Search Results (925)

In this work, the production of iron-containing powders by the electroplasma dispersion method was investigated under various discharge regimes and in electrolytes of different natures (NaCl and Na₂CO₃). The influence of technological parameters on particle morphology, phase composition, and elemental content was analyzed using X-ray diffraction (XRD), scanning electron microscopy with energy-dispersive spectroscopy (SEM/EDS), as well as laser particle size distribution analysis. It was found that the single-stage mode at 350 V in NaCl electrolyte led to the formation of predominantly irregularly shaped and fragmented particles, with a limited amount of spherical powders. The two-stage mode (350 V for 5 s followed by 250 V) in NaCl ensured a more stable formation of spherical particles with sizes of 60–80 μm; however, it was accompanied by intensive surface oxidation. The highest fraction of spherical powders was obtained in a Na₂CO₃ electrolyte under the two-stage mode, where homogeneous spheres with diameters of 20–75 μm and smooth surfaces were formed. According to EDS analysis, the powders consisted mainly of iron and oxygen, while in the samples synthesized in Na₂CO₃, the presence of sodium was detected, indicating the formation of mixed Na–Fe–O oxide phases. XRD confirmed the presence of a metallic α-Fe matrix along with oxide phases Fe₂O₃ and Fe₃O₄, while granulometric analysis (D50 ≈ 55 μm) revealed a relatively narrow particle size distribution. The obtained results demonstrate that variation in the discharge regime and electrolyte composition enables targeted control over the morphology and phase composition of the powders, making the electroplasma method a promising approach for producing metallic powders with tailored properties. Full article

Beta (β)-type Ti-29Nb-13Ta-4.6Zr (TNTZ) alloys combine low modulus with biocompatibility but require improved surface properties for long-term implantation. This study aimed to enhance the surface mechanical strength and antibacterial performance of TNTZ by applying TiN and TiAlN coatings via PVD. Notably, TiAlN was deposited on TNTZ for the first time, enabling a direct side-by-side comparison with TiN under identical deposition conditions. Dense TiN (~1.06 μm) and TiAlN (~1.73 μm) coatings were deposited onto solution-treated TNTZ and characterized by X-ray diffraction, scanning probe microscopy, Vickers microhardness, Rockwell indentation test (VDI 3198), static water contact angle measurements, and a Kirby–Bauer disk-diffusion antibacterial assay against Escherichia coli (E. coli). Both coatings formed face-centered cubic (FCC) structures with smooth interfaces (Ra ≤ 5.3 nm) while preserving the single-phase β matrix of the substrate. The hardness increased from 192 HV (uncoated) to 1059 HV (TiN) and 1468 HV (TiAlN), and the adhesion quality was rated as HF2 and HF1, respectively. The surface wettability changed from hydrophilic (48°) to moderately hydrophobic (82°) with TiN and highly hydrophobic (103°) with TiAlN. Similarly, the diameter of the no-growth zones increased to 18.02 mm (TiN) and 19.09 mm (TiAlN) compared to 17.65 mm for uncoated TNTZ. The findings indicate that TiAlN, in particular, provided improved hardness, adhesion, and hydrophobicity. Preliminary bacteriostatic screening under diffusion conditions suggested a modest relative antibacterial response, though the effect was not statistically significant between coated and uncoated TNTZ. Statistical analysis confirmed no significant difference between the groups (p > 0.05), indicating that only a preliminary bacteriostatic trend— rather than a definitive antibacterial effect—was observed. Both nitride coatings strengthened TNTZ without compromising its structural integrity, making TiAlN-coated TNTZ a promising candidate for next-generation orthopedic implants. Full article

We address the inverse spectral problem for a PT-symmetric Dirac operator with discontinuity conditions imposed along the entire real axis—a configuration that has not been explicitly solved in prior literature. Our approach constructs fundamental solutions via convergent recursive series expansions and establishes their linear independence through a constant Wronskian. We derive explicit formulas for transmission and reflection coefficients, assemble them into a PT-symmetric scattering matrix, and demonstrate how both spectral and scattering data uniquely determine the underlying complex-valued, discontinuous potentials. Unlike classical treatments, which assume smoothness or limited discontinuities, our framework handles full-axis discontinuities within a non-Hermitian setting, proving uniqueness and providing a constructive recovery algorithm. This method not only generalizes existing inverse scattering theory to PT-symmetric discontinuous operators but also offers direct applicability to optical waveguides, metamaterials, and quantum field models where gain–loss mechanisms and zero-width resonances are critical. Full article

A subset of chondrocytes in various human cartilage tissues, including neoplastic, regenerative, and normal cartilage, expresses α-smooth muscle actin (α-SMA), a protein typically found in smooth muscle cells. These α-SMA-containing chondrocytes, termed myochondrocytes and myochondroblasts, may play important roles in cartilage physiology, regeneration, and structural integrity, particularly in auricular and articular cartilage. This review synthesizes current knowledge regarding the terminology, distribution, and biological significance of these cells across normal, osteoarthritic, transplanted, and neoplastic cartilage. We summarize key findings from immunohistochemical studies using markers such as S-100, α-SMA, and SOX9, along with ultrastructural confirmation of myofilament bundles via electron microscopy. Current evidence suggests that myochondrocytes exhibit enhanced regenerative potential and contribute to matrix remodeling. Furthermore, their presence reflects the inherent cellular heterogeneity of cartilage, potentially arising from transdifferentiation processes involving fibroblasts, mesenchymal stem cells, or chondroblasts. Finally, TGF-β1 and PDGF-BB are identified as a critical modulator of α-SMA expression and chondrocyte phenotype. A deeper understanding of nature and function of myochondrocytes and myochondroblasts may improve interpretations of cartilage pathology and inform strategies for tissue engineering and cartilage repair. This review highlights the need for further investigation into the molecular regulation and functional roles of these cells in both physiological and pathological contexts. Full article

Articular cartilage (AC) is a specialised connective tissue covering joint surfaces. It enables smooth movement, distributes mechanical loads, and protects the underlying bone. In response to loading, AC adapts by modifying both its thickness and composition. AC is organised in different zones, with low cellularity and a high abundance of extracellular matrix (ECM). Mechanical overloading or immobilisation can lead to structural changes, potentially resulting in osteoarthritis (OA), for which no causal treatment currently exists. However, smaller defects can be treated using chondrocyte/cartilage transplantation or tissue engineering. A better understanding of the molecular composition of AC at different locations is essential to improve such therapeutic approaches. For this purpose, we performed a comprehensive analysis of porcine femoral knee cartilage at eight defined anatomical sites. Cartilage thickness and proteoglycan (PG) content were analysed histologically, while specific ECM proteins were assessed by proteomics and validated by immunohistochemistry and Western blot. Significant differences were identified, particularly between medial and lateral compartments, in terms of cartilage thickness, PG abundance, and ECM composition. Some proteins also showed zone-specific localisation patterns. These structural differences likely reflect adaptation to mechanical loading and should be considered to optimise future cartilage repair and tissue engineering strategies. Full article

The direction-of-arrival (DOA) estimation of multiple linear frequency modulation (LFM) signals typically requires the construction of a spatial time–frequency distribution (STFD) matrix via linear transforms or quadratic time–frequency distributions (QTFD) before joint spatial time–frequency estimation. Extensive research has been conducted on DOA estimation of LFM signals with overlapped instantaneous frequency (IF) trajectories and significantly different chirp rates. However, when LFM signals have the same chirp rate and slightly different initial frequencies with parallel and close IF trajectories, their linear transforms suffer from low resolution and quadratic distributions and are affected by cross-terms, both of which reduce accuracy. To address this problem, this study proposes a DOA estimation algorithm based on QTFD and the Hough transform. First, QTFD is used to improve the resolution and apply both spatial and directional smoothing to eliminate cross-terms. Second, the Hough transform is employed for IF estimation instead of threshold filtering to enhance accuracy. Finally, DOA results are obtained via time–frequency filtering and the multiple signal classification (MUSIC) algorithm. Experiments show that for two LFM signals at a −5 dB signal-to-noise ratio (SNR), the proposed algorithm improves accuracy by approximately 43.2% compared to similar algorithms and effectively estimates the DOA in underdetermined cases. Thus, the proposed algorithm enhances the DOA estimation accuracy for multiple LFM signals, is robust to noise, and expands the application scenarios of joint spatial time–frequency estimation. Full article

This review covers the roles of cartilage oligomeric matrix protein (COMP), an established biomarker of cartilage breakdown in pathological tissues in osteoarthritis, and in emerging areas in extracellular matrix and vascular remodeling associated with trauma, fibrosis and cancer. COMP is produced by chondrocytes, tenocytes, myofibroblasts, and in some specialized tissue contexts, endothelial and vascular smooth muscle cells. COMP expression by tendon and cartilage cells is sensitive to weight bearing and tensional mechanical stimulation. Vascular smooth muscle cells are sensitive to shear forces which regulate COMP expression in vascular tissues in atherosclerosis and in carotid stenosis. COMP is a multivalent bridging molecule that stabilizes tissues. It facilitates the signaling of TGF-β and BMP-2 in chondrogenesis, osteogenesis, tissue fibrosis, vascular and ECM remodeling and tumor development by providing a multimeric environment through which growth factor binding and receptor activation can occur. Engineered COMP proteins have been used as molecular templates in the development of chimeric therapeutic proteins of potential application in repair biology. Tie2 (Angiopoietin-1 receptor, Tyrosine-protein kinase receptor TEK), when activated by an engineered COMP-inspired angiopoietin-2 pentamer, is a potent angiogenic molecule of obvious application in wound healing. COMP’s multifunctional properties show it is much more than a biomolecular marker protein through its ability to participate in many biological processes. Further studies are warranted to fully explore the biology of this fascinating molecule, particularly in the wound repair processes. Full article

The medial longitudinal arch is fundamental to weight distribution, balance, and lower limb biomechanics, and its evaluation is important for identifying abnormalities such as flatfoot or high arch. Traditional clinical methods for assessing the foot arch index (FAI) are often constrained by limited accessibility and inconsistent accuracy. To overcome these limitations, this study proposes a deep learning-based plantar pressure measurement system (DLPPMS) designed for accurate and affordable static foot arch evaluation. The system integrates two resistive pressure sensor arrays combined into a 24 × 24 matrix to acquire plantar pressure data in real time. To enhance spatial resolution and improve the fidelity of pressure distribution, Bessel interpolation is employed to generate smooth, high-resolution plantar pressure maps. Deep learning-based pose estimation and instance segmentation models are further applied to isolate the plantar region and identify anatomical keypoints relevant for FAI computation. The system was validated on participants with flatfoot, normal arch, and high arch conditions, demonstrating high segmentation accuracy, reliable keypoint localization, and consistent FAI estimation with minimal error compared to reference values. These results confirm that the DLPPMS provides accurate, repeatable, and low-cost assessment of the medial longitudinal arch under static conditions. Overall, this work highlights the potential of combining pressure sensing, interpolation algorithms, and deep learning into a portable and scalable system, offering promising applications not only for clinical diagnostics but also for biomechanical research, preventive healthcare, and rehabilitation monitoring. Full article

Aims: Surgery remains the only definitive treatment option for abdominal aortic aneurysms (AAA), as no conclusive evidence supports drug effectiveness in preventing AAA growth. Although type 2 diabetes (T2D) is an important cardiovascular risk factor, patients with T2D show reduced AAA presence and growth, associated with metformin use. We aimed to investigate the potential benefits of metformin on AAA using proteomics and in vitro experiments. Methods: Proteomics analysis using tandem mass spectrometry was performed on aortic smooth muscle cells (SMCs) from non-pathological controls (C-SMC, n = 8), non-diabetic (ND, n = 19) and diabetic (D, n = 5) AAA patients. Key findings were subsequently validated in aortic tissue using mass spectrometry-based proteomics. SMCs were cultured with/without metformin and analyzed. Results: Comparison of the proteome of SMCs from ND-AAA patients with controls revealed a reduction in proteins associated with metabolic processes and mitochondrial function. Cytoskeletal and extracellular matrix (ECM) proteins were elevated in ND-AAA-SMCs versus C-SMCs, with a similar cluster of mechanosensitive proteins being increased in ND-AAA-SMCs versus D-AAA-SMCs. D-AAA-SMCs showed an improved metabolic and antioxidant profile, enriched in pentose phosphate pathway proteins responsible for NAD(P)H generation (G6PD, PGD) and NAD(P)H-dependent antioxidants (NQO1, CBR1, AKR1C1, AKR1B1, GSTM1), all regulated by NRF2, an antioxidant transcription factor. Over half of the proteins identified in the protein–protein interaction network, constructed from proteins with higher expression in D-AAA SMCs versus ND-AAA SMCs, were verified in D-AAA aortic tissue. In vitro, metformin causes a shift from aerobic to anaerobic metabolism, increased AMPK activation and elevated mitochondrial biogenesis, indicated by increased PGC-1α expression. Metformin increased the gene expression of PGD, CBR1 and the protein expression of NQO1, with enhanced translocation of pNRF2 to the nucleus, due to reduced KEAP1 as negative regulator of NRF2. Consequently, metformin enhanced the gene expression of well-known antioxidant regulators SOD2 and CAT. Conclusions: This study identified significant differences in the proteome of SMCs derived from controls, ND-AAA and D-AAA patients. It highlights distinct pathways in relation to mechanosensing, metabolism and redox balance as therapeutic targets of metformin that may underlie its inhibition of AAA progression. Full article

In recent years, remarkable advancements have been achieved in hyperspectral unmixing (HU). Sparse unmixing, in which models mix pixels as linear combinations of endmembers and their corresponding fractional abundances, has become a dominant paradigm in hyperspectral image analysis. To address the inherent limitations of spectral-only approaches, spatial contextual information has been integrated into unmixing. In this article, a superpixel collaborative sparse unmixing algorithm with graph differential operator (SCSU–GDO), is proposed, which effectively integrates superpixel-based local collaboration with graph differential spatial regularization. The proposed algorithm contains three key steps. First, superpixel segmentation partitions the hyperspectral image into homogeneous regions, leveraging boundary information to preserve structural coherence. Subsequently, a local collaborative weighted sparse regression model is formulated to jointly enforce data fidelity and sparsity constraints on abundance estimation. Finally, to enhance spatial consistency, the Laplacian matrix derived from graph learning is decomposed into a graph differential operator, adaptively capturing local smoothness and structural discontinuities within the image. Comprehensive experiments on three datasets prove the accuracy, robustness, and practical efficacy of the proposed method. Full article

Ischemic stroke is one of the leading causes of morbidity and mortality worldwide, with carotid atherosclerosis being its key etiological factor. MicroRNA-21 (miR-21) regulates intracellular signal pathways responsible for vascular changes and ischemic brain injury, and is recognized as a potential diagnostic and prognostic biomarker. It modifies the activity of macrophages (MΦ) and vascular smooth muscle cells, causing inflammation and affecting the stability of atherosclerotic plaques. A deficiency of miR-21 in macrophages stimulates the inflammatory response and plaque growth. It promotes both the synthesis of extracellular matrix, stabilizing the plaque, and the degradation of the fibrin cap, which leads to plaque instability. The effect of miR-21 on endothelial cells differs: it stimulates both NO· synthesis and inflammation. During ischemic stroke, miR-21 demonstrates neuroprotective effects by modulating post-ischemic inflammation and protecting the integrity of the blood–brain barrier. Therapy targeting miR-21 shows potential in experimental models, but it requires cell-specific delivery and precise timing. Further research efforts should focus on the effects of miR-21 on different cell types, as well as the development of new technologies for diagnostic and therapeutic applications. Full article

Background: Large porous particles (LPPs) offer significant potential in drug delivery due to their porous structure and suitable particle size and shape, which can improve powder dispersibility and control drug release. Methods: In this study, sustained-release large porous microparticles of mannitol, PVA, and diclofenac sodium (MPDs) were developed using a spray drying technique. The influence of PVA co-spray drying and its concentration (0–40%) on the characteristics of the spray-dried particles was investigated. Results: Co-spray drying with PVA enhanced particle morphology, producing MPDs with a spherical shape and smooth surface, which minimized particle adhesion. This improvement correlated with a low Carr’s Index value (17.56%), indicating favorable particle dispersibility and aerosol performance. The large geometric diameter (>5 μm) of the MPDs, coupled with their low bulk density (<0.1 g/cm³), suggested potential for inhalation use. FTIR, XRD, and DSC analyses revealed that PVA altered the polymorphic form of mannitol, with the MPDs exhibiting a mixture of the α and δ forms. In vitro dissolution tests demonstrated that PVA co-spray drying effectively prolonged drug release, with the formulation containing 40% PVA (MPD-4) showing an optimal release profile. The release kinetics followed first-order Higuchi models, suggesting drug release occurred through a matrix diffusion mechanism facilitated by the porous structure. Conclusions: These findings demonstrate the feasibility of engineering large porous microparticles with tailored release characteristics and physicochemical properties suitable for further development in inhalable or other controlled-release dosage forms. Full article

Foodborne pathogenic biofilms pose significant challenges to food safety due to their enhanced resistance to conventional antimicrobial agents. In this study, we evaluated the synergistic antibiofilm activity of oregano essential oil (OEO) from Lippia graveolens and the lytic bacteriophage phiLLS against six foodborne bacteria. GC–MS analysis achieved a 100% identification ratio, revealing that OEO was mainly composed of carvacrol (58.9%), p-cymene (28.6%), γ-terpinene (2.9%), and caryophyllene (2.6%). The MIC and MBC of OEO were 1 and 2 mg/mL, respectively, for all strains except E. coli BALL1119 (MIC = 2 mg/mL). We assessed biofilm biomass by crystal violet (CV) staining and metabolic activity using the TTC assay under both individual and combined treatments, monitored 9-hour planktonic growth kinetics to calculate Bliss and HSA synergy indexes, and employed atomic force microscopy (AFM) to visualize nanoscale alterations in Staphylococcus aureus and Escherichia coli BALL1119 biofilms. Combined OEO (2 mg/mL) and phiLLS (MOI 1) treatments achieved significantly greater biofilm biomass reduction than single agents, notably yielding >70% inhibition of S. aureus biofilms (p < 0.05) and a Bliss synergy index of 10.8% in E. coli BALL1119 growth kinetics, whereas other strains were additive. In biofilm assays, S. aureus and Salmonella spp. showed the highest reductions in biomass (CV) (71.0% and 67.8%, ΔHSA = 27.0% and 17.4%; ΔBliss = 21.1% and 13.8%) and metabolic activity (TTC) (68.6% and 48.5%). AFM revealed that OEO alone smoothed the extracellular matrix (averaging a 35% reduction in roughness), whereas the combined treatment caused fracturing (≈68 nm roughness) and prominent lytic pits. Although variability in S. aureus biofilm architecture precluded statistically significant pairwise comparisons, AFM topography and consistent trends in Ra/Rz parameters provided clear visual corroboration of the significant reductions detected by CV and TTC assays. These complementary data indicate that OEO primes the biofilm matrix for enhanced phage-mediated collapse, offering a green, two-step strategy for controlling resilient foodborne biofilms. Full article

Atherosclerosis is now recognized as a chronic inflammatory disease of the arterial wall, in which perivascular adipose tissue (PVAT) has evolved from a passive structural component to a key player in regulating vascular homeostasis and the pathophysiology of atherosclerosis, playing an active, not just structural, role. PVAT surrounds blood vessels and influences them metabolically, immunologically, and vascularly by secreting adipokines, cytokines, and other bioactive mediators. Under physiological conditions, PVAT has protective roles, as it produces adiponectin, nitric oxide (NO), and other vasodilatory factors that help maintain vascular tone and reduce inflammation. In particular, brown-like PVAT (rich in Uncoupling Protein-1 (UCP1) and mitochondria) offers significant vasoprotective effects. Under pathological conditions (obesity, dyslipidemia, insulin resistance), PVAT undergoes a phenotypic transition towards a pro-inflammatory profile by increasing leptin, tumor necrosis factor-alpha (TNF-α), and interleukin-6 (IL-6) secretion and decreasing adiponectin, contributing to endothelial dysfunction, vascular smooth muscle cell (VSMC) proliferation, local immune cell recruitment, extracellular matrix (ECM) remodeling, and fibrosis. PVAT plays a complex role in vascular health and disease, interacting with systemic metabolism through the secretion of bioactive molecules. Metabolic imbalances can promote PVAT inflammation. Epigenetic alterations and micro ribonucleic acid (miRNAs) can influence PVAT inflammation, and modern imaging methods for PVAT assessment, such as the fat attenuation index (FAI) and artificial intelligence-assisted radiomic profiling, may become predictive biomarkers of cardiac risk. Future directions aim to identify biomarkers and develop targeted therapies that modulate PVAT inflammation and dysfunction in the context of cardiovascular diseases. Full article

Aiming at the problem of harrowing target feature extraction for one-dimensional radar signals in the strong sea clutter background, this paper proposes a weak target detection method based on the combination of multi-modal time-frequency map fusion and deep learning in the sea clutter background. The one-dimensional signal is converted into three gray-scale maps with complementary characteristics by three signal processing methods: normalized continuous wavelet transform, Normalized Smooth Pseudo Wigner-Ville Distribution, and recurrence plot; the resulting two-dimensional grayscale maps are adaptively mapped to the R, G, and B channels through an adaptive weighting matrix for feature fusion, ultimately generating a fused color image. Subsequently, an improved multi-modal EfficientNetV2s classification framework was constructed, wherein the decision threshold of the Softmax layer was optimized to achieve controllable false alarm rates for weak signal detection. Experiments are carried out on the IPIX dataset and the China Yantai dataset, and the proposed method achieves certain improvement in detection performance compared with existing detection methods. Full article