Search Results (6)

Age-related macular degeneration (AMD) is a leading cause of visual impairment and blindness in older adults. Its pathogenesis involves multiple factors, including aging, environmental influences, genetic predisposition, oxidative stress, metabolic dysfunction, and immune dysregulation. Currently, AMD treatment focuses primarily on wet AMD, managed through repeated intravitreal injections of anti-vascular endothelial growth factor (VEGF) therapies. While anti-VEGF agents represent a major breakthrough in wet AMD care, repeated injections may lead to incomplete responses or resistance in some patients, and carry a risk of progressive fibrosis. Artemisinin (ART) and its derivatives, originally developed as antimalarial drugs, exhibit a broad spectrum of pleiotropic activities beyond their established use, including anti-inflammatory, anti-angiogenic, antioxidant, anti-fibrotic, mitochondrial regulatory, lipid metabolic, and immunosuppressive effects. These properties position ART as a promising therapeutic candidate for AMD. A growing interest in ART-based therapies for AMD has emerged in recent years, with numerous studies demonstrating their potential benefits. However, no comprehensive review has systematically summarized the specific roles of ART and its derivatives in AMD pathogenesis and treatment. This paper aims to fill the knowledge gap by synthesizing the therapeutic efficacy and molecular mechanisms of ART and its derivatives in AMD, thereby providing a foundation for future investigations. Full article

Background: Neurodegenerative diseases (NGD) encompass a range of progressive neurological conditions, such as Alzheimer’s disease (AD) and Parkinson’s disease (PD), characterised by the gradual deterioration of neuronal structure and function. This degeneration manifests as cognitive decline, movement impairment, and dementia. Our focus in this investigation is on PD, a neurodegenerative disorder characterized by the loss of dopamine-producing neurons in the brain, leading to motor disturbances. Early detection of PD is paramount for enhancing quality of life through timely intervention and tailored treatment. However, the subtle nature of initial symptoms, like slow movements, tremors, muscle rigidity, and psychological changes, often reduce daily task performance and complicate early diagnosis. Method: To assist medical professionals in timely diagnosis of PD, we introduce a cutting-edge Multimodal Diagnosis framework (PMMD). Based on deep learning techniques, the PMMD framework integrates imaging, handwriting, drawing, and clinical data to accurately detect PD. Notably, it incorporates cross-modal attention, a methodology previously unexplored within the area, which facilitates the modelling of interactions between different data modalities. Results: The proposed method exhibited an accuracy of 96% on the independent tests set. Comparative analysis against state-of-the-art models, along with an in-depth exploration of attention mechanisms, highlights the efficacy of PMMD in PD classification. Conclusions: The obtained results highlight exciting new prospects for the use of handwriting as a biomarker, along with other information, for optimal model performance. PMMD’s success in integrating diverse data sources through cross-modal attention underscores its potential as a robust diagnostic decision support tool for accurately diagnosing PD. Full article

Ultra-widefield (UWF) retinal imaging stands as a pivotal modality for detecting major eye diseases such as diabetic retinopathy and retinal detachment. However, UWF exhibits a well-documented limitation in terms of low resolution and artifacts in the macular area, thereby constraining its clinical diagnostic accuracy, particularly for macular diseases like age-related macular degeneration. Conventional supervised super-resolution techniques aim to address this limitation by enhancing the resolution of the macular region through the utilization of meticulously paired and aligned fundus image ground truths. However, obtaining such refined paired ground truths is a formidable challenge. To tackle this issue, we propose an unpaired, degradation-aware, super-resolution technique for enhancing UWF retinal images. Our approach leverages recent advancements in deep learning: specifically, by employing generative adversarial networks and attention mechanisms. Notably, our method excels at enhancing and super-resolving UWF images without relying on paired, clean ground truths. Through extensive experimentation and evaluation, we demonstrate that our approach not only produces visually pleasing results but also establishes state-of-the-art performance in enhancing and super-resolving UWF retinal images. We anticipate that our method will contribute to improving the accuracy of clinical assessments and treatments, ultimately leading to better patient outcomes. Full article

In this work, the design of a wideband low-noise amplifier (LNA) using a resistive feedback network is proposed for potential multi-band sensing, communication, and radar applications. For achieving wide operational bandwidth and flat in-band characteristics simultaneously, the proposed LNA employs a variety of circuit design techniques, including a voltage–current (shunt–shunt) negative feedback configuration, inductive emitter degeneration, a main branch with an added cascode stage, and the shunt-peaking technique. The use of a feedback network and emitter degeneration provides broadened transfer characteristics for multi-octave coverage and a real impedance for input matching, respectively. In addition, the cascode stage pushes the band-limiting low-frequency pole, due to the Miller capacitance, to a higher frequency. Lastly, the shunt-peaking approach is optimized for the compensation of a gain reduction at higher frequency bands. The wideband LNA proposed in this study is fabricated using a commercial 0.13 μm silicon-germanium (SiGe) BiCMOS process, employing SiGe heterojunction bipolar transistors (HBTs) as the circuit’s core active elements in the main branch. The measurement results show an operational bandwidth of 2.0–29.2 GHz, a noise figure of 4.16 dB (below 26.5 GHz, which was the measurement limit), and a total power consumption of 23.1 mW under a supply voltage of 3.3 V. Regarding the nonlinearity associated with large-signal behavior, the proposed LNA exhibits an input 1-dB compression (IP1dB) point of −5.42 dBm at 12 GHz. These performance numbers confirm the strong viability of the proposed approach in comparison with other state-of-the-art designs. Full article

Optical coherence tomography (OCT) is widely used to detect and classify retinal diseases. However, OCT-image-based manual detection by ophthalmologists is prone to errors and subjectivity. Thus, various automation methods have been proposed; however, improvements in detection accuracy are required. Particularly, automated techniques using deep learning on OCT images are being developed to detect various retinal disorders at an early stage. Here, we propose a deep learning-based automatic method for detecting and classifying retinal diseases using OCT images. The diseases include age-related macular degeneration, branch retinal vein occlusion, central retinal vein occlusion, central serous chorioretinopathy, and diabetic macular edema. The proposed method comprises four main steps: three pretrained models, DenseNet-201, InceptionV3, and ResNet-50, are first modified according to the nature of the dataset, after which the features are extracted via transfer learning. The extracted features are improved, and the best features are selected using ant colony optimization. Finally, the best features are passed to the k-nearest neighbors and support vector machine algorithms for final classification. The proposed method, evaluated using OCT retinal images collected from Soonchunhyang University Bucheon Hospital, demonstrates an accuracy of 99.1% with the incorporation of ACO. Without ACO, the accuracy achieved is 97.4%. Furthermore, the proposed method exhibits state-of-the-art performance and outperforms existing techniques in terms of accuracy. Full article

This article investigates a principle inscribed at the top of most codes of ethics for curators: they should always “serve the public good.” No self-respecting curator would ever admit to serve “the private good,” that is, the good of the few, whether that of an elite in power or of a circle of friends or allies. The principle of “serving the public good” is inalienable and unquestionable even in situations where it is most open to doubt. However, what exactly is the meaning of this seemingly “true” and on all accounts “universal” principle: “to serve the public good”? To address this question, I look at this principle for the way it is perceived as being both majestic in its impressive widespread acceptance and cloaked in ridicule for being so often disregarded. I will argue—with an example taken from the history of curating—that it is not the meaning attached to the principle that counts, but the respect that it enjoins. I conclude by drawing a few remarks on how the value of the “good” remains, after the principle has been cast aside and the priority of respect is acknowledged, a ghost on the horizon of all curators’ work. Full article