Search Results (89)

Objective: Quantib^® Prostate is a commercially available machine learning software (MLS) used for prostate magnetic resonance imaging (MRI), approved by the Food and Drug Administration (FDA) and Conformité Européenne (CE). We aimed to assess the agreement between biparametric MRI (bpMRI) interpretations produced by radiologists and by Quantib^® alone. Material and Methods: This single-centre, retrospective, and observational study included 188 bpMRI scans from the Innlandet Hospital Trust, Norway. Radiologists’ Prostate Imaging Reporting and Data System (PI-RADS) scores were compared with scores produced by the Quantib^® Prostate PI-RADS, which was used as an autonomous software. Additionally, for exploratory purposes, the PI-RADS scores were compared with biopsy findings, using the radiologist’s scorings to determine biopsy indications. Results: For PI-RADS scores generated by radiologists vs. those produced by Quantib^® Prostate, the weighted kappa was 0.49 (95% CI: 0.37–0.59), and 46% of the PI-RADS scores differed by at least one unit. In the sample, 125 men (66%) had a prostate biopsy, and for male patients with PI-RADS scores of 4–5, the probability of detecting cancer grades 2–5 via biopsies was 64% (95% CI: 56–71) for radiologists and 58% (95% CI: 54–62) for Quantib^® Prostate. The main limitations of this study are the retrospective, single-centre design, the small sample size, the use of Quantib^® Prostate in an autonomous manner, and verification bias, as the biopsy decisions were based solely on PI-RADS scoring by radiologists. Conclusions: We identified inter-rater variability between PI-RADS scores generated by Quantib^® Prostate and radiologists’ assessments, and the agreement between the two scoring groups is moderate. Our results suggest that Quantib^® Prostate requires further clinical comparative studies before it is used in clinical routines. Full article

(1) Background: Provisional restorations play a crucial role in maintaining oral function and must exhibit adequate mechanical properties, particularly fracture resistance, to ensure structural integrity throughout the provisional phase. The aim of this study was to compare the flexural strength and modulus of materials used for provisional dental prostheses, with and without fiber or metal reinforcement. (2) Methods: Standardized specimens (2 × 2 × 25 mm) were fabricated from an acrylic resin (Unifast LC), a 3D-printed resin (NextDent C&B), and a bis-acryl resin (Luxatemp Fluorescence). For each material, four experimental subgroups were established: no reinforcement, two types of glass fiber reinforcement (EverStick C&B and EverStick Post NET), and metal reinforcement. Specimens were subjected to a three-point bending test. Flexural strength and flexural modulus were analyzed using a two-way, non-parametric ANOVA with the aligned rank transform. The significance level was set at 0.05. (3) Results: Material type and reinforcement strategy significantly affected flexural strength and flexural modulus. Fiber reinforcement with EverStick C&B yielded the highest values across all materials, particularly in the acrylic resin. Metal reinforcement showed moderate improvements, whereas EverStick NET had limited or no effect and reduced strength in the bis-acryl resin. Reinforced specimens exhibited altered fracture behavior, preventing complete separation after failure. (4) Conclusions: Fiber reinforcement, particularly with EverStick C&B, significantly enhances the flexural strength and modulus of provisional materials. The reinforcement performance is dependent on its type and material interaction, modifying fracture behavior by preventing complete separation. Full article

Mass-based circularity indicators, such as ISO 59020:2024, quantify material recovery as a share of total throughput but do not account for chemical composition or functional performance, as a consequence of their sector-agnostic design. In copper metallurgical systems, trace tramp elements (e.g., As, Sb, Bi, Fe, Sn, Ni) present in WEEE-derived scrap, anode slimes, and refinery residues can significantly reduce electrical conductivity. Even at nominal purities of ≥99.7 wt.% Cu, conductivity may drop to 85.0–88.0% IACS, as illustrated by selected reported cases—a level of functional degradation that remains undetected by mass-based accounting. Analysis of Grade A cathode standards (EN 1978:2022, LME Cu-CATH-1, ASTM B115-10:2021) shows that impurity limits as low as 2 ppm (Bi) constrain the achievable share of secondary feed in closed-loop recycling. For a specific flash-smelting–refinery configuration, modeling indicates that secondary feed shares above approximately 30% may lead to impurity accumulation beyond the stated specification constraints unless low-impurity primary copper is introduced. This study introduces the Quality-Adjusted Circularity Indicator (QACI), a conceptual, specification-constrained indicator framework that applies a dilution factor f_dil derived from a binary blending mass balance to adjust ISO 59020:2024 inflow-based circularity indicators using a feed-composition blending constraint anchored to Grade A specification limits. The QACI functions as a feed-composition screening indicator operating at the anode blending stage and does not represent a correction of the full electrorefining system. Parametric scenario analysis across six stylized impurity configurations shows that, at identical mass-based circularity (C_mass = 25%), the QACI ranges from 7.1% to 25.0%. This corresponds to a 1.3- to 3.5-fold difference between the mass-based and quality-adjusted indicator values under the stated feed-composition assumptions, illustrating the potential overestimation introduced when feed-quality constraints are not considered. This ratio quantifies the divergence between two indicator values under stylized conditions and should not be interpreted as a directly measured fold-difference in actual loop-closure performance. Positioned within the ISO 59020:2024 Annex C complementary method space, the QACI is positioned as a first-order screening approach of existing circularity metrics that may inform future research discussion of quality-differentiated approaches in EU secondary metals policy. Full article

Background/Objectives: Standard mechanical compression applied during screening mammography is a primary barrier that reduces patient compliance. Current guidelines attempt to standardize compression based solely on the one-dimensional “breast thickness” measured by the device. This study aimed to investigate the effects of three-axis anatomical breast dimensions, applied compression force, menstrual cycle phases, and BI-RADS breast density patterns on pain scores during mammography within a comprehensive biomechanical model. Methods: This retrospective cohort study included 443 female patients who underwent routine screening or diagnostic mammography. Patients with a history of breast implants, lactation, or prior breast surgery that could alter tissue biomechanics were excluded. Maximum pain scores (1–10 on a Visual Analog Scale [VAS]) were recorded. Transverse, anteroposterior, and superoinferior breast biometric measurements for each patient were calculated using advanced radiological workstations. Data were analyzed using One-Way ANOVA and Multiple Linear Regression (OLS) models. Results: The mean age of the participants was 49.7 ± 9.4 years, the mean applied compression force was 62.4 ± 10.3 N, and the mean pain score was 2.03 ± 2.12 (range: 1–10). The multiple linear regression analysis was statistically significant overall (F = 2.516, p = 0.015). Having a BI-RADS Type D (extremely dense) breast pattern was identified as the strongest independent factor associated with an increased pain score (p = 0.082, coefficient = 1.219). Age showed a trend toward a negative effect on pain (p = 0.072), while compression force showed a trend toward a positive effect (p = 0.067). Conversely, breast thickness (p = 0.231) and the three-dimensional mean breast size index (p = 0.568) demonstrated no independent predictive power. The menstrual cycle phase did not reach independent significance in the multivariate regression model (p = 0.117); however, non-parametric univariate analysis revealed a significant difference in pain across hormonal groups (Kruskal–Wallis H = 10.04, p = 0.039), with actively menstruating and luteal-phase women reporting higher pain than menopausal women. Conclusions: The pain experienced during mammography depends on the internal fibroglandular architecture (elasticity and stiffness) of the tissue rather than its external volumetric dimensions. Notably, neither device-measured breast thickness nor manually calculated three-dimensional breast dimensions independently predicted pain, challenging the widespread assumption that breast size drives mammographic discomfort. “One-size-fits-all” or thickness-based compression strategies should be abandoned in routine practice. Instead, “personalized compression” protocols that prioritize patient comfort without compromising image quality should be developed, particularly for younger patients and those with BI-RADS Type D, and to a lesser extent Type C, density patterns. Full article

Background/Objectives: Assisted verticalization and supported upright activity are relevant components of rehabilitation in adults with severe acquired brain injury (sABI), although patient selection and implementation remain challenging. This retrospective observational study aimed primarily to describe the implementation feasibility and documented safety of GRILLO-based training in routine inpatient multidisciplinary rehabilitation, and secondarily to report exploratory pre–post functional changes. Methods: We reviewed clinical records of 34 adults screened or considered for GRILLO-based training at Centro Cardinal Ferrari KOS, Italy, between June 2022 and December 2024. GRILLO training was delivered as part of standard care and not as an experimental intervention. Functional outcomes included the Barthel Index (BI), Trunk Control Test (TCT), Tinetti Balance Scale, and Tinetti Gait subscale, extracted from routine documentation. Non-parametric descriptive analyses were used. Results: Of 34 screened patients, 4 did not meet diagnostic criteria for ABI, 5 interrupted training because of pain or poor tolerance to prolonged upright positioning, and 3 were not included because of poor compliance/motivation or an incomplete clinical pathway. The paired functional-analysis cohort comprised 22 patients: 20 (91%) completed 15 sessions and 2 (9%) completed 10 sessions. No serious device-related adverse events were documented in available clinical records, although minor adverse events were not systematically monitored. Among patients with paired observations, median BI increased from 16 to 22.5 (median change, +3; p = 0.008; n = 20), median TCT from 72 to 74 (median change, +12; p < 0.001; n = 21), and median Tinetti Balance Scale from 1 to 2 (median change, +1; p = 0.006; n = 22). Individual responses were heterogeneous and floor effects were evident, especially for balance and gait-related measures. Conclusions: In this retrospective real-world cohort, GRILLO-based training could be implemented in selected severely impaired inpatients, but feasibility may be overestimated if interrupted and non-completing cases are not considered. The non-completion cases may suggest that feasibility depends not only on initial clinical indication, but also on the appropriate timing of introduction, tolerance to prolonged upright physical effort, pain/discomfort, motivation, and behavioral engagement. The retrospective design, survivorship bias, non-systematic adverse-event monitoring, concurrent multidisciplinary rehabilitation, and absence of a comparator group preclude conclusions regarding device-specific safety or efficacy. Nevertheless, these preliminary findings support further prospective controlled studies. Full article

Background: Temporary dental crowns are an essential component of fixed prosthodontic treatment, protecting prepared teeth and maintaining occlusal function and aesthetics until delivery of the definitive restoration. Their clinical performance is strongly influenced by their internal microstructure, which directly affects mechanical behavior. Therefore, the aim of this study was to compare the internal porosity and fracture resistance of temporary dental crowns fabricated using conventional and 3D-printing techniques. Materials and Methods: This in vitro study compared the porosity and fracture resistance of three materials for provisional restorations: a bis-acrylic resin (Protemp^TM 4), an autopolymerizing resin (Success CD), and a 3D-printed light-curing resin (V-Print c&b temp). Thirty-six standardized single-unit crowns (n = 12 per group) were fabricated. All specimens were analyzed using high-resolution micro-computed tomography to determine total crown volume, pore volume, and relative porosity. Fracture resistance was evaluated under monotonic compressive loading in a universal testing machine. Data were analyzed using appropriate parametric or non-parametric statistical tests (α = 0.05). Results: The 3D-printed material exhibited the lowest mean porosity (0.0029%), whereas Protemp^TM 4 and Success CD showed substantially higher porosity values. However, Protemp^TM 4 demonstrated the highest mean fracture resistance, followed by the 3D-printed resin and Success CD. No direct correlation between porosity and fracture resistance was observed, indicating that material chemistry and internal bonding play a more decisive role than void content alone. Conclusions: These findings suggest that 3D printing improves structural homogeneity, while bis-acrylic materials provide superior load-bearing capacity, and that each fabrication method offers distinct advantages depending on clinical requirements. Full article

Accurate detection of prostate cancer suspicious areas in biparametric MRI (bpMRI) remains challenging because of severe lesion-to-background imbalance, limited lesion contrast, and inter-reader variability in lesion delineation. Unlike prior approaches that collapse inter-reader disagreement into a single consensus label, this study makes three contributions: (1) an adapted nnU-Net framework with prostate-centered preprocessing to reduce voxel-level class imbalance; (2) a class-imbalance-aware composite loss combining Dice, binary cross-entropy, and tailored focal loss to improve sensitivity to small and low-contrast lesions; and (3) a multi-expert learning strategy that preserves reader-specific annotations as separate supervision targets and aggregates predictions at the ensemble level. The method was developed on a single-center dataset of 378 bpMRI studies independently annotated by three board-certified radiologists. Of these, 323 studies were used for model development with patient-level 5-fold cross-validation, and 55 studies were reserved as a fixed independent test set. Compared with our previously published U-Net baseline, the proposed consensus-based nnU-Net improved Average Precision (AP) from 0.69 to 0.75, AUROC from 0.92 to 0.96, and the PI-CAI score from 0.81 to 0.85 on the independent test set. In addition, the multi-expert approach further improved AP to 0.81 versus 0.76 (+6.6%, p < 0.01), AUROC to 0.99 versus 0.95 (+4.2%, p < 0.01), and the PI-CAI score to 0.90 versus 0.86 (+4.7%). These findings demonstrate that explicitly preserving expert disagreement as a training signal, combined with anatomically targeted preprocessing and tailored loss design, substantially improves prostate lesion detection in bpMRI, providing a strong basis for future multi center external validation. Full article

Background: We tested whether a contrast-free protocol can reproduce contrast-enhanced VI-RADS scoring and whether reader expertise influences results. Methods: In this retrospective single-center study (January–December 2024), 65 patients (69 lesions) underwent bladder multiparametric MRI. Two blinded radiologists assigned VI-RADS scores using only T2-weighted and diffusion-weighted imaging (biparametric, non-contrast MRI): an expert (>15 years in urogenital radiology) in genitourinary MRI and a non-expert (5 years of experience in genitorurinary radiology). Two complementary reference standards were used. For reproducibility analysis, the reference standard was the VI-RADS score from the original clinical report based on the full multiparametric examination including contrast-enhanced imaging. For diagnostic accuracy analysis, histopathology was used as the reference standard for muscle-invasive versus non-muscle-invasive disease. Agreement was evaluated with confusion matrices, overall agreement, and weighted Cohen’s kappa. Discrimination for high likelihood of muscle invasion (VI-RADS ≥ 4) was assessed with receiver operating characteristic analysis. Results: Reference scores were VI-RADS 2 (34.8%), 3 (14.5%), 4 (20.3%), and 5 (30.4%). Agreement was higher for the expert than the non-expert (73.9% vs. 56.5%; weighted kappa 0.74 [95% confidence interval 0.56–0.89] vs. 0.58 [0.37–0.75]). The area under the curve for VI-RADS ≥ 4 was 0.87 (0.78–0.95) for the expert and 0.81 (0.69–0.91) for the non-expert. Sensitivity at a biparametric threshold of VI-RADS ≥ 4 was 88.6% for both readers; specificity was 85.3% vs. 73.5%. Post-resection cases showed more discrepancies, mainly overstaging. Conclusions: Contrast-free biparametric MRI may approximate multiparametric VI-RADS scoring only in treatment-naïve pre-TURBT cases with clearly low-risk, non-equivocal imaging features, but performance is reader-dependent and less reliable in equivocal, higher-risk, and post-resection examinations. Contrast-enhanced multiparametric MRI remains preferred for staging. Full article

Background: Multiparametric MRI is central to detecting clinically significant prostate cancer (csPCa), but diagnostic accuracy depends on reader experience. Artificial intelligence (AI) tools may support prostate MRI interpretation and reduce inter-reader variability. This study compared the detection rate of a trial, non-commercial version an AI-based software (PAROS) with that of an experienced radiologist. Methods: This retrospective single-center study included 150 patients who underwent prostate MRI followed by combined systematic and MRI-targeted transperineal biopsy. MRI examinations were interpreted by an experienced radiologist according to PI-RADS v2.1 and independently analyzed using a precommercial trial version of PAROS operating on biparametric MRI. Histopathology served as the reference standard. Detection rate was evaluated using sensitivity, specificity, and positive and negative likelihood ratios (PLR and NLR) at PI-RADS thresholds ≥3 and ≥4. Results: CsPCa was present in 63.3% of patients. At both PI-RADS thresholds, PAROS and the radiologist showed comparable sensitivity and specificity, wuth extremely low NLRs, indicating excellent rule-out capability. PLRs were modest and similar at PI-RADS ≥ 3 (1.26 vs. 1.42) and 1.88 for both at PI-RADS ≥ 4. PAROS detected more lesions, particularly in the transition zone. Conclusions: PAROS achieved csPCa detection comparable to an experienced radiologist, supporting its role as a decision-support tool in prostate MRI interpretation. Full article

Parallel iterative schemes are widely used for the simultaneous computation of all distinct roots of nonlinear equations in scientific computing and engineering. While high-order parallel methods can provide substantial acceleration, their practical performance is often dominated by the choice of internal real-valued parameters introduced by correction/acceleration mechanisms, which may strongly affect convergence speed and numerical robustness. Classical parameter-selection strategies—based on analytical sufficient conditions, trial-and-error experimentation, or qualitative dynamical diagnostics (basins of attraction, bifurcation-style inspection, and parameter planes)—are typically problem-dependent, expensive to scale, and difficult to automate reproducibly. In this work, we propose a data-driven framework for systematic parameter optimization based on finite-time contractivity profiling. The approach uses k-nearest neighbors (kNN) micro-series analysis to estimate a proxy profile of the largest Lyapunov exponent (LLE) along the iteration index, summarizing the transient contraction/expansion behavior of the solver trajectories. Two profile-based scores, the minimum score $S_{min}$ and the moment score $S_{\mathrm{mom}}$, are introduced to rank candidate parameter pairs and to construct stability landscapes over $(\alpha ,\beta )$ grids. As a testbed, we apply the framework to a bi-parametric two-step parallel Weierstrass-type scheme and demonstrate that the learned parameter regions yield faster and more reliable convergence than generic or manually tuned choices. Extensive numerical experiments show that the proposed profiling-based optimization consistently improves convergence rate and robustness across the considered nonlinear test problems, providing a scalable and reproducible alternative to heuristic and dynamical-system-based tuning. Full article

Background: MRI-based radiomics has shown promise in men with prostate cancer (PCa); however, successful clinical implementation is contingent upon on reproducible measurements. Purpose: We assessed the reproducibility of radiomics features extracted from bi-parametric prostate MRI (bpMRI) in prostate lesions and non-tumoral prostate tissue in men with PCa undergoing active surveillance (AS). Methods: This retrospective study included 47 men with biopsy-proven PCa undergoing AS (mean 68.9 ± 8.2 years, mean PSA density [PSAD] 0.08 ± 0.03 ng/mL/mL) who underwent two bpMRI approximately 12 months apart (range, 10–14 months; December 2018 to April 2020). The reproducibility of radiomics measurements was assessed using the same MRI platform (3T Skyra, Siemens Healthineers; inter-platform) (n = 37), different MRI vendors (Skyra, Siemens Healthineers; 3T Discovery MR750, GE Healthcare; inter-platform) (n = 10), and between observers (n = 10). Shape/1st-/2nd-order radiomics features were extracted from regions of interest on axial T2-weighted (T2-WI), diffusion-weighted imaging (DWI, b1600), and apparent diffusion coefficient (ADC) maps on prostate lesions, non-tumoral peripheral zones (PZs), and transition zones (TZs) using software. Reproducibility was evaluated by calculating the intraclass correlation coefficient (ICC) and coefficient of variation (CV). Associations of clinical variables and prostate volume were assessed. Results: PCa diagnoses included Gleason grade groups 1 (n = 46) and 2 (n = 1)]. Thirty-seven lesions (mean size 0.9 ± 0.4 cm) in 31 patients had PI-RADS v2.1 scores of 2 (n = 3)/3 (n = 12)/4 (n = 21)/5 (n = 1); 16 patients demonstrated diffuse PI-RADS 2 changes. Lesion radiomics features from T2-WI yielded a high proportion of good/moderate ICCs (intra-platform, 77.8%; inter-platform, 56.5%), whereas most DWI/ADC features yielded poor reproducibility. Similar results were observed for non-tumoral PZ/TZ. Intra-platform CVs were lowest for T2-WI lesion features (13.6%) and background PZ/TZ (<13.3%), while DWI/ADC exceeded 20%. Inter-platform CVs were lowest for lesions on T2-WI and were <18% for DWI/ADC; all background PZ/TZ CVs were < 16.4%. Inter-observer analyses showed good/moderate ICCs across all sequences and regions (57.4–92.6%). The distribution of ICC and CV values did not differ between intra- and inter-platform analyses (p > 0.05). Higher reproducibility (ICC > 0.5) was associated with larger prostate volume (intra-platform diagnostic odds ratio [DOR] = 2.58, 95% confidence interval [95%CI], 1.35–3.80, p = 0.01; inter-platform DOR = 3.48, 95%CI 1.79–5.17, p = 0.01) and older age (inter-platform DOR = 5.30, 95%CI 3.75–6.85, p < 0.01). Conclusions: Radiomics measurements from T2-WI demonstrated better intra-/inter-platform reproducibility than DWI/ADC for prostate lesions and non-tumoral tissue. Patient factors (larger prostate volumes and older age) influence radiomics stability. The optimization of diffusion-based radiomics features is needed to improve reproducibility given the essential role of DWI in prostate MRI. Full article

Background/Objectives: Robotic-assisted radical prostatectomy (RARP) is a standard treatment for localized and locally advanced prostate cancer; however, optimizing oncologic control while preserving urinary continence and erectile function remains challenging. Advances in preoperative imaging, molecular diagnostics, artificial intelligence (AI), and intraoperative assessment have the potential to refine surgical planning and execution. This review summarizes contemporary evidence on advanced imaging and intraoperative technologies used to optimize RARP outcomes. Methods: A narrative literature review was conducted of English-language studies published between 2015 and 2025 using PubMed/MEDLINE, Scopus, and Google Scholar. Studies evaluating multi-parametric and bi-parametric MRI, prostate-specific membrane antigen-based positron emission tomography/computed tomography (PSMA PET/CT), AI-assisted tumor modeling, and intraoperative histologic or molecular imaging techniques in the context of robotic-assisted radical prostatectomy were included. Evidence from randomized controlled trials, prospective and retrospective studies, technical feasibility reports, and expert consensus statements was reviewed. Results: MRI remains central to anatomic mapping and local staging but consistently underestimates true tumor extent, with implications for margin control. AI-assisted platforms improve tumor contouring accuracy and may meaningfully influence surgical decision-making. PSMA-based imaging enhances detection of extra-prostatic extension and nodal disease and shows early promise for ex vivo and intraoperative guidance. Intraoperative margin assessment techniques are supported by randomized evidence demonstrating improved functional outcomes without compromising short-term oncologic safety and emerging digital histologic technologies offer scalable alternatives for real-time margin evaluation. Conclusions: Integration of advanced anatomic, molecular, and intraoperative imaging technologies represents an evolving multimodal paradigm in RARP. Combined use of MRI, PSMA-based imaging, AI-assisted modeling, and rapid histologic assessment may enable more precise, individualized surgery that balances oncologic control with functional preservation. Further validation is required to define optimal implementation in routine clinical practice. Full article

(1) Background: Hot pot bases are susceptible to phthalate (PAE) contamination due to their high lipid content. Standard risk models often fail to capture extreme values, leading to biased exposure estimates. This study characterized dibutyl phthalate (DBP) and bis(2-ethylhexyl) phthalate (DEHP) contamination using a hybrid modeling framework to ensure precise risk profiling. (2) Methods: A total of 91 samples were analyzed via GC-MS. Concentration data were fitted using traditional parametric, extreme value mixture (EVMM), and finite mixture models. Probabilistic dietary risks were assessed for Chinese demographic groups using 10,000-iteration Monte Carlo simulations. (3) Results: DEHP (detection rate: 55%) and DBP (32%) were best modeled by a two-component Gamma mixture and a Lognormal–Generalized Pareto distribution, respectively. These advanced models significantly outperformed conventional distributions in capturing upper-tail extremes. Crucially, all hazard quotients (HQs) remained below the safety threshold of 1, indicating acceptable risk, although children aged 7–13 exhibited the highest calculated risk (Max DEHP HQ = 0.68). (4) Conclusions: Although current exposure levels are within safe limits, the heavy-tailed distributions identify potential sporadic high-exposure events that traditional models overlook, specifically highlighting the relative vulnerability of children aged 7–13. This study validates that hybrid statistical approaches offer superior precision for analyzing skewed contamination data. Consequently, these findings provide a critical scientific basis for refining regulatory monitoring and implementing targeted source-tracking measures to mitigate long-tail food safety risks. Full article

The paper shows an alternative perspective of the reduced chi-square as a measure of the goodness of fitting methods. The reduced chi-square is given by the ratio of the fitting over the propagation errors, that is, a universal relationship that holds for any linearity, but not for a nonlinearly parameterized fitting model. We begin by providing the proof for the traditional examples of one-parametric fitting of a constant and the bi-parametric fitting of a linear model, and then, for the general case of any linearly multi-parameterized model. We also show that this characterization is not generally true for nonlinearly parameterized fitting. Finally, we demonstrate these theoretical developments with an application in real data from the plasma protons in the heliosphere. Full article

Background: Dopaminergic signaling is a key mechanism in behavioral regulation and impulse control. While DAT1 promoter methylation has been linked to behavioral dysregulation in clinical groups, its role in high-functioning populations such as elite athletes remains unclear. Objectives: To compare DAT1 promoter methylation, DAT1 VNTR genotype, and impulsivity between elite combat sport athletes and matched controls, and to assess potential gene–environment interactions. Methods: The study included 209 male participants (100 elite combat athletes, 109 controls). Methylation of 33 CpG sites within the DAT1 promoter was quantified from peripheral blood DNA. DAT1 VNTR genotypes were determined via PCR and gel electrophoresis. Impulsivity was assessed using the Barratt Impulsiveness Scale (BIS-11). Group differences and interactions were analyzed using analysis of variance (ANOVA), non-parametric tests, and post hoc comparisons. Results: Athletes displayed significantly higher overall DAT1 promoter methylation and lower impulsivity scores across all BIS-11 subscales compared with controls. A significant group × genotype interaction for methylation indicated genotype-specific epigenetic differences by athletic status. No differences in VNTR genotype or allele frequencies were observed. Conclusions: Elevated DAT1 promoter methylation in elite athletes may be associated with enhanced behavioral control, potentially reflecting neurobiological adaptations to high-intensity training. These results highlight the need to integrate genetic and epigenetic perspectives in sports science. Longitudinal and multi-omics studies are warranted to determine causal links and evaluate the potential of epigenetic markers as indicators of performance-related traits. Full article