Purpose: To elucidate patterns of macular ganglion cell-inner plexiform layer (GCIPL) defects by Cirrus optical coherence tomography (OCT) and examine the spatial relationship between GCIPL defect and visual field (VF) defect patterns. Methods: A total of 116 eyes of 116 normal subjects and 111 eyes of 111 glaucoma patients were included. The 227 study subjects underwent Cirrus OCT imaging in macular cube mode and reliable standard VF testing. Two ophthalmologists blindly classified GCIPL defect patterns and VF defects. The frequency distribution of GCIPL defect patterns and spatial relationships between GCIPL defects and VF defects were investigated. Results: GCIPL defect patterns were classified as minimal, inner, outer, diffuse mild, diffuse severe, inferior confined, inferior dominant, superior confined, and superior dominant defects in normal controls (71.6%, 7.8%, 4.3%, 1.7%, 0%, 10.3%, 1.7%, 1.7%, and 0.9%, respectively) and in glaucoma patients (11.7%, 3.6%, 4.5%, 7.2%, 21.6%, 22.5%, 18.0%, 4.5%, and 6.3%, respectively). In mild and moderate glaucoma patients, the inferior confined type was most frequent (21.9% and 50.0%, respectively). However, the diffuse severe type was most frequent (59.1%) in advanced glaucoma patients. The locations of the VF defects corresponded to the locations of the GCIPL defects in glaucoma patients (P=0.012). Conclusions: Glaucomatous damage of the macula was common and more frequent in the inferior retina. GCIPL defect patterns as determined by SD-OCT imaging corresponded well with central VF defects. It seems macular GCIPL analysis may be useful for evaluating glaucomatous optic neuropathy.

This study addresses the growing demand for scalable data encryption by evaluating the performance of AES (Advanced Encryption Standard) encryption and decryption using CBC (Cipher Block Chaining) and CTR (Counter Mode) modes across various CPU (Central Processing Unit) and GPU (Graphics Processing Unit) hardware models. The objective is to highlight GPU acceleration benefits and propose an optimized hybrid CPU–GPU workflow for large-scale data security. Methods include benchmarking encryption performance with provided data, mathematical models, and computational analysis. The results indicate significant performance gains with GPU acceleration, particularly for large datasets, and demonstrate that the hybrid CPU–GPU approach balances speed and resource utilization efficiently.

Although presepsin, a crucial biomarker for the diagnosis and management of sepsis, has gained prominence in contemporary medical research, its relationship with routine laboratory parameters, including demographic data and hospital blood test data, remains underexplored. This study integrates machine learning with explainable artificial intelligence (XAI) to provide insights into the relationship between presepsin and these parameters. Advanced machine learning classifiers provide a multilateral view of data and play an important role in highlighting the interrelationships between presepsin and other parameters. XAI enhances analysis by ensuring transparency in the model's decisions, especially in selecting key parameters that significantly enhance classification accuracy. Utilizing XAI, this study successfully identified critical parameters that increased the predictive accuracy for sepsis patients, achieving a remarkable ROC AUC of 0.97 and an accuracy of 0.94. This breakthrough is possibly attributed to the comprehensive utilization of XAI in refining parameter selection, thus leading to these significant predictive metrics. The presence of missing data in datasets is another concern; this study addresses it by employing Extreme Gradient Boosting (XGBoost) to manage missing data, effectively mitigating potential biases while preserving both the accuracy and relevance of the results. The perspective of examining data from higher dimensions using machine learning transcends traditional observation and analysis. The findings of this study hold the potential to enhance patient diagnoses and treatment, underscoring the value of merging traditional research methods with advanced analytical tools.

Spiking neural networks (SNNs) rely on precise spike timing for computation, however, their performance is often suffers jitter-induced errors and constraints on synaptic weight updates. In this study, we address this challenge by expanding the neuronal integration window, enhancing temporal robustness while maintaining efficient learning dynamics. We introduce a material-driven approach to expand the operational window of artificial neuron device, which is defined as the difference between the threshold voltage (Vth) and holding voltage (Vhold), in ovonic threshold switch (OTS)-based neurons, demonstrating its direct impact on synaptic weight updates and error mitigation. Carbon doping in GeTe-based OTS devices is employed to systematically modulate trap depth variations under an electric field, achieving precise control over Vth and Vhold. Electrical measurements confirm that an optimal 3.4% carbon concentration maximizes the operational window, stabilizing threshold switching and ensuring reliable neuronal operation. To reveal the atomic-scale mechanisms behind this behavior, we perform density functional theory (DFT) simulations, analyzing coordination number, bond angles to elucidate how carbon incorporation modifies trap distributions and influences device characteristics. Finally, we assess the practical impact of operational window expansion by implementing the optimized OTS neurons in a learning framework based on the tempotron learning rule, revealing enhanced spike timing robustness and reduced synaptic weight update constraints. This study provides a scalable pathway toward more reliable spike-based neuromorphic computing to advance the next generation of artificial intelligence hardware.

Background/Objectives: Digital intraoral photography is widely used for clinical documentation, longitudinal monitoring, and AI-assisted dental image analysis. However, specular highlights caused by saliva and intense illumination can obscure tooth texture and compromise image fidelity. This study aimed to develop an automated method for removing specular highlights from intraoral images while preserving tooth surface texture. Methods: A three-stage pipeline consisting of adaptive threshold prediction, mask generation, and image inpainting was proposed. Initially, the Hue, Saturation, Value (HSV) statistical features were extracted from each image and used to train a regression model that predicts an image-specific threshold. Subsequently, the predicted threshold was applied in the CIE LAB color space, followed by a condition-based dynamic adjustment algorithm to refine the mask area and distribution. Finally, an Aggregated Contextual Transformation (AOT)-based generator network was used to restore the masked regions. Results: The proposed dynamic adjustment reduced over-masking compared with regression-only processing and better preserved tooth surface texture. Pixel distribution analysis demonstrated a lower distributional discrepancy, with the Wasserstein distance reduced from 2.9601 to 1.3505 and the Kullback–Leibler divergence reduced from 0.3451 to 0.0618. In the clinical expert evaluation, the proposed method was preferred in 69.5% of the 200 evaluation responses, and the preference difference was statistically significant (p < 0.001). Conclusions: As a proof-of-concept study conducted under controlled conditions using synthetic images, the proposed pipeline reduced specular highlights while better preserving tooth surface texture than the baseline approaches. These findings suggest that the pipeline may support standardized preprocessing of dental image datasets, although broader applications such as long-term monitoring and AI-assisted diagnostic workflows require validation on real clinical photographs.

Low-cost RGB intraoral cameras are accessible alternatives to intraoral scanners; however, generating panoramic images is challenging due to narrow fields of view, textureless surfaces, and specular highlights. This study proposes a robust stitching framework and identifies optimal acquisition parameters to overcome these limitations. All experiments were conducted exclusively on a mandibular dental phantom model. Geometric consistency was further validated using repeated physical measurements of mandibular arch dimensions as ground-truth references. We employed a deep learning-based approach using SuperPoint and SuperGlue to extract and match features in texture-poor environments, enhanced by a central-reference stitching strategy to minimize cumulative drift errors. To validate the feasibility in a controlled setting, we conducted experiments on dental phantoms varying working distances (1.5–3.0 cm) and overlap ratios. The proposed method detected approximately 19–20 times more valid inliers than SIFT, significantly improving matching stability. Experimental results indicated that a working distance of 2.5 cm offers the optimal balance between stitching success rate and image detail for handheld operation, while a 1/3 overlap ratio yielded superior geometric integrity. This system demonstrates that robust 2D dental mapping is achievable with consumer-grade sensors when combined with advanced deep feature matching and optimized acquisition protocols.