDIV benchmarks [6, 18]. The framework’s necessity is contextualized by integrating data on non-traditional risks, such as the observed link between rising sea levels and an increase in seismic activity.

Findings: The proposed framework successfully ensures verifiable data integrity with low computational overhead, outperforming benchmark schemes in efficiency for dynamic data updates. Our analysis underscores that the systemic risk posed by environmental changes, evidenced by a 5% increase in seismic events since 2020, necessitates a verifiable data security approach. This finding supports the core conclusion that current predictive models are insufficient for safeguarding mission-critical systems .

Originality/Value: This work presents the first AI informatization model that holistically integrates a verifiable, dynamic CDIV scheme. Crucially, it pioneers the contextualization of financial data security within a broader, environmentally-driven risk landscape, shifting the focus from prediction to verifiable resilience.

International Journal of Multidisciplinary Research and Studies

The Clinical and Psychosocial Phenotype of Fibromyalgia Syndrome in Female Patients: A Comprehensive Review of Etiology, Burden, and Management

ROBUST FRAMEWORK FOR ACCOUNTING INFORMATIZATION: INTEGRATING A CLOUD DATA INTEGRITY VERIFICATION MODEL