Abstract: Background: Chronic kidney disease (CKD) is characterized by accelerated aging and decline in physical function. Klotho, an anti-aging protein predominantly expressed in the kidney, plays a crucial role in mineral metabolism and longevity. Exercise has been proposed as a non-pharmacological strategy to enhance Klotho expression; however, clinical evidence in hemodialysis patients remains limited. Objective: This study aimed to explore the association between exercise and plasma Klotho levels using a combined case study and cross-sectional design. Methods: This study included: (1) A prospective case study evaluating the effects of high-intensity interval training (HIIT) in a hemodialysis patient. (2) A cross-sectional analysis comparing plasma Klotho levels between hemodialysis patients (n=24) and healthy controls (n=18) and assessing their association with habitual physical activity. Plasma Klotho levels were measured using ELISA. Statistical analyses included the Mann–Whitney U test and Spearman’s correlation coefficient. Results: In the case study, improvements in muscle strength and exercise tolerance were observed following HIIT, allowing the patient to resume daily occupational activities. In the cross-sectional analysis, plasma Klotho levels were significantly lower in hemodialysis patients than in healthy controls (p=0.0001). A moderate positive correlation was observed between exercise habits and plasma Klotho levels in hemodialysis patients (r=0.52, p=0.02), whereas no significant association was found in healthy individuals. Conclusion: These findings suggest that exercise therapy may exert potential anti-aging implications in hemodialysis patients through modulation of Klotho expression. This study provides translational evidence linking clinical rehabilitation and molecular aging pathways.

Abstract: Purpose: This review article investigates the significance of AI-driven e-banking services through a holistic conceptual model considering ethical trust, fraud prevention, and consumer purchase decision-making in the context of Bangladesh's online banking services. Methodology: A narrative literature review was conducted, synthesizing peer-reviewed articles published between 2024-2025 from major databases including Scopus, Web of Science, and IEEE Xplore. The UTAUT2 framework provides the theoretical foundation. Findings: AI adoption in Bangladeshi e-banking enhances customer experience, risk management, process automation, financial inclusion, and regulatory compliance. Ethical trust comprising transparency, fairness, data privacy, reliability, and digital inclusion mediates the relationship between AI implementation and consumer decision-making. Fraud prevention acts as a critical enabler, reducing perceived risk through real-time monitoring and secure authentication. Originality: This study provides the first integrated analysis of AI's tripartite role in Bangladeshi e-banking, extending UTAUT2 by incorporating ethical trust and fraud prevention as mediating mechanisms. For policymakers at Bangladesh Bank, the findings offer evidence-based guidance for developing AI governance frameworks. For commercial banks, the study illuminates specific drivers of ethical trust and user acceptance.

Abstract: Cardiovascular complications are the main cause of early mortality in elderly patients after hip fracture surgery. Echocardiography, although suggested by guidelines to improve risk stratification, is frequently omitted for the risk to delay surgery. Aim of the study was to evaluate whether preoperative echocardiographic in patients with hip fracture effectively delays surgery and which echocardiographic abnormalities are associated with in-hospital mortality. The study included hip fracture patients aged > 70 years admitted in the period January 1, 2019, to December 31, 2024, to the Hip fracture Unit of a teaching tertiary hospital. Echocardiography was indicated according to clinical criteria (detection of heart murmur, pathological electrocardiographic changes, known heart disease and the presence of > 2 coronary risk factors). In the study entered 2272 patients, 1593 had indication for preoperative echocardiography that was performed in 1502. Mean age was significantly higher in ECHO than in NO-ECHO group (85.4 ± 8 vs. 80.5 ± 11 years, p < 0.0001). ECHO group patients had more frequently at least two comorbidities. In-hospital mortality was 7.3% in ECHO patients compared to 2.3% in NO-ECHO patients. At multivariate analysis showed decreased left ventricular ejection fraction and pulmonary hypertension other than age, anemia, reduced functional capacity expressed as lost BADL and cancer were independent predictors of in-hospital mortality. Echocardiography identifies a population at a high risk of in-hospital mortality, three times higher compared to the group of NO ECHO patients. A reduced left ventricular ejection fraction and an increase in pulmonary pressure are independent predictors of in hospital mortality.

Abstract: APD is widely adopted in the management of end-stage renal disease (ESRD) and offers flexi-bility and improved quality of life, but bacterial infections, particularly peritonitis, are still a major constraint, which frequently results in hospitalization, catheter failure, and hemodialysis. Early diagnosis is important but difficult because of the non-specific clinical manifestations and delays related to the traditional diagnostic techniques like culture-based analysis. “To overcome these restrictions, this paper suggests a new explainable machine learning model to early identify bacterial infections in APD patients based on multimodal data streams, such as clinical, lab, and time-series dialysis data, to identify both fixed and dynamic infection onset patterns”. The framework uses a hybrid characteristic of feature engineering, which is a combination of statistical selection techniques and clinically relevant indicators to improve predictive performance, and Supervised learning models of high accuracy like the Random Forest, SVM, and Gradient Boosting are applied. One of the contributions of this work is the incorporation of explainable artificial intelligence through SHAP that leads to a clear interpretation of model predictions and the determination of key risk factors that will affect the development of the infection and thus enhance clinical trust and usability. The experimental findings indicate that the given approach greatly enhances the accuracy of early detection as compared to the conventional ones, allowing timely intervention, minimizing complications, and improving the overall outcomes of the treatment, which underscores its potential as a scalable and clinically applicable decision support system to manage APD.

Abstract: The development of new chronobiotics, substances capable of selectively modulating the parameters of circadian rhythms, is hampered by the fragmented nature and limited volume of available experimental data.In the present study, a comprehensive evaluation of the applicability of the SMILES-Transformer architecture to the classification of circadian rhythm modulators was performed using the specialised ChronobioticsDB resource, and the first systematic virtual screening of the SAVI (Synthetically Accessible Virtual Inventory) library of synthetically accessible compounds for chronobiotic activity was carried out. Rigorous protocols were applied for model training and validation: Data-Efficient Modeling (DEM) assessment with 20 repeats, repeated scaffold validation (5 × 5), and a comparative analysis of training strategies (feature-based vs. end-to-end fine-tuning). The influence of three variants of circadian-effect labelling (raw, aggregated, and expert-curated) and three loss functions (BCE, Focal Loss, and Asymmetric Loss) on the quality of multi-label classification was investigated. The results demonstrate that systematic hyperparameter optimisation in end-to-end mode provides the best predictive performance (ROC-AUC 0.666 for the effect_coarse task), whereas standard fine-tuning without optimisation leads to overfitting (ROC-AUC 0.470). Scaffold validation confirmed the ability of the model to generalise to structurally novel compounds (ROC-AUC 0.587). Expert aggregation of labels improved the recognition of rare classes (F1-macro 0.254 versus 0.148 for the raw labelling). Based on the trained models, a consensus virtual screening of the SAVI library was performed using four independent classifiers (classf, effect_coarse, target, mechanism). From more than five million compounds, 10,000 of the most promising candidates were selected, among which 34 super-candidates (consensus score > 0.9) and 435 strong candidates (> 0.8) were identified. Analysis of the predicted targets revealed dominance of the CLOCK-BMAL1 complex (60.49%), while among effects the circadian phase shift prevailed (37%). All identified candidates are synthetically accessible and are recommended for prioritised experimental verification.

Abstract: Caveolae are specialized plasma membrane microdomains whose structure and signaling functions are highly sensitive to nutritional status. They operate as dynamic, metabolically responsive units whose stability depends on membrane cholesterol, sphingolipids, fatty acid composition, and insulin regulated metabolic cues. Dietary lipids, glucose availability, amino acid balance, and micronutrient dependent antioxidant defenses all influence caveolar assembly, membrane curvature, and caveolin expression. Saturated fats, hyperglycemia, and oxidative stress destabilize caveolae by altering lipid packing, promoting caveolin mislocalization, and increasing lipid and protein oxidation. In contrast, unsaturated fatty acids, antioxidant vitamins, polyphenols, and adequate zinc and selenium support membrane fluidity, redox balance, and caveolar integrity. Dietary patterns exert integrated effects: Western style diets impair caveolin 1 expression and endothelial structure, whereas Mediterranean and plant based diets enhance lipid handling and insulin sensitivity, conditions favorable for maintaining functional caveolae. Caveolae also act as nutrient sensing platforms that coordinate insulin receptor signaling, nitric oxide production, and lipid uptake, amplifying the systemic impact of nutritional perturbations. Disruption of caveolae contributes to metabolic disease by impairing adipocyte lipid storage, endothelial nitric oxide signaling, and skeletal muscle glucose uptake. Understanding how nutrition modulates caveolae provides a mechanistic link between diet and metabolic health and highlights membrane targeted nutritional strategies as potential therapeutic approaches.

Abstract: Auto rickshaw drivers face significant occupational health risks due to prolonged sedentary behaviour, poor ergonomics, and exposure to environmental pollutants, yet systematic longitudinal assessments of their health deterioration remain scarce. We conducted a cross-sectional study involving 102 auto rickshaw drivers in Agartala, India, to evaluate longitudinal trends in mapping Eco-health inequities in the urban informal sector. This study conducted a cross-sectional survey of 102 auto-rickshaw service provider in the urban informal sector of Agartala, to assess and mapping of health inequalities. This study was involving body mass index (BMI), vital capacity and scoliosis prevalence. Participants/Samples were selected via/through incidental convenience sampling and data were collected through/following structured interviews, anthropometric measurements, spirometry, and spinal curvature assessments using a baseline inclinometer. The results revealed a concerning trend of increasing BMI with driving tenure, rising from 25.1±3.03 for drivers with 0–9 years of experience to 29.36±2.94 for those with ≥20 years, indicating a high prevalence of overweight and obesity. Moreover, vital capacity declined from 3.3±0.49 litres in novice drivers to 3.2±0.61 litres in veterans, suggesting a decline in respiratory function over time. Scoliosis was prevalent in 91% of participants, with 74% showing severe curvature (≥5°), and lateral deviations were predominantly left-sided (55.72% cervical, 70% thoracic, 64.29% lumbar), likely due to asymmetric driving postures. These findings highlight the cumulative health deterioration associated with prolonged occupational exposure, emphasising the urgent need for ergonomic interventions and lifestyle modifications. The study also provides novel longitudinal insights into the health challenges faced by auto rickshaw drivers, laying the foundation for targeted public health strategies to mitigate occupational hazards and improve their overall well-being. The study also provides novel longitudinal insights into the health challenges faced by auto rickshaw drivers. Findings suggested the inclusive foundation for targeted public health strategies to mitigate occupational health hazards and improve their overall well-being.

Abstract: The most widely used bankruptcy predictor, Altman’s Z-Score, assigns a positive coefficient to asset turnover: faster firms are rated safer. Under crisis conditions, that assumption reverses. We introduce the Solvency Margin (SM), a diagnostic calculable from standard financial statements that measures, in dollars, how far an organization is from the threshold where operations become impossible. Unlike static liquidity ratios, the SM yields a concrete speed limit: the maximum operating velocity at which an organization can survive a defined shock. We validate the SM against pre-crisis financial data across three crises in two domains. In the automotive sector, SM computed from FY2019 filings showed directional predictive power among ten major automakers in both the 2021 semiconductor shortage (ρ = 0.50, p = 0.14) and the 2020 COVID-19 pandemic (ρ = 0.53, p = 0.12; ρ = 0.70, p = 0.036 excluding one governance-driven outlier). In the 2023 U.S. banking crisis, SM augmented with a Deposit Stability Factor predicted crisis outcomes among eighteen regional banks (Spearman ρ = 0.62, p = 0.006), correctly ranking three of four failed institutions in the bottom three positions. Monte Carlo simulation (450,000+ runs) confirms threshold behavior across a wide range of conditions. We present a five-step calculation method and a three-lever decision framework for practitioners.

Abstract: The Universal Declaration on the Human Genome and Human Rights gave genomics an enduring human-rights grammar built around dignity, equality, privacy, and the symbolic idea that the human genome is the heritage of humanity [1]. That grammar remains indispensable, but it is no longer sufficient. Contemporary genomic practices are not confined to laboratory science or bedside counseling: they unfold within data-intensive, computational, and commercially mediated infrastructures that classify persons, govern access to care, and redistribute risk across families, communities, and generations. This article asks a sharper question than the usual privacy-versus-innovation framing: what is the normative object of genomic rights under conditions of predictive biology? The article argues that genomic rights should be interpreted not merely as personality rights protecting individuals from misuse, but as governance rights aimed at shaping how genomic prediction, circulation, ownership, and benefit-sharing are organized. The argument proceeds in four steps. First, it reconstructs the normative architecture of the UNESCO framework and its connections with broader human-rights law, including privacy, equality, and the right to enjoy the benefits of scientific progress [1-6]. Second, it shows why mainstream approaches centered on consent, confidentiality, and anti-discrimination are necessary but analytically insufficient in the face of algorithmic profiling, cross-sector data drift, and unequal access to genomic benefit [7-10]. Third, it proposes four analytic concepts—algorithmic genomic biopower, conditional genomic sovereignty, anticipatory dignity, and multilevel genomic justice—as a vocabulary for contemporary governance. Fourth, it tests that framework against six boundary cases that reveal where conventional bioethics becomes descriptively weak or normatively thin [11-24]. The article concludes that the most important contemporary question is no longer whether genomics can be reconciled with human rights in principle, but who governs predictive biological futures, through which institutions, and for whose benefit. A rights-based response adequate to that problem must move from downstream protection toward upstream governance, from exclusively individual consent toward relational and collective accountability, and from formal access to innovation toward justice in the distribution of genomic risk and benefit.

Abstract: Cancer is increasingly recognized as a metabolic disease influenced by nutritional factors, with multi-omics technologies and artificial intelligence (AI), particularly machine learning (ML), enabling integrative analyses of diet, metabolism, and tumor biology interactions. This study aimed to synthesize evidence on these approaches for understanding the nutrition–metabolism–cancer axis and assess their translational potential in oncology, especially in low-resource settings. A PRISMA-compliant systematic review and meta-analysis searched PubMed, EMBASE, and Cochrane databases from 2018 to 2025, including studies on human cancers using ≥2 omics layers integrated via AI/ML and addressing nutritional/metabolic exposures. Random-effects pooling evaluated area under the curve (AUC), odds ratios (OR), and clinical endpoints, with subgroup analyses and quality assessments via QUADAS-2, ROBINS-I, TRIPOD, and PRISMA-AI. From 4812 records, 42 studies were included, yielding a pooled AUC of 0.88 (95% CI: 0.86–0.91) and OR of 2.4 (95% CI: 1.2–3.5), demonstrating encouraging but early-stage exploratory evidence of predictive performance. Cancer-specific signatures emerged in colorectal, breast, pancreatic, liver, and hematologic malignancies. A conceptual translational framework was proposed, integrating nutrition, omics, AI/ML, and oncology to illustrate a potential implementation pathway for developing countries like Saudi Arabia. These findings represent preliminary, hypothesis-generating evidence; the proposed framework requires prospective validation before clinical deployment, particularly in resource-limited settings.

Abstract: The essence of free trade zones lies in addressing development challenges through institutional opening-up and innovation-driven growth. Sustainable development constitutes the fundamental goal of free trade zone construction, opening-up and innovation serve as the core driving forces for their development, and a sound business environment acts as a critical guarantee for their efficient operation. Therefore, based on the panel data of 22 free trade zones in China from 2013 to 2022, this paper adopts Principal Component Analysis (PCA) and Analytic Hierarchy Process (AHP) to conduct a comprehensive evaluation of their sustainable development levels from six dimensions: environmental optimization, economic development, opening-up, radiation-driven capacity, business environment, and scientific and technological innovation. The results indicate that, first, the overall comprehensive scores of free trade zones in sustainable development show an upward trend with obvious regional divergence in growth rates. Coastal free trade zones maintain robust growth momentum, inland ones achieve steady progress, and border free trade zones witness modest growth. Second, the comprehensive scores of the 22 free trade zones in 2022 present a gradient distribution, reflecting prominent regional development imbalance. On this basis, targeted policy recommendations are put forward in this paper.

Abstract: Renal cell carcinoma (RCC) remains a biologically heterogeneous disease with variable clinical outcomes, underscoring the need for robust biomarkers to guide risk stratification and therapeutic decision-making. Despite advances in immune checkpoint inhibitors and targeted therapies, clinically validated biomarkers are lacking, particularly in the perioperative setting. Kidney injury molecule-1 (KIM-1; also known as HAVCR1 or TIM-1) has emerged as a promising candidate with strong biological and clinical rationale. KIM-1 is a transmembrane glycoprotein minimally expressed in normal kidney tissue but markedly upregulated in injured and dedifferentiated proximal tubular epithelial cells, the cell of origin for clear cell RCC. Its extracellular domain is shed into circulation and urine, enabling non-invasive quantification. Beyond its role as a marker of renal injury, KIM-1 is implicated in immune modulation, chronic inflammation, and tumor biology, supporting its role as a dynamic indicator of tumor burden and disease aggressiveness. This review presents the current evidence supporting KIM-1 as a circulating biomarker and therapeutic target in RCC and discusses emerging strategies to address disease heterogeneity through biomarker-driven approaches. We examine its biological role, clinical utility in early detection and postoperative risk stratification, integration with other emerging biomarkers, and its development as a target for antibody–drug conjugates. The review concludes with a summary of the evolving landscape of KIM-1–directed biomarker strategies in RCC, which hold promise to refine patient selection, improve risk-adapted management, and advance precision oncology in this complex disease.

Abstract: We investigate the continuation problem for the three-dimensional incompressible Navier–Stokes equations from a structural, assumption-free perspective. Using the exact Fourier–helical representation and a dyadic shell decomposition, the nonlinear term is reformulated in terms of triadic interactions, allowing a scale-resolved analysis of energy transfer. Within this framework, we establish a complete structural reduction of the nonlinear dynamics. All cross-scale and non-coherent interactions are shown to be perturbatively controlled on every finite time interval and cannot produce non-integrable accumulation in weighted Sobolev norms on compact subintervals. As a result, any potential finite-time blow-up must be supported by a sharply restricted class of residual mechanisms. More precisely, we show that non-integrable accumulation of positive Sobolev-weighted transfer can occur only through either large-transfer same-scale interactions or endpoint accumulation of perturbative remainder contributions. All other interaction channels are excluded as possible sources of divergence by structural and energetic arguments. The analysis is entirely assumption-free and does not rely on any phase closure, temporal localization, or statistical modeling. It therefore provides a complete obstruction formulation of the continuation problem: blow-up is reduced to the viability of a minimal set of explicitly identified mechanisms. We further show that these residual mechanisms persist because the incompressible Navier–Stokes equations do not constitute a thermodynamically complete system. Interpreting the incompressible equations as a singular limit of the compressible formulation, we identify the loss of entropy-based dissipation as the structural origin of the missing control on positive nonlinear transfer. Motivated by this observation, we introduce a minimal ε-retained thermodynamic extension that restores a remnant of the free-energy dissipation mechanism. Under this extension, we show that the positive transfer becomes integrable and that both residual blow-up mechanisms are eliminated under the stated closure condition. This yields a precise conditional closure of the continuation problem. The results clarify the exact scope and limitation of Navier–Stokes-based analysis and reduce the global regularity problem to the question of whether a thermodynamic-type dissipation principle can be rigorously derived within, or as a limit of, the governing equations.

Abstract: This study examined the potential of AI-assisted tools to improve English language learning for neurodiverse students (with ADHD, dyslexia, or autism) in low-resource settings in Iran, considering student and teacher perspectives and students’ lan-guage-learning outcomes. The study used a convergent mixed-methods design, and 142 neurodiverse learners and 97 teachers participated through surveys, a 4-week ex-perimental study involving 30 learners (15 AI intervention, 15 controls), and semi-structured interviews with 15 learners, 10 teachers, and five parents. The out-comes were positive: learners stated that they enjoy adaptive features such as multi-modal input and gamification (M=4.2/5) and are motivated by them, and teachers found inclusivity to be important but perceived low confidence (M=2.7/5) because of the training gaps. The AI group showed substantial improvements in vocabulary (+16.3, d=1.21), reading comprehension (+13.3, d=1.05), and oral fluency (+9.2 wpm, d=0.89) compared to controls. Qualitative themes emphasized personalization as em-powerment, as well as obstacles such as infrastructural constraints, exam-based cur-ricula, and cultural cynicism. Recommendations were provided on the transformative power of AI in promoting equity and the need to train teachers and make changes in low-resource schools.

Abstract: Acquiring world knowledge directly from visual observation is fundamental to Artificial General Intelligence (AGI). To support this capability, the Vision World Model (VWM) has emerged as a key paradigm, which learns how the world evolves over time from visual streams. However, recent progress has been driven by diverse research communities, resulting in inconsistent problem formulations, disconnected taxonomies, and divergent evaluation protocols. We argue that addressing this gap requires a conceptual shift: vision should not be treated merely as an input modality, but as the primary driver shaping how world models are represented, learned, and evaluated. Guided by this vision-centric perspective, we introduce a unified framework that organizes VWM research into three core components: vision encoding, knowledge learning, and controllable simulation, and use it to analyze existing model designs and evaluation methodologies. Finally, we outline future research directions that emphasize stronger physical and causal grounding, more meaningful evaluation beyond visual appearance, and scaling toward more general and reliable world modeling capabilities.

Abstract: While joint hypermobility can result from various medical conditions, it is most commonly associated with a group of related genetic conditions that affect connective tissue known as Ehlers–Danlos syndromes (EDSs). As there is currently no specific genetic testing for the diagnosis of Ehlers–Danlos hypermobility syndrome (hEDS), diagnosis is strictly made based on clinical criteria, which include physical features such as pain and family history, in addition to a scoring system known as the Beighton Score—a 9-point scale used to measure joint hypermobility—with a score of >4 considered significant. While hEDS often causes chronic muscle and joint pain, the underlying mechanisms remains poorly understood. Dysautonomia, characterized by common symptoms such as anxiety, vertigo, and increased heart rate when standing (orthostatic intolerance), in addition to multiple gastrointestinal symptoms, is highly prevalent among hEDS patients. We hypothesize that hypermobility due to ligamentous instability of the upper cervical spine, C1 and C2, results in impingement of the carotid sheath, the carotid artery and, more significantly, the vagus nerve, thus explaining the myriad symptoms that accompany hEDS. We also propose the novel use of extracorporeal shock wave therapy (ESWT) to treat this instability.

Abstract: The Hill cipher has historically lacked the confusion and diffusion properties required for modern cryptographic use. This paper presents the Multidimensional Hill Substitution-Permutation Network (MD-Hill-SPN), a 128-bit, 12-round block cipher combining three elements: (1) a hierarchical matrix diffusion layer operating at 4×4, 8×8, and 16×16 byte scales over GF(2⁸); (2) two AES S-box substitution layers per round; and (3) Argon2id memory-hard key derivation. Metric sessions used a SHA-256 domain-separator surrogate for Argon2id for computational tractability; Argon2id is the specified production KDF. Two independent runs of the full metric suite yield: (a) full plaintext avalanche from round 1 (mean 63.97–64.67 of 128 bits, ideal 64); (b) the differential-probability sampling floor of 2×10⁻⁵ reached at round 4 (50,000 of 50,000 output differences distinct, both sessions); (c) algebraic-degree lower-bound saturation at the maximum observable value from round 1; (d) linear bias indistinguishable from random (combined exceedance 4.40%, below the 4.55% noise floor); and (e) branch numbers at the Singleton (MDS) bound for every tier (B = 5 for 4×4, B = 9 for 8×8, B = 17 for 16×16), computed exhaustively over weight-1 inputs. MD-Hill-SPN therefore moves beyond theoretical construction to empirically validated confusion and diffusion properties stronger than prior Hill-cipher variants.

Abstract: The distributional specification in Markov-switching GARCH models has historically been driven by empirical convention rather than statistical theory. This paper derives the two-regime MS-GARCH specification from the Maximum Entropy Principle, providing an information-theoretic motivation for Student-t regime-conditional innovations in cryptocurrency volatility modelling. The framework is applied to five major cryptocurrencies, Bitcoin, Ethereum, Ripple, Litecoin, and Bitcoin Cash, over the period January 2017 to March 2026, comprising 15,834 daily observations spanning six complete market cycles. Three principal findings emerge. First, a Calm-Phase Fragility pattern is identified: four of five assets exhibit calm-regime half-lives below one trading day (0.48 to 1.16 days), with turbulence the dominant long-run state (stationary turbulent probability in [0.451, 0.771] across all assets), establishing turbulence rather than calm as the structural baseline of the cryptocurrency ecosystem. Second, the Maximum Entropy derivation yields endogenous Student-t degrees of freedom, with heavy-tailed turbulent innovations (degrees of freedom approximately 4.5) confirmed across all assets, validating the MaxEnt constraint framework empirically. Third, near-unity turbulent GARCH persistence drives MS-GARCH point forecasts toward the persistence ceiling, consistent with an information-theoretic bound on predictability when the calm half-life collapses below one trading day; HAR-RV achieves the lowest QLIKE loss for three of five assets under these near-critical conditions. Cross-asset consistency is confirmed across seven statistical indicators including Hill tail exponents in [2.31, 3.26], Hurst exponents in [0.543, 0.577], and Wald tests rejecting parameter homogeneity at p &lt; 0.001 for all assets. The framework is formalised as a deployable expert system for real-time regime monitoring and risk management.

Abstract: Introduction: Ovarian cancer remains the most lethal gynecological malignancy worldwide, primarily due to late-stage diagnosis, extensive molecular heterogeneity, and the development of therapeutic resistance. Although advances in cytoreductive surgery and platinum-based chemotherapy have improved short-term disease control, durable long-term survival improvements remain modest, particularly in patients with recurrent or platinum-resistant disease. Rapid progress in molecular profiling, targeted therapeutics, and artificial intelligence–based diagnostic tools has transformed the understanding and management of ovarian cancer. However, translating these scientific advances into consistent clinical benefit remains challenging due to therapeutic resistance, limited validation of emerging biomarkers, and disparities in access to precision oncology. Methods: This narrative review synthesizes current evidence from peer-reviewed clinical trials, translational studies, and systematic reviews examining molecular pathogenesis, early detection strategies, and therapeutic developments in ovarian cancer. Literature was identified through structured searches of major biomedical databases, focusing on studies evaluating molecular biomarkers, artificial intelligence–driven diagnostic approaches, targeted therapies—including poly (ADP-ribose) polymerase inhibitors and anti-angiogenic agents—and emerging treatment modalities such as immunotherapy, antibody–drug conjugates, and cellular therapies. Particular emphasis was placed on identifying conflicting findings, methodological limitations, and translational barriers affecting clinical implementation. Results: Advances in genomic and molecular characterization have established ovarian cancer as a biologically heterogeneous disease comprising multiple histological and molecular subtypes with distinct clinical behavior and therapeutic responsiveness. Targeted therapies, particularly PARP inhibitors, have significantly improved progression-free survival in patients with homologous recombination deficiency; however, long-term efficacy is frequently limited by acquired resistance mechanisms, including restoration of homologous recombination function and activation of alternative DNA repair pathways. Emerging diagnostic technologies—including circulating tumor DNA, multi-omics biomarker panels, and artificial intelligence–based predictive models—demonstrate promising diagnostic accuracy for early-stage disease detection. Nevertheless, many of these technologies remain in early clinical development and require large-scale prospective validation before routine adoption in clinical practice. Discussion: Despite substantial scientific progress, several translational gaps continue to limit the real-world impact of precision oncology in ovarian cancer. Variability in biomarker performance across populations, heterogeneity in study design, and reliance on retrospective datasets complicate interpretation of current evidence. In addition, the relatively modest response rates observed with immunotherapy highlight the importance of understanding tumor immune evasion mechanisms and optimizing combination treatment strategies. Emerging therapies, including antibody–drug conjugates and chimeric antigen receptor T-cell therapies, show encouraging early clinical activity but remain under active investigation. Addressing these challenges will require interdisciplinary collaboration, standardized biomarker validation frameworks, and integration of computational tools into routine clinical workflows. Conclusion: Ovarian cancer management is undergoing a paradigm shift toward precision oncology driven by advances in molecular biology, biomarker discovery, and targeted therapeutics. However, durable improvements in survival will depend on overcoming therapeutic resistance, validating early detection strategies in diverse populations, and ensuring equitable access to advanced diagnostics and personalized treatments. Future research should prioritize prospective validation of emerging technologies, development of biomarker-guided treatment strategies, and translation of scientific innovation into sustainable clinical outcomes.

Abstract: The development of efficient, visible light responsive and magnetically recoverable photocatalysts remains a critical challenge in wastewater remediation, particularly for the degradation of persistent azo dyes. In this study, a hierarchical nanocomposite consisting of NH₂-MIL-88B(Fe)-derived Fe₃O₄@porous carbon coupled with graphitic carbon nitride (g-C₃N₄) was successfully synthesized via a controlled pyrolysis and heterostructure assembling strategy. The NH₂-MIL-88B(Fe) precursor was synthesized solvothermally and subsequently carbonized at 500 °C under a nitrogen atmosphere to yield Fe₃O₄ nanoparticles embedded in a porous carbon matrix. The Fe₃O₄@porous carbon was then integrated with exfoliated g-C₃N₄ through ultrasonication assisted self-assembling to form a heterojunction nanocomposite. Structural, morphological, and optical characterizations confirmed the formation of a hierarchical porous architecture with enhanced visible light absorption and efficient charge separation. The photocatalytic performance was evaluated using methyl orange (MO) and Congo red (CR) dyes under visible light irradiation at λ > 420 nm, achieving degradation efficiencies of 98.6% and 96.8%, respectively, within 90 minutes at a catalyst dosage of 0.5 g L⁻¹. The composite exhibited excellent magnetic recoverability with a saturation magnetization of 32.4 emu g⁻¹, enabling facile separation using an external magnetic field. Mechanistic investigations revealed a Z scheme charge transfer pathway with dominant reactive species including •OH and •O₂⁻ radicals. The nanocomposite maintained over 92% of its photocatalytic efficiency after five cycles, demonstrating high stability and reusability. This work highlights a scalable strategy for designing multifunctional photocatalysts for environmental applications.