Search Results (1,117)

Multiple endocrine neoplasia (MEN) syndromes are rare hereditary disorders characterized by the development of multiple endocrine and non-endocrine tumours with variable penetrance and age-dependent expression. Although uncommon, these syndromes are highly relevant from both biological and clinical perspectives, as they exemplify the direct link between germline genetic alterations and tumorigenesis. Early tumour detection is critical in MEN syndromes because many associated neoplasms—such as medullary thyroid carcinoma (MTC), pancreatic neuroendocrine tumours (NETs), pheochromocytomas, and parathyroid disease—may remain clinically silent for prolonged periods while retaining malignant potential. Delayed diagnosis is associated with advanced disease and worse outcomes, whereas early identification enables curative or organ-preserving interventions. This clinical challenge has driven the development of integrated diagnostic strategies combining genetic testing, biochemical markers, and imaging. Among these, genetic testing plays a pivotal role, providing definitive diagnosis, enabling family screening, and guiding risk-adapted surveillance. The aim of this review is to provide a comprehensive synthesis of genetically driven diagnostics in MEN syndromes, outlining the current state of the art and future directions in precision medicine. Full article

Water level monitoring is closely linked to the safety of production and daily activities along riverbanks, making real-time and high-precision water level measurement an urgent technical demand. The feature extraction backbone of the Unet model is modified, and the lightweight MobileNet V2 network is adopted in this paper. The constructed network achieves significantly higher computational efficiency than standard convolutions, effectively overcoming the limited real-time performance of conventional water level measurement methods. Furthermore, the coordinate attention (CA) mechanism is integrated into the skip connections of Unet to strengthen the network’s capability to extract key features for water level segmentation, thereby further improving the accuracy of water level detection. A novel piecewise linear fitting method for water level line measurement based on monocular vision is proposed, and field-measured water level data are adopted to verify the calculation results. The main achievements of the improved model include the following: (1) Compared with the baseline model, the improved model MCUnet (MobileNet V2 + CA + Unet) achieves a 5.77% increase in accuracy and a 25.71% improvement in inference speed on the experimental water surface recognition dataset. (2) Taking the field-observed water level as the reference, the mean absolute error of the proposed image-based water level monitoring method reaches approximately 1.69 cm. (3) In comparison with DeepLab, U2net and Unet, the MCUnet model gains accuracy improvements of 4.47%, 2.81% and 5.77% respectively, with the detection frame rate increased by 12 FPS, 15 FPS and 11 FPS correspondingly. Through this work, the paper can provide some theoretical support and technical references for overcoming the limitations of conventional water level measuring devices, including strict installation requirements, limited measurement precision, high deployment and maintenance costs, and cumbersome data processing. Full article

Global agriculture faces a dual imperative: increase food production to meet rising demand while simultaneously reducing environmental impacts and resource inefficiencies. Addressing this challenge requires repositioning ruminant nutrition as the intelligent nexus linking crop and livestock production within Integrated Crop–Livestock Systems (ICLS). In this role, nutrition becomes central to restoring ecological, nutritional, and economic synergies that have been fragmented by decades of agricultural specialization. While ICLS provides the ecological foundation, Precision Livestock Farming delivers the technological and analytical infrastructure necessary to operationalize integration at the individual-animal level. Real-time sensing, Internet of Things platforms, and Artificial Intelligence (AI) enable dynamic monitoring of animal physiology, behavior, and environmental interactions across scales. A key advancement in this evolution is the development of Hybrid Intelligent Mechanistic Models (HIMM), which integrate biologically grounded mechanistic models with data-driven AI approaches. By combining interpretability with adaptive learning, HIMM enhances predictive accuracy, extrapolative capacity, and decision transparency, enabling the creation of digital twins that simulate biological responses before management interventions are implemented. Such architectures extend precision nutrition beyond feed efficiency and methane mitigation to include nutrient density and product quality, thereby linking different ecosystem processes directly to human dietary needs. Integrating nutrition with advanced modeling and monitoring tools can help livestock systems move beyond static “net-zero” benchmarks toward sustainable strategies that are responsive to local production contexts. In this reframed paradigm, nutrition is not merely a production input but the central analytical framework that computationally links biological mechanisms, environmental stewardship, technological innovation, and human health within sustainable ruminant systems. Full article

Floodplain wetlands are globally important ecosystems, yet altered hydrological regimes increasingly disrupt the balance between woody and non-woody vegetation. In Australia’s regulated Murray–Darling Basin, it remains unclear whether woody plant encroachment represents a persistent shift toward terrestrialisation or a dynamic process that can be periodically reversed by flooding. This study quantified long-term patterns of woody-vegetation encroachment and retreat across 32,000 ha of mapped wetlands in the mid-Murrumbidgee River floodplain from 1988 to 2023, and assessed how hydrological variability and floodplain connectivity mediate these dynamics. Using open, analysis-ready Earth observation data from Digital Earth Australia (DEA) within the Open Data Cube (ODC) framework, we combined DEA Land Cover for transition mapping, Water Observations for hydrological masking, Landsat surface reflectance for Enhanced Vegetation Index (EVI)-based spectral plausibility testing, and the Wetlands Insight Tool for qualitative temporal context. Woody-vegetation dynamics were strongly non-linear and closely linked to alternating drought and flood phases. During the Millennium Drought (2001–2009), mapped woody-cover decline exceeded 50% of wetland area in some sub-regions, whereas the post-drought recovery interval (2008–2013) produced encroachment exceeding 40% in the most affected areas. Across the full 35-year record, mean encroachment rates ranged from 85 to 250 ha yr⁻¹ among sub-regions, summing to approximately 865 ha yr⁻¹ of woody expansion across the floodplain, while retreat rates were lower overall (approximately 634 ha yr⁻¹), resulting in a net expansion of woody cover. Local hydrological connectivity strongly mediated these responses: infrequently inundated wetlands showed persistent terrestrialisation, whereas more frequently inundated, better-connected wetlands experienced periodic flood-driven retreat. Landsat-derived EVI broadly supported the mapped transitions, indicating general consistency with canopy greening and canopy decline, supporting the ecological plausibility of the detected changes. This open DEA–ODC workflow provides a transparent, transferable framework for operational wetland monitoring and demonstrates that maintaining natural flood frequency, duration, and connectivity is essential for sustaining the resilience of regulated floodplain systems. Full article

In the context of global climate change and China’s dual-carbon strategy, this analysis examines how land-use transition is associated with land-use carbon effects in China’s resource-based cities. From the perspective of urban development stages, an analytical framework is built by linking development stage, land-use structure, and carbon source–sink structure. Using 262 resource-based cities from 2011 to 2023, we estimate land-use-related carbon emissions, carbon sequestration, and net land-use carbon effects with the carbon emission coefficient method and analyze their spatiotemporal patterns and driving factors using GeoDetector. The results show clear differences among city types. Mature cities form the largest group. Growth cities show the fastest expansion of impervious surfaces, while regenerative cities present signs of ecological recovery. This suggests that land-use transition is not simply the expansion of impervious surfaces, but a stage-dependent process of structural change. Land-use carbon effects also differ across stages. Mature cities maintain high and stable carbon-source effects. Growth cities exhibit increasing carbon-source effects, declining cities show reduced emissions but limited improvement in the carbon source–sink structure, and regenerative cities show improved carbon-sink capacity under ecological restoration. Overall, net land-use carbon effects follow a rise–decline–rebound pattern and show clear spatial heterogeneity and visually apparent clustering patterns. Population size has strong explanatory power, while interactions between socioeconomic and land-use factors further shape spatial differences. These results support stage-specific low-carbon transition strategies. Full article

Pituitary apoplexy is an uncommon but clinically urgent complication that often involves intrasellar hemorrhage and tissue necrosis. The mechanisms linking acute tissue injury to the inflammatory tumor microenvironment remain incompletely defined. Here, we characterized the apoplexy-associated microenvironment and examined whether macrophage mechanosensitive signaling contributes to inflammatory amplification and tissue damage in pituitary neuroendocrine tumors (PitNETs). We combined single-cell RNA sequencing (scRNA-seq), histological validation, clinical stratification, and in vitro functional assays using apoplectic and non-apoplectic human PitNET specimens. Macrophage state transitions, intercellular communication, and transcriptional regulatory programs were analyzed, followed by an experimental assessment of the PIEZO1–Ca²⁺ axis and macrophage-conditioned medium-induced tumor cell death. Histological validation confirmed macrophage accumulation in apoplectic PitNETs, including a 1.67-fold increase in IBA-1-positive cells (p < 0.001). CellChat-inferred interaction metrics increased descriptively in apoplectic samples. Apoplectic tissues showed higher TNF-α expression (3.00-fold; p < 0.0001) and higher PIEZO1 fluorescence in IBA-1-positive regions (1.39-fold; p = 0.001). Yoda1 increased Calcium 520 fluorescence in macrophages (1.72-fold; p = 0.002), whereas Piezo1 knockdown reduced the Yoda1-associated response (p = 0.003). Conditioned medium from activated macrophages increased total Annexin V/PI-positive death in AtT-20 cells (0.53 ± 0.53% to 32.48 ± 1.14%; p < 0.001) and GH3 cells (0.82 ± 0.50% to 30.92 ± 1.11%; p < 0.001); Piezo1 knockdown or TNF-α neutralization attenuated this effect. Clinically, pathological necrosis was associated with higher symptom frequencies and a greater adjusted likelihood of two or more clinical symptoms. Together, these findings indicate that PIEZO1-related macrophage signaling may participate in TNF-α-associated tumor cell necroptosis in pituitary apoplexy. Pathological necrosis was linked to greater acute symptom burden and perioperative hormonal abnormalities, suggesting that it may identify a clinically severe apoplexy subtype. Full article

Background: Falls among older adults are a major public health concern associated with injury, disability, reduced mobility, and loss of independence. Functional impairment, chronic diseases, and unsafe home environments may increase the risk of falls. This study examined environmental, functional, and health-related factors linked to falls among community-dwelling older adults in Iran. Methods: A comparative cross-sectional study was conducted among 329 community-dwelling older adults. Data were collected using standardized assessments of functional ability, home safety, health status, and fall history. Conventional regression and Elastic Net analyses were applied to identify significant predictors of falls. Results: Overall, 28.6% of participants reported at least one fall during the previous 12 months. Falls were significantly more common among females, adults aged ≥85 years, individuals without a spouse, and those with lower educational levels. Fallers showed poorer mobility, balance, and functional independence, greater fear of falling, and a higher risk of home accidents (all p < 0.001). Elastic Net analysis identified use of movement aids as the strongest risk factor, whereas better Performance-Oriented Mobility Assessment (POMA) scores were the main protective factor. Conclusions: Falls among community-dwelling older adults appear to result from the interaction of physical, medical, socioeconomic, and environmental factors. These findings highlight the need for multidimensional fall-prevention strategies in primary care settings. Full article

Polymer coatings are integral to nearly every modern cardiovascular and endovascular device, including drug-eluting stents (DESs) and drug-coated balloons (DCBs), bioabsorbable vascular scaffolds (BVSs), occluders, grafts, and catheter and guidewire hydrophilic surfaces. Persistent complications, including late stent thrombosis, delayed endothelialization, hypersensitivity, and restenosis, show that coatings actively shape biological responses rather than acting as inert drug carriers. Their surface chemistry, drug release kinetics, and degradation behavior are upstream determinants of blood– and tissue–material responses that govern healing and failure. This review frames coating selection as a structure–property–biological response problem. It surveys the major classes of synthetic polymer coatings and the defining surface and bulk properties. This review also examines how composition and architecture control drug release, and traces the interfacial cascade of protein adsorption, coagulation and complement activation, platelet and leukocyte responses, and neutrophil extracellular trap (NET) formation. These mechanisms are linked to contemporary design strategies that improve hemocompatibility, limit thrombosis, promote endothelial recovery, and tune degradation, and to the standardization and translation gaps that remain. The central message is that polymer coatings are not biologically equivalent. Their surface chemistries and degradation profiles determine the thrombo-inflammatory outcomes. Therefore, coating design should be guided by intended biological response, not drug release alone. Full article

Background: Sepsis is a life-threatening syndrome characterized by a dysregulated host response and organ dysfunction and remains a major cause of mortality in intensive care units (ICUs). Early risk stratification is essential for clinical management. C-C motif chemokine ligand 4 (CCL4), a pro-inflammatory chemokine involved in immune cell recruitment, may reflect the severity of systemic inflammation; however, its prognostic value in adult patients with sepsis has not been fully elucidated. Methods: In this prospective, single-center observational study, 75 adult patients with sepsis admitted to the ICU were enrolled. Plasma CCL4 levels were measured at admission using an enzyme-linked immunosorbent assay (ELISA). Logistic regression, receiver operating characteristic (ROC) curve analysis, DeLong testing, and reclassification analyses using net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were performed. Results: CCL4 levels were significantly higher in non-survivors than survivors (1784 ± 752 vs. 1397 ± 528 pg/mL, p = 0.011). In multivariable analysis, the CCL4 (odds ratio [OR] 1.001, p = 0.023) and Pitt bacteremia score (OR 1.523, p = 0.003) were independently associated with ICU mortality. CCL4 alone showed modest discriminative performance (AUC 0.645, 95% confidence interval [CI] 0.508–0.782). However, the addition of CCL4 to clinical severity scores significantly improved discrimination, with the highest observed in the combined model (AUC 0.885). Reclassification analyses further supported the incremental prognostic value of CCL4. Conclusions: CCL4 is independently associated with ICU mortality in sepsis, and its integration with clinical severity scores may improve prognostic accuracy for risk stratification. Full article

The rapid adoption of electric vehicles (EVs) creates a planning challenge for the Kampala-Wakiso metropolitan region in Uganda, where the electricity grid already faces local network constraints. This study applies the EVPV-Simulator, an open-source geospatial modelling framework that links mobility demand, charging demand, and EV-PV complementarity, to assess projected charging demand and solar integration potential in the Kampala-Wakiso metropolitan region. By simulating the charging requirements of a projected fleet of 60,000 EVs, the study identifies a pronounced evening charging peak concentrated in residential areas and weakly aligned with daytime solar availability. Under the base-case charging pattern, increasing PV capacity raises the self-sufficiency potential, but has limited influence on the evening peak. In the base-case with 40 MW of installed PV capacity, the self-sufficiency ratio reaches 39.6%, while peak demand falls by only 0.20%. A charging location sensitivity analysis then shows that temporal alignment improves substantially when charging shifts from home towards workplaces and Points of Interest (POI). In a selected daytime oriented scenario with 40% workplace charging and 60% POI charging, the self-sufficiency potential reaches 68.97% and the mean daily maximum net load falls to about 18 MW at 40 MW of installed PV capacity. These results show that the value of solar integration depends strongly on where charging occurs, and that daytime charging access should be treated as a central variable in EV infrastructure planning. The study provides a planning oriented basis for future work incorporating feeder level validation, explicit PV siting constraints, and storage. Full article

Background/Objectives: This study assessed whether endometriosis is associated with an increased risk of placenta accreta spectrum (PAS) disorders and investigated if assisted reproductive technology (ART) further increases the risk in patients with endometriosis. Methods: This retrospective study used multi-institutional data from the TriNetX database to identify patients who experienced delivery on or before 31 December 2024, with a prior diagnosis of endometriosis and ART therapy, as coded by CPT and ICD-10 codes. The primary outcomes included up to 7-day perinatal results, such as PAS (accreta, increta, percreta), and maternal complications, including peripartum hysterectomy, transfusion, postpartum hemorrhage (PPH), ICU admission, and sepsis. Risk ratios, 95% confidence intervals, and p-values were calculated for endometriosis versus no endometriosis and endometriosis patients with ART versus without ART. Results: Out of 3,487,612 patients identified, 24,341 had a prior diagnosis of endometriosis prior to propensity score matching. Propensity score matching was used to control for age, demographic variables, previous procedures, and comorbidities. Compared to controls, endometriosis was linked to a higher risk of PAS disorders (RR 1.74), including accreta (RR 2.22), increta (RR 2.50), and percreta (RR 1.59). Additional complications included peripartum hysterectomy (RR 1.72), transfusion (RR 1.26), PPH (RR 1.35), ICU admission (RR 1.43), and sepsis (RR 1.56). Patients conceived via ART faced greater risks of PAS disorders (RR 2.00), accreta (RR 2.14), hysterectomy (RR 1.63), transfusion (RR 2.10), and PPH (RR 1.66). Conclusions: This study shows a positive link between endometriosis and the risk of PAS disorders and maternal complications, and the use of ART in patients with endometriosis further increases this risk, emphasizing the importance of comprehensive counseling and a multidisciplinary approach to delivery planning for this high-risk group. Full article

Monitoring immunosuppression in kidney transplant recipients remains challenging, as conventional therapeutic drug monitoring (TDM) reflects pharmacokinetic exposure rather than the overall functional immune state. Torque teno virus (TTV), a non-pathogenic virus, has emerged as a potential complementary biomarker of the net state of immunosuppression. This review evaluates the current evidence regarding the utility of TTV load in this context, focusing on its correlation with standard pharmacokinetic markers, the analytical performance of quantitative PCR assays, its role as an integrated marker of immunosuppression, and its predictive value for clinical outcomes. Available data indicate that TTV load shows weak and inconsistent correlations with individual drug levels, such as tacrolimus trough concentrations, supporting its role as a complementary rather than substitutive tool. qPCR-based assays demonstrate generally good sensitivity and reproducibility, although inter-assay variability and lack of standardization remain important limitations. Clinically, higher TTV levels have been associated with an increased risk of opportunistic infections, whereas lower levels have been linked to acute rejection, suggesting a potential association between TTV viremia and immune status. TTV monitoring may represent a promising complementary approach for a more individualized assessment of immunosuppression. However, further prospective and interventional studies are required to validate standardized thresholds and determine whether TTV-guided strategies improve transplant outcomes compared with conventional monitoring. Full article

Petri nets represent a highly versatile mathematical formalism for modeling discrete event and hybrid systems. For the development of modern complex production processes for Industry 4.0, integrating these formal models with industrial communication standards is an appropriate and effective option. The main aim of the proposed article is to design a new web-based software tool for the modeling, simulation, and control of mechatronic systems with OPC Unified Architecture support. To accomplish this task, an original software solution called PetriLink is proposed. This platform leverages an intuitive graphical interface and significantly expands the formalism by combining hybrid Petri nets with Colored Petri Nets (CPN) data extensions and a reactive OPC UA subscription model. These new features greatly expand the area of systems that can be modeled and controlled, bridging the gap between theoretical academic tools and practical industrial automation. Furthermore, the structural flexibility of the implemented Petri net models enables the explicit representation of symmetric cyber-physical architectures, as well as the design of asymmetric, event-driven control strategies (e.g., using inhibitor and reset arcs) for enhanced system robustness. The platform was evaluated on a reference net of 5000 places and 2500 transitions, where an incremental dirty-flag evaluation mechanism keeps the per-step engine cost below 1 ms for sparse industrial markings and at about 350 µs for a moderate workload of one hundred concurrent tokens, yielding a speed-up of up to roughly three orders of magnitude over naive full re-evaluation and confirming consistent soft real-time behavior on commodity hardware. Offering a graphical environment for the design of discrete event and hybrid system control algorithms, it can be used for education, research and practice in cyber-physical systems (Industry 4.0). Full article

Nickel ferrite (NiFe₂O₄) is one of the most studied inverse-spinel ferrites because it combines moderate saturation magnetization, comparatively high electrical resistivity, chemical stability, and broad synthesis flexibility. Yet the literature shows that the measured structure and magnetism of NiFe₂O₄ are not intrinsic constants; they evolve strongly with dimensionality, size, thickness, strain state, cation distribution, surface spin disorder, and synthesis pathway. This review develops a unified theoretical and literature-based interpretation of how dimensionality reshapes the structural and magnetic behavior of NiFe₂O₄ across bulk ceramics, nanoparticles, one-dimensional nanostructures, polycrystalline thin films, and ultrathin epitaxial films. The review is anchored in the two uploaded nickel ferrite attachments and expanded using internet-sourced journal literature on spinel inversion, surface effects, mechanochemical synthesis, sputtered and pulsed laser deposited thin films, and epitaxial ultrathin-film anomalies. The central novelty of this article is the formulation of a dimensionality-dependent framework in which the observed magnetic response is governed by a competition among three coupled factors: (i) the cation-distribution function, which controls the A–B superexchange balance and therefore the net ferrimagnetic moment; (ii) the microstructural coherence function, which measures how crystallinity, strain, defects, and anti-phase boundaries preserve or degrade exchange continuity; and (iii) the surface/interface spin-order parameter, which quantifies the loss or reconfiguration of magnetic order at free surfaces and buried interfaces. Within this framework, bulk NiFe₂O₄ behaves as a near-equilibrium inverse spinel with relatively stable magnetization, whereas nanoscale NiFe₂O₄ experiences strong spin canting and finite-size suppression due to the growing fraction of disordered surface spins. Thin films introduce a distinct regime in which strain, texture, anti-phase boundaries, substrate mismatch, and growth kinetics determine both anisotropy and magnetization. In ultrathin epitaxial films, off-equilibrium cation redistribution and interface-controlled electronic reconstruction may even generate magnetization values far above bulk expectations. The review also compares major synthesis routes—solid-state reaction, sol–gel, co-precipitation, hydrothermal growth, reactive milling, combustion, pulsed laser deposition, and radio-frequency sputtering—and explains why each route biases the final dimensionality-dependent properties differently. A set of word-style equations is provided to formalize spinel inversion, finite-size suppression, anisotropy scaling, coercivity trends, and superparamagnetic crossover. Beyond summarizing the field, the review proposes a regime map linking dimensionality to characteristic structural defects and magnetic signatures, and it identifies unresolved questions concerning the true origin of enhanced magnetization in ultrathin NiFe₂O₄, the interplay between anti-phase boundaries and strain, and the distinction between intrinsic inversion changes and extrinsic substrate artifacts. The resulting article offers a submission-ready, originality-focused review that positions dimensionality as the master variable governing structure–magnetism correlations in nickel ferrite. Full article

This study proposes a novel simulation-driven intelligent framework for the performance and reliability assessment of renewable energy-integrated pavement systems by unifying coupled multiphysics finite element modeling, structured dataset generation, and graph-based artificial intelligence within a single computational paradigm. The proposed pavement is formulated as a seven-layer multifunctional infrastructure system comprising the asphalt surface, intermediate binder, base layer, thermoelectric energy layer, piezoelectric insert zone, subbase, and subgrade soil, thereby enabling simultaneous consideration of structural load transfer, thermal gradient-driven energy harvesting, moisture-sensitive support behavior, and reliability-oriented performance interpretation. A three-dimensional thermo-hydro-mechanical Abaqus model was developed to simulate the concurrent effects of moving wheel load, solar heat flux, rainfall infiltration, and internal moisture diffusion, and it was subsequently used to construct an AI-ready dataset containing 6000 simulation cases and 68 variables spanning geometric, material, environmental, traffic, uncertainty, structural, thermal, hydraulic, renewable-energy, and probabilistic reliability descriptors. To preserve the physical hierarchy of the layered pavement within the learning process, a Layer-Coupled Reliability Graph Operator Network (LaRGO-Net) was proposed, in which pavement layers are represented as interacting graph nodes linked through adaptive interlayer coupling and optimized through multi-task, physics-aware, and coupling-consistent learning. Experimental evaluation across nine progressive configurations demonstrated a monotonic improvement from baseline dense and graph-convolution models to the full LaRGO-Net formulation. The final model achieved the best overall performance with mean RMSE = 0.040, mean MAE = 0.028, mean $R^2=0.994$, and reliability prediction accuracy characterized by F1 = 99.21 and AUC = 99.53. These results confirm that the proposed framework provides a highly accurate, physically interpretable, and reliability-aware surrogate for next-generation pavement systems capable of simultaneously supporting structural serviceability, renewable-energy functionality, and intelligent decision-making. Full article