Search Results (495)

Immunotherapy can reduce treatment-related side effects but shows limited efficacy in “cold tumors,” whose immunosuppressive tumor immune microenvironment is characterized by abundant M2 macrophages and poor T cell infiltration. Because biopsy-based qualitative assessment of the tumor microenvironment is invasive and conventional imaging lacks functional information, this study aimed to develop an M2 macrophage-targeted theranostic agent enabling non-invasive photoacoustic (PA) imaging and pH-triggered cytotoxicity. A pullulan-based nanogel conjugated with mannose and near-infrared dye (IR-820) was further functionalized with the pH-responsive doxorubicin (DOX) prodrug, Aldoxorubicin, to develop Pullulan-mannose-IR820-Aldoxorubicin (PMID) nanogel. PMID was successfully synthesized, and the resulting self-assembled nanogels (<100 nm) exhibited a highly negative ζ-potential, near-infrared absorption peaks at 780 and 850 nm, and PA contrast comparable to IR-820 at 850 nm excitation. Dialysis studies demonstrated suppressed drug release at neutral pH (~20%) but accelerated release under acidic conditions, reaching ~80% within 48 h at pH 5.5, consistent with hydrazone hydrolysis and supporting tumor/lysosome-activated delivery. In RAW264.7 macrophages, PMID nanogel showed preferential uptake by M2-poralized versus M1-polarized macrophages, outperforming non-mannosylated PID nanogel and IR-820, and produced the strongest PA signal in M2 macrophage pellets. PMID nanogel also induced the highest concentration-dependent cytotoxicity in M2 macrophages, and microscopy indicated lysosomal accumulation of the nanogel with partial nuclear localization of released DOX. These findings support the use of PMID nanogel as M2 macrophage-targeted PA contrast agents and pH-responsive drug carriers with the potential to deplete immunosuppressive macrophages, modulate cold tumor microenvironments, and improve precision cancer theranostics. Full article

Anti-CD38 monoclonal antibodies have substantially improved outcomes in multiple myeloma (MM). Although daratumumab and isatuximab target the same antigen, accumulating evidence indicates that they differ in epitope recognition, biological activity, and immunomodulatory properties, suggesting these agents may not be therapeutically interchangeable. This review summarizes the molecular and immunological mechanisms underlying their distinct antitumor effects and their implications for treatment selection. Isatuximab binds near the catalytic site of CD38, resulting in potent enzymatic inhibition, enhanced antibody internalization, FOXM1 suppression, and reactive oxygen species-mediated cytotoxicity, which may preferentially target MM cells harboring 1q21 amplification. In contrast, daratumumab exerts prominent Fc-dependent immune effects, including trogocytosis-mediated downregulation of CD38 and VLA-4, suppression of cell adhesion-mediated drug resistance, and modulation of the immune microenvironment, potentially enhancing subsequent T-cell-redirecting therapies. We further discuss the relevance of these mechanistic differences to measurable residual disease, extramedullary disease, and sequencing with BCMA- and GPRC5D-directed immunotherapies. Finally, we propose a biology-guided treatment-selection model integrating genomic alterations, tumor biology, and immune remodeling to support precision medicine for patients with MM. Full article

Background/Objectives: This study aimed to develop an artificial intelligence-based method for generating virtual post-contrast T1 maps and extracellular volume (ECV) maps from native T1 maps and to evaluate its performance. Methods: The proposed method was based on a modified self-consistent recursive diffusion bridge framework to generate virtual post-contrast T1 maps from native T1 maps. Cardiac magnetic resonance (CMR) data were collected from consecutive patients with suspected myocardial disease. A total of 813 well-registered image slices were selected for model development and evaluation. On an unseen test set of native T1 maps, the trained model generated virtual post-contrast T1 maps, which were subsequently combined with the corresponding native T1 maps to compute ECV maps. Results: The myocardial T1 values derived from the reference and virtual post-contrast T1 maps revealed similar distributions, although a systematic offset between the distribution peaks was observed. Following ECV transformation, this offset was substantially reduced. In the held-out test cohort, the virtual myocardial ECV showed acceptable agreement with the reference ECV, achieving a mean root mean square error (RMSE) of 3.05%, despite noticeable slice-to-slice variability (R² = 0.585; Bland–Altman 95% limits of agreement, −5.98% to +6.06%). Conclusions: The proposed method enabled the generation of post-contrast T1 and ECV maps directly from native T1 maps without the administration of gadolinium-based contrast agents during CMR. These findings suggest that the proposed approach represents a promising contrast-free, non-invasive alternative for myocardial tissue characterization, with the potential to reduce examination costs, eliminate contrast-agent-related risks, and improve patient safety. Full article

Chagas disease, caused by Trypanosoma cruzi, remains a neglected tropical infection with limited therapeutic options and significant drug-related toxicity. Endophytic fungi are emerging as sustainable sources of bioactive metabolites with potential antiparasitic activity. In this study, endophytic fungi isolated from the medicinal plant Lippia alba were screened for trypanocidal activity. Crude extracts from seven isolates were tested against epimastigote and amastigote forms of T. cruzi, together with cytotoxicity assays in J774A.1 murine macrophages. The crude extract of Montagnula sp. exhibited the strongest trypanocidal activity (IC₅₀ = 22.1 ± 0.5 μg/mL for epimastigotes and 29.2 ± 3.0 μg/mL for amastigotes), comparable to benznidazole, with low toxicity (CC₅₀ > 600 μg/mL) and a high selectivity index (>26). Morphological and mitochondrial analyses demonstrated preservation of host-cell integrity and mitochondrial function, in contrast to the reference drug. UHPLC-ESI-HRMS profiling identified phenolic and terpenoid compounds, including caffeic acid, ferulic acid, naringenin, apigenin, and ursolic acid, which may underlie the observed biological activity. To our knowledge, this study provides the first evidence of trypanocidal activity in an endophytic Montagnula species and highlights endophytic fungi as promising platforms for the discovery of novel anti-T. cruzi agents. Full article

Background/Objectives: Artificial intelligence (AI) can support lesion detection in gadolinium-based contrast agent-enhanced (GBCA-enhanced) breast MRI. However, its effectiveness on virtual contrast-enhanced (vCE) images remains unclear. This feasibility study evaluated the publicly available MAMA-MIA nnU-Net model trained on GBCA-enhanced data using an independent cohort of both GBCA-enhanced and vCE breast MRI. Methods: This IRB-approved retrospective study included the publicly available nnU-Net model trained on n = 1506 MAMA-MIA breast MRI scans and a cohort of n = 2126 in-house 3T breast MRI scans. A generative adversarial network (Pix2Pix-GAN) was developed on n = 1870 of the in-house scans and used to generate vCE data on the remaining independent n = 256 in-house cases. The MAMA-MIA nnU-net was applied to both GBCA-enhanced (GBCA) and corresponding vCE images. Ground-truth segmentations of malignant lesions served to calculate the Dice score, Hausdorff distance, and lesion dimension differences. Results: The final test set comprised n = 250 cases (n = 69 malignant, n = 181 benign). Lesion detection rates were 91% (n = 63/n = 69; 95% confidence interval (CI): 82.3–96.0%) for GBCA and 84% (n = 58/n = 69; 95% CI: 73.7–90.9%) for vCE. Two lesions missed in GBCA were identified by vCE. The Hausdorff distances were similar (GBCA: 6.4 (IQR: 3.2–9.3; 95% CI: 5.2–7.8) mm; vCE: 6.7 (IQR: 3.9–9.7; 95% CI: 5.3–8.0) mm, p = 0.564). The Dice scores showed minor differences (GBCA: 0.829 (IQR: 0.723–0.900; 95% CI: 0.786–0.865) vs. vCE: 0.826 (IQR: 0.720–0.857; 95% CI: 0.770–0.836); p < 0.001). vCE images had slightly higher non-target tissue segmentation (median 6072 mm³ vs. 5754 mm³). Conclusions: A GBCA-trained algorithm demonstrated some cross-domain transferability to vCE images, albeit with a reduced case-level sensitivity of 84% (95% CI: 73.7–90.9%) vs. 91% (95% CI: 82.3–96.0%). Based on these preliminary results, further research, including larger cohorts and more diverse datasets, is warranted. Full article

The transition toward a net-zero economy requires buildings to evolve from passive consumers into Grid-Interactive Efficient Buildings (GEBs). Traditional Building Energy Management Systems (BEMSs) lack the dynamic intelligence needed to control stochastic energy flows and solve multi-objective optimization problems. To systematically map this technological shift, this study conducts a Systematic Literature Review (SLR) following PRISMA guidelines, analyzing a curated corpus of 144 studies (135 primary technical papers and 9 review articles). Due to the significant diversity in methodological approaches within cyber-physical testbeds and IoT architectures discovered through the literature review process, a qualitative narrative and architectural synthesis was conducted rather than a quantitative meta-analysis. Based on this framework, this review examines emerging paradigms for Cognitive Buildings based on Artificial Intelligence of Things (AIoT), edge computing, and semantic interoperability. This review discusses the evolution of algorithms from predictive Deep Learning (DL) and Deep Reinforcement Learning (DRL) to newer approaches such as Agentic AI and Physics-Informed Neural Networks (PINNs). These new methods address the fundamental “sim-to-real” gap while ensuring thermodynamic consistency and safety in physical actuation. It also presents strategic applications in multi-objective optimization of HVAC systems, demand response, energy arbitrage, and predictive maintenance. Moreover, this review tackles major real-world deployment issues by introducing Federated Learning for data privacy, Transfer Learning for portfolio scaling, and TinyML for overcoming the computational carbon paradox of “Green AI.” By quantifying this paradox, the review contrasts the massive computational carbon footprint of cloud-based model training against the milliwatt-class efficiency of localized edge deployments. Overall, this review outlines potential research directions toward the development of autonomous Cognitive Digital Twins (CDTs) and Human-Centric Personal Comfort Models (PCMs). Full article

Renibacterium salmoninarum, the etiological agent of bacterial kidney disease, is a facultative intracellular pathogen whose interaction with salmonid phagocytic cells remains poorly resolved at the protein level. Here, we aimed to define the temporal protein-abundance architecture of SHK-1 macrophage-like cells after R. salmoninarum inoculation and to test whether this response supports broad canonical cell-death pathway engagement. We used label-free quantitative LC-MS/MS proteomics to profile SHK-1 cells over a 48 h post-inoculation time course. Because the design included a single non-infected T0 baseline, analyses were framed as baseline-referenced post-inoculation comparisons rather than a fully controlled mock time course. Of 6842 proteins retained for statistical modeling, 2254 were strictly differentially abundant in at least one contrast relative to T0 (adjusted p < 0.05 and |log₂FC| ≥ 0.585). Perturbation was strongest at 1–2 h and progressively contracted at later time points. Among 1278 recurrent proteins, k-means clustering resolved four temporal modules capturing coordinated remodeling of lysosomal, immunometabolic, cytoskeletal, stress-response, and antioxidant programs. A curated cell-death panel spanning apoptosis, pyroptosis, necroptosis, ferroptosis, and PANoptosis yielded only three detected markers; CASP3 and MLKL met the strict threshold, whereas ACSL4 remained sub-threshold. Overall, early host-cell remodeling, rather than broad canonical death-program execution, was the predominant proteomic signature of SHK-1 cells during the first 48 h after R. salmoninarum inoculation. Full article

Land-use changes and unsustainable agricultural practices can alter soil properties, thereby increasing soil erodibility and the risk of land degradation. This study assessed the impact of converting forest to grassland and cropland on soil erodibility in the Kolubara watershed (western Serbia) using soil samples collected at two depths (0–15 and 15–30 cm). Soil erodibility was determined using the following indicators: clay ratio (CR), soil structure stability index (SSI), mean weight diameter (MWD), soil organic carbon cementing agent index (SCAI), saturated hydraulic conductivity (Ks), the K-factor, and a comprehensive soil erodibility index (CSEI) calculated by a weighted summation method. Most soil indicators differed significantly among land uses. Forest soils exhibited the highest MWD (2.94 mm), Ks (1119.15 mm h⁻¹), and SSI (5.86), whereas the lowest values were recorded in cropland soils (1.64 mm, 29.68 mm h⁻¹, and 3.07, respectively). In contrast, cropland soils showed the highest CR (0.005) and K-factor (0.038 t ha h ha⁻¹ MJ⁻¹ mm⁻¹), while the lowest values occurred in forest soils (0.003 and 0.032 t ha h ha⁻¹ MJ⁻¹ mm⁻¹). The significantly higher CSEI in cropland (0.75) compared with forest soils (0.62) corresponded to reduced soil structural stability and lower organic matter–related indicators. Grassland soils generally showed intermediate values for most indicators. Soil depth significantly influenced only SSI and Ks. Differences in soil erodibility among land uses are closely related to soil physical and chemical properties, particularly soil organic carbon and soil structure-related properties (total porosity and bulk density). These findings emphasize the substantial impact of land-use change on soil erodibility and highlight the need to implement effective soil conservation practices to improve soil stability and mitigate erosion. Full article

Atopic dermatitis (AD) is a common chronic inflammatory condition of the skin that has increased dramatically over the past decade and significantly impacts individual quality of life. Corticosteroids are still the primary therapy for AD, but there are limitations to their continued use due to potential adverse effects, particularly when used in sensitive areas. Topical calcineurin inhibitors (CNIs), such as tacrolimus and pimecrolimus, are available as a safe, steroid-sparing alternative that directly inhibit calcineurin-mediated activation of T cells and have been shown to be efficacious according to varying clinical study designs including randomized controlled trials, registry studies and meta-analyses. Although there was controversy regarding the safety of CNIs subsequent to the FDA’s black-box warning in 2006, the preponderance of evidence supports their continued safety when used as directed. In contrast to biologics and JAK inhibitors, CNIs occupy an inherently unique therapeutic niche for use in pediatric patients, have demonstrated historical efficacy, and can provide localized affordable treatment in sensitive areas including the face, eyelids and intertriginous surfaces. Furthermore, the role of CNIs in the context of precision dermatology continues to be defined through new innovations including barrier-repair strategies used in combination with topical medications, microneedle systems, and nanocarrier formulations. Hence, the role of CNIs in the current AD treatment paradigm is crucial and lies at the interface between topical corticosteroids and systemic immunomodulatory agents. The narrative review discusses recent advances in formulation strategies, combination approaches, and targeted delivery systems, underscoring how CNIs continue to bridge established practice and emerging therapeutic innovation in AD. Full article

Background/Objectives: Contrast-enhanced (CE) breast MRI is highly sensitive for evaluating breast cancer extent and response to neoadjuvant therapy (NAT) but requires intravenous administration of gadolinium-based contrast agents (GBCA), increasing cost, time, patient discomfort, and health concerns. This study explored the feasibility of reducing GBCA use in treatment monitoring using a deep learning (DL) model to synthesize CE-MRI from non-contrast MRI. Methods: This IRB-approved retrospective pilot study evaluated women with breast cancer enrolled in an ongoing trial using serial MRI to monitor NAT prior to surgery. A pre-trained DL model was used to synthesize CE-MRI from T1-, T2-, and diffusion-weighted MRI. Changes in tumor volume at early (post-1-cycle NAT) and mid-treatment were measured on synthetic and acquired CE-MRI. Performance for predicting residual cancer burden (RCB) class 0/1 was evaluated using AUC and compared with DeLong’s test. Results: 27 women were included in the study (median age, 47 years [range = 28–75]); 14 (52%) achieved RCB class 0 and six (22%) achieved class 1. Synthetic CE-MRI-derived tumor volumes showed strong correlation with those from acquired CE-MRI at pre-treatment ($\rho$ = 0.92, p < 0.001) and early treatment ($\rho$ = 0.83, p < 0.001), but lower agreement at mid-treatment ($\rho$ = 0.57, p = 0.002). Change in tumor volume on synthetic CE-MRI was numerically similar to acquired CE-MRI for predicting RCB class 0/1 vs. 2/3 at both early (AUC = 0.84 vs. 0.86, p = 0.83) and mid-treatment (AUC = 0.73 vs. 0.75, p = 0.80). Conclusions: Synthetic CE-MRI demonstrates preliminary feasibility as a non-contrast surrogate for predicting favorable outcomes (RCB class 0/1) in this pilot study, but inconsistencies in tumor volume measurement vs. acquired CE-MRI warrant further model refinement and validation. Full article

Objectives: Contrast-enhanced magnetic resonance imaging (CE-MRI) plays an important role in the diagnosis, treatment monitoring, and follow-up of brain tumors. However, the use of gadolinium-based contrast agents (GBCAs) is limited in patients with contraindications, such as severe renal impairment or situations requiring repeated examinations. This study aimed to develop a diffusion model-based Difference-Aware Guided Control Network (DAGCN) for synthesizing high-quality contrast-enhanced T1-weighted MRI (T1-CE) from non-contrast T1-weighted images in combination with an auxiliary sequence. Methods: Using the BraTS 2021 dataset, we proposed a two-stage generative framework that first localizes lesion-related enhancement cues and then guides image synthesis. In the first stage, a Difference-Aware Fusion and Prediction (DAFP) module was designed to extract complementary information from non-contrast T1-weighted images and an auxiliary sequence (T2-weighted or FLAIR) through dual-branch feature extraction and cross-modal channel attention fusion, followed by prediction of a lesion-related discrepancy map. In the second stage, the predicted discrepancy map was concatenated with the original T1-weighted images and introduced into a ControlNet-guided diffusion model to constrain the reverse denoising process and generate the target T1-CE image. Model performance was evaluated by visual comparison, quantitative metrics including peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), visual information fidelity (VIF), and normalized cross-correlation (NCC), as well as blinded radiologist scoring of image quality (IQ), clinical replaceability (IC), contrast enhancement (CE), and lesion conformity (CF). Results: DAGCN generated synthetic T1-CE images with preserved global anatomical structure and faithful local lesion enhancement without the need for contrast agent administration. Compared with baseline methods, DAGCN achieved the highest PSNR and NCC under both T1 + T2 and T1 + FLAIR settings, while showing competitive SSIM and VIF performance. Visual comparison and radiologist-based subjective evaluation further indicated improved lesion-focused enhancement fidelity and reduced false-positive enhancement. Among the two auxiliary sequence settings, the T1 + FLAIR configuration provided more specific lesion localization and cleaner background suppression than the T1 + T2 configuration, particularly by reducing interference from cerebrospinal fluid signals. Conclusions: The proposed DAGCN framework enables the synthesis of clinically informative contrast-enhanced-like MRI from non-contrast multi-sequence inputs and may provide a promising alternative for patients in whom gadolinium administration is contraindicated or should be avoided. In particular, the FLAIR-guided setting showed advantages in lesion specificity, background cleanliness, and overall diagnostic quality. Full article

Therapeutic cancer vaccines represent a rational immunotherapeutic strategy aimed at inducing tumor-specific adaptive immune responses in patients with established malignancies. In contrast to prophylactic vaccines, these approaches must function within immunosuppressive tumor microenvironments characterized by antigenic heterogeneity, immune dysfunction, and dynamic tumor evolution. Effective vaccine design requires the integration of three essential components: the selection of appropriate tumor-associated or tumor-specific antigens, efficient delivery platforms that enable antigen presentation, and adjuvant systems that promote robust T-cell priming and expansion. Initial clinical investigations in B-cell malignancies and multiple myeloma demonstrated that idiotype-based vaccines can elicit tumor-specific immune responses. However, durable clinical benefit has been inconsistent, reflecting limitations in antigen selection, suboptimal immunogenicity, and tumor-mediated immune evasion. Over the past decade, advances in tumor genomics, next-generation sequencing, and immune monitoring have enabled the development of next-generation vaccine platforms, including dendritic cell-based approaches, personalized neoantigen vaccines, and mRNA-based technologies. Emerging evidence suggests that vaccine efficacy is highly dependent on disease context. Biologically favorable settings such as minimal residual disease (MRD) and post-transplant immune reconstitution provide reduced tumor burden and improved immune competence, thereby enhancing the likelihood of effective immune priming. In parallel, combination strategies incorporating immune checkpoint inhibitors, immunomodulatory agents, and cellular therapies are increasingly being explored to overcome tumor-induced immunosuppression. This review synthesizes current knowledge of therapeutic cancer vaccines in B-cell malignancies and multiple myeloma, with emphasis on immunologic mechanisms, antigen selection, vaccine platforms, and clinical evidence. We further propose a conceptual framework integrating tumor biology, immune context, and combination strategies to guide the rational development of next-generation vaccine therapies. Full article

The genus Burkholderia encompasses both plant pathogenic and beneficial species, yet the genomic determinants underlying this lifestyle divergence remain poorly understood. Using 16S rRNA sequencing of 100 rice cultivars, our companion study demonstrated that resistant varieties are enriched in beneficial Burkholderiaceae, leading to the isolation of three phenotypically contrasting strains. Here, we present comparative genomic analyses of non-pathogenic biocontrol strain Burkholderia vietnamiensis J14EpLeaf2 and pathogenic strains Burkholderia gladioli A1EpSeed5 and Burkholderia cepacia J14Eple. Pathogenic strains possess significantly larger genomes (8.36–8.46 Mb) enriched in mobile genetic elements compared to the streamlined 6.95 Mb genome of B. vietnamiensis. CAZyme analysis revealed broader repertoires of glycoside hydrolases and polysaccharide lyases in pathogens, consistent with enhanced plant cell wall degradation. B. gladioli possesses a complete T3SS and expanded T6SS with 301 predicted effectors, while B. cepacia lacks structural T3SS genes but harbors 271 candidate effectors predicted to be secreted via alternative secretion pathways, compared to 180 in B. vietnamiensis. Notably, B. cepacia harbors cystic fibrosis-associated markers (cable pili, ZmpA/ZmpB), raising significant biosafety concerns that preclude its agricultural application. LC-MS validated IAA, ornibactin, and AHL production in B. vietnamiensis, supporting its plant growth-promoting and biocontrol functions. Computational PPI networks predicted distinct interaction landscapes requiring experimental validation. This study provides a genomic framework for distinguishing pathogenic from beneficial Burkholderia and supports B. vietnamiensis as a safe biocontrol agent while cautioning against B. cepacia J14Eple. Full article

This study examines how urban transportation systems influence the spatial spread of infectious diseases by developing a modified Susceptible–Exposed–Infected–Recovered (SEIR) model with explicit intercity travel dynamics. The model distinguishes between two mobility mechanisms: travel volume, represented by the departure rate g, and travel speed, represented by the arrival rate $\alpha$. Using the next-generation matrix (NGM) approach, we derive the basic reproduction number $R_0$ and analyse how within-city and transit-phase transmission contribute to epidemic spread. The results show that travel volume and travel speed affect mobility-driven transmission through distinct mechanisms. Increasing g increases the number of travelers entering the transit system and therefore amplifies the aggregate number of transit-mediated infections, although the per-capita transit reproduction expression is governed primarily by $\alpha$ and ${\beta }_d^T$ under the reduced next generation matrix formulation formulation. By contrast, increasing $\alpha$ shortens the time spent in transit, reduces the exposure window during travel, and lowers the per-capita contribution of transit-based infection to $R_0$. Numerical simulations illustrate these effects and support the conclusion that reducing travel volume can mitigate intercity epidemic spread by decreasing the number of potentially exposed travelers. Comparative case studies for Brazil, New Zealand, China, and Algeria are used to evaluate the model under different epidemiological settings and socioeconomic contexts. These socioeconomic indicators are treated as contextual background rather than as direct inputs to the mathematical model. The qualitative predictions of the ordinary differential equation (ODE) model are further cross-validated using an agent-based simulation implemented in NetLogo. Overall, the study shows that separating travel volume from travel speed provides a more precise understanding of mobility-driven disease transmission and can support the design of targeted travel-related control measures. Full article

Purpose: Myocardial iron overload has been demonstrated to have a heterogeneous distribution. A segmental T2* CMR approach, with correction factors applied to account for artifacts, has been demonstrated to be feasible and has permitted a reduction in cardiac morbidity and mortality, by better capturing the heterogeneous distribution of myocardial iron overload. To the best of our knowledge, commercially available software does not provide a segmental T2* technique. Our aims were to prospectively examine a large population of healthy volunteers, stratified by sex and age, using the Black Blood MEGE T2* mapping technique, to obtain normative values of the myocardium, to assess their relationship with physiological variables, and to fix correction factors for a segmental approach by using a commercially available software. Methods: Fifty healthy subjects (M:F = 1:1, 20–69 years) underwent CMR without a contrast agent. Segmental T2* values were obtained using cvi42 software; global values were the mean. Inter-study, and intra- and inter-operator reproducibility were assessed to confirm the stability of the acquired data. The association of T2* values with physiological characteristics, and myocardial wall thickness were assessed. The fluctuation of all segments versus the mid-septum was calculated to obtain a correction factor for each segment for the software used. Regional T2* differences were examined. A p-value <0.05 was considered statistically significant. Results: Twenty-five males and females, five for each decade (mean age 43 ± 13.8 years), were included. The native T2* values in all subjects averaged at 34.03 ± 6.65 ms (range 29.9–37.9 ms). Reproducibility analyses showed good correlations between the various datasets (ICC > 0.80). A weakly negative correlation was observed between age and T2* (p = 0.04). Segmental correction factors were developed and found to be significantly different from correction factors developed by non-commercially available software on non-state-of-the-art technology for sequences and scanners. Conclusions: Age-specific normative values and higher normal cut-off values than the conservative 20 ms are recommended to avoid systematic biases in the identification of pathological findings. Moreover, the correction factors developed by using the most reproducible Black Blood MEGE sequences and a commercially available software on a scanner of the current era could be a significant step toward spreading a more sensitive T2* segmental approach in the clinical arena worldwide. Full article