Search Results (204)

This study proposes a deep learning-based, non-contact method for detecting elevated intraocular pressure (IOP) by integrating Scheimpflug images with corneal biomechanical features. Glaucoma, the leading cause of irreversible blindness worldwide, requires accurate IOP monitoring for early diagnosis and effective treatment. Traditional IOP measurements are often influenced by corneal biomechanical variability, leading to inaccurate readings. To address these limitations, we present a multi-modal framework incorporating CycleGAN for data augmentation, Swin Transformer for visual feature extraction, and the Kolmogorov–Arnold Network (KAN) for efficient fusion of heterogeneous data. KAN approximates complex nonlinear relationships with fewer parameters, making it effective in small-sample scenarios with intricate variable dependencies. A diverse dataset was constructed and augmented to alleviate data scarcity and class imbalance. By combining Scheimpflug imaging with clinical parameters, the model effectively integrates multi-source information to improve high IOP prediction accuracy. Experiments on a real-world private hospital dataset show that the model achieves a diagnostic accuracy of 0.91, outperforming traditional approaches. Grad-CAM visualizations identify critical anatomical regions, such as corneal thickness and anterior chamber depth, that correlate with IOP changes. These findings underscore the role of corneal structure in IOP regulation and suggest new directions for non-invasive, biomechanics-informed IOP screening. Full article

Glaucoma is a leading cause of irreversible blindness globally, with early diagnosis being crucial to preventing vision loss. Traditional diagnostic methods, including fundus photography, OCT imaging, and perimetry, often fall short in sensitivity and fail to integrate structural and functional data. This study proposes a novel multi-modal diagnostic framework that combines convolutional neural networks (CNNs), vision transformers (ViTs), and quantum-enhanced layers to improve glaucoma detection accuracy and efficiency. The framework integrates fundus images, OCT scans, and clinical biomarkers, leveraging their complementary strengths through a weighted fusion mechanism. Datasets, including the GRAPE and other public and clinical sources, were used, ensuring diverse demographic representation and supporting generalizability. The model was trained and validated using cross-entropy loss, L2 regularization, and adaptive learning strategies, achieving an accuracy of 96%, sensitivity of 94%, and an AUC of 0.97—outperforming CNN-only and ViT-only approaches. Additionally, the quantum-enhanced architecture reduced computational complexity from O(n²) to O (log n), enabling real-time deployment with a 40% reduction in FLOPs. The proposed system addresses key limitations of previous methods in terms of computational cost, data integration, and interpretability. The proposed system addresses key limitations of previous methods in terms of computational cost, data integration, and interpretability. This framework offers a scalable and clinically viable tool for early glaucoma detection, supporting personalized care and improving diagnostic workflows in ophthalmology. Full article

Background/Objectives: Diabetic retinopathy (DR) remains one of the primary causes of preventable vision impairment worldwide, particularly among individuals with long-standing diabetes. The progressive damage of retinal microvasculature can lead to irreversible blindness if not detected and managed at an early stage. Therefore, the development of reliable, non-invasive, and automated screening tools has become increasingly vital in modern ophthalmology. With the evolution of medical imaging technologies, Optical Coherence Tomography (OCT) has emerged as a valuable modality for capturing high-resolution cross-sectional images of retinal structures. In parallel, machine learning has shown considerable promise in supporting early disease recognition by uncovering complex and often imperceptible patterns in image data. Methods: This study introduces a novel framework for the early detection of DR through multifractal analysis of OCT images. Multifractal features, extracted using a box-counting approach, provide quantitative descriptors that reflect the structural irregularities of retinal tissue associated with pathological changes. Results: A comparative evaluation of several machine learning algorithms was conducted to assess classification performance. Among them, the Multi-Layer Perceptron (MLP) achieved the highest predictive accuracy, with a score of $98.02\%$, along with precision, recall, and F1-score values of $98.24\%$, $97.80\%$, and $98.01\%$, respectively. Conclusions: These results highlight the strength of combining OCT imaging with multifractal geometry and deep learning methods to build robust and scalable systems for DR screening. The proposed approach could contribute significantly to improving early diagnosis, clinical decision-making, and patient outcomes in diabetic eye care. Full article

Cataract remains the leading cause of blindness worldwide, and the number of individuals affected by this condition is expected to rise significantly due to global population ageing. Early diagnosis is crucial, as delayed treatment may result in irreversible vision loss. This study explores and presents a mobile application for Android devices designed for the detection of cataracts using deep learning models. The proposed solution utilizes a multi-stage classification approach to analyze ocular images acquired with a slit lamp, sourced from the Nuclear Cataract Database for Biomedical and Machine Learning Applications. The process involves identifying pathological features and assessing the severity of the detected condition, enabling comprehensive characterization of the NC (nuclear cataract) of cataract progression based on the LOCS III scale classification. The evaluation included a range of convolutional neural network architectures, from larger models like VGG16 and ResNet50, to lighter alternatives such as VGG11, ResNet18, MobileNetV2, and EfficientNet-B0. All models demonstrated comparable performance, with classification accuracies exceeding 91–94.5%. The trained models were optimized for mobile deployment, enabling real-time analysis of eye images captured with the device camera or selected from local storage. The presented mobile application, trained and validated on authentic clinician-labeled pictures, represents a significant advancement over existing mobile tools. The preliminary evaluations demonstrated a high accuracy in cataract detection and severity grading. These results confirm the approach is feasible and will serve as the foundation for ongoing development and extensions. Full article

Alzheimer’s disease (AD) is a chronic and complex neurodegenerative disorder characterized by progressive cognitive decline, memory loss, and irreversible impairment of brain functions. The etiology of AD is multifactorial, involving a complex interplay of genetic, environmental, and physiological factors, including the aggregation of amyloid-β (Aβ) and oxidative stress (OS). The role of OS in AD pathogenesis is of particular significance, given that an imbalance between oxidants and antioxidants promotes cellular damage, exacerbates Aβ deposition, and leads to cognitive deterioration. Despite extensive research, current therapeutic strategies have largely failed, likely due to the use of single-target drugs unable to halt the multifactorial progression of the disease. In this study, we investigated the synergistic therapeutic effect of plant-derived bioactive compounds Withanone, Apigenin, Bacoside A, Baicalin, and Thymoquinone in combination with N,N-Dimethyltryptamine (NN-DMT), a psychedelic molecule. We used a transgenic Caenorhabditis elegans model to assess the behavioral and molecular outcomes following compound exposure. Motility assays, thioflavin S staining, and survival assays under oxidative stress were employed to evaluate the treatment efficacy. The results of the behavioral and molecular analyses indicated that the combination therapy exhibited a higher efficacy than the monotherapies, leading to a significant reduction in age-related motility defects in the AD model. Furthermore, the combination treatment substantially reduced Aβ plaque burden, enhanced survival following OS insult, and demonstrated a synergistic effect in mitigating AD-related hallmarks. Taken together, these findings support the potential of combining NN-DMT with specific bioactive compounds as a promising multi-target therapeutic approach for AD. Full article

Cellular senescence is a fundamental mechanism in aging, marked by irreversible growth arrest and diverse functional changes, including, but not limited to, the development of a senescence-associated secretory phenotype (SASP). While transient senescence contributes to beneficial processes such as tissue repair and tumor suppression, the persistent accumulation of senescent cells is implicated in tissue dysfunction, chronic inflammation, and age-related diseases. Notably, the SASP can exert both pro-inflammatory and immunosuppressive effects, depending on cell type, tissue context, and temporal dynamics, particularly in early stages where it may be profibrotic and immunomodulatory. Recent advances in senotherapeutics have led to two principal strategies for targeting senescent cells: senolytics, which selectively induce their apoptosis, and senomorphics, which modulate deleterious aspects of the senescence phenotype, including the SASP, without removing the cells. This review critically examines the molecular mechanisms, therapeutic agents, and clinical potential of both approaches in the context of anti-aging interventions. We discuss major classes of senolytics, such as tyrosine kinase inhibitors, BCL-2 family inhibitors, and natural polyphenols, alongside senomorphics including mTOR and JAK inhibitors, rapalogs, and epigenetic modulators. Additionally, we explore the biological heterogeneity of senescent cells, challenges in developing specific biomarkers, and the dualistic role of senescence in physiological versus pathological states. The review also highlights emerging tools, such as targeted delivery systems, multi-omics integration, and AI-assisted drug discovery, which are advancing precision geroscience and shaping future anti-aging strategies. Full article

Invertase (INV) irreversibly converts sucrose to glucose and fructose during processes such as differentiation and organ development in plants, especially during the development of trichomes. Systematic identification and analysis of INVs in Salicaceae remain limited. Here, INV genes in Populus deltoides and Salix suchowensis were investigated, and their chromosomal localization, collinearity, gene structures, cis-regulatory elements, and phylogenetic relationships were comprehensively analyzed. Twenty and seventeen INVs were found, respectively, in P. deltoides and S. suchowensis, most of which were derived from a common ancestor and exhibited similar chromosomal distribution and high collinearity. Orthologs between the two species showed conservation of gene structures and promoter regulatory elements. Multi-species phylogenetic analysis identified an evolutionary clade associated with seed fiber development in P. deltoides and S. suchowensis. Further evaluation of INV expression in female catkins at various stages of seed fiber formation verified the predominance of PdeVINV1, PdeVINV2, PdeVINV3, and PdeVINV4 in P. deltoides, as well as SsuVINV1 and SsuVINV2 in S. suchowensis, during critical phases of catkin fiber differentiation. These genes are likely to have significant regulatory roles in the initiation and development of catkin fiber cells. These findings provide a reference for future functional studies of INVs. Full article

Lithium-ion batteries (LIBs), crucial in modern advanced energy storage systems, inherently experience several side reactions during operation, with the formation of a solid electrolyte interface (SEI) and lithium plating being the most significant. These side reactions, which deplete lithium ions and lead to the clogging of negative electrode pores, considerably impair the battery’s cycle life and overall performance. This study introduces a numerical model for the battery aging process, grounded in existing research on SEI formation and its temperature-dependent aging kinetics. The model aims to elucidate how variations in the porosity of the negative electrode impact the battery’s cycle life. The study initially focuses on analyzing the principal mechanisms behind pore clogging in LIBs’ negative electrodes following extensive charge/discharge cycles. Subsequently, the study conducts numerical simulations to thoroughly investigate the effects of various non-uniform porosity structures in the negative electrode, encompassing both linear and gradient configurations, on the battery’s cycle life. Additionally, the investigation conducts a comparative analysis to determine how different gradients in porosity structures influence pore clogging. It also delves into a detailed exploration of heat generation associated with the linear porosity structure of the negative electrode. The results indicate that the accumulation of the SEI layer significantly reduces porosity. This reduction, in turn, affects the conductivity and alters the current density during the SEI reaction. Notably, the linear porosity structure exhibits a significant advantage over traditional structures, especially in terms of reducing pore clogging and minimizing irreversible heat generation. In summary, this study presents a multi-physics and detailed numerical model to evaluate the impact of variations in negative electrode porosity on the cycle life of LIBs. Furthermore, it provides essential theoretical support for battery design and performance optimization, particularly in the determination of pore structures and material selection. Full article

Background: Glaucoma is a leading cause of irreversible visual loss worldwide, characterized by progressive retinal ganglion cell (RGC) degeneration and optic nerve damage. Current therapies mainly focus on lowering intraocular pressure (IOP), yet fail to address pressure-independent neurodegenerative mechanisms. Melatonin, an endogenously produced indoleamine, has gained attention for its potential in modulating both IOP and neurodegeneration through diverse cellular pathways. This review evaluates the therapeutic relevance of melatonin in glaucoma by examining its mechanistic actions and emerging delivery approaches. Methods: A comprehensive literature search was conducted via PubMed and Medline to identify studies published between 2000 and 2025 on melatonin’s roles in glaucoma. Included articles discussed its effects on IOP regulation, RGC survival, oxidative stress, mitochondrial integrity, and inflammation. Results: Evidence supports melatonin’s involvement in IOP reduction via MT receptor activation and its synergism with adrenergic and enzymatic regulators. Moreover, it protects RGCs by mitigating oxidative stress, preventing mitochondrial dysfunction, and inhibiting apoptotic and inflammatory cascades. Recent advances in ocular drug delivery systems enhance its bioavailability and therapeutic potential. Conclusions: Melatonin represents a multi-target candidate for glaucoma treatment. Further clinical studies are necessary to establish optimal dosing strategies, delivery methods, and long-term safety in patients. Full article

The advancement of modern concretes, such as printable concrete, fluid concrete with adapted rheology, and ultra-high-performance concrete, has increased the importance of understanding structural build-up in cement-based materials. This process, which describes the time-dependent evolution of rheological properties, is a key factor to ensure the stability of concrete by influencing segregation, bleeding, formwork pressure, numerical modeling, and multi-layer casting. As a result, the structural build-up of cementitious materials has become a significant area of research in recent years. The structural build-up of cement based-materials results from both a reversible part (thixotropic behavior), driven by colloidal interactions, and an irreversible part, caused by cement hydration and the formation of C-S-H bridges. Various experimental techniques have been developed to investigate these processes, with various factors affecting the thixotropic behavior and overall structural build-up of cement suspensions. This review provides a comprehensive analysis of the current understanding of structural build-up in cement pastes. It covers measurement methods and key influencing factors, including the water-to-binder ratio (w/b), admixtures, temperature, and supplementary cementitious materials (SCMs). By consolidating the existing knowledge and identifying research gaps, this review aims to contribute to the development of sustainable, high-performance cement-based materials suitable for modern construction techniques. Full article

Rural tourism has become a key driver of rural revitalization in China, contributing to poverty alleviation while also irreversibly altering the spatial evolution of rural settlements. In the ecologically fragile regions of northwest China, the rapid expansion of rural tourism destinations has raised ecological concerns, particularly regarding land resource utilization. Therefore, it is crucial to examine the phenomenon of industrial agglomeration in the evolution of rural tourism within the context of tourism-driven poverty alleviation. This study uses Lanzhou City as a case study and employs nearest neighbor analysis and kernel density estimation to analyze the spatial agglomeration patterns of rural tourism destinations, focusing on agglomeration forms, scales, and patterns. Additionally, it explores the spatial coupling distribution between agglomeration levels and influencing factors. The results show that from 1987 to 2022, the development of rural tourism destinations in Lanzhou City has progressed through several stages, from initial emergence to rapid growth. The form of industrial agglomeration has shifted from a dispersed to a clustered distribution, gradually expanding from urban centers to peripheral areas. The spatial agglomeration follows a multi-core hierarchical point-axial diffusion model, forming multiple core and sub-core agglomeration zones of varying scales. This transformation is primarily driven by geographical factors, transportation accessibility, and the presence of high-quality tourist attractions. However, a comparison of land use changes and ecological vulnerability indices over multiple periods indicates that the industrial agglomeration of rural tourism has led to irregular land use patterns and ecosystem instability. Finally, based on the complex relationship between rural tourism development, industrial agglomeration, and ecological sustainability, this study proposes strategies for the development of rural tourism in Lanzhou City, with the aim of providing valuable insights for the development of rural tourism in ecologically fragile regions of China. Full article

Alzheimer’s disease, an irreversible neurodegenerative disorder, manifests through the progressive deterioration of memory and cognitive functions. While magnetic resonance imaging has become an indispensable neuroimaging modality for Alzheimer’s disease diagnosis and monitoring, current diagnostic paradigms predominantly rely on single-time-point data analysis, neglecting the inherent longitudinal nature of neuroimaging applications. Therefore, in this paper, we propose a multi-task feature selection algorithm for Alzheimer’s disease classification based on longitudinal imaging and hypergraphs (THM2TFS). Our methodology establishes a multi-task learning framework where feature selection at each temporal interval is treated as an individual task within each imaging modality. To address temporal dependencies, we implement group sparse regularization with two critical components: (1) a hypergraph-induced regularization term that captures high-order structural relationships among subjects through hypergraph Laplacian modeling, and (2) a fused sparse Laplacian regularization term that encodes progressive pathological changes in brain regions across time points. The selected features are subsequently integrated via multi-kernel support vector machines for final classification. We used functional magnetic resonance imaging and structural functional magnetic resonance imaging data from Alzheimer’s Disease Neuroimaging Initiative at four different time points (baseline (T1), 6th month (T2), 12th month (T3), and 24th month (T4)) to evaluate our method. The experimental results show that the accuracy rates of 96.75%, 93.45, and 83.78 for the three groups of classification tasks (AD vs. NC, MCI vs. NC and AD vs. MCI) are obtained, respectively, which indicates that the proposed method can not only capture the relevant information between longitudinal image data well, but also the classification accuracy of Alzheimer’s disease is improved, and it helps to identify the biomarkers associated with Alzheimer’s disease. Full article

Infectious bronchitis virus (IBV) is an enveloped, positive-sense, single-stranded RNA virus belonging to the genus Gammacoronavirus. It primarily infects avian species, causing respiratory and renal disease and irreversible damage to the oviduct, which can lead to high mortality rates in chickens. The lack of rapid and reliable diagnostic tools for on-farm IBV detection hampers timely disease management and control measures. The introduction of DNA biosensors offers a promising approach for the sensitive and specific detection of IBV, facilitating rapid identification and intervention. In this study, an electrochemical DNA biosensor with a multi-walled carbon nanotube (MWCNT)-modified gold electrode was developed for IBV detection. The biosensor targeted the target-specific 5′ untranslated region (5′-UTR) of the IBV genome. Under optimal conditions, the immobilization and hybridization efficiencies were evaluated by cyclic voltammetry (CV) and differential pulse voltammetry (DPV), with methylene blue as a redox indicator. The developed DNA biosensor demonstrated a dynamic detection range from 2.0 × 10⁻¹² to 2.0 × 10⁻⁵ mol L⁻¹, with a limit of detection (LOD) of 2.6 nM and a limit of quantification (LOQ) of 0.79 nM. Validation using a small subset of clinical samples, including crude complementary DNA, and polymerase chain reaction products, showed high recovery rates ranging from 95.41% to 99.55%. While these findings highlight the potential of the proposed DNA biosensor as an innovative diagnostic tool for IBV detection, this study remains a proof of concept. However, further validation using a large number of clinical samples is essential to assess its feasibility, robustness, and practical application in a real-world farm setting Full article

Background: For infants, withholding or withdrawal of feeding is ethically permissible when the child is imminently dying or chronically and irreversibly comatose. It can also be appropriate in cases of medical futility with a low chance of survival. However, there is much contention in situations where the medical prognosis is uncertain. Case presentation: Annie is a 6-week-old neonate with antenatally acquired cystic encephalomalacia, a chronic non-progressive neurological condition. Her future neurological outcome is uncertain. She is putting on weight in the NICU with stable cardiorespiratory status on room air and tolerates full nasogastric tube feeding but requires frequent oropharyngeal suctioning. Her parents ask to stop tube feeding and allow Annie to die. They deem she has a poor quality of life and is experiencing tremendous suffering. Discussion: Parents’ perceptions of “best interest” and “physical suffering” are explored, alongside those of the healthcare team. Concomitant issues like feeding withdrawal and moral distress are examined in context—that of a newborn where developmental outcomes and disease trajectory are unclear. Conceptual frameworks, empirical evidence and consensus-based ethics guidelines informed a rich and multi-dimensional exposition of a difficult and value-laden decision. Conclusions: While instinctively legitimate, enteral feeding in an infant, in this case with severe neurological impairment, is ultimately still a medical intervention. In contrast to prevailing conventions within adult medicine, the careful and nuanced consideration of benefits and burdens from different stakeholders’ perspectives is critical before any deliberate withdrawal to allow natural death. Full article

Aiming at the irreversible demagnetization of permanent magnet synchronous motors (PMSMs) under extreme working conditions, a fault diagnosis method for permanent magnet demagnetization based on multi-parameter estimation is proposed in this paper. This scheme aims to provide technical support for enhancing the safety and reliability of permanent magnet motor drive systems. In the proposed scheme, multiple operating states of the motor are acquired by injecting sinusoidal current signals into the d-axis, ensuring that the parameter estimation equation satisfies the full rank condition. Furthermore, the accurate dq-axis inductance parameters are obtained based on a recursive least square method. Subsequently, a dual extended Kalman filter is employed to acquire real-time permanent magnet flux linkage data of PMSMs, and the estimation data between the two algorithms are transferred to each other to eliminate the bias of permanent magnet flux estimation caused by a parameter mismatch. Finally, accurate evaluation of the remanence level of the rotor permanent magnet and demagnetization fault diagnosis can be achieved based on the obtained permanent magnet flux linkage parameters. The experimental results show that the relative estimation errors of the dq-axis inductance and permanent magnet flux linkage are within 5%, which can realize the effective diagnosis of demagnetization fault and high-precision condition monitoring of a permanent magnet health. Full article