Abstract: This paper presents a further step in the development of scale invariant fully conformal cosmology (FCC), formulated in our previous study. Whereas the previous paper focused mainly on the global cosmological consequences of the fully conformal metric and their confrontation with selected astrophysical data, here we analyze its local gravitational and background consequences. On the background of the fully conformal metric we formulate an effective generalization of the weak Schwarzschild field in the corresponding FCC global coordinates and derive from it the associated modified intensity of the Newtonian central field. We further derive the cosmological state/constitutive equation p = − ε/3 as a direct consequence of the fully conformal metric rather than as an ad hoc additional postulate. Likewise, within the fully conformal metric, spatial flatness and the critical density ρcrit are understood as direct consequences of this metric structure rather than as independently postulated inputs. From the condition of global equilibrium between negative cosmological pressure and the gravitational cohesive pressure of homogeneously distributed matter, the effective particulate fraction is obtained as β ≈ 0.45 of the total critical density ρcrit. For the relatively well-confirmed baryonic matter fraction , this stable-equilibrium condition then leads to the corresponding particulate fraction of collisionless dark matter , which is in principle determined by the global cosmological equilibrium within this framework. Because direct identification of the entire dark fraction with standard collisionless cold dark matter would very probably be incompatible with the main structural observables, we discuss an effective phenomenological decomposition into a structuring cold dark matter component (cdm) and an almost homogeneous residual warm-dark-matter-like component (wdm). In this interpretation, the paper preserves the previously introduced global FCC framework while simultaneously providing a concrete background prediction for the matter content and a physically motivated basis for further testing of structure formation within scale invariant fully conformal cosmology.

Abstract: Chronic stress, circadian disruption, sedentary behavior, industrialized diets and disturbances in the gut microbiome have created an evolutionary mismatch between ancestral physiology and the modern environment. Generation X (Gen X) women (born between 1965–1980) are the first cohort to enter midlife having lived their entire adult lives within these conditions while also carrying distinct cohort-specific factors shaped by major economic and cultural transitions. The interaction of evolutionary mismatch and Gen X pressures destabilizes hormonal regulation, increases allostatic load and impairs mitochondrial function, contributing to fatigue, metabolic inflexibility and cognitive dysfunction during perimenopause and menopause, with implications for postmenopausal health and long-term disease risk. Women with polycystic ovary syndrome have reduced insulin sensitivity and a heightened proinflammatory response that makes them more susceptible to Gen X evolutionary mismatch pressures. This paper synthesizes evidence from evolutionary biology, endocrinology, neuroscience and lifestyle medicine to present an integrated model explaining the mechanisms driving midlife symptomatology in Gen X women. The model places midlife dysfunction within an evolutionary mismatch context, where modern environmental exposures and cohort-specific demands interact with hormonal, immune and metabolic changes to drive convergent pathophysiological mechanisms. A tiered recovery framework is proposed, targeting allostatic load reduction, circadian realignment, restoration of metabolic flexibility, and integration of mitochondrial, musculoskeletal and gut–brain–endocrine signaling systems.

Abstract: The color origin of precious coral, a highly valued organic polycrystalline gemstone, has long remained elusive. In this study, an integrated approach employing spectrophotometry, Raman, FTIR, and UV-Vis spectroscopy, coupled with Spearman correlation analysis, was utilized to investigate a color-graded series of precious coral samples ranging from white to red. The results demonstrate that the calcareous skeleton consists exclusively of calcite. The actual chromophores are identified as a blend of multiple distinct polyene species, characterized by Raman shifts at 1126 and 1515 cm⁻¹. Inherently exhibiting a red-orange hue, the progressive accumulation of these polyenes drives a systematic color transition from orange to red.Both absorption bands at 314 nm and 532 nm in the UV-Vis spectra originate from the polyene pigment molecules. Specifically, the broad 532 nm band is dominated by π-π* electronic transitions. As the pigment concentration increases, this band exhibits pronounced broadening and enhancement, accompanied by a redshift of the maximum absorption peak. This spectral evolution leads to an intensified absorption in the yellow-orange region, elucidating the intrinsic mechanism underlying the color transition of precious coral from orange to red with increasing pigment content. This work lays a solid foundation for the non-destructive identification of precious corals and future research on their color genesis.

Abstract: Background/Objectives: The optimal upfront modality selection for real-world aneurysmal subarachnoid hemorrhage (aSAH) remains uncertain. This study evaluated the clinical outcomes associated with an institutional practice change from an endovascular treatment (EVT)-first approach to a more individualized upfront modality-selection strategy. Methods: This single-center retrospective before-and-after cohort study included consecutive patients with aSAH who underwent aneurysm securing during two fixed time periods (pre-change: 1 May 2023 to 31 July 2024; post-change: 1 August 2024 to 31 October 2025). The primary outcome was a favorable 90-day modified Rankin Scale (mRS) score of 0–2. The primary adjusted analysis used Firth penalized logistic regression with adjustment for age (per 10-year increase), pre-morbid mRS ≥ 2, and admission World Federation of Neurosurgical Societies (WFNS) grade IV–V. Conventional logistic regression was performed as a sensitivity analysis. The full 90-day mRS distribution (0–6) was also evaluated using an adjusted proportional odds model. Results: A total of 104 patients were included (pre-change, n = 48; post-change, n = 56). The distribution of securing modalities changed substantially between the two periods (EVT, 79.2% vs. 37.5%; microsurgery, 20.8% vs. 62.5%; p < 0.001). Favorable outcomes occurred in 25/48 patients (52.1%) in the pre-change period and 36/56 patients (64.3%) in the post-change period (p = 0.235). In adjusted analyses, the post-change period was associated with higher odds of a favorable outcome (adjusted odds ratio [aOR], 3.82; 95% confidence interval [CI], 1.31–12.79; p = 0.009), consistent with the sensitivity analysis (aOR, 4.41; 95% CI, 1.43–15.95; p = 0.009). Shift analysis also favored the post-change period (adjusted common OR, 2.36; 95% CI, 1.15–4.91; p = 0.021). Secondary outcomes and perioperative complications were similar between the two periods. Conclusions: In this single-center retrospective before-and-after study, an institutional practice change toward more individualized upfront modality selection was associated with more favorable adjusted 90-day functional outcomes in patients with aSAH. These findings support the potential clinical relevance of individualized modality selection in real-world aSAH management, although confirmation in multicenter studies is warranted.

Abstract: The growing adoption of distributed renewable energy systems (DRES) calls for ad-vanced planning methodologies capable of addressing their inherent complexity and multi-dimensional trade-offs. Multi-Criteria Decision-Making (MCDM) frameworks are widely used to balance diverse objectives, but their effectiveness depends heavily on the selection of criteria, weighting techniques, and integration methods. This paper undertakes a systematic review of existing literature to analyse how MCDM ap-proaches have been applied in the planning and optimization of DRES projects. The review focuses on the criteria considered in MCDM, the techniques used to assign their relative importance, and the methods employed to integrate these weights into mul-ti-objective evaluations. The analysis draws from a diverse set of peer-reviewed pa-pers, examining economic, technical, environmental, and social dimensions, as well as the relationships between project-specific features and the criteria selection process. Results show that social criteria remain underrepresented both in terms of frequency and of relative importance in the evaluation process, while economic criteria are the most used and influential, underlining the need for more balanced, context-sensitive, and socially inclusive MCDM frameworks. Among MCDM methods and weighting methods, TOPSIS and AHP are by far the most common approaches, respectively. This review provides a foundation for future research aimed at improving the adaptability and effectiveness of MCDM frameworks in DRES.

Abstract: This paper proposes a novel framework, HyperGAT-BERT-RAS, that integrates: (1) a Hy perGraph Attention Network (HyperGAT) with BERT for enhanced semantic representa-tion; (2) a Reference Answer Set (RAS) constructed via clustering of full-score answers; (3) Siamese Neural Networks (SNNs) for similarity-based scoring; and (4) GPT-4-based data augmentation to address class imbalance. Experiments on the Ohsumed and ASAP-5 da-tasets demonstrate that: (i) HyperGAT-BERT achieves 0.7317 accuracy on Ohsumed text classification, outperforming baseline HyperGAT by 2.69%; (ii) the full Hyper-GAT-BERT-RAS achieves 0.7991 accuracy and 0.7956 F1-score, with RAS contributing the most to performance gains (4.34% accuracy drop when removed); (iii) GPT-4 augmentation improves Quadratic Weighted Kappa from 0.584 to 0.880 and minority-class (scores 2–3) F1 by 15.3%. These improvements translate into more reliable scoring of diverse student answers, reduced teacher grading burden, and enhanced feasibility of AI-assisted forma-tive assessment in real classrooms. Ablation and error analyses confirm the contribution of each component. The framework advances ASAG by synergizing graph-based relational modeling, pretrained language understanding, and knowledge-guided scoring.

Abstract: Background/Objectives: This study evaluates the prognostic value of baseline inflam-matory biomarkers neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), lymphocyte-to-monocyte ratio (LMR), systemic immune-inflammation index (SII) and pan-immune-inflammation value (PIV) in advanced cutaneous melanoma treated with first-line immunotherapy. Methods: This multicenter retrospective study included 162 patients with unresectable stage III/IV cutaneous melanoma treated with first-line pembrolizumab, nivolumab, or nivolumab plus ipilimumab. Biomarkers were calculated from complete blood counts obtained within 30 days before treatment start. Cut-offs were defined by ROC analysis. Progression free survival (PFS) and overall survival (OS) were analyzed using Kaplan–Meier and Cox regression. Response was assessed by RECIST v1.1. Results: Higher baseline NLR, PLR, MLR, SII and PIV were more common in patients with adverse baseline features, including liver metastases, elevated LDH and poorer ECOG performance status. Patients with biomarker values below the cut-offs had sig-nificantly longer PFS and OS. In multivariable models adjusted for clinical covariates, PIV remained independently associated with the duration of PFS and OS; MLR inde-pendently predicted PFS, while PLR independently predicted OS. Conclusions: Baseline inflammatory biomarkers from routine blood counts provide useful prognostic information in advanced melanoma treated with first-line ICIs. PIV showed the most consistent independent association with survival outcomes and may support initial risk stratification alongside LDH, ECOG and metastasis pattern. However, prospective validation in independent cohorts is needed before routine clinical implementation.

Abstract: Metal binder jetting (MBJ) is an additive manufacturing (AM) process that offers advantages such as high speed, low cost, and low residual stress, compared to the prevalent fusion-based metal AM methods. However, a major barrier to MBJ is the low density of manufactured parts, which restricts part quality and limits its applications. One key process parameter that affects part density is the packing density of the powder bed. In general, a higher packing density is preferable in MBJ. Although research has been conducted to enhance the packing density ex-situ, most proposed approaches lack robustness when applied to real-world printing, where environmental variations and stochastic powder behavior introduce inconsistencies. An in-situ sensing method for packing density can mitigate these issues in several ways. It enables the implementation of feedback control strategies to regulate packing density during printing, contributes to comprehensive in-situ process monitoring, and provides quantitative data to support post-processing analysis and optimization. However, effective in-situ methods for accurately sensing packing density remain limited. To fill this research gap, two methods, namely ultrasound (acoustic) and recoating-force sensing, are proposed as potential approaches for in-situ sensing of powder packing density. Using a dedicated test platform, their responses to different powder bed packing densities are measured and compared. The results show a strong correlation between packing density and the sensor measurements, with differing levels of estimation confidence, demonstrating promising potential for their implementation as in-situ packing density sensors. Furthermore, the concept of sensor fusion is tested by combining the force-sensing and acoustic-sensing data, leading to improvements in the estimation confidence.

Abstract: When placing dental implants, xenografts are most commonly used clinically to compensate for insufficient bone volume of patients. However, xenografts have limitations including low osteoinductive capacity and prolonged healing time. The aim of this study was to evaluate the effects of non-thermal plasma-treated bovine cancellous bone graft on new bone formation, graft resorption, bone marrow formation, and vascularization in a rabbit calvarial defect model. Twenty-four adult male New Zealand white rabbits received bilateral 8-mm critical-size calvarial defects. One defect was filled with untreated SANTA-OSS® (control) and the contralateral defect with plasma-treated SANTA-OSS using the ACTILINK™ Reborn device. Animals were sacrificed at 2, 4, and 8 weeks (n=8 per group) for histomorphometric analysis. The plasma-treated group showed significantly higher new bone area (14.12 ± 0.69%, 18.93 ± 0.68%, and 32.72 ± 0.61% at 2, 4, and 8 weeks) than the control at all time points (p < 0.05). In addition, the experimental group exhibited accelerated graft resorption, larger bone marrow area, greater blood vessel area, and more TRAP-positive osteoclasts compared with the control (p < 0.05). Within the limitations of this study, plasma treatment significantly enhanced new bone formation, accelerated graft resorption, promoted bone marrow development, and increased vascularization.

Abstract: Soil salinization severely limits the stable production of garlic (Allium sativum L.) and compromises the postharvest storability of seed cloves as industrial planting materials. This study evaluated the morpho-physiological, photosynthetic (JIP-test), and postharvest responses of a shoot-dominant ('C-P') and a root-dominant ('J-L') garlic cultivar to graded salinity (0, 50, 200 mM NaCl) in a hydroponic system, with or without seed-clove priming using a novel commercial biostimulant. Results showed 50 mM NaCl significantly inhibited shoot growth, while 200 mM nearly arrested growth and induced clove decay. Under moderate salinity, LE priming exhibited cultivar-dependent mitigation. In 'C-P', it promoted root branching, enhanced soluble sugar accumulation, and improved postharvest tissue hydration. In 'J-L', biostimulant elevated leaf SPAD values, fully reversed stress-induced clove yellowing, and significantly suppressed postharvest fungal decay during cold storage. In conclusion, garlic's response to salinity is fundamentally dictated by intrinsic resource allocation strategies. Rather than merely promoting growth, biostimulant priming optimizes photosynthetic energy fluxes and reshapes metabolism. This tailored approach effectively preserves the visual marketability of susceptible cultivars while enhancing Osmo protectant accumulation and hydration in vigorous morphotypes, providing a sustainable strategy to safeguard industrial raw materials in salinized controlled cultivation systems.

Abstract: Mathematics, as actually practiced, operates as a federated system: practitioners work within autonomous domain-specific axiomatizations (geometry, algebra, analysis) and construct explicit bridges only when cross-domain reasoning is required. This organization is not accidental; it is a structural adaptation that safeguards local decidability and algorithmic efficiency. Yet the dominant foundational narrative still operates on the Compiler Myth—the belief that all mathematics must theoretically compile down into ZFC set theory to achieve rigor. We argue that this monolithic reductionism confuses representational universality with logical priority. Embedding a decidable (tame) domain into an undecidable (wild) one does not clarify foundations; it imposes a crippling epistemic overhead. It buries efficient, domain-specific decision procedures under general proof search and destroys the native structural immunities of the object. We introduce the Decidability Threshold — a litmus test based on Negation, Representability, and Discrete Unboundedness — to explain why mathematicians instinctively isolate tame domains from wild ones. Finally, we distinguish the Mathematician (builder of formal systems) from the Scientist (consumer modeling empirical reality). We argue that federalism is not a failure of unification, but the primary safeguard preventing the scientist from inadvertently importing uncomputable, undecidable paradoxes into physical theories. We show that for empirical applications, syntactic safety is insufficient; valid scientific modeling must be strictly confined to the constructively computable sub-fragments of these domains.

Abstract: Following the global financial crisis, the transition to IFRS 9’s forward-looking Ex-pected Credit Loss (ECL) model has introduced significant implementation complexity, particularly in emerging markets facing data limitations. This study investigates the heterogeneous ECL compliance strategies adopted within the Cambodian banking sector during a period of heightened credit stress, marked by a system-wide non-performing loan ratio of 8.6%. Utilizing a multiple-case study design and replication logic, a quali-tative content analysis was conducted on the 2024 audited financial statements of 13 representative institutions, ranging from market leaders to international subsidiaries. The findings reveal a pronounced technical divide: market leaders utilize advanced internal statistical methods, such as cohort analysis, while international subsidiaries rely on top-down parent-group proxy models to bridge local data gaps. A “macro-correlation paradox” was identified, where certain institutions prioritize faithful representation by excluding macroeconomic variables when statistical links to historical defaults remain weak. Furthermore, a significant transparency gap exists, where granular disclosures are leveraged as strategic communication tools to signal institutional safety. These results suggest that ECL compliance in data-limited environments is a strategic management choice rather than a standardized technical exercise, highlighting the need for regulatory standardization of modeling assumptions to improve inter-bank comparability.

Abstract: Low-light image enhancement aims to recover high-quality visuals from poorly illuminated inputs, yet existing methods often suffer from over-enhancement, noise amplification, and semantic inconsistency in complex scenes. In this paper, we propose SeMaNet, a novel semantic-guided framework that integrates textual priors with a hybrid Transformer-Mamba architecture for controllable and efficient low-light enhancement. Our approach begins by leveraging pre-trained CLIP to generate semantically meaningful attention maps from natural language prompts, enabling interpretable region-aware enhancement without requiring pixel-level annotations. These semantic priors are then fused with illumination estimates and raw image features through a cross-attention mechanism, allowing dynamic interaction among multi-modal cues. To balance global context modeling and computational efficiency, we design a U-Net-based restoration network that interleaves Transformer blocks for long-range dependency capture and Mamba layers for linear-time sequence processing. Furthermore, our method explicitly models the image formation process via a perturbation-aware Retinex decomposition, enhancing physical plausibility. Extensive experiments on LOL v1, LOL-v2-real, LOL-v2-synthetic, SID, SMID, and SDSD-out datasets demonstrate that SeMaNet achieves state-of-the-art performance in both quantitative metrics (PSNR, SSIM) and qualitative quality, particularly excelling in preserving semantic coherence and fine details under challenging lighting conditions. The hybrid architecture also offers superior inference efficiency compared to pure Transformer-based models.

Abstract: We propose a structural framework for understanding quantum computational advantage based on admissible continuation of configurations. Within this framework, quantum computation is interpreted as the organization of admissible histories whose contributions combine through phase coherence, in a manner related to path-integral formulations of quantum mechanics. We identify three fundamental structural resources: the multiplicity of admissible histories, the persistence of phase coherence, and the non-factorizable structure of continuation constraints (entanglement). We introduce the notion of effective coherent multiplicity as a measure of the portion of history space that contributes constructively to computational outcomes, and formulate a structural speedup conjecture relating superpolynomial quantum advantage to its growth under bounded instability. This perspective provides a unified explanation of both the power and the limitations of quantum computation, clarifying why unstructured problems admit limited speedup while problems with strong global structure can exhibit substantial advantage. The framework complements standard circuit-based complexity theory by relating computational power to the organization of admissible-history space.

Abstract: Background: Substance Use Disorder (SUD) is characterized by recurrent craving episodes frequently associated with emotional dysregulation and altered reward processing. This study aimed to evaluate whether emotional states associated with craving episodes can be detected through automated facial emotion recognition during controlled emotional induction. Methods: Forty-one participants completed a 14-day ecological momentary assessment (EMA) monitoring anxiety and craving levels, followed by an emotional induction task using standardized stimuli from the EmoMadrid database and addiction-related images. Facial expressions were recorded and analyzed in real time using a computational facial emotion recognition model trained on the FER-2013 dataset. Results: Participants with SUD exhibited significantly reduced positive emotional valence and activation in response to positive stimuli compared with HC (p &lt; 0.01). Item-level analyses revealed that most differences occurred in stimuli depicting social interactions. Positive emotions and energy were linked to less intense cravings and shorter substance use. People with SUD showed more fear and less disgust in their facial expressions than controls (p = 0.02). Conclusions: These results suggest that people with SUD have changes in how they process emotions, showing less response to positive things and unique facial expressions related to craving.

Abstract: Pathological complete response (pCR) after neoadjuvant therapy is an important indicator of treatment response and prognosis in breast cancer. Multi-modal breast MRI provides complementary information for pCR prediction, but existing methods often assume complete modality availability and do not fully exploit the complementary value of radiomics and deep features. To address these limitations, we propose a radiomics-guided multi-modal learning framework for pCR prediction from breast MRI under incomplete modality settings. The model employs a multi-branch 2.5D encoder to extract modality-specific features, a radiomics-guided gating module to enhance deep representations with handcrafted priors, and a masked fusion strategy to adaptively integrate available modalities while excluding missing ones. Experiments on the I-SPY1 Trial dataset show that the proposed method achieves promising performance and maintains robustness under incomplete modality settings. These results suggest that the proposed framework can effectively integrate multi-modal MRI and radiomics information for pCR prediction and shows potential under incomplete modality settings.

Abstract: This study presents a hybrid additive manufacturing approach to fabricate bioinspired stainless steel 316L-copper (SS316L-Cu) multimaterial structures using laser powder bed fusion (LPBF). The present study incorporates honeycomb lattice structures with varying wall thicknesses (0.25 mm, 0.5 mm, 0.75 mm, and 1.0 mm) to investigate the effect of geometric parameters on mechanical performance. Mechanical testing was conducted according to ISO 6892 standards, and the results revealed a strong dependence of tensile strength and ductility on lattice thickness. Copper (Cu) infiltration into SS316L lattice structures improved ductility by 30% compared to the monolithic SS316L lattice, with minimal compromise in tensile strength. To complement experimental results, molecular dynamics (MD) simulations were performed to study atomic-scale deformation and validate the trend of strength enhancement with increasing wall thickness. The findings demonstrate the potential of combining LPBF and liquid Cu infiltration to develop multifunctional, mechanically robust, and thermally conductive metallic composites. This approach provides valuable insight into structure–property relationships and supports the design of next-generation multifunctional composites for structural and thermal applications.

Abstract: The growing deployment of green hydrogen technologies is increasing pressure on freshwater resources, motivating the exploration of alternative water sources that do not compete with human consumption. In this work, the direct use of untreated produced water from the Shushufindi 78 oil well (Ecuador) as an electrolyte for the hydrogen evolution reaction (HER) was experimentally evaluated. A comprehensive physicochemical characterization (ICP-OES, anions, BTEX), combined with electrochemical techniques (cyclic voltammetry, Tafel analysis, electrochemical impedance spectroscopy) and gas chromatography, was performed to correlate electrolyte composition with electrochemical performance. Despite the high salinity (~52 ± 5 g·kg⁻¹) and complex matrix composition, hydrogen production was achieved without pretreatment. Absolute hydrogen quantification yielded 10.29 µmol after 4 h of electrolysis, corresponding to a Faradaic efficiency of 43.8% and an electrical efficiency of 54.1% under non-optimized conditions. Comparative gas chromatography experiments using different electrolyte compositions revealed that alkaline systems, particularly mixtures of produced water with KOH, enhance hydrogen production, as evidenced by increased chromatographic peak areas. Impedance analysis showed reduced ohmic losses with KOH addition, while mineral scaling (CaCO₃ and Mg (OH)₂) increased interfacial resistance and reduced catalytic activity. These results demonstrate the feasibility of using produced water as an electrolyte for hydrogen production, highlighting the critical role of electrolyte composition within a circular economy framework.

Abstract: Forests of Lualaba Province (DR Congo) form a compositionally complex mosaic of dry dense forest, gallery forest, and Miombo woodland. Yet, categorical land-cover maps impose discrete boundaries on these inherently continuous vegetation gradients, systematically discarding subpixel compositional information critical for forest monitoring and carbon accounting. The magnitude of this information loss at the landscape scale, however, remains largely unquantified. In this study, we train a Multi-Output Neural Network (MONN) using Sentinel-2 spectral and textural predictors (2025) to estimate the proportional cover of three forest types across the province. Model performance is benchmarked against a normalised Random Forest (RF) using spatial block cross-validation. Categorical information loss is quantified pixel-wise using two complementary metrics, dominant class proportion and Shannon compositional entropy, alongside a derived interpretive quantity, categorical information loss. The MONN slightly outperformed RF (R² = 0.648 vs 0.630; RMSE = 0.224 vs 0.229), yet the results reveal a fundamentally heterogeneous landscape structure. The mean dominant-class proportion was only 56.2%, indicating that categorical maps discard, on average, 43.8% of compositional information per pixel. Only 7.9% of forested pixels exceeded the 75% dominance threshold, while Shannon entropy reached 74.1% of its theoretical maximum, indicating that forest types coexist in near-equal proportions across most pixels. This renders categorical attribution structurally inadequate for most of the forested landscape. Across 92.1% of forested pixels, no single forest type achieved clear dominance. These results show that compositional mixing is the dominant structural condition of the landscape, and that compositional mapping is essential for representing tropical forest structure in heterogeneous drylands. By formally quantifying categorical information loss at the landscape scale, this study shows that continuous compositional mapping converts this structural ambiguity into a spatially explicit ecological signal, with direct implications for monitoring vegetation dynamics and biodiversity, highlighting a structural source of error in carbon stock estimation in tropical dry forests.

Abstract: Background/Objectives Ki‑67 is widely used as an immunohistochemical marker of tumor proliferation in hormone receptor–positive (HR‑positive), HER2‑negative breast cancer, but its inter-pretation is limited by variability and uncertain concordance with genomic assays. The Oncotype DX® Recurrence Score (RS) is a validated multigene assay with established prognostic and predictive utility. This study evaluated the relationship between Ki‑67 and RS in clinical practice. Methods We retrospectively analyzed women in Greece with early‑stage estrogen receptor–positive, HER2‑negative breast cancer without distant metastasis (pM0) who under-went Oncotype DX testing between 2020 and 2023. Eligible patients were node nega-tive or postmenopausal with node positive disease. RS was categorized as low (0–25) or high (>25). Ki‑67 was assessed using binary (< 20% vs ≥20%) and three‑tier (≤5%, >5%–< 30%, ≥30%) classifications. Associations were analyzed using correlation, con-cordance, and nonparametric methods. Results Among 2,967 patients, the median RS was 16, with similar distributions across nodal subgroups. Ki‑67 and RS demonstrated a moderate positive correlation as continuous variables (r = 0.42, p <  0.001). After stratification, associations with RS were observed only in tumors with high Ki‑67 expression, whereas no correlation was detected in low or intermediate groups. RS distributions differed significantly across Ki‑67 strata. Overall concordance between Ki‑67‑based proliferation categories and RS‑based ge-nomic risk was 56.2%, with discordant cases in both directions. Conclusions Ki‑67 shows a moderate association with Oncotype DX RS, but substantial discordance indicates it should not substitute genomic testing in HR‑positive/HER2‑negative early breast cancer.