Human Eye Research Articles

Seven Principles of Anatomical Pedagogy in Proverbs: A Hermeneutic Study of Numerical Proverbs Based on Proverbs 6:16-19

The pedagogical element is powerful in the Book of Proverbs as the primary teaching material for wisdom seekers. Interestingly, in Proverbs 6:16-19, a unique pedagogical element emerges because it is organized based on the anatomy of the human body, eyes, tongue, hands, heart, and feet. It closes with a complete picture of the human being, a false witness. Some previous studies have only focused on discussing it in terms of the structure of Proverbs 6 in the opening (prologue) of Proverbs 1-9 without examining its pedagogical elements. For this reason, this study aims to explore the pedagogical principles in Proverbs 6:16-19 as an essential lesson for a life pleasing to God and very unique because of the peculiarities of human anatomy. Based on the Hermeneutic Analysis of Wisdom Literature Sub Genre, this study found several things, namely: First, Synthetic parallelism in verse 16 emphasizes the understanding of God's hatred and disgust for sin; Second, Seven pedagogical principles of Proverbs numerical anatomy, namely: humility, honesty, mercy, purity, deliberation, honesty, and unity. The results of this study serve as a warning for believers to practice the principles of pedagogy as moral and ethical values.

Phenolic Profile, Antioxidant Activity, Chemical Composition, and Elements of Merlot Wine Stored in Toasted Oak Barrels

Wine ageing represents an important stage during wine production when the final wine composition is formed. In this study, 2020 and 2021 vintage Merlot red wines were subjected to 12-month ageing in a stainless-steel tank, Excellence oak barrels with medium, medium-plus and medium-long toasting, and a Premium oak barrel with medium toasting. The aim was to investigate the influence of different ageing vessels on the main chemical composition, element content, phenolic profile, antioxidant activity, and wine colour during ageing. The results showed that changes in ethanol, total sugars, pH, and density were minimal, mostly not significant. Slight changes in malic and lactic acid concentration occurred due to malolactic fermentation. Statistically, more changes that are significant occurred in the phenolic profile, and they affected the antioxidant activity of the wine. In both wine vintages, anthocyanin content decreased, followed by an increase in polymeric colour. Elements and individual phenolic compounds changed significantly, depending on vessel type, ageing time, wine vintage, and initial concentrations. The PCA biplots of the mentioned compounds showed that vessel type had a significant influence on wine composition, especially after 12 months of ageing. According to the CIELab parameters, a slight colour change occurred in both wine vintages, but this is not visible to the human eye. According to the obtained results, various changes in the phenolic profile of Merlot wine occurred during ageing, which strongly depended on the ageing vessel used, the ageing time, and the initial wine composition.

Eye-Inspired Single-Pixel Imaging with Lateral Inhibition and Variable Resolution for Special Unmanned Vehicle Applications in Tunnel Inspection

This study presents a cutting-edge imaging technique for special unmanned vehicles (UAVs) designed to enhance tunnel inspection capabilities. This technique integrates ghost imaging inspired by the human visual system with lateral inhibition and variable resolution to improve environmental perception in challenging conditions, such as poor lighting and dust. By emulating the high-resolution foveal vision of the human eye, this method significantly enhances the efficiency and quality of image reconstruction for fine targets within the region of interest (ROI). This method utilizes non-uniform speckle patterns coupled with lateral inhibition to augment optical nonlinearity, leading to superior image quality and contrast. Lateral inhibition effectively suppresses background noise, thereby improving the imaging efficiency and substantially increasing the signal-to-noise ratio (SNR) in noisy environments. Extensive indoor experiments and field tests in actual tunnel settings validated the performance of this method. Variable-resolution sampling reduced the number of samples required by 50%, enhancing the reconstruction efficiency without compromising image quality. Field tests demonstrated the system’s ability to successfully image fine targets, such as cables, under dim and dusty conditions, achieving SNRs from 13.5 dB at 10% sampling to 27.7 dB at full sampling. The results underscore the potential of this technique for enhancing environmental perception in special unmanned vehicles, especially in GPS-denied environments with poor lighting and dust.

Inhibitory Effect of Oxibiol on the Process of Protein Modification by Water-Soluble Products of Photo-Oxidative Destruction of Bisretinoid A2E

It has previously been shown that heteroaromatic antioxidant oxibiol (N-acetylcysteinate 6-hydroxy-2-aminobenzothiazole) inhibits the process of fructosylation of serum albumin. The aim of this study is to elucidate the inhibitory effect of oxibiol on modification of proteins by photodegradation products of bisretinoid A2E, the main fluorophore of lipofuscin granules in retinal pigment epithelial cells of the human eye. It was shown that unlike a water-soluble fraction fraction from non-irradiated A2E-liposomes, a water-soluble fraction formed after irradiation of A2E-liposomes with visible light significantly modified albumin in a day after incubation at 37°C. Oxibiol in millimolar concentrations effectively inhibited this process. The inhibitory effect of oxybiol could be attributed to its antioxidant activity and the ability to compete with reactive aldehydes formed during the photooxidative degradation of A2E-cardiolipin liposomes. The acute toxicity of oxibiol in mice after intraperitoneal injection was studied, the values of LD 10 and LD 50 were determined. The results obtained demonstrate that oxibiol can be used in pharmacology for preventing and curing diseases associated with the development of oxidative stress in various fields of medicine, primarily in ophthalmology.

Regulation of corneal epithelial differentiation: miR-141-3p promotes the arrest of cell proliferation and enhances the expression of terminal phenotype.

In recent years, different laboratories have provided evidence on the role of miRNAs in regulation of corneal epithelial metabolism, permeability and wound healing, as well as their alteration after surgery and in some ocular pathologies. We searched the available databases reporting miRNA expression in the human eye, looking for miRNAs highly expressed in central cornea, which could be crucial for maintenance of the epithelial phenotype. Using the rabbit RCE1(5T5) cell line as a model of corneal epithelial differentiation, we describe the participation of miR-141-3p as a possible negative regulator of the proliferative/migratory phenotype in corneal epithelial cells. The expression of miR-141-3p followed a time course similar to the differentiation-linked KRT3 cytokeratin, being delayed 24-48 hours relative to PAX6 expression; such result suggested that miR-141-3p only regulates the expression of terminal phenotype. Inhibition of miR-141-3p led to increased cell proliferation and motility, and induced the expression of molecular makers characteristic of an Epithelial Mesenchymal Transition (EMT). Comparison between the transcriptional profile of cells in which miR-141-3p was knocked down, and the transcriptomes from proliferative non-differentiated and differentiated stratified epithelia suggest that miR-141-3p is involved in the expression of terminal differentiation mediating the arrest of cell proliferation and inhibiting the EMT in highly motile early differentiating cells.

Iris Recognition Using Artificial Neural Network

Iris recognition has emerged as one of the most widely used and effective techniques for personal verification. It leverages the unique features of the human eye, specifically the distinct patterns found in the iris or, more precisely, the pupil. Biometric systems based on iris scanning analyze these unique iris patterns to confirm and authenticate an individual's identity. They are widely used as human eye has unique features particularly the iris. Human iris have random variations and complex texture which are stable with time even visible between identical twins. It’s replication and forgery are almost impossible. For training and testing our model we used CASIA-Iris-Interval database as camera used to collect this data uses a circular NIR LED array [1].Near Infrared LED has a suitable luminous flux for imaging human iris for recognition. Our results demonstrate that the ANN model outperforms traditional machine learning algorithms, such as Support Vector Machines (SVM) and k-Nearest Neighbors (k-NN), in terms of both accuracy and robustness [5]. The model showed high accuracy in identifying iris patterns, even under varying conditions like changes in lighting, pupil dilation, and partial occlusions. This highlights the potential of deep learning techniques to overcome the challenges faced by conventional biometric systems, which often struggle with high-dimensional data and manual feature extraction [18].

Head-mounted displays based on human eye condition variation patterns

In response to the practical demands for a wide field of view (FOV), long exit pupil distance, and high image quality in head-mounted displays, an initial structure was built based on the principles of catadioptric lenses in a folded optical path (pancake optics) and the field curvature correction formula. Subsequently, using even-order aspheric surfaces, a VR optical system was developed with a FOV of 2w=110∘, an exit pupil distance of 15 mm, and an exit pupil diameter of 8 mm. Compared to traditional designs, a human eye model was introduced as the image plane to construct a VR–human eye optical system analysis model, allowing for direct analysis of the retinal image quality under different states of the human eye. After that, based on medical statistical data, the impact of different light intensity conditions on the retinal image quality of the VR optical system was explored. Experimental results showed that changes in light intensity affect the pupil diameter, which, in turn, influences the final image quality of the system. To solve the issues, two solutions were proposed: adjusting the screen distance and changing the lens spacing. Both methods significantly improved the imaging quality on the human retina, with an increase of over 30% in the average MTF value. A comparison of these methods was conducted, providing valuable references for future VR optical system designs.

Introducing Aberrations into Human Eye Models with High Precision for Objective Visual Quality Evaluation

Objective visual quality evaluation using optical human eye models with actual eye aberration characteristics provides an important reference for clinical vision diagnosis and treatment. To ensure accurate evaluation, it is typically necessary to precisely introduce eye aberrations into the eye model by adjusting the model’s surface shape. However, the detailed relationship between eye aberrations and surface shapes is unclear, causing challenges in aberration introduction. In this paper, a method that enables the accurate introduction of target aberrations into an eye model for objective visual quality evaluation is proposed. The eye aberrations generated by the model’s surface are analyzed based on optical nodal aberration theory. The aberration calculation formulas are proposed and proved by a design example. Using the proposed method, the objective visual quality evaluation is conducted for the patients who underwent refractive surgery. The clinically measured postoperative eye aberrations are accurately introduced into the eye model with an error of less than 1.0 × 10−4 μm. The objective visual evaluation results are consistent with the statistical results of clinical contrast sensitivity tests. With the proposed method, it is expected to aid in clinical visual quality testing and assess the effect of refractive correction.

Quality Assessment of MRI-Radiomics-Based Machine Learning Methods in Classification of Brain Tumors: Systematic Review.

Brain tumors present a complex challenge in clinical oncology, where precise diagnosis and classification are pivotal for effective treatment planning. Radiomics, a burgeoning field in neuro-oncology, involves extracting and analyzing numerous quantitative features from medical images. This approach captures subtle spatial and textural information imperceptible to the human eye. However, implementation in clinical practice is still distant, and concerns have been raised regarding the methodological quality of radiomic studies. A systematic literature search was performed to identify original articles focused on the use of radiomics for brain tumors from 2015 based on the inclusion and exclusion criteria. The radiomic features train machine learning models for glioma classification, and data are split into training and testing subsets to validate the model accuracy, reliability, and generalizability. The present study systematically reviews the status of radiomic studies concerning brain tumors, also using the radiomics quality score (RQS) to assess the quality of the methodology used in each study. A systematic search of PubMed identified 300 articles, with 18 studies meeting the inclusion criteria for qualitative synthesis. These studies collectively demonstrate the potential of radiomics-based machine learning models in accurately distinguishing between glioma subtypes and grades. Various imaging modalities, including MRI, PET/CT, and advanced techniques like ASL and DTI, were utilized to extract radiomic features for analysis. Machine learning algorithms such as deep learning networks, support vector machines, random forests, and logistic regression were applied to develop predictive models. The present study indicates high accuracies in glioma classification, outperforming traditional imaging methods and inexperienced radiologists in some cases. Further validation and standardization efforts are warranted to facilitate the clinical integration of radiomics into routine practice, ultimately enhancing glioma management and patient outcomes. Open science practices: Machine learning using MRI radiomic features provides a simple, noninvasive, and cost-effective method for glioma classification, enhancing transparency, reproducibility, and collaboration within the scientific community.

Design of a Non-Destructive Seed Counting Instrument for Rapeseed Pods Based on Transmission Imaging

Pod counting of rapeseed is a critical step in breeding, cultivation, and agricultural machinery research. Currently, this process relies entirely on manual labor, which is both labor-intensive and inefficient. This study aims to develop a semi-automatic counting instrument based on transmission image processing and proposes a new algorithm for processing transmission images of pods to achieve non-destructive, accurate, and rapid determination of the seed count per pod. Initially, the U-NET network was used to segment and remove the stem and beak from the pod image; subsequently, adaptive contrast enhancement was applied to adjust the contrast of the G-channel image of the pod to an appropriate range, effectively eliminating the influence of different varieties and maturity levels on the translucency of the pod skin. After enhancing the contrast, the Sauvola algorithm was employed for threshold segmentation to remove the pod skin, followed by thinning and dilation of the binary image to extract and remove the central ridge lines, detecting the number and area of connected domains. Finally, the seed count was determined based on the ratio of each connected domain’s area to the mean area of all connected domains. A transmission imaging device that mimics the human eye’s method of counting seeds was designed, incorporating an LED transmission light source, photoelectric switch-triggered imaging slot, an industrial camera, and an integrated packaging frame. Human–machine interaction software based on PyQt5 was developed, integrating functions such as communication between upper and lower machines, image acquisition, storage, and processing. Operators simply need to place the pod in an upright position into the imaging device, where its transmission image will be automatically captured and processed. The results are displayed on a touchscreen and stored in Excel spreadsheets. The experimental results show that the instrument is accurate, user-friendly, and significantly reduces labor intensity. For various varieties of rapeseed pods, the seed counting accuracy reached 97.2% with a throughput of 372 pods/h, both of which are significantly better than manual counting and have considerable potential for practical applications.

Mixed Bayesian Stackelberg Strategies for Robust Adversarial Classifiers

Deep neural networks (DNNs) have achieved state-of-the-art performance in classification tasks; however, they are susceptible to small perturbations that are seemingly imperceptible to the human eye but are enough to fool the network into misclassifying images. To develop more robust DNNs against adversarial attacks, research methods have focused on exploring the interaction between a machine learning classifier and a single adversary. However, these methods do not adequately model the real-world scenarios in which these classifiers are deployed. In this research paper, we address this gap and propose an adversarial learning algorithm with multiple adversaries using Bayesian Stackelberg games to model the interaction between the learner and multiple adversaries. We conclude that the nested Bayesian Stackelberg method is a useful strategy for developing adversarial learning algorithms to improve the robustness of DNNs. This strategy can serve as a benchmark in future defense attempts to create DNNs that resist adversarial attacks.

Advances in Ophthalmic Organ-on-a-Chip Models: Bridging Translational Gaps in Disease Modeling and Drug Screening

Background: Organ-on-a-chip models have emerged as transformative tools in ophthalmology, offering physiologically relevant platforms for studying ocular diseases and testing therapeutic interventions. These microfluidic devices replicate human eye tissue architecture, addressing limitations of traditional in vitro and animal models. Methods: A narrative review of recent advancements in organ-on-a-chip technology was conducted, focusing on models simulating ocular structures like the retina and cornea and their applications in studying diseases such as dry eye disease (DED), age-related macular degeneration (AMD), and glaucoma. Results: Advanced organ-on-a-chip models successfully mimic key ocular features, providing insights into disease mechanisms and therapeutic responses. Innovations in microengineering and cellular integration have enhanced these platforms’ translational potential, though challenges like scalability and regulatory validation persist. Conclusions: Organ-on-a-chip models are poised to enhance preclinical research and clinical applications in ophthalmology. Addressing scalability and regulatory hurdles will be key to unlocking their full potential in drug discovery and disease modeling.

The Strain Response to Intraocular Pressure Increase in the Lamina Cribrosa of Control Subjects and Glaucoma Patients.

The purpose of this study was to measure biomechanical strains in the lamina cribrosa (LC) of living human eyes undergoing intraocular pressure (IOP) increase. Healthy control subjects and patients with glaucoma underwent optical coherence tomographic (OCT) imaging of the LC before and after wearing of swim goggles that increased IOP (57 image pairs, 39 persons). Digital volume correlation was used to measure biomechanical strains in optic nerve head tissue and change in depth of the anterior border of the LC. The mean IOP increase in both glaucoma and control eyes was 7.1 millimeters of mercury (mm Hg) after application of the goggles. Among glaucoma eyes, strains that were significant were: contractile Ezz (average = -0.33%, P = 0.0005), contractile Eθθ (average = -0.23%, P = 0.03), Emax (average = 0.83%, P < 0.0001), and Γmax (average = 0.95%, P < 0.0001), whereas the average anterior LC depth (ALD) decreased by 2.39µm (anterior; P = 0.0002). In glaucoma eyes, shear strain Ezθ was greater with worse mean deviation (MD) and visual function index (P = 0.044 and P = 0.006, respectively, multivariate models). Strain compliance for Erθ, Ezθ, and Eθθ all increased with greater MD worsening prior to imaging (P = 0.04, P = 0.007, and P = 0.03). LC strains were measurable 20 minutes after IOP increase, producing axial compression and greater peripheral strain than centrally. Some strain compliances were greater with worse existing visual field loss or with more progressive past field loss. Biomechanical strains are related to measures of glaucoma damage, supporting the hypothesis that optic nerve head biomechanical responses represent a noninvasive biomarker for glaucoma.

Non-invasive vital parameter detection using neuromorphic cameras

Abstract The non-invasive detection of vital parameters is gaining significance in the medical field through the use of various established and novel camera technologies and the use of artificial intelligence for data processing. A recently emerging type of imaging sensor, the neuromorphic camera, mimics the way the human eye perceives changes in its vision by responding to local changes in brightness for each pixel. The use of these neuromorphic cameras in the medical field is mostly unexplored and offers new possibilities. This research experiment is designed as a proof of concept for the use of neuromorphic cameras to detect the heart rate and respiration rate of a person by monitoring movement and subtle vibrations of the abdominal and chest area. The experimental setup consisted of a DVXplorer camera positioned 30 cm from a healthy person, directed at the abdominal and chest area. The respiration rate was detected with a mean absolute error of 0.66 bpm. The heart rate was detected with an accuracy of 95 % and a mean absolute error of less than 2 bpm. This shows the possibility to detect the heart rate and respiration rate of a person reliably, using neuromorphic cameras under controlled conditions. Future research will focus on improving reliability under more challenging environmental circumstances like varying light conditions and movement of the patient.

Enhancing Student Learning of the Physics of the Human Eye in the Context of Computer Vision Syndrome During COVID-19

Enhancing Student Learning of the Physics of the Human Eye in the Context of Computer Vision Syndrome During COVID-19

Fast segment anything model applied in a low-cost camera-based system for oyster growth monitoring

The increase in the global population has been accompanied by an increase in demand for animal protein, consequently driving the growth of the aquaculture sector as a sustainable solution to satisfy this demand. Observing the growth of animals in farm environments is vital to assess the efficiency of feeding methods, identify health problems or environmental stressors, and help implement sustainable practices. The application of cameras enables remote monitoring of various environments and activities in real time, providing quick insights, improving security measures, and facilitating decision-making based on the collected data. This paper proposes a low-cost camera system and an image processing-based strategy to monitor and measure the size of Pacific oysters inside a tank. The fast segment anything model algorithm is implemented in the Python programming language to perform image segmentation and generate object masks for subsequent processing. The size of oysters is obtained by measuring the distance between the parallel lines of the bounding box that is generated surrounding them. The largest absolute errors and relative errors for the estimates were 3 mm and 5.13%, respectively, when compared to measurements made manually. Furthermore, the width and height have standard deviations of 0.6 and 0.3 mm, respectively, indicating low data dispersion and good repeatability of the algorithm. Since the accuracy of the human eye in estimating a dimension without the aid of a distance measuring instrument tends to be lower than that obtained by the proposed tool, it is believed that the approach can significantly contribute to the management of oyster farm production, providing strategic insights based on animal growth for producers.

Retinal cytoarchitecture is preserved in an organotypic perfused human and porcine eye model

Pathobiology of the intact human retina has been challenging to study due to its relative inaccessibility and limited sample availability. Thus, there is a great need for new translational models that can maintain human retinal integrity and cytoarchitecture. The role of physiologic intraocular pressure (IOP) and fluid flow on retinal tissue has not been well studied. Here, we present an ex vivo organotypic model to assess the impact of physiological intraocular perfusion on retinal cytoarchitecture and cell survival. We demonstrate that retinal cytoarchitecture is remarkably well preserved following re-establishment of physiological IOP and aqueous humor dynamics for up to 24 h in ex vivo whole globe porcine and human eyes, comparable to freshly preserved control eyes. Accordingly, cell death was minimized in the perfused retinas, which also displayed normal markers of cellular metabolism and astrogliosis. These results are in marked contrast to contralateral control eyes without active perfusion, which displayed excessive cell death and disrupted cytoarchitecture at the same time point. These experiments demonstrate the critical impact that physiological pressure and fluid flow have on retinal tissue, and introduce a new pre-clinical model to study human and porcine retinal health and degeneration in a relevant biomechanical setting.

Ocular drug delivery systems: A comprehensive review of innovative formulations, challenges and opportunities

The human eye's complex anatomy and physiology pose significant challenges to effective ocular drug delivery. This comprehensive review explores the eye's structure, ocular disorders such as cataracts, conjunctivitis, glaucoma, and fungal keratitis, and various ocular drug delivery systems. The review discusses the advantages and disadvantages of ocular drug delivery systems, including topical administration, intravitreal injection, subconjunctival routes, and implantable devices. Novel formulations such as nanosuspensions, microemulsions, liposomes, and niosomes are examined for their potential to overcome anatomical limitations and enhance bioavailability. The mechanisms of drug release, including diffusion, osmosis, and bio-erosion, are also discussed. Furthermore, the review highlights the importance of targeting specific ocular tissues and overcoming defensive mechanisms to achieve optimal therapeutic efficacy.

Handwritten Hiragana Letter Detection Using CNN

Hiragana is one of the primary alphabets used in Japanese. Hiragana is a phonetic symbol; each letter represents one syllable. Hiragana letters are formed from curved lines and strokes. However, detecting Hiragana letters causes many errors because people still rely on their vision to detect the letters, especially people familiar with them for the first time. It will be difficult and not very clear to read the letters. Therefore, a Convolutional Neural Network (CNN) method is used to detect handwritten Hiragana letters and help people who first get to know Hiragana letters when the letters are too complicated for human eyes to detect. This research uses the YOLOv8 model as a handwritten Hiragana letter detection algorithm. The Hiragana letters to be detected are basic letters with 46 characters. This research uses the YOLOv8 model run on Google Collaboratory with the Ultralytics library version 8.0.20 using the Python programming language. The dataset is collected from the internet and annotated using the Roboflow framework and dataset 4600 Hiragana letters. From the test results, the best model is YOLOv8l using SGD optimizer and learning rate 0.01 with a precision value of 98.5%, recall value of 95.7%, f1-score value of 97.1%, and mAP value of 95.5%. In the future, we aim to expand the number of datasets and employ a broader range of hyperparameter values to optimize the classification precision and accuracy of the Hiragana Letter Detection system.

Detecting Conjunctival Hyperemia Using an Effective Machine Learning based Method

Among the many complex sensory organs, the human eye stands out. Prevention of eye illnesses is of the utmost importance since irreversible vision loss might result from postponed treatment. Therefore, to manage the continuous course of eye illnesses, early identification and monitoring are vital. Hyperemia, or redness, from increased blood flow, is one sign of conjunctivitis, an eye disorder defined by inflammation of the conjunctiva. Many illnesses and problems are often curable or significantly reducible with the help of the greatest medicines, sophisticated procedures, and early, accurate diagnosis by medical experts. Because there is a severe lack of diagnostic specialists, patients with vision difficulties are often not given the care they need because their conditions are not properly diagnosed. Segmentation approaches are crucial for detecting and quantifying hyperemic areas, which are necessary for diagnosing and evaluating conjunctivitis. This paper presents a very efficient machine learning framework for the detection of eye problems. The system does this by integrating segmentation approaches with feature extraction techniques. The use of the discrete cosine transform (DCT) generates feature vectors from the segmented regions of interest. Random forests and neural networks are two machine learning classifiers that are learned using feature vectors. This approach has a 96% success rate and has potential to assist ophthalmologists in assessing the severity of illnesses in objective and accurate manner. This strategy might be advantageous for adopting ICT-based solutions in public health systems in remote regions.