Search Results (12,283)

Macroeconomic systems are increasingly characterized by fractal structures, entropy-generating processes, and chaotic dynamics that challenge the assumptions of traditional regression methods. The presence of self-similarity, fractal structure, and sensitivity to initial conditions suggests that macroeconomic variables evolve through complex interactions that cannot be adequately explained within an equilibrium-based method. Motivated by this perspective, this paper tested the relationships between oil prices and macroeconomic variables in the United States over the period of 1960–2024 using a suggested fractal regression approach. The analysis proceeds in two stages. In the first stage, fractal, entropy, and chaotic structures of the variables were analyzed by employing entropy measures, Lyapunov exponents, attractor diagnostics by including Lorenz and Julia structures, and tests for fractal dimension: d parameter (GPH) and d parameter (Phillips), and long range dependendeceLo’s Modified R/S, and Hurst–Mandelbrot R/S. Our results explored evidence of fractal structure, complexity, and chaotic behavior within the selected macroeconomic series by indicating the presence of nonlinear dynamics and sensitivity to initial conditions. In the second stage, a proposed chaotic–fractal-based regression model is employed to explore the transmission mechanism of oil price to economic growth, inflation, and unemployment. By directly incorporating Lyapunov and fractal-based measures into the regression method, the model captured nonlinear interactions that are overlooked by traditional methods. The results revealed that oil price shocks generate chaotic and fractal effects across macroeconomic variables and that these effects vary according to the degree of chaotic divergence embedded in the system. Overall, the results suggested the interconnected roles of fractality, entropy, and chaos in shaping macroeconomic dynamics and showed the importance of chaos- and fractal-based modeling methods for understanding the economic consequences of energy shocks and their policy implications. Full article

Background: Cigarette smoking promotes persistent systemic alterations in COPD, yet the interplay among genetic susceptibility, oxidative stress, immune dysregulation, impaired control of persistent viral replication, and long-term outcomes remains incompletely understood. Methods: We conducted an observational study in 102 patients aged ≥70 years with severe-to-very-severe COPD undergoing pulmonary rehabilitation. Current smokers (n = 38) were compared with never/former smokers (n = 64). Analyses included Chr15q25 genotyping (rs16969968), oxidative stress biomarkers (tail intensity, 8-OHdG, MDA, and bilirubin), hematological and immunological parameters, α7nAChR expression, TTV load as a surrogate marker of immune competence, latent virus prevalence, and five-year survival assessed by multivariable Cox regression. Results: Current smokers exhibited significantly higher DNA damage (tail intensity, p = 0.001; 8-OHdG, p = 0.002), lower bilirubin levels (p = 0.031), increased neutrophil and CD4⁺ T-cell counts (p = 0.031 and p = 0.028, respectively), altered α7nAChR expression on CD4⁺ T cells (p = 0.030), and higher TTV load (p = 0.002) than never/former smokers. The rs16969968 AA genotype was more frequent among current smokers. In survival analyses, an elevated WBC count was independently associated with increased mortality risk (HR 1.12, 95% CI 1.01–1.23; p = 0.035), whereas higher bilirubin levels showed a protective association. TTV load, smoking status, and FEV1 were not independently associated with mortality. Conclusions: In severe-to-very-severe COPD, smoking is associated with a distinct biological profile characterized by enhanced oxidative DNA damage, systemic inflammation, immune remodeling, reduced antioxidant defenses, and impaired control of persistent viral replication. WBC and bilirubin emerged as the biomarkers most consistently associated with long-term outcomes. These findings support integrated biological profiling as a tool for risk stratification and precision-guided rehabilitation in advanced COPD. Full article

Age-associated metabolic dysfunction is commonly defined by abnormalities in adiposity, glucose regulation, lipid metabolism, and blood pressure. Although clinically useful, these criteria do not fully capture the biological heterogeneity that explains why older adults with similar metabolic profiles may follow divergent trajectories toward type 2 diabetes, cardiovascular disease, metabolic dysfunction-associated steatotic liver disease, frailty or multimorbidity. This narrative Review summarizes clinical, translational, and mechanistic evidence on the biological processes that shape metabolic aging, with particular emphasis on inflammaging, immunosenescence, cellular senescence, oxidative stress, mitochondrial dysfunction, adipose tissue dysfunction, endothelial injury, and inflammation-related microRNAs. We first discuss how chronic low-grade inflammation and immune remodeling alter the interpretation of conventional metabolic syndrome components in older adults. We then review redox imbalance and mitochondrial stress as amplifiers of insulin resistance, lipid injury, vascular dysfunction, and tissue remodeling. The review also examines inflammation-related microRNAs, including circulating and extracellular-vesicle-associated miRNAs, as post-transcriptional regulators that may connect inflammatory, metabolic, and redox pathways. Finally, we discuss how conventional metabolic markers may be integrated with inflammatory mediators, oxidative-stress indicators, adipokines, endothelial and senescence-related markers, and miRNA profiles to improve biological interpretation of metabolic risk. Within this context, we present the Inflammaging–Redox–InflammamiR Axis as a conceptual framework for organizing these overlapping mechanisms rather than as an established diagnostic or causal model. The proposed biomarker tiers and candidate risk phenotypes are author-derived, hypothesis-generating constructs intended to guide future longitudinal and interventional research. Clinical translation will require standardized assays, longitudinal validation, external replication, and intervention studies. Full article

The replacement of n-hexane in vegetable oil extraction remains a significant challenge due to environmental and health concerns. This study integrates thermodynamic modeling and kinetic analysis to evaluate green solvents for soybean oil extraction. Solvent–triglyceride interactions were predicted using Conductor-like Screening Model for Real Solvents (COSMO-RS), employing σ-surfaces, σ-profiles, σ-potentials, activity coefficients at infinite dilution (γ∞), and relative solubility descriptors ($x_{RS}$ and $w_{RS}$). Representative triglycerides were modeled using DFT-optimized structures. Based on these predictions and sustainability criteria, cyclopentyl methyl ether (CPME), 2-methyltetrahydrofuran (2-MeTHF), tert-butyl methyl ether (TBME), and ethyl acetate were experimentally evaluated against n-hexane using accelerated solvent extraction (ASE) at 100 °C. CPME and 2-MeTHF achieved the highest extraction yields, exceeding n-hexane, while TBME showed comparable performance and ethyl acetate underperformed. Kinetic analysis using the hot-ball diffusion model revealed a two-stage mechanism: an initial solvation-controlled stage followed by a diffusion-controlled regime. COSMO-RS predictions correlated strongly with early-stage extraction behavior, whereas diffusion coefficients highlighted the influence of mass transfer properties at later stages. The proposed COSMO-RS, experimental extraction, and kinetic modeling framework, validated here for soybean oil, offers a transferable and resource-efficient platform for designing sustainable solvent-based extraction processes across diverse oilseed and natural product matrices. Full article

Warming profoundly influences soil respiration in terrestrial ecosystems, thereby altering global carbon cycling. Understanding the trends and drivers of soil respiration changes in forest ecosystems under warming is essential for assessing regional carbon budgets and ecosystem carbon sink/source dynamics. In this study, a one-year warming experiment was conducted using open-top chambers in a Taxodium hybrid (Zhongshanshan) ecosystem in the northern Jiangsu coastal area, China. Treatments included control (CK) and warming (W), focusing on soil respiration components (soil respiration, Rs; heterotrophic respiration, Rh; autotrophic respiration, Ra) and associated soil hydrothermal and nutrient factors. Results showed that both warming and season significantly affected Rs, Rh, and Ra, all exhibiting a unimodal seasonal pattern peaking in summer. Warming increased winter Ra by 117.39% (p < 0.001). Bivariate models (temperature and moisture) explained more variation in respiration (R² = 0.720–0.893) than univariate models. Correlation analysis indicated that under control conditions, Rs components were significantly positively correlated with microbial biomass carbon (MBC), ammonium nitrogen (NH₄⁺-N), and available phosphorus (AP). After warming, these positive correlations with MBC and AP persisted; however, negative correlations emerged with soil organic carbon (SOC) and its stoichiometric ratios (C:N, C:P). Additionally, Ra showed negative correlations with easily oxidizable carbon (EOC), total nitrogen (TN), and N:P. Overall, these findings suggest that climate warming may enhance soil respiration in the Taxodium hybrid (Zhongshanshan) ecosystem by altering soil thermal-hydrological and nutrient factors, although further validation is needed. Full article

Orbital remote sensing platforms increasingly rely on CNN-based object detection for real-time situational awareness. However, deploying these models on spaceborne edge devices is challenging because of stringent Size, Weight, and Power (SWaP) constraints. In addition, the branch-and-merge topology of conventional single-stage detectors increases on-chip memory usage and introduces pipeline stalls, limiting efficient FPGA implementation. To address these challenges, we proposed RS-YOLO, an object detection algorithm developed through a hardware–software co-design approach. Structural re-parameterization converts heterogeneous branches into a sequential stream of padding-free convolutions, producing a deterministic dataflow and reducing per-state combinational control complexity and data-path multiplexing overhead. To mitigate the high-entropy concentration at the center of the re-parameterized kernels, we further introduce a spatial heterogeneous quantization (SHQ) engine. The SHQ engine assigns 16-bit precision to the central coefficients while preserving vectorized 8-bit computation for peripheral elements, reducing quantization errors for small targets with minimal hardware overhead. Experimental results on the Xilinx Zynq-7020 platform show that the proposed system consumes only 2.24 W while achieving a mean Average Precision (mAP) of 0.887 on the NWPU VHR-10 dataset, representing a 1.4% decrease compared with the FP32 baseline. The system also achieves an energy efficiency of 15.19 GOPS/W, demonstrating an effective balance between hardware efficiency and detection performance for resource-constrained edge platforms such as micro-satellite payloads. Full article

Background/Objectives: The main objective of this study was to estimate 1- and 3-year overall survival and relative survival in patients aged >65 years who underwent surgery for a second contralateral hip fracture. Methods: Retrospective cohort study including patients aged ≥65 years who underwent surgery for a first or second contralateral hip fracture between June 2010 and December 2021. Overall survival (OS) was estimated from the date of surgery to death from any cause or end of follow-up. Relative survival (RS) was calculated as the ratio between observed survival and expected survival derived from general population mortality tables for Catalonia implemented in the WebSurvCa application. Results: A total of 2642 patients were included (2467 primary fractures; 175 s contralateral fractures). Patients with a second fracture were older and had worse pre-fracture functional and cognitive status. At 3 years, overall survival was 53.8% (95% CI: 51.7–55.9) after a first fracture and 43.7% (95% CI: 36.3–52.4) after a second contralateral fracture. Relative survival estimates were 70.5% (95% CI: 67.7–73.3) and 59.0% (95% CI: 49.1–70.8), respectively. Divergence between expected and relative survival became more evident at 3 years than at 1 year. Advanced age, male sex, and worse pre-fracture functional or cognitive status were associated with poorer survival in both cohorts. Conclusions: A second contralateral hip fracture may represent a clinically relevant marker of increased vulnerability and greater clinical complexity, associated with substantial excess mortality compared with general population matched by age and sex. Relative survival highlights the true prognostic burden of a primary or second fracture and supports intensive secondary prevention and tailored follow-up. Full article

Background: C-reactive protein (CRP) is a well-established marker of systemic inflammation, while Growth Differentiation Factor-15 (GDF-15) has emerged as a potential biomarker of cellular stress. Their relative perioperative dynamics remain incompletely defined. This prospective observational study aimed to explicitly characterize and compare the longitudinal dynamics of CRP and GDF-15 in patients undergoing elective knee arthroplasty. Characterizing these biomarkers in a controlled acute surgical model provides clinical relevance by mapping systemic acute-phase inflammation against cellular stress pathways, which may offer a baseline reference for evaluating chronic tissue-wasting states. Methods: This prospective observational study included 47 patients undergoing elective knee arthroplasty. After excluding nine patients with elevated baseline CRP (>10 mg/L), serial measurements of CRP and GDF-15 were analysed daily in 38 patients from pre-operation to postoperative Days 1–3. Results: CRP rose sharply after surgery, peaking on Day 3 (median: 206.0 mg/L vs. baseline: 3.0 mg/L), representing a maximal increase exceeding 6100%. GDF-15 levels also increased progressively, peaking on Day 3 (median: 1682.5 pg/mL vs. baseline: 968.8 pg/mL), which represented a statistically significant but more modest rise of 33%. CRP and GDF-15 were significantly correlated on postoperative day 1 (rs = 0.53, p = 0.001) but not days 2 and 3. Baseline GDF-15, but not CRP, was significantly associated with oral hypoglycaemic agent use and vitamin B12 supplementation. Compared to values reported in recent randomized trials of ghrelin, anti-GDF-15, and anti-IL-6 therapies, GDF-15 levels in this surgical cohort remained lower, while CRP approached levels observed in such studies. Conclusions: CRP and GDF-15 rise in parallel following surgical injury; however, GDF-15 shows a considerably lower sensitivity to the inflammatory response. These findings suggest a complementary role of GDF-15 alongside CRP in profiling the perioperative stress response. While its relatively modest acute elevation limits its utility as a primary marker of acute inflammation, this surgical stress model serves as a reference that may help contextualize biomarker profiles in chronic inflammatory or wasting conditions, such as cancer cachexia, rather than serving as a direct tool for cachexia management. Full article

Background: Osteoprotegerin (OPG) is best known for regulating bone remodeling, vascular calcification, apoptosis, and immune responses. Recent studies also suggest that OPG may play an important role in diabetic microvascular complications such as diabetic retinopathy (DR). Objectives: The aim of our cross-sectional case–control study was to assess the relationship between the OPG variants (rs2073618 and rs3134069) and DR in a large cohort of subjects with type 2 diabetes (T2DM). Methods: A total of 1554 subjects with T2DM were enrolled in this cross-sectional case–control study: 577 with DR and 977 without DR. Genotyping was performed using StepOne Real-Time PCR with TaqMan genotyping assays. Results: Our data indicated that the prevalence of the C minor allele was significantly higher among patients with DR and type 2 diabetes in both polymorphisms. Furthermore, individuals carrying the CC genotype of the rs2073618 had a 1.42-fold increased likelihood of DR, while carriers of the CC or CA genotypes of the rs3134069 exhibited a 1.55-fold higher likelihood of DR. Conclusions: Taken together with existing evidence, these results suggest that OPG polymorphisms may be involved in the development of DR. Full article

Pb-Zn resources are critical strategic assets for many nations. The Dian-Dongbei (northeastern Yunnan) region in Yunnan Province is a significant production area for these resources in China, boasting considerable prospecting potential. However, conventional exploration methods are increasingly inadequate, as they often fail to rapidly and effectively identify concealed mineralization information. To tackle this challenge, we propose a hybrid GAN-ResNet convolutional neural network methodology. This approach constructs a data-driven prospecting model for Pb-Zn deposits in the Dian-Dongbei region, utilizing multi-source geoscientific data encompassing geology, geophysics, geochemistry, and remote sensing (Geo-Phys-Chem-RS) to conduct quantitative mineral prospectivity mapping. A GAN model was introduced to augment the multi-source geoscientific data based on the concepts of random down-sampling and pseudo-window size. The quality of the generated synthetic samples was evaluated using the Peak Signal-to-Noise Ratio (PSNR) metric. The results show that the synthetic samples achieved an average PSNR value of 33.67 dB, effectively preserving the original features of the geoscientific data. This confirms the feasibility and quality of the data generated by this augmentation method. Furthermore, when applied to train the ResNet model, this augmented data effectively increased the prediction accuracy from 0.765 to 0.842. The results demonstrate that the integrated GAN-ResNet method produces prediction maps with higher accuracy. Moreover, it significantly refines and narrows down the target areas with high mineralization potential. This precision can substantially reduce exploration costs, representing a marked improvement in prediction efficacy. Full article

Background: Stroke remains a leading cause of death and long-term disability worldwide. Sleep disturbances are increasingly recognized as potential factors influencing recovery after stroke. Therefore, in this study we examined the associations of sleep disturbances and metabolic markers with post-stroke outcomes. Methods: We conducted a retrospective study of adult patients with stroke-related presentations. The primary analysis included 270 patients with follow-up mRS data. Extracted variables included demographics, vascular risk factors, stroke subtype, imaging findings, sleep features, and selected laboratory markers. Functional outcome was classified as favorable, mRS 0–2, or unfavorable, mRS 3–6. Recurrent stroke burden was analyzed as 0–1 versus ≥2 documented events. Associations and predictive performance were assessed using group comparisons, LASSO logistic regression, and random forest models with repeated 10-fold cross-validation. Results: Among 270 stroke patients, 214 (79.3%) had favorable outcomes and 56 (20.7%) had unfavorable outcomes. Sleep disturbances were common, especially nocturnal awakenings (59.6%), increased sleep apnea risk (44.4%), circadian rhythm disturbances (28.9%), and insomnia (23.7%). Unfavorable outcomes were linked to older age, cardio-aortic embolism, large vessel/cortical stroke, abnormal vascular imaging, insomnia, and lower HDL. In LASSO analysis, age, steno-occlusive/atherosclerotic imaging, cardio-aortic embolism, and insomnia predicted unfavorable outcome, while HDL was protective. For recurrent stroke, small artery occlusion and hypertension with diabetes were retained. In predictive modeling, the best random forest model showed good discrimination AUC values of 0.791 ± 0.0126. Conclusions: Poorer stroke outcomes were associated with vascular factors, insomnia, and low HDL; recurrent events were mainly associated with small artery occlusion. Full article

Background: Over the past two decades, the widespread use of flow diversion stents (FDS) has significantly expanded treatment options for patients with intracranial aneurysms (IA). However, FDS implementation requires antiplatelet therapy (APT), the optimal regimen for which remains undefined. This study aimed to evaluate light transmission aggregometry (LTA) as a routine tool for monitoring antiplatelet therapy during flow diversion to examine whether an LTA-based residual-reactivity cut-off is associated with thromboembolic complications and to assess the agreement between LTA and VerifyNow (VN) P2Y12 reaction units (PRU), rather than to establish definitive safety thresholds. Methods: A retrospective analysis was conducted across two expert centers on 771 patients. Based on selection criteria, 203 IA patients who underwent FDS implantation between 2019 and 2023 with LTA-guided APT (clopidogrel plus aspirin) were included; LTA was used for monitoring in all 203 patients, and a subset of 84 patients additionally underwent VerifyNow testing for method comparison. The primary outcome was clinically significant thromboembolic complications (TECs) leading to a reduction in quality of life by ≥1 point on the modified Rankin scale (mRS). The secondary outcome was intracranial hemorrhage. Results: Among the 203 patients (203 FDS implantations), dual aggregometry control (LTA + VN) was performed in 84 cases. At the 12-month follow-up, complete or near-complete aneurysm occlusion was radiologically confirmed in 83.7% of patients. A favorable functional outcome (mRS ≤ 2) at 12 months was observed in 97.5% of cases. Thromboembolic complications occurred in 7 patients (3.4%) and hemorrhagic complications in 4 (2.0%). On receiver operating characteristic (ROC) analysis, the control-LTA cut-off of 44% showed only weak, statistically non-significant discrimination for thromboembolic events (area under the curve, AUC 0.700, 95% confidence interval, CI 0.479–0.920; p = 0.073). In the 84 patients tested with both assays, LTA and VerifyNow PRU correlated only moderately (Spearman’s ρ = 0.51; Pearson’s r = 0.53) and showed wide Bland–Altman limits of agreement, indicating that the two methods are not clinically interchangeable. Conclusions: Routine platelet-function aggregometry may be a useful tool for monitoring APT during flow diversion. In this retrospective cohort, lower residual platelet reactivity on LTA was associated with fewer thromboembolic and hemorrhagic events; however, given the small number of outcome events (n = 7) and an LTA cut-off whose discriminative ability did not reach statistical significance, this association is exploratory and hypothesis-generating rather than an established safety threshold. LTA and VerifyNow PRU were not clinically interchangeable, so no VerifyNow cut-off is proposed. Prospective, adequately powered studies with outcomes stratified directly by each assay are required to define and validate clinically applicable thresholds. Full article

Background: A widely cited 2024 analysis reported that only 5% of therapeutic interventions tested in animals obtain regulatory approval for human use, reviving the debate about the predictive value of animal models. This review asks how the scientific community has interpreted that finding and what collective position is emerging. Methods: The literature citing the original study was assembled from public citation indexes and classified by argumentative stance into five categories: defences of animal experimentation, critiques, methodological and disease-specific analyses, the author’s own position, and work developing non-animal methods; no new animal data were generated. Results: Across the corpus, attempts to strengthen the validity of animal studies tended to reduce rather than reinforce their apparent translational signal; the low translation rate recurred across unrelated disease domains; and even defenders of animal models increasingly restricted their claims to specific contexts while conceding poor translation. In parallel, non-animal methods—including organoids, organ-on-chip systems, and computational models—were repeatedly described as equalling or surpassing animal models in several fields. Conclusions: The scientific, ethical, economic and regulatory cases now converge, supporting a deliberately planned transition toward human-relevant methods rather than its deferral. Full article

Accurate prediction of tunnel water inflow in water-rich fault zones is important for groundwater control design and construction risk prevention. In this study, a per-linear-meter tunnel water inflow database containing 425 valid samples was established through orthogonal numerical simulations based on a three-dimensional steady-state seepage model with a grouting ring. The input variables included four hydraulic and grouting parameters and two excavation-position descriptors, namely the excavation-position distance and excavation-position category, thereby reflecting both the water-blocking effect of grouting reinforcement and the spatial variation in water inflow as the excavation face approached the fault zone. Considering that the samples were generated from 25 orthogonal simulation cases at different excavation positions, grouped validation was adopted to reduce information leakage at the simulation-case level. Four baseline machine learning models, including SVM, RF, XGBoost, and CatBoost, were evaluated using ten repeated grouped hold-out validations. CatBoost achieved the best overall baseline generalization performance, with an average test R² of 0.6209 ± 0.0405, MAE of 0.1084 ± 0.0079, and RMSE of 0.1555 ± 0.0085. CatBoost was therefore selected for further hyperparameter optimization. Subsequently, random search, Bayesian optimization, the Osprey Optimization Algorithm, and the Grey Wolf Optimizer were compared under the same search space and computational budget. Hyperparameter optimization was conducted only within the training set using grouped cross-validation, and the independent grouped test set was used only for final evaluation. The results showed that the unoptimized CatBoost model achieved the best overall balance between prediction accuracy, stability, and computational efficiency. Although RS-CatBoost slightly improved MAE and MAPE among the optimized models, none of the optimization strategies consistently outperformed the unoptimized CatBoost baseline, indicating that the choice of hyperparameter optimization algorithm played a secondary role under the current dataset and grouped-validation framework. The proposed framework is intended as a preliminary modeling reference under controlled numerical simulation conditions, and its practical engineering reliability requires further validation using field monitoring data or independent benchmark cases. Full article

Helicobacter pylori possesses an unusually high number of restriction–modification (R-M) systems relative to its small genome, contributing to a methylome increasingly implicated in bacterial gene regulation. In this study, we analyzed the methylomes of two mutant strains of H. pylori 26695: ∆rdxA (control) and ∆rdxA/∆arsS. Each mutant was cultivated under neutral (pH 7) and acidic (pH 5) growth conditions. We identified one conspicuous hypomethylated region of 21 kBp possessing 21 annotated genes across each methylome. Notably, over 600 protein coding regions and 10 different promoters displayed differential methylation between pH conditions, including several virulence factors. The vacA gene, encoding the Vacuolating Cytotoxin A, exhibited eight differentially methylated positions between pH 7 and pH 5 within the H. pylori 26695 control mutant methylome, potentially contributing to its previously documented 32-fold down regulation of mRNA in acidic environments. pH-dependent methylation changes were widespread within the cag pathogenicity island, genes encoding cell envelope proteins including adhesin-encoding sabA, babA, and hopQ, and numerous flagellar-associated genes. These results reveal the plasticity of the H. pylori methylome and suggest that DNA methylation is responsive to environmental pH in both ArsRS-dependent and independent manners. Methylome dynamics may serve as an important layer of gene regulation in acclimation to hostile gastric environments and promote persistent infection. Full article