Coral reef aorta (CRA) is a rare and severe form of atherosclerotic disease, marked by dense, irregular calcifications that project into the aortic lumen [1]. CRA exhibits an exophytic growth pattern, resulting in pronounced intraluminal obstruction [2]. Mechanisms of CRA are not understood; however, extent of calcification is usually out of proportion to atherosclerosis and probably related to abnormal calcium metabolism and systemic inflammatory state [1]. CRA more commonly affects women, presents with symptoms related to vascular compromise, is often found in the suprarenal and juxtarenal segments of the aorta, and is challenging to treat, often requiring open repair [2, 3].We report a case of CRA involving both the thoracic aorta and subclavian artery, resulting in symptoms of subclavian steal.

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia globally, marked by chaotic electrical impulses and irregular atrial contraction. At the cellular level, defective calcium (Ca+2) handling plays a central role in AF pathogenesis, with the sarco/endoplasmic reticulum Ca+2-ATPase (SERCA) pump being a critical determinant of intracellular Ca+2 reuptake and myocardial relaxation. Recent research has uncovered a class of small transmembrane micropeptides, phospholamban (PLB), sarcolipin (SLN), dwarf open reading frame (DWORF), myoregulin (MLN), endoregulin (ELN) and another-regulin (ALN), that directly modulate SERCA activity. Interestingly, these micropeptides exhibit chamber-specific expression and diverse regulatory mechanisms, functioning as inhibitors, uncouplers or facilitators of SERCA activity and are increasingly linked to atrial arrhythmogenesis. This review synthesizes current understanding on function of SERCA micropeptides, highlighting their distinct roles in atrial versus ventricular excitation-contraction (E-C) coupling. We explore evidence from genetically modified animal models and patient-derived data to elucidate how dysregulation of these peptides, particularly SLN and PLB, contributes to abnormal EC-coupling like Ca+2 cycling, delayed afterdepolarizations, oxidative stress and atrial remodeling. We also examine the influence of systemic metabolic regulators, such as thyroid hormones, catecholamines, dexamethasone and various exercise paradigms on micropeptide expression and function, offering insight into their intersection with AF progression. By dissecting the spatial, temporal and metabolic regulation of SERCA micropeptides, this review aims to offer current understanding about chamber-specific modulation of Ca+2 homeostasis and use these insights towards treatment of AF.

Aiming at the problem of low segmentation accuracy caused by complex lesion morphology and irregular lesion boundaries in medical images, a segmentation model for medical images based on DEC-UNet is proposed in the research. A dual-encoder architecture based on C-FEM and RCW-Transformer is introduced to simultaneously extract local and global features, where C-FEM is used to capture local features such as textures and boundaries, RCW-Transformer is used to capture global features such as overall morphology and spatial location. To sufficiently fuse the features extracted by the dual encoders, DCA is introduced to compute cross-attention weights between local and global features, learning their similarities and differences and preserving discriminative information from each encoder. The fused features are transmitted to the corresponding decoder levels through the skip connections. In the decoder, CARAFE is adopted for adaptive upsampling to reduce the loss of edge features, thereby improving the segmentation accuracy. Experiments are conducted on the Kvasir-SEG, ACDC, and Synapse datasets respectively. Compared with CNN-based, Transformer-based, and CNN-Transformer-based models, DEC-UNet achieves the highest DSC of 89.38%, 91.24%, and 93.83% respectively, and the lowest HD of 9.43 mm, 8.13 mm, and 6.64 mm respectively. The results demonstrate that DEC-UNet improves the accuracy of medical image segmentation.

Long before the first symptoms of psychosis were categorized, late eighteenth-century practitioners of psychiatry had frequently noted that their patients suffered from acute stomach pain, skin rashes, and heart irregularities, suggesting at least a handful of them to think of an underlying mind-body pathology. The psychiatric community began debating the connection between mental illness and the body more frequently by the late nineteenth century. Throughout the late 20th century, the body in schizophrenia was largely viewed as a vessel for side effects, a repository for the metabolic consequences of antipsychotic medication. However, we are currently witnessing a visceral turn in the field, a theoretical and methodological shift moving from the brain-only models to mind-body models of psychopathology. This shift acknowledges that the brain isn't just a distinct entity; it is inextricably tethered to the rhythmic oscillations of the visceral organs. This article tries to trace the evolution of the methods used to understand the gut brain axis better. How the understanding evolved from merely trying to understand situational reactivity to a how broader stress-regulatory networks function in the body. The evolution of the brain-gut axis in schizophrenia, from the analog stress tests of 1982 to the network coupling models of 2025, reveals a clear trajectory. We have moved from viewing visceral symptoms as noise to recognizing them as a crucial signal. Current evidence suggests that the gut is key to the pathophysiology of psychiatric disorders.

Polycystic ovarian syndrome (PCOS) is an intricate endocrine and metabolic disorder affecting reproductive-age women. This research focused on assessing the therapeutic potential of harmine in letrozole-induced PCOS rat model. PCOS was induced in all the groups except normal control by oral administration of letrozole (1 mg/kg) for consecutive 28 days. After confirmation of PCOS, normal and disease controls were treated with vehicle, clomiphene citrate (5.25 mg/kg) was administered as a standard drug, and harmine as treatment compound at 2.5, 5.0, and 10 mg/kg orally for 28 days daily. Letrozole administration in diseased rats displayed disrupted estrous cycle irregularity, insulin resistance, cystic ovarian morphology, and hyperandrogenism along with upregulation of metabolic and lipid profile and alteration in hormonal and hematological parameters. Treatment with harmine (2.5-10 mg/kg) was remarkably (p < 0.05) normalize the metabolic and lipid profile, restored the hormonal imbalance, and improved ovarian histology. Treatment with harmine (2.5-10 mg/kg) notably reduced the serum level TNF-α, and IL-6 in contrast to disease control. Harmine-treated rats exhibited the down regulation of mRNA expression of TNF-α, IL-6, PGR, and Keap-1 while upregulated FSHR, CYP19A1, and Nfr-2 as equated to disease control. However, harmine demonstrated dose-dependent improvement in mRNA expression that linked with ovarian function. These findings concluded that harmine exhibited promising therapeutic potential for PCOS by modulating metabolic, endocrine, anti-oxidant, anti-inflammatory, and steroidogenic markers. Besides, harmine was safe in acute usage and LD50 > 2000 mg/kg.

Accurate predictions of heathland plant species are crucial for ecological monitoring and assessing biodiversity. Previous research has predominantly utilised convolutional neural networks (CNNs), which process images arranged on a regular grid. Although CNNs are effective at extracting local visual features, they frequently do not capture the irregular spatial relationships characteristic of heathland vegetation. Earlier approaches employing graph neural networks (GNNs) for plant classification have generally relied on simple or dataset-wide graph constructions, with limited consideration for edges that represent the actual morphological structure of plants. This study presents PlantGraphNet, a hybrid CNN-GNN framework that integrates visual and structural information for heathland plant species classification. PlantGraphNet extracts image-specific keypoints and local descriptors to construct graphs, where nodes correspond to plant regions and edges encode spatial relationships. The resulting graphs are processed using graph convolution and graph attention layers to capture relational context, while a CNN backbone provides complementary appearance features. These two modalities are combined into a unified representation for classification. To ensure scalability, PlantGraphNet employs distributed data-parallel training, enabling efficient gradient synchronisation across devices and near-linear performance scaling. When evaluated on Danish aerial heathland datasets, PlantGraphNet achieves a precision of 98.98%, substantially outperforming CNN-only baselines. In addition to improved accuracy, the explicit graph construction increases interpretability by associating classification outcomes with specific plant structures. These findings indicate that integrating CNN-derived features with purposefully designed graph representations of plant morphology yields a robust and interpretable approach for fine-grained heathland plant species classification.

Non-invasive monitoring of deep-tissue physiological processes from the body surface is technically challenging due to rapid signal attenuation and severe motion artefacts, primarily caused by high contact impedance and mechanical mismatch in conventional electrodes. Here we directly spray-coat a biocompatible two-dimensional nanosheet ink onto the human body, forming microscopically conformal van der Waals thin films that are intrinsically stretchable and mechanically adaptive to non-Euclidean, hairy and dynamically evolving body surfaces, thereby creating electrically functionalized body surfaces. Compared with commercial solutions, the resulting electrically functionalized body surface exhibits substantially lower contact impedance and markedly reduced extrinsic motion artefacts, enabling reliable bioelectrical signal acquisition from irregular body surfaces and robust monitoring of biopotential and bioimpedance modulations associated with deep-tissue physiological processes, including blood circulation, muscle movement and brain activity. This technology advances capability for continuous, non-invasive monitoring of deep-tissue activities during routine body movements.

The present work reports an investigation of the thermomechanical dynamic viscoelastic properties of a magnetorheological (MR) elastomer embedded with electrolytic iron particles (EIP, 40% wt, irregular shapes) in a silicone matrix. Unlike conventional MREs produced from spherical carbonyl iron particles, irregular flake-shaped EIPs offer greater particle-particle interactions and enhanced field sensitivity. The temperature rise from 30 to 70 °C significantly influences the matrix properties, altering the magnetic field-induced changes in the modulus as particle orientation with the field improves and friction between the particles and the matrix decreases. The thermomechanical dynamic viscoelastic properties of the present MRE confirm these interactions, along with the effects of strain amplitude (0.01 to 10%), frequency rate (0.1-40 Hz), and applied magnetic field strength (0.016 to 640 mT). The increase in temperature from 30 to 70 °C shows an overall 12.5% decrease in G' while there is a 67% increase in G" for silicone rubber. In MRE40, there is an 18.9% reduction in the storage modulus and a 4% increase in the loss modulus. This variation indicates that the high aspect ratio of the iron particles initially provides higher stiffness, but above a certain temperature, thermal energy overcomes it, leading to a higher rate of decrease of G' with temperature. Additionally, the time-temperature superposition (TTS) master curve is established for the present MRE. The damping behavior also varies with both temperature and field intensity, demonstrating the material's dual sensitivity. This work introduces a novel method for creating thermally adjustable MREs using flake-shaped magnetic fillers, which could enhance vibration control isolation pads used to minimize thrust force, operating under varying temperature conditions.

Dust accumulation is a major challenge for PV systems in arid regions, reducing efficiency and increasing system costs. This study analyzes the influence of dust on the optimal sizing of a stand-alone PV-battery system for a typical household in Saudi Arabia. Dust samples were examined using SEM and EDS, showing irregular particles (1-100 µm, average ~ 50 µm) composed mainly of oxygen (58.6%) and silicon (11.8%). These particles reduced glass transmittance from 1 (clean) to 0.704, 0.496, and 0.349 at dust densities of 0.7, 1.4, and 2.1 mg/cm2, respectively. A MATLAB-based techno-economic model was developed to optimize system design under each condition while ensuring 0% loss of power supply probability. Under clean conditions, to meet a typical household load of approximately 2 kW, the optimal system required 10.5 kWp PV (21 modules) and 5 batteries at an LCOE of 0.22 $/kWh. At 0.7 mg/cm2, the requirement increased to 15 kWp PV (30 modules) with 5 batteries at 0.27 $/kWh. For 1.4 mg/cm2, 19.5 kWp PV (39 modules) and 6 batteries were needed, raising LCOE to 0.33 $/kWh. At 2.1 mg/cm2, the system required 21 kWp PV (42 modules) and 9 batteries, resulting in 0.40 $/kWh, an 82% cost increase. Results highlight the need for dust-mitigation strategies to ensure reliable and cost-effective PV system performance overall.

The extensive use of industrial chemicals increases the likelihood of accidental or deliberate environmental release, potentially occurring in occupational, public, or military settings, with skin contact being an important route of exposure. The effectiveness of subsequent skin decontamination depends on the physicochemical properties and absorption profiles of the chemicals involved, as well as the decontamination method applied. This study assessed three solution-based decontamination approaches, hard water, 2% soapy water, and Dahlgren Decon Skin Soap against three representative compounds: chloroacetone, chloroacetonitrile, and crotonaldehyde. For all chemicals tested, statistically significant differences (p < 0.05) were more commonly observed between decontaminated and untreated samples than among the decontamination treatments themselves. These findings indicate that decontamination reliably reduces dermal permeation, even though the specific formulations did not consistently produce distinguishable differences in efficacy. These results support the practical value of prompt decontamination, highlighting that readily accessible solutions can meaningfully mitigate dermal exposure risks in real‑world chemical release scenarios.