Search Results (3,958)

Despite the widespread success of convolutional deep learning frameworks in computer vision, significant limitations persist in medical image analysis. These include low image quality caused by noise and artifacts, limited data availability compromising robustness on unseen data, class imbalance leading to biased predictions, and insufficient feature representation, as conventional CNNs often fail to capture subtle patterns and complex dependencies. To address these challenges, we propose DAME (Diffusion-Augmented Meta-Learning Ensemble), a unified architecture that integrates hybrid modeling with generative learning using the Denoising Diffusion Probabilistic Model (DDPM). The DDPM component improves resolution, augments scarce data, and mitigates class imbalance. A hybrid backbone combining CNN, Vision Transformer (ViT), and CBAM captures both local dependencies and long-range spatial relationships, while CBAM further enhances feature representation by adaptively emphasizing informative regions. Predictions from multiple hybrids are aggregated, and a logistic regression meta classifier learns from these outputs to produce robust decisions. The framework is evaluated on the HAM10000 dataset, a benchmark for multi-class skin cancer classification. Explainable AI is incorporated through Grad CAM, providing visual insights into the decision-making process. This synergy mitigates CNN limitations and demonstrates superior generalizability, achieving 98.6% accuracy, 0.986 precision, 0.986 recall, and a 0.986 F1-score, significantly outperforming existing approaches. Overall, the proposed framework enables accurate, interpretable, and reliable medical image diagnosis through the joint optimization of contextual modeling, feature discrimination, and data generation. Full article

Propane is a typical volatile organic compound (VOC) in coal chemical processing and petroleum refining. However, coexisting SO₂ significantly impairs its catalytic oxidative removal, potentially causing catalyst poisoning and deactivation. This study systematically elucidated the inhibitory effects of SO₂ on the catalytic oxidation of propane over the Ru@CoMn₂O₄ catalyst system. Under continuous exposure to 30 ppm SO₂, propane conversion plummeted by 30% within two hours. Mechanistic studies revealed that SO₂ selectively bound to high-valent Mn sites rather than preferentially interacting with Co sites, leading to the formation of MnSO₄ particles. These particles were directly corroborated by X-ray diffraction (XRD) and transmission electron microscopy (TEM) analyses. After four hours of exposure to SO₂, roughly 11.8 mole percent of manganese in the catalyst was converted into MnSO₄. These deposits physically blocked active sites, reduced specific surface area, and disrupted redox cycling. As a result, their combined effects diminished performance progressively, ultimately leading to complete deactivation. Furthermore, in situ diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) confirmed that SO₂ suppressed C=C bond oxidation in propane intermediates, thereby directly limiting conversion efficiency. Combining qualitative and quantitative methods, we characterized SO₂-induced poisoning during propane oxidation. This work provides guidelines and strategies for designing anti-sulfur catalysts at the elemental scale for the catalytic combustion of low-carbon alkanes. Full article

Microfluidics enables precise control of fluid movement within microchannels, facilitating the generation of microdroplets at high frequencies. This technology provides a unique platform for conducting biological and chemical experiments, enhancing throughput and sensitivity, particularly in single-cell analysis. The microdroplet environment enhances interactions between cells and gene delivery materials, resulting in greater contact area, higher reagent concentration, and improved diffusion for both eukaryotic and prokaryotic cells. This review discusses the advantages and limitations of transfection and transformation within microdroplet technologies, highlighting their potential to improve gene editing efficiency while addressing challenges related to delivery mechanisms and cellular uptake rates. The integration of microdroplet technology with advanced gene editing tools, such as CRISPR/Cas9, promises to streamline processes and improve outcomes in various applications, including therapeutic interventions, vaccine development, regenerative medicine, and personalized medicine. These advancements could lead to more precise targeting of genetic modifications, resulting in tailored therapies that better meet individual patient needs. Overall, the integration of gene delivery in microdroplets represents a significant leap in biotechnology, enhancing the efficacy of gene delivery systems and opening new avenues for research and development in precision medicine. Full article

Background: CT perfusion (CTP) is increasingly used in the evaluation of acute ischemic stroke (AIS) and may complement non-contrast CT (NCCT) and CT angiography (CTA). This review aimed to assess the role of CTP in patient selection for reperfusion therapy, its prognostic value, and the influence of technical factors, collateral assessment, and post-processing software. Methods: A literature search of PubMed, DOAJ, and Google Scholar (2014–2025) identified 119 articles; after screening, 39 met inclusion criteria. Only studies on adult AIS patients investigated with CTP were included. Data were synthesized across eight thematic categories: core/penumbra estimation, prognosis, treatment selection, collateral assessment, software validation, technical parameters, reliability, and safety. Results: CTP improved identification of infarct core, penumbra, and collateral status, aiding patient selection for endovascular therapy, particularly beyond 6 h. Limitations included variability in tissue thresholds, “ghost infarct core,” and differences across software. Technical advances, such as “one-stop-shop” protocols and low-kV acquisition, reduced treatment delays and radiation. Reliability studies showed CTP to be less accurate than diffusion-weighted MRI, while safety analyses confirmed a low risk of contrast-induced nephropathy. Conclusions: CTP enhances patient stratification and outcome prediction, supporting individualized treatment strategies. Standardization of protocols and validation of software remain necessary before CTP can serve as a reliable alternative to MRI-DWI. Full article

Reliable forecasting of tropical cyclone (TC) intensity—particularly rapid intensification (RI) events—remains a major challenge in meteorology, largely due to the inherent difficulty of accurately quantifying predictive uncertainty. Traditional numerical approaches are computationally expensive, while statistical models often fail to capture the highly nonlinear relationships involved. Mainstream machine learning models typically provide only deterministic point forecasts and lack the ability to represent uncertainty. To address this limitation, we propose Tropical Cyclone Diffusion Model (TCDM), the first conditional diffusion-based probabilistic forecasting framework for TC intensity. TCDM integrates multimodal meteorological data, including satellite imagery, re-analysis fields, and environmental predictors, to directly generate the full probability distribution of future intensities. Experimental results show that TCDM not only achieves highly competitive deterministic accuracy (low MAE and RMSE; high $R^2$), but also delivers high-quality probabilistic forecasts (low CRPS; high PICP). Moreover, it substantially improves RI detection by achieving higher hit rates with fewer false alarms. Compared with traditional ensemble-based methods, TCDM provides a more efficient and flexible approach to probabilistic forecasting, offering valuable support for TC risk assessment and disaster preparedness. Full article

The glass-forming ability of short-chain alkanes remains a fundamental challenge in condensed matter physics. This study investigates the structural properties of n-hexane (C₆H₁₄) and decane (C₁₀H₂₂) under two distinct cooling regimes using Raman spectroscopy: fast cooling (~50–100 K/s via contact freezing on a copper substrate at 77 K) and conventional cooling (~1–5 K/s). Despite employing rapid cooling protocols, both alkanes underwent crystallization without forming amorphous phases. n-Hexane formed a defective crystalline structure characterized by broad spectral bands (FWHM ~40–45 cm⁻¹) and diffuse phase transitions in the 180–200 K range, while decane exhibited highly ordered crystalline structures with sharp spectral features (FWHM ~15–20 cm⁻¹) and abrupt transitions at 220–240 K. Quantitative analysis of characteristic Raman bands (skeletal deformations, C-C stretching, and C-H stretching vibrations) revealed fundamental differences in crystallization mechanisms related to molecular chain length. The study demonstrates that contact freezing methods are fundamentally incapable of achieving the extreme cooling rates (>10⁴ K/s) and ultra-thin film conditions (<1 μm) necessary for alkane vitrification. These findings establish spectroscopic diagnostic criteria for distinguishing between defective and well-ordered crystalline structures and define the limitations of conventional cryogenic techniques for glass formation in alkanes. Full article

Transdermal hydrogel films were fabricated from gellan gum, chitosan, and agar–agar, employing glutaraldehyde as a covalent crosslinker. The obtained formulation exhibited structural stability, pH-sensitive swelling, and high biocompatibility without the participation of metal ions. FTIR spectra showed the emergence of a characteristic imine (C=N) vibration near 1630 cm⁻¹, confirming covalent network formation through Schiff-base reactions. SEM imaging revealed a homogeneous porous architecture (45–120 μm) that enhances moisture absorption and molecular diffusion. The swelling ratio reached 410 ± 12% at pH 9.18 and 275 ± 9% at pH 4.01, evidencing pronounced pH responsiveness. Mechanical strength measured 0.82 ± 0.03 MPa with elongation of 42 ± 2%, ensuring flexibility for skin application. The temperature-controlled release of methylene blue achieved 78 ± 4% at 40 °C after 24 h, consistent with diffusion-limited transport. This gellan–chitosan–agar hydrogel network crosslinked with glutaraldehyde represents a stable, pH-responsive, and biocompatible platform suitable for wound care and transdermal drug delivery. Full article

Concussions, classified as a type of mild traumatic brain injury (mTBI), are frequently underdiagnosed due to the subjective nature of symptoms and limitations in existing diagnostic methodologies. Current clinical evaluations, including tools such as the Sport Concussion Assessment Tool 5 (SCAT5), Balance Error Scoring System (BESS), and Vestibular Ocular Motor Screening (VOMS), demonstrate high sensitivity and specificity but often fail to capture the full complexity of concussive injuries. Emerging diagnostic approaches, such as blood biomarkers (for example, glial fibrillary acidic protein (GFAP), ubiquitin C-terminal hydrolase-L1 (UCH-L1), S100 calcium-binding protein B (S100B), and tau) and advanced neuroimaging techniques (for example, diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI)), show promise but remain impractical for routine clinical use due to accessibility and standardization challenges. This review examines objective markers, including neuroimaging, electrophysiological measures (for example, Electroencephalography (EEG), Magnetoencephalography (MEG)), and real-time diagnostic tools, as complementary strategies to enhance traditional clinical evaluations. Findings indicate that while clinical assessments remain central to concussion diagnosis, integrating them with advanced imaging and electrophysiological tools can provide more accurate diagnostics and recovery tracking. Biomarkers, although not yet ready for widespread use, hold significant potential for future applications. Further research is required to validate these methods and establish standardized protocols to facilitate their integration into clinical practice. Full article

Degenerative cervical myelopathy (DCM) is a common cause of spinal cord dysfunction in adults and is frequently accompanied by pain, a symptom that remains under-recognised despite its profound impact on quality of life. Conventional magnetic resonance imaging (MRI) is indispensable for identifying structural spinal cord compression; however, it is unable to detect early microstructural alterations, particularly those that may contribute to pain pathophysiology. This narrative review critically appraises the limitations of standard MRI in the diagnostic assessment of DCM and examines the expanding role of advanced imaging modalities—most notably diffusion tensor imaging (DTI)—in evaluating spinal cord integrity. DTI-derived parameters, including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD), demonstrate sensitivity to axonal and myelin injury. For example, reductions in FA and AD have been linked to axonal disruption in sensory pathways, while elevations in RD suggest demyelination, a hallmark of neuropathic pain. Despite this potential, the widespread implementation of DTI is constrained by technical heterogeneity, limited accessibility, and the absence of standardised protocols. Future research priorities include the incorporation of pain-specific imaging endpoints, longitudinal validation across diverse cohorts, and integration with artificial intelligence frameworks to enable automated analysis and predictive modelling. Collectively, these advances hold promise for enabling earlier diagnosis, refined symptom stratification, and more personalised therapeutic strategies in DCM. Full article

With the rapid advancement of hyperspectral remote sensing technology, the security of hyperspectral images (HSIs) has become a critical concern. However, traditional image encryption methods—designed primarily for grayscale or RGB images—fail to address the high dimensionality, large data volume, and spectral-domain characteristics inherent to HSIs. Existing chaotic encryption schemes often suffer from limited chaotic performance, narrow parameter ranges, and inadequate spectral protection, leaving HSIs vulnerable to spectral feature extraction and statistical attacks. To overcome these limitations, this paper proposes a novel hyperspectral image encryption algorithm based on a newly designed two-dimensional cross-coupled hyperchaotic map (2D-CSCM), which synergistically integrates Cubic, Sinusoidal, and Chebyshev maps. The 2D-CSCM exhibits superior hyperchaotic behavior, including a wider hyperchaotic parameter range, enhanced randomness, and higher complexity, as validated by Lyapunov exponents, sample entropy, and NIST tests. Building on this, a layered encryption framework is introduced: spectral-band scrambling to conceal spectral curves while preserving spatial structure, spatial pixel permutation to disrupt correlation, and a bit-level diffusion mechanism based on dynamic DNA encoding, specifically designed to secure high bit-depth digital number (DN) values (typically >8 bits). Experimental results on multiple HSI datasets demonstrate that the proposed algorithm achieves near-ideal information entropy (up to 15.8107 for 16-bit data), negligible adjacent-pixel correlation (below 0.01), and strong resistance to statistical, cropping, and differential attacks (NPCR ≈ 99.998%, UACI ≈ 33.30%). The algorithm not only ensures comprehensive encryption of both spectral and spatial information but also supports lossless decryption, offering a robust and practical solution for secure storage and transmission of hyperspectral remote sensing imagery. Full article

Onychomycosis is a therapeutically challenging fungal infection. Systemic antifungals are limited by adverse effects and drug interactions, while topical therapies may fail to achieve therapeutic nail bed concentrations. Nitric oxide (NO), a small, diffusible free radical with broad-spectrum antimicrobial activity, offers a novel approach to overcoming these barriers. We assessed the penetration and subsequent efficacy of a nitric oxide–releasing gel (NORG) in the treatment of onychomycosis. Ex vivo human nail models assessed NORG’s transungual penetration and antifungal activity via colorimetric, immunohistochemical, and microbiological assays. NORG eradicated Trichophyton mentagrophytes completely (0 CFU/g), outperforming terbinafine (3.58 ± 0.2 log₁₀ CFU/g). In an ex vivo infection model, NORG achieved fungal clearance within 14 days, continuing to Day 30 treatment end, with no regrowth during 21 days of incubation post-treatment. Clinical data from patients with onychomycosis who received topical NORG therapy show that NORG penetrated the nail plate and nail bed, as evidenced by s-nitrosothiol accumulation and progressive discoloration. The NORG formulation demonstrates in vitro efficacy; controlled trials are warranted to fully assess clinical efficacy and safety of this NORG formulation in humans, and establish optimal treatment protocols. Full article

Recent denoising methods based on diffusion models typically formulate the task as a conditional generation process initialized from a standard Gaussian distribution. However, such stochastic initialization often leads to redundant sampling steps and unstable results due to the neglect of structured noise characteristics. To address these limitations, we propose a novel framework that directly bridges the probabilistic distributions of noisy and clean images while jointly modeling structured noise. We introduce Dual-diffusion Brownian Bridge Coupled Sampling (DBBCS) the first framework to incorporate Brownian bridge diffusion into image denoising. DBBCS synchronously models the distributions of clean images and structural noise via two coupled diffusion processes. Unlike conventional diffusion models, our method starts sampling directly from noisy observations and jointly optimizes image reconstruction and noise estimation through a coupled posterior sampling scheme. This allows for dynamic refinement of intermediate states by adaptively updating the sampling gradients using residual feedback from both image and noise paths. Specifically, DBBCS employs two parallel Brownian bridge models to learn the distributions of clean images and noise. During inference, their respective residual processes regulate each other to progressively enhance both denoising and noise estimation. A consistency constraint is enforced among the estimated noise, the reconstructed image, and the original noisy input to ensure stable and physically coherent results. Extensive experiments on standard benchmarks demonstrate that DBBCS achieves superior performance in both visual fidelity and quantitative metrics, offering a robust and efficient solution to image denoising. Full article

Background/Objectives: Olive leaf (Olea europaea L.), a by-product of olive oil production, is rich in bioactive phenolics but limited in application due to poor solubility and stability. To improve their bioavailability, this study presents a comparative encapsulation strategy using three phospholipid-based liposomal systems (AL, PG90, and PH90) loaded with ethanolic olive leaf extract. Methods: Liposomes were characterized by physicochemical parameters, encapsulation efficiency (EE), antioxidant activity, morphology, release kinetics under simulated physiological conditions, and 60-day stability. To the best of our knowledge, this is the first direct comparison of AL, PG90, and PH90 matrices for olive leaf extract encapsulation. Results: HPLC and GC-MS confirmed successful encapsulation, with oleuropein showing the highest EE (up to 76.18%). PH90 favored retention of non-polar triterpenes, while AL and PG90 preferentially encapsulated polar flavonoid glycosides. FT-IR analysis verified extract integration into phospholipid bilayers. Antioxidant activity remained high in all loaded formulations, with negligible activity in empty liposomes. Extract-loaded systems exhibited reduced particle size, higher viscosity, and more negative electrophoretic mobility, enhancing colloidal stability. PG90 liposomes displayed the most stable mobility profile over 60 days. Transmission electron microscopy and nanoparticle tracking analysis revealed formulation-dependent vesicle morphology and concentration profiles. Release studies demonstrated significantly prolonged polyphenol diffusion from PG90 liposomes compared to the free extract. Conclusions: Phospholipid composition critically governs encapsulation selectivity, stability, and release behavior. Tailored liposomal systems offer a promising strategy to enhance the stability and delivery of olive leaf polyphenols, supporting their application in bioactive delivery platforms. Full article

Understanding the relationship between state-of-charge (SOC) and anisotropic lithium diffusion is essential for improving the durability of Ni-rich layered oxide cathodes. However, quantitative insights into directional lithium diffusivity and its influence on mechanical degradation remain limited. In this study, molecular dynamics (MD) simulations were performed for LiNi_xMn_yCo_zO₂ (NMC) compositions with varying nickel content and SOC levels to reveal composition- and direction-dependent lithium transport behavior. The numerical indices in NMC compositions (e.g., NMC111, NMC532, NMC811) indicate the relative molar ratios of Ni, Mn, and Co, respectively, in LiNi_xMn_yCo_zO₂. The results show that lithium diffusion is enhanced at low SOC, owing to the abundance of vacant sites, while diffusion along the out-of-plane (c-axis) direction is strongly constrained, particularly in Ni-rich systems. To bridge the atomistic and continuum scales, the SOC-dependent anisotropic diffusivities obtained from MD simulations were incorporated into a chemo-mechanical finite-element model of an NMC811 particle. The coupled analysis demonstrates that anisotropic and SOC-dependent diffusion accelerates lithium depletion and stress localization, elucidating the origin of particle cracking in Ni-rich cathodes. This multiscale framework provides quantitative parameters and mechanistic understanding critical for designing durable next-generation lithium-ion batteries. Full article

Background: Anemoside B4 (AB4), the major bioactive saponin from Pulsatilla chinensis, exhibits anti-inflammatory, anti-tumor, anti-apoptotic, and analgesic properties. However, its clinical translation for ulcerative colitis (UC) is constrained by poor epithelial permeability and low oral bioavailability. Objective: This study’s objective was to engineer and optimize thermosensitive rectal in situ gels (ISGs) of AB4, incorporating suitable absorption enhancers to improve mucosal permeation, bioavailability, and therapeutic efficacy against UC. Methods: Screening of effective permeation enhancers was conducted using Caco-2 cell monolayers and Franz diffusion cells. Critical formulation variables such as poloxamer 407 (P407), poloxamer 188 (P188), and hydroxypropyl methyl cellulose (HPMC) were optimized, employing single-factor experiments coupled with the Box–Behnken design response surface methodology (BBD-RSM). Comprehensive characterization encompassed in vitro release kinetics, in vivo pharmacokinetics, rectal tissue tolerability, rectal retention time, and pharmacodynamic efficacy in a UC model. Results: We used 2.5% hydroxypropyl-β-cyclodextrin (HP-β-CD) and 1.0% sodium caprate (SC) as the appropriate absorption enhancers, and the amounts of P407, P188, and HPMC were 17.41%, 4.07%, and 0.44%, respectively, to yield the corresponding in situ gels HP-β-CD-AB4-ISG and SC-AB4-ISG. The gel characterization, such as gelation temperature, gelation time, pH, gelation strength, etc., was in accordance with requirements. The ISGs did not stimulate or damage rectal tissue and remained in the rectum for a prolonged period. More importantly, an improvement in bioavailability and alleviation of UC were noted. Conclusion: Absorption enhancer-assisted, poloxamer-based thermosensitive rectal ISGs provide a safe, convenient, and effective platform for targeted delivery of AB4 to the colorectum. This strategy addresses key limitations of oral dosing and warrants further clinical development for UC and related colorectal inflammatory diseases. Full article