Abstract:

Blue crab (Callinectes sapidus) populations are of substantial ecological and economic importance. As a keystone species, C. sapidus plays a critical role in maintaining estuarine food webs while also supporting one of the most consumed and economically valuable seafood industries in Delaware and Maryland. This study investigated the presence of Callinectes sapidus Reovirus 1 (CsRV1) in C. sapidus collected from Rehoboth Bay, Delaware, USA, using reverse transcription–quantitative polymerase chain reaction (RT-qPCR) and evaluated potential associations between viral occurrence and physicochemical parameters, including temperature, salinity, pH, turbidity, alkalinity, calcium hardness, nitrite, and chlorophyll-a. A total of 18 traps were deployed across six study sites encompassing oyster aquaculture areas, artificial oyster reefs, and control sites with minimal structural habitat. CsRV1 was detected in blue crabs from Rehoboth Bay, confirming the presence of the virus within the Delaware Inland Bays; however, detections were limited to a small subset of sampled individuals. Among the environmental parameters examined, salinity exhibited the greatest interannual variability, while other physicochemical conditions remained relatively consistent across site types and sampling periods. Overall, environmental conditions during the study period were within ranges considered suitable for C. sapidus, indicating that the population is likely to experience limited environmental stress and minimal disease-related impacts under current conditions.

Abstract: Background: Large language models (LLMs) are increasingly used as diagnostic support tools. However, their sensitivity to prompt framing (especially the gender assigned to the physician persona) is poorly understood for neglected tropical diseases (NTDs). Objective: Compare the diagnostic performance of four LLMs prompted as male or female infectious disease specialists using anonymized cases of Chagas disease (CD) and visceral leishmaniasis (VL). Methods: This experimental, paired study evaluated ChatGPT-4o, LLaMA 3 70B, Meditron-70B, and Mixtral 8x7B across 12 cases per disease (n=24), from real records at a Brazilian teaching hospital. The primary outcome was top-five diagnostic accuracy. A committee of five infectious disease specialists assessed the biological plausibility of all differentials. Paired comparisons used Wilcoxon signed-rank tests; 95% confidence intervals were calculated using the Wilson score method. Results: For VL, the female prompt yielded numerically higher accuracy in all four models (gains of 8.3–16.7 percentage points), with ChatGPT-4o reaching 91.7%. For CD, ChatGPT-4o achieved 100% under both conditions; LLaMA 3 70B and Mixtral 8x7B improved by 16.7 percentage points with the female prompt, while Meditron-70B remained at 16.7%. No between-prompt differences reached statistical significance (all p>0.05). ChatGPT-4o produced the highest proportion of biologically plausible diagnoses, while Meditron-70B generated the most implausible hypotheses. Conclusion: The gender assigned to the physician persona produced subtle, nonsignificant variations in LLM diagnostic accuracy, with a consistent numerical trend favoring the female prompt in open-weight models. These findings underscore the importance of prompt standardization and systematic bias evaluation in AI-assisted diagnostics for NTDs.

Abstract: From the standpoint of both specialized sociological inquiry (Misztal, 2011) and sociolinguistics (Goutsos & Bella, 2022), the term "vulnerability" may be considered a late-modern neologism used to describe a specific psycho-emotional state. It is a concept found in the fields of psychology and psychiatry, as well as the specialized sociology of emotions. By definition, vulnerability denotes the quality of being susceptible—of being easily overwhelmed or unable to effectively withstand a threat or an attack. There is also the view, within the context of establishing a formal definition, that considers transparency, receptivity, and sensitivity as synonyms of vulnerability—provided that courage is present as a prerequisite. In other words, vulnerability is the emotion we experience during periods of uncertainty, risk, and emotional exposure.

Abstract: As indicated by its title, this study challenges the common view that as complex systems cities emerge from the bottom-up. It suggests, firstly, that that this common view is a consequence of applying the various complexity theories to the dynamics of cities by means of analogy to material media namely to complex systems such as Benard cells or laser. Secondly, that when examining cities from first principles of human media that concern cognition, behavior and brain dynamics, cities emerge in a top-down manner. Thirdly, that the dynamics of cities is characterized by the simultaneous coexistence of bottom-up and top-down processes so that the question is not bottom-up or top-down, but rather how these apparent negating processes co-exist.

Abstract: It is necessary to develop a sensitive and selective analytical method for detecting organophosphate insecticides, such as malathion, for environmental protection. Herein, we have designed an innovative sensing platform that incorporates silver nanoparticles (AgNPs) into molecularly imprinted polymers (MIPs), with AgNPs synthesized via in situ silver ion reduction during the precipitation polymerization of the MIP. Integrating AgNPs into MIP allows us to leverage both the selectivity and high sensitivity of molecular imprinting technology and the enhanced surface-enhanced Raman scattering (SERS) properties of AgNPs. The sensors demonstrate a linear detection range of 0.005-5 µg/ml and a limit of detection (LOD) of 0.005 µg/ml for malathion in water solution. The sensor is tested and evaluated in spiked drinking and tap water, obtaining recovery rates ranging from 93% to 100.5%. The AgNPs@MIP SERS sensor provides a rapid, selective, and sensitive approach for malathion detection, promising to develop an analytical tool for environmental and agricultural monitoring of organophosphate compounds.

Abstract: U.S. banks are investing unprecedented amounts in artificial intelligence, with annual spending at institutions like JPMorgan Chase, Bank of America, and Citigroup now exceeding $2–$4 billion each. Yet a critical national financial resilience problem persists: most U.S. banks cannot confidently determine whether these massive AI investments generate positive risk-adjusted returns, creating capital allocation inefficiency and potential systemic vulnerability. This research proposal outlines a comprehensive mixed-methods research design for investigating how senior executives in U.S. global banks govern enterprise AI investments, manage emerging financial risks, and measure return on investment when scaling AI across national banking operations. Drawing on the Resource-Based View, Paradox Theory, and the Technology-Organization-Environment framework, this proposal develops an integrated conceptual framework linking AI governance mechanisms, operating model configurations, and multi-dimensional ROI measurement specifically calibrated to the U.S. regulatory environment (Federal Reserve, OCC, FDIC). The proposed study would employ an embedded multiple-case design with semi-structured interviews of 30–40 C-Suite executives across 6–8 U.S.-headquartered global banks, supplemented by secondary analysis of SEC filings, FRED economic data, FDIC call reports, and Model Risk Management documentation. We propose a novel risk-adjusted ROI calculation framework incorporating direct financial benefits, indirect value creation, strategic option pricing, and probabilistic risk adjustments aligned with U.S. banking stress testing practices. Anticipated methodological barriers include organizational resistance, access constraints to senior executives, and causal attribution challenges—each addressed with specific mitigation strategies outlined in this proposal. This proposal aims to contribute empirically validated ROI measurement tools for executive decision-making at U.S. systemically important financial institutions and demonstrates a scholar-practitioner approach to bridging academic rigor with national financial stability priorities.

Abstract: Urban street lighting remains a significant source of energy consumption in cities, largely due to static operation and limited responsiveness to real-time conditions. This inefficiency increases operational costs and environmental impact, especially in rapidly urbanizing regions. To address this issue, this study investigates IoT-enabled smart street lighting as an adaptive and data-driven solution within smart city frameworks. The work focuses on the growing body of research in this domain and examines its evolution, technical structure, and emerging environmental role. The study aims to provide a structured synthesis that connects research trends with system-level design, while highlighting the transition from energy-focused systems to multifunctional urban platforms. A bibliometric-driven and thematic review approach is adopted. A dataset of 151 publications was analyzed using Bibliometrix and Biblioshiny tools to extract trends, collaboration patterns, and research themes. This analysis is complemented by a qualitative evaluation of system architectures, sensing technologies, communication models, and control strategies. The findings indicate a sustained annual growth rate of 14.87% and a highly collaborative research landscape, with an average of 3.97 authors per study. The results also reveal that energy efficiency remains the dominant focus, while environmental integration is emerging but still underrepresented. The study further identifies key gaps related to scalability, sensor reliability, and the lack of standardized evaluation metrics. The outcomes provide a comprehensive roadmap for future research and support the development of scalable, intelligent, and sustainable lighting systems. The proposed insights are applicable to urban environments globally, particularly in regions seeking cost-effective and energy-efficient infrastructure solutions.

Abstract: Research and Development (R&D) represents a strategic pillar of the petroleum industry, where technological innovation drives competitiveness, and the transition toward sustainable and cleaner energy systems. However, measuring the performance of R&D projects remains a complex challenge because their outcomes are often intangible, uncertain, and multidimensional. Traditional Key Performance Indicators (KPIs)—such as cost, time, and number of deliverables—provide only a partial view of effectiveness. R&D performance assessment must therefore consider the intrinsic nature of the activity. Reverse engineering emphasizes replication and adaptation of existing technologies, while innovation-driven R&D seeks to create novel solutions. Accordingly, the selection of performance indicators must differ across these categories. To avoid biased evaluation, the framework integrates B. Roy’s (1996) Multi-Criteria Decision Analysis (MCDA) approach, enabling prioritization of criteria aligned with each project’s objectives and complexity (Martinsuo et al., 2022). Moreover, in R&D environments, traditional indicators such as cost and time act as strategic signals rather than mere management metrics. Cost data guide managerial decisions on partnerships, external funding, and open innovation when internal resources are limited. Similarly, adherence to schedule directly influences technological relevance—delays may result in obsolescence, missed market windows, or loss of first-mover advantage (Tsinopoulos & Al-Zu’bi, 2023). To move beyond simple cost and time metrics, this study revisits the meaning of “performance” in R&D and explores multi-dimensional evaluation tools capable of capturing both tangible and intangible value creation, by integrating five novel dimensions: knowledge creation and diffusion, innovation velocity, dynamic strategic alignment, team and organizational health, and resilience under uncertainty. Beyond its conceptual formulation, the framework has been numerically applied to a portfolio of 10 ongoing R&D projects spanning renewable energy, digitalization of upstream processes, advanced materials, and industrial decarbonization. Each project was scored on a standardized 0–10 scale across the five dimensions, allowing for fine-grained benchmarking and identification of strengths and gaps. For example, Projects 3 and 7 achieved high innovation velocity scores (≥ 9) but lagged in resilience metrics (< 5), indicating exposure to external risks. Conversely, Projects 5 and 9 showed strong knowledge diffusion and team health (scores of 8–10) but slower strategic alignment (< 6). The analysis demonstrates how the proposed framework can generate actionable dashboards for managers, enabling more balanced resource allocation, improved project selection, and proactive mitigation of weaknesses. Applications in industry, academia, and public R&D contexts are also explored, illustrating how this systemic, ecosystem-aware approach moves performance management beyond a narrow project-level perspective to a dynamic, portfolio-wide view. The results provide both theoretical contributions and practical tools for R&D managers seeking to measure and enhance the multidimensional value of their projects.

Abstract: Background/Objectives: Hepatitis B vaccines (HBVs) have been recommended since 1991 for all U.S. infants starting at birth. Several studies have examined associated reports to the U.S. Vaccine Adverse Events Reporting System (VAERS) and found no unexpected safety signals, but no study has systematically quantified and characterized the reports. Methods: A range of outcomes reported to VAERS between 1990 and 2025 was assessed, stratifying by HBV type, age, sex, and vaccine dose. The outcomes included death, SIDS, seizures, developmental delay, encephalitis; and four symptom groups that may reflect potential signs of encephalitis (crying, fever, sleepiness, and gastrointestinal disorders). Results: 1,793 deaths have been reported to VAERS since 1990 following administration of HBVs. Of these, 1493 were for infants aged 0-12 months, of whom 64% were in the 2-3 month age group and 60% were male. Most death reports occurred in infants receiving HBV co-administered with other vaccines or in combination shots like Comvax and Pediarix. The rate of SIDS was nearly twice as high in males compared to females in the 2-3 month age group. Other adverse events were reported at more similar rates for males and females, but all outcomes in infants occurred most frequently at age 2-3 months. Conclusions: Male infants aged 2-3 months who receive multiple vaccines at once appear more vulnerable to adverse events than other groups. Since the extent of underreporting to VAERS is not well known, characterizing the patterns in the existing reports is more informative than judging whether they are cause for concern.

Abstract: The escalating demand for sustainable metallurgical practices necessitates innovative approaches to manganese production. The smelting-aluminothermic reduction of hydrogen pre-reduced manganese ores in a direct current (DC) arc furnace offers a resilient and sustainable trajectory for optimizing manganese recovery efficiencies while minimizing waste generation under low-carbon operating conditions. This study presents a comparative of smelting-aluminothermic reduction of two Mn ores pre-reduced with hydrogen using two distinct approaches, namely, a packed-bed vertical retort and a plasma rotary furnace. A 200 kW DC arc furnace was used for smelting. The scope of this assessment integrates technical, environmental and operational metrics of smelting-aluminothermic reduction. For energy, the considered metrics are power stability metrics, specific energy requirement, furnace thermal efficiency and load factor/power-on time. The metrics considered for material are reductant efficiency, elemental accountability, elemental recovery, elemental deportment and slag-to-metal ratio. For process sustainability, refractory and electrode consumption were considered. The environmental indicators considered includes CO2-equivalent emissions per ton of product, dust and particulate emissions, NOx/SOx emissions. This research provides critical insights into the viability and environmental advantages of hydrogen pre-reduction coupled with smelting-aluminothermic reduction for cleaner manganese production.

Abstract: Small unmanned aerial vehicle (UAV) acoustic signatures have become increas-ingly relevant not only from the perspective of environmental noise mitigation, but also for detectability, surveillance vulnerability, and low-observable aerial system design. While most prior studies focus on rotor-noise reduction through high-fidelity computa-tional fluid dynamics (CFD) or experimental testing, comparatively fewer studies ad-dress reduced-order computational frameworks capable of rapidly predicting both acoustic signatures and detection distances under varying operating conditions. This study presents a physics-informed reduced-order computational aeroacoustic framework integrating blade passing frequency harmonic modeling, aeroacoustic scaling laws, atmospheric propagation, and beamforming-informed detectability metrics for rapid prediction of small UAV acoustic signatures. The methodology combines harmonic spectral synthesis, rotational speed scaling, source propagation modeling, and sig-nal-to-noise-based detection criteria to estimate sound pressure spectra, directional acoustic signatures, and acoustic detection distance envelopes. Computational results indicate strong agreement with trends reported in published UAV aeroacoustic ex-periments and suggest that propeller operating speed dominates both acoustic signature growth and detectability range. For representative multirotor conditions, modeled detection distances vary from approximately 80 m to over 200 m depending on rotational speed and ambient noise floor, while reduced source signature scenarios can reduce detectability by up to 30%. The proposed framework provides a computationally efficient tool for rapid aeroacoustic assessment, acoustic signature management, and preliminary low-observable UAV design.

Abstract: Introduction: Extreme weather events are increasing in frequency and intensity due to climate change, contributing to substantial morbidity and mortality globally, particularly among vulnerable populations. In the UK, climate adaptation within health systems remains insufficiently developed. However, there is limited understanding of the tools currently available for the identification and management of populations at risk during extreme weather. This study aims to systematically characterise UK-based climate adaptation tools used in healthcare settings. Methods: Environmental scanning was conducted, because no centralised database exists for climate adaptation tools in healthcare, and many relevant resources are not captured in traditional academic or grey literature repositories. Structured Google searching by two independent reviewers enabled identification of publicly available and practice-oriented tools accessed in real-world settings. Eligible resources included UK-based tools designed for healthcare professionals, local authorities, or patients that incorporate meteorological data to mitigate climate-related health risks. Results: Nine tools met inclusion criteria, comprising e-learning platforms, online dashboards, integrated clinical software, and structured workflows. Most targeted healthcare professionals, with few targeting local authorities and none targeting patient self-management. Data inputs and outcomes measures were heterogeneous, spanning risks related to heat, cold, flooding, and air pollution. Reporting was inconsistent, as nearly half the tools were not publicly accessible and all demonstrated limited transparency. Conclusion: The current landscape of UK climate adaptation tools in healthcare is fragmented, with variability in accessibility, evidence, and scope. Clearer reporting and greater coordination in how such tools are catalogued may support more consistent and equitable responses to climate-related health risks.

Abstract: Air pollution poses a serious environmental and public health problem in Bishkek, Kyrgyzstan, especially during the winter months when the concentration of particulate matter increases dramatically. Despite the urgency of the problem, there are fewer than eight monitoring stations in the city, which leaves large urban areas without proper air quality control. This article presents the first systematic study of image-based AQI assessment for Bishkek, which explores whether transfer learning models can extract visual cues related to environmental pollution from on-site urban photographs under real-world uncontrolled conditions. Two hybrid deep learning architectures, VGG16 and EfficientNetB0, each augmented with scalar PM2.5 input data, were trained and evaluated on a locally collected dataset of 1,014 image pairs–AQI. EfficientNetB0 consistently outperformed VGG16 on all three evaluation indicators, reducing RMSE by 15.5% (66.49 vs. 78.71) and MAE by 16.6% (49.00 vs. 58.78). Both models demonstrated a partial predictive signal in the AQI range from low to moderate, confirming that visual features related to the atmosphere can be detected even based on small datasets from local sources. The performance limitations reflect the scale of the dataset and sparse sensor infrastructure, rather than the lack of a studied structure, which is consistent with similar pilot studies conducted under similar data constraints. This work establishes a basic and methodological framework for future image-based air quality monitoring in Central Asia and identifies key bottlenecks — the size of the dataset, tag interference caused by geographic mismatches in sensor images, and the density of monitoring stations - that should be addressed in future work.

Abstract: Tofersen is a gene-targeted therapy for superoxide dismutase 1 (SOD1)-associated amyotrophic lateral sclerosis (ALS), but neurofilament light chain (NfL) may not fully capture the biological response to treatment. We performed a multicentre retrospective longitudinal study including 24 patients with SOD1-ALS treated with intrathecal tofersen at four Italian referral centres between 2022 and 2025. Cerebrospinal fluid (CSF) and serum biomarkers were assessed at baseline, month 3, month 6, and last available administration using single-molecule array assays to quantify NfL, glial fibrillary acidic protein (GFAP), ubiquitin C-terminal hydrolase L1 (UCHL-1), and total tau. NfL decreased after treatment initiation in both CSF and serum, providing the clearest pharmacodynamic signal. In contrast, CSF GFAP increased progressively over follow-up, while CSF total tau and UCHL-1 rose mainly at later timepoints; serum GFAP, total tau, and UCHL-1 also showed increases during follow-up. ALS Functional Rating Scale-Revised trajectories were broadly stable, whereas disease progression rate was lower at last follow-up than at baseline. Greater reductions in CSF NfL were observed in pathogenic versus uncertain SOD1 variants, and early serum NfL and UCHL-1 changes were associated with longer-term changes in disease progression. These findings suggest that longitudinal multi-analyte profiling may refine biological response stratification beyond NfL alone in tofersen-treated SOD1-ALS.

Abstract: Infrastructure expansion in Indonesia has increased the demand for paving blocks, raising concerns regarding cement production costs and environmental impact. This study investigates the comparative effectiveness of pineapple leaf fibre (PALF) and sisal fibre as natural reinforcements to enhance paving block performance. An experimental design was employed with fibre contents varying from 0% to 7% by cement volume. Specimens were cured for 28 days and tested for water absorption and compressive strength; analysis was performed using descriptive statistics and two-way ANOVA. Results indicated that fibre content significantly influenced both response variables (p < 0.001). Water absorption increased monotonically with fibre content, while compressive strength exhibited an inverted-U relationship with a distinct optimum at 3% fibre addition. Sisal fibre exhibited greater mechanical enhancement than PALF, achieving a maximum strength of 15.2 MPa at 3% (R² = 0.973), meeting Indonesian National Standard SNI 03-0691-1996 Class B requirements (minimum 12.5 MPa). A significant interaction between fibre type and fibre content was identified for compressive strength (F = 3.697, p = 0.012), confirming that the response to dosage differs between the two species. These findings demonstrate the potential of agricultural waste fibres for producing sustainable, eco-friendly paving blocks, supporting circular economy principles in the construction industry.

Abstract: This study advances a modular microfoundational framework to examine how individual-level actions aggregate into macro-level technological innovation capabilities and operational performance in innovation intermediaries in emerging economies. Grounded in microfoundations theory (Coleman's bathtub model) and cybernetic principles (Viable System Model), we dissect three interdependent modules to diagnose systemic issues within institutional voids: (i) macro-level system viability and technological emergence; (ii) meso-level organizational practices mediating R&D collaboration; and (iii) micro-level behaviors of boundary-spanning agents driving knowledge integration. Empirical evidence from a Brazilian Research and Technology Organization (RTO) reveals how context-specific microfoundations determine operational efficiency and technological emergence. Theoretically, we contribute by operationalizing Coleman's micro-macro link to enable cross-context benchmarking of innovation intermediaries and decoding how meso-micro-level actions co-evolve with ecosystem-level innovation. By shifting the diagnostic focus to the fine-grained dynamics of individuals and their interactions, our study offers actionable levers for managers and policymakers to optimize operational viability in contexts of institutional uncertainty. Implications for innovation policy, ecosystem governance, and the design of intermediary organizations in late-development settings are discussed.

Abstract: Background: Allergic conjunctivitis (AC) is a frequent inflammatory ocular surface disease that significantly affects quality of life, particularly in children. Current treatments mainly provide temporary symptom relief and often require prolonged use. Bacterial suspensions have emerged as potential immunomodulatory treatments for other allergies, but have not been completely explored in ocular allergy. Objective: To describe the clinical ophthalmological and quality of life changes in patients with AC treated with a bacterial suspension (BS) as complementary therapy. Methods: A before-and-after clinical study was conducted in 5 children aged 6 to 12 years with a diagnosis of moderate-to-severe persistent allergic conjunctivitis and negative skin prick test results. Clinical ocular signs and symptoms, quality of life, and changes in CD19+IL-10+ cells were assessed. Results: After 90 days of BS treatment, a significant reduction in allergic symptoms, including itching, light sensitivity, and burning, was observed, along with a marked reduction of ocular inflammation. Evaluation of quality of life revealed improvement across all evaluated domains and an increase in CD19+IL-10+ cells. Conclusions: BS therapy demonstrated favorable clinical and immune-modulatory effects in children with AC, supporting its potential as a promising complementary therapeutic option.

Abstract: The rapid proliferation of unmanned aerial vehicles (UAVs) in urban and peri-urban environments has increased concern regarding drone-generated acoustic emissions, particularly in multirotor platforms whose tonal and broadband noise is strongly influenced by propeller blade geometry. This study presents a CFD-based aeroacoustic assessment framework to examine the influence of key geometric modifications on the acoustic signature of a representative multirotor propeller while preserving aerodynamic performance. A baseline quadrotor propeller was analyzed using Reynolds-Averaged Navier–Stokes (RANS) simulations coupled with the Ffowcs Williams–Hawkings (FW-H) acoustic analogy and Brooks–Pope–Marcolini (BPM) broadband noise estimation. The blade geometry was parameterized in terms of leading-edge sweep, tip chord, blade twist, and trailing-edge serration features, and representative low-noise configurations were evaluated under operating conditions ranging from 3000 to 6000 RPM and advance ratios between 0 and 0.3. The results indicate that combined swept-serrated geometries provide the most favorable noise–performance trade-off, with a predicted reduction of up to 4.8 dB(A) relative to the baseline at the design condition, while maintaining thrust and figure of merit within practical engineering margins. The proposed framework provides a transferable computational basis for the systematic design of low-noise propellers for surveillance UAVs, commercial multirotors, and emerging urban air mobility applications.

Abstract: Lipedema is a chronic, progressive adipose tissue disorder predominantly affecting women and has been widely proposed as an estrogen-dependent condition despite the lack of objective causal evidence. In contrast, increasing data implicate genetic heterogeneity, endothelial dysfunction, and altered vascular permeability as central features of the disease. This review critically reassesses the estrogen-dependence hypothesis in light of emerging genetic and vascular evidence. These findings highlight molecular pathways linking endothelial dysfunction and adipose tissue dysregulation as central features of the disease. Methods: A narrative literature review was conducted using PubMed, Cochrane Library, and Google Scholar databases. Searches combined the terms “lipedema,” “lipoedema,” “estrogen,” “hormonal dependence,” “genetic polymorphism,” “endothelial dysfunction,” “vascular permeability,” “microangiopathy,” and “adipose tissue,”. Original research articles, reviews, consensus statements, and experimental studies were included. Given the narrative design, no formal inclusion criteria, quality assessment, or meta-analytic procedures were applied. Results: Across multiple cohorts, no studies demonstrated that estrogen levels, estrogen receptor expression, aromatase activity, or estrogen-related signaling pathways act as primary causal triggers of lipedema. Conversely, consistent genetic, transcriptomic, and histopathological findings reveal marked genetic heterogeneity, dysregulated adipose tissue proliferation, extracellular matrix remodeling, microangiopathy, and increased endothelial permeability. Variants affecting adipogenesis, connective tissue integrity, vascular function, and lymphatic regulation have been repeatedly identified, alongside early endothelial structural and functional abnormalities. Conclusion: Current evidence does not consistently support classifying lipedema as an estrogen-dependent disease. While estrogen may modulate inflammatory and metabolic processes relevant to disease expression, its role appears secondary rather than causative. Genetic predisposition and vascular dysfunction emerge as more consistent contributors to lipedema pathophysiology, supporting integrative, mechanism-based models to guide future research and clinical approaches.

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.