Vision-based estimation of fatigue and engagement in cognitive training sessions.

Computerized cognitive training (CCT) is a scalable, well-tolerated intervention that has promise for slowing cognitive decline. The effectiveness of CCT is often affected by a lack of effective engagement. Mental fatigue is a the primary factor for compromising effective engagement in CCT, particularly in older adults at risk for dementia. There is a need for scalable, automated measures that can constantly monitor and reliably detect mental fatigue during CCT. Here, we develop and validate a novel Recurrent Video Transformer (RVT) method for monitoring real-time mental fatigue in older adults with mild cognitive impairment using their video-recorded facial gestures during CCT. The RVT model achieved the highest balanced accuracy (79.58%) and precision (0.82) compared to the prior models for binary and multi-class classification of mental fatigue. We also validated our model by significantly relating to reaction time across CCT tasks (Waldχ2=5.16,p=0.023). By leveraging dynamic temporal information, the RVT model demonstrates the potential to accurately measure real-time mental fatigue, laying the foundation for future CCT research aiming to enhance effective engagement by timely prevention of mental fatigue.

Sensing and feeling: an overview.

Emotional experiences are driven, in part, by the way we process and integrate information from different sensory modalities. Understanding how perceptual and emotional systems interact to give rise to subjective feelings is an important, complex and challenging issue, requiring new approaches and integrative thinking that fuses the fundamentals of low-level sensory perception with higher-level cognitive and affective processes. The Theme Issue 'Sensing and feeling: an integrative approach to sensory processing and emotional experience' showcases fifteen theoretical, empirical, and review articles from experts working at the intersection of perception and emotion, encompassing multiple sensory systems (visual, auditory, tactile and interoceptive), clinical and non-clinical perspectives (e.g. affective disorders and hearing loss), contextual and social perspectives, and complex emotional experiences in special populations. Articles in Part 1 emphasize recent advances across fields in sensory and emotion science and give insights into future directions. Each article in Part 2 provides more detailed and specific methodological approaches or theoretical models, and focuses on basic mechanisms linking sensation to emotional experience. This article is part of the theme issue 'Sensing and feeling: an integrative approach to sensory processing and emotional experience'.

Guided image generation for improved surgical image segmentation.

The lack of large datasets and high-quality annotated data often limits the development of accurate and robust machine-learning models within the medical and surgical domains. In the machine learning community, generative models have recently demonstrated that it is possible to produce novel and diverse synthetic images that closely resemble reality while controlling their content with various types of annotations. However, generative models have not been yet fully explored in the surgical domain, partially due to the lack of large datasets and due to specific challenges present in the surgical domain such as the large anatomical diversity. We propose Surgery-GAN, a novel generative model that produces synthetic images from segmentation maps. Our architecture produces surgical images with improved quality when compared to early generative models thanks to the combination of channel- and pixel-level normalization layers that boost image quality while granting adherence to the input segmentation map. While state-of-the-art generative models often generate overfitted images, lacking diversity, or containing unrealistic artefacts such as cartooning; experiments demonstrate that Surgery-GAN is able to generate novel, realistic, and diverse surgical images in three different surgical datasets: cholecystectomy, partial nephrectomy, and radical prostatectomy. In addition, we investigate whether the use of synthetic images together with real ones can be used to improve the performance of other machine-learning models. Specifically, we use Surgery-GAN to generate large synthetic datasets which we then use to train five different segmentation models. Results demonstrate that using our synthetic images always improves the mean segmentation performance with respect to only using real images. For example, when considering radical prostatectomy, we can boost the mean segmentation performance by up to 5.43%. More interestingly, experimental results indicate that the performance improvement is larger in the set of classes that are under-represented in the training sets, where the performance boost of specific classes reaches up to 61.6%.

Ensemble approach of transfer learning and vision transformer leveraging explainable AI for disease diagnosis: An advancement towards smart healthcare 5.0.

Smart healthcare has advanced the medical industry with the integration of data-driven approaches. Artificial intelligence and machine learning provided remarkable progress, but there is a lack of transparency and interpretability in such applications. To overcome such limitations, explainable AI (EXAI) provided a promising result. This paper applied the EXAI for disease diagnosis in the advancement of smart healthcare. The paper combined the approach of transfer learning, vision transformer, and explainable AI and designed an ensemble approach for prediction of disease and its severity. The result is evaluated on a dataset of Alzheimer's disease. The result analysis compared the performance of transfer learning models with the ensemble model of transfer learning and vision transformer. For training, InceptionV3, VGG19, Resnet50, and Densenet121 transfer learning models were selected for ensembling with vision transformer. The result compares the performance of two models: a transfer learning (TL) model and an ensemble transfer learning (Ensemble TL) model combined with vision transformer (ViT) on ADNI dataset. For the TL model, the accuracy is 58 %, precision is 52 %, recall is 42 %, and the F1-score is 44 %. Whereas, the Ensemble TL model with ViT shows significantly improved performance i.e., 96 % of accuracy, 94 % of precision, 90 % of recall and 92 % of F1-score on ADNI dataset. This shows the efficacy of the ensemble model over transfer learning models.

Professional perspective of vision therapy worldwide among optometrists and ophthalmologists.

CLINICAL RELEVANCE: Perception of optometrists and ophthalmologists of vision therapy (VT) as well as their methods for performing the sessions is of great importance for understanding the problems that professionals face and what should be improved in their clinical practice. BACKGROUND: The objective of this study was to determine the perception of VT and protocols used by eye care professionals globally. METHODS: A cross-sectional study was performed among eye care professionals internationally. The participants completed via online (Google Forms) a questionnaire of 40 questions on their perception and clinical practice of VT. The survey only permitted one answer per email address and was only collected if professionals gave their consent to participate. RESULTS: A total of 205 professionals from 43 countries answered the questionnaire (171 optometrists and 34 ophthalmologists). Accommodation and convergence problems were the main indication reported for VT (47.8%), followed by amblyopia (26.3%). The principal negative factor associated to VT was the limited number of professionals dedicated to this field (55.6%). The most common program of VT was the combination of VT sessions at home and at office (85.5%), with a great variability in terms of duration. Few professionals made use of the new technologies when programming VT, such as eye trackers (2.7%) or virtual reality (6.4%). European professionals surveyed had a more negative perception of VT (p < 0.031). CONCLUSIONS: Eye care professionals globally perceive VT as a scientifically based procedure. Overall, they believe that it has low recognition and prestige, especially ophthalmologists. A great variability in the programming of VT sessions was found, which highlights the need for standardised protocols that professionals could follow in their clinical practice.

Awareness of Visual Offset Reduces but Does Not Eliminate Joint Repositioning Errors in Virtual Reality.

The present study investigated the effect of visual offset (visuo-proprioceptive mismatch) in joint repositioning task in a three-dimensional virtual reality (VR) environment when participants were instructed to ignore vision. Twenty-five physically healthy young individuals performed shoulder joint position sense test. Repositioning accuracy was tested under two visual conditions, accurate and offset visions, and two instructions, no guidance or ignore vision. In accurate vision trials, the virtual hand of the tested limb seen in VR was congruent with where the participant placed their hand. In the offset vision condition, the virtual hand was seen 8° above or below their actual hand in the vertical plane. Repositioning error (i.e. constant error) in offset vision trials was lower when the participants were instructed to ignore vision compared to when no instruction about the visual offset was given (p < 0.001). However, constant error in offset vision trials was larger than accurate vision trials when the participants tried to ignore vision in both visual conditions (p < 0.001). Our results suggest that humans may be able to down-weight vision to some extent by conscious effort, while the influence of vision is difficult to eliminate when vision is present.

Low-cost, portable, easy-to-use kiosks to facilitate home-cage testing of non-human primates during vision-based behavioral tasks.

Non-human primates (NHPs), especially rhesus macaques, have significantly contributed to our understanding of the neural computations underlying human vision. Besides the established homologies in the visual brain areas between these species and our ability to probe detailed neural mechanisms in monkeys at multiple scales, NHPs' ability to perform human-like visual behavior makes them an extremely appealing animal model of human vision. Traditionally, such behavioral studies have been conducted in controlled laboratory settings, offering experimenters tight control over variables like luminance, eye movements, and auditory interference. However, in-lab experiments have several constraints, including limited experimental time, the need for dedicated human experimenters, additional lab space requirements, invasive surgeries for head-post implants, and extra time and training for chairing and head restraints. To overcome these limitations, we propose adopting home-cage behavioral training and testing of NHPs, enabling the administration of many vision-based behavioral tasks simultaneously across multiple monkeys with reduced human personnel requirements, no NHP head restraint, and monkeys' unrestricted access to experiments. In this article, we present a portable, low-cost, easy-to-use kiosk system developed to conduct home-cage vision-based behavioral tasks in NHPs. We provide details of its operation and build to enable more open-source development of this technology. Furthermore, we present validation results using behavioral measurements performed in the lab and in NHP home cages, demonstrating the system's reliability and potential to enhance the efficiency and flexibility of NHP behavioral research.

Monitoring Berlin Heart EXCOR by computer vision: A preliminary implementation and evaluation.

BACKGROUND: EXCOR Pediatric is one of the most commonly used ventricular assist devices (VAD) for small children; it requires visual inspection of the diaphragm movement to assess its operating status. Although this visual inspection can only be performed by trained medical professionals, it can also be attempted by the recent advances in computer vision technology. METHODS: Movement of the diaphragm in the operating EXCOR VAD was recorded as movies and annotated frame-by-frame in three classes according to the state of the diaphragm: "fill," "mid," and "empty." Three models, MobileNetV3, EfficientNetV2, and MobileViT, were trained using the frames, and their performance was evaluated based on the accuracy and area under the receiver operating characteristic curve (AROC). RESULTS: A total of 152 movies were available from two participants. Only the 10 mL pumps were used. Ninety-eight movies were used for annotation and frame extraction, and 7949 frames per class were included in the analysis. The macro-average accuracies of the three models were 0.88, 0.91, and 0.93, and the AROC were 0.99, 0.98, and 0.99 for MobileNetV3, EfficientNetV2, and MobileViT, respectively. CONCLUSION: Image recognition models based on lightweight deep neural networks could detect the diaphragm state of EXCOR VAD with sufficient accuracy, although there were limited variations in the dataset. This suggests the potential of computer vision for the automated monitoring of the EXCOR diaphragm position.

Deep Learning - Methods to Amplify Epidemiological Data Collection and Analyses.

Deep learning is a subfield of artificial intelligence and machine learning based mostly on neural networks and often combined with attention algorithms that has been used to detect and identify objects in text, audio, images, and video. Serghiou and Rough (Am J Epidemiol. 0000;000(00):0000-0000) present a primer for epidemiologists on deep learning models. These models provide substantial opportunities for epidemiologists to expand and amplify their research in both data collection and analyses by increasing the geographic reach of studies, including more research subjects, and working with large or high dimensional data. The tools for implementing deep learning methods are not quite yet as straightforward or ubiquitous for epidemiologists as traditional regression methods found in standard statistical software, but there are exciting opportunities for interdisciplinary collaboration with deep learning experts, just as epidemiologists have with statisticians, healthcare providers, urban planners, and other professionals. Despite the novelty of these methods, epidemiological principles of assessing bias, study design, interpretation and others still apply when implementing deep learning methods or assessing the findings of studies that have used them.

Can repetitive transcranial magnetic stimulation influence the visual cortex of adults with amblyopia? - systematic review.

Amblyopia is the most frequent cause of monocular vision loss. Transcranial Magnetic Stimulation (TMS) has been used to improve several vision parameters of the amblyopic eye in adulthood. This study is relevant in order to evaluate TMS effects and to raise awareness of the need for further research. Transcranial Magnetic Stimulation (TMS) is a neuromodulation technique capable of changing cortical excitability. In the last decade, it has been used to improve visual parameters in amblyopic patients. The main goal of this systematic review is to evaluate the influence of TMS in the amblyopic eye, in the visual parameters of amblyopic patients. Searches were done in PubMed and Embase databases, and a combined search strategy was performed using the following Mesh, EMBASE, and keywords: 'Amblyopia', 'Transcranial Magnetic Stimulation', and 'theta burst stimulation'. This review included randomised controlled studies, descriptive cases, and clinical case studies with adult amblyopes. All articles that had any of the following characteristics were excluded: children or animal studies, reviews, pathologies other than amblyopia, and other techniques rather than repetitive TMS (rTMS), or Theta Burst Stimulation (TBS). A total of 42 articles were found, of which only four studies (46 amblyopes) meet the criteria above. Three of the articles found significant improvement after one session of continuous TBS (cTBS) in visual parameters like visual acuity, contrast sensitivity, suppressive imbalance, and stereoacuity. One study found a significant visual improvement with 10 Hz rTMS. Only one stimulation-related dropout was reported. The few existing studies found in this review seem to show that through the usage of high-frequency rTMS and cTBS, it is possible to re-balance the eyes of an adult amblyope. However, despite the promising results, further research with larger randomised double-blind studies is needed for a better understanding of this process.

A dataset of over one thousand computed tomography scans of battery cells.

Battery technology is increasingly important for global electrification efforts. However, batteries are highly sensitive to small manufacturing variations that can induce reliability or safety issues. An important technology for battery quality control is computed tomography (CT) scanning, which is widely used for non-destructive 3D inspection across a variety of clinical and industrial applications. Historically, however, the utility of CT scanning for high-volume manufacturing has been limited by its low throughput as well as the difficulty of handling its large file sizes. In this work, we present a dataset of over one thousand CT scans of as-produced commercially available batteries. The dataset spans various chemistries (lithium-ion and sodium-ion) as well as various battery form factors (cylindrical, pouch, and prismatic). We evaluate seven different battery types in total. The manufacturing variability and the presence of battery defects can be observed via this dataset. This dataset may be of interest to scientists and engineers working on battery technology, computer vision, or both.

Comparison of deep learning architectures for predicting amyloid positivity in Alzheimer's disease, mild cognitive impairment, and healthy aging, from T1-weighted brain structural MRI.

Abnormal ß-amyloid (Aß) accumulation in the brain is an early indicator of Alzheimer's disease (AD) and is typically assessed through invasive procedures such as PET (positron emission tomography) or CSF (cerebrospinal fluid) assays. As new anti-Alzheimer's treatments can now successfully target amyloid pathology, there is a growing interest in predicting Aß positivity (Aß+) from less invasive, more widely available types of brain scans, such as T1-weighted (T1w) MRI. Here we compare multiple approaches to infer Aß + from standard anatomical MRI: (1) classical machine learning algorithms, including logistic regression, XGBoost, and shallow artificial neural networks, (2) deep learning models based on 2D and 3D convolutional neural networks (CNNs), (3) a hybrid ANN-CNN, combining the strengths of shallow and deep neural networks, (4) transfer learning models based on CNNs, and (5) 3D Vision Transformers. All models were trained on paired MRI/PET data from 1,847 elderly participants (mean age: 75.1 yrs. ± 7.6SD; 863 females/984 males; 661 healthy controls, 889 with mild cognitive impairment (MCI), and 297 with Dementia), scanned as part of the Alzheimer's Disease Neuroimaging Initiative. We evaluated each model's balanced accuracy and F1 scores. While further tests on more diverse data are warranted, deep learning models trained on standard MRI showed promise for estimating Aß + status, at least in people with MCI. This may offer a potential screening option before resorting to more invasive procedures.

The severity of dry eye symptoms and risk factors among university students in Saudi Arabia: a cross-sectional study.

Dry eye syndrome (DES) is a tear film disorder caused by increased tear evaporation or decreased production. The heavy workload on the eye and the increased usage of digital screens may decrease blink frequency, leading to an increased evaporation rate and an upsurge in the incidence and severity of DES. This study aims to assess the severity of DES symptoms and the risk factors among university students. A cross-sectional study was conducted at Umm AlQura University to evaluate the severity of DES among students and explore its potential association with digital screen use. Validated questionnaires were used to assess the severity of DES and digital screen usage. The study included 457 participants, of which 13% had symptoms suggestive of severe DES. Furthermore, multiple risk factors had a significant association with the severity of DES, including gender, use of monitor filters, monitor and room brightness, and smoking habits. DES symptoms were prevalent among university students, particularly female students. Although there was no significant association with the duration of screen usage and collage distribution. Other factors however, such as the usage of screen monitors and the brightness of both the monitor and the room, were significantly associated with the severity of DES symptoms.

Consequences of eye movements for spatial selectivity.

What determines spatial tuning in the visual system? Standard views rely on the assumption that spatial information is directly inherited from the relative position of photoreceptors and shaped by neuronal connectivity.1,2 However, human eyes are always in motion during fixation,3,4,5,6 so retinal neurons receive temporal modulations that depend on the interaction of the spatial structure of the stimulus with eye movements. It has long been hypothesized that these modulations might contribute to spatial encoding,7,8,9,10,11,12 a proposal supported by several recent observations.13,14,15,16 A fundamental, yet untested, consequence of this encoding strategy is that spatial tuning is not hard-wired in the visual system but critically depends on how the fixational motion of the eye shapes the temporal structure of the signals impinging onto the retina. Here we used high-resolution techniques for eye-tracking17 and gaze-contingent display control18 to quantitatively test this distinctive prediction. We examined how contrast sensitivity, a hallmark of spatial vision, is influenced by fixational motion, both during normal active fixation and when the spatiotemporal stimulus on the retina is altered to mimic changes in fixational control. We showed that visual sensitivity closely follows the strength of the luminance modulations delivered within a narrow temporal bandwidth, so changes in fixational motion have opposite visual effects at low and high spatial frequencies. By identifying a key role for oculomotor activity in spatial selectivity, these findings have important implications for the perceptual consequences of abnormal eye movements, the sources of perceptual variability, and the function of oculomotor control.

Deep learning of Parkinson's movement from video, without human-defined measures.

BACKGROUND: The core clinical sign of Parkinson's disease (PD) is bradykinesia, for which a standard test is finger tapping: the clinician observes a person repetitively tap finger and thumb together. That requires an expert eye, a scarce resource, and even experts show variability and inaccuracy. Existing applications of technology to finger tapping reduce the tapping signal to one-dimensional measures, with researcher-defined features derived from those measures. OBJECTIVES: (1) To apply a deep learning neural network directly to video of finger tapping, without human-defined measures/features, and determine classification accuracy for idiopathic PD versus controls. (2) To visualise the features learned by the model. METHODS: 152 smartphone videos of 10s finger tapping were collected from 40 people with PD and 37 controls. We down-sampled pixel dimensions and videos were split into 1 s clips. A 3D convolutional neural network was trained on these clips. RESULTS: For discriminating PD from controls, our model showed training accuracy 0.91, and test accuracy 0.69, with test precision 0.73, test recall 0.76 and test AUROC 0.76. We also report class activation maps for the five most predictive features. These show the spatial and temporal sections of video upon which the network focuses attention to make a prediction, including an apparent dropping thumb movement distinct for the PD group. CONCLUSIONS: A deep learning neural network can be applied directly to standard video of finger tapping, to distinguish PD from controls, without a requirement to extract a one-dimensional signal from the video, or pre-define tapping features.

Survey on pattern of myopia in school children in Hangzhou after the COVID-19 pandemic: a school-based vision screening study.

BACKGROUND: Myopia is a major health issue around the world. Myopia in children has increased significantly during the COVID-19 pandemic in China, but reports are scarce on the prevalence of myopia following the pandemic. This study collected vision screening data of school children in China for five consecutive years to observe the changes in myopia after the pandemic and compare the observed prevalence of myopia before and after the pandemic. METHODS: A school-based vision screening study used stratified samplings to collect the vision screening data in school children aged 6-13 from 45 primary schools in Hangzhou. Vision screening data including uncorrected visual acuity(UCVA) and spherical equivalent refraction(SER). Calculating the mean of SER and the prevalence of myopia and hyperopia from 2019 to 2023. RESULTS: A total of 79,068 screening results (158,136 eyes) were included in the analysis. A substantial myopic shift (approximately -0.30 diopters [D] on average) was found in 2020 and 2021 compared with 2019 in all age groups and a substantial myopic shift (approximately 0.4 D on average) was found in 2022 compared with 2021. A slight myopic shift (approximately -0.14 D on average) was found in 2023 compared with 2022. The prevalence of myopia in all age groups was the highest for five years in 2020 or 2021, which was 31.3% for 6-year-olds, 43.0% for 7-year-olds, and 53.7% for 8-year-olds. A positive change in the prevalence rate of myopia was found at 6 years old (0.59%, 0.12%, 0.36%, 0.25%, p < 0.001). The change in prevalence rate in myopia was shifted slightly in children aged 10-13 years. Children aged 8 to 13 years had a slight increase in myopia prevalence from 2022 to 2023. The prevalence of hyperopia was low and stable in all grade groups, ranging from 0.7% to 2.2% over five years. CONCLUSION: Myopia in children has increased rapidly during the COVID-19 pandemic. After the pandemic, the prevalence of myopia in children gradually decreased temporarily and then rebounded. Myopic shift was more apparent in younger children. Myopic shift in children may be related to the reduction of outdoor time, less light, and near work habits, and further research is needed.

Visual, ocular surface, and extraocular diagnostic criteria for determining the prevalence of computer vision syndrome: a cross-sectional smart-survey-based study.

Background: The American Optometric Association defines computer vision syndrome (CVS), also known as digital eye strain, as "a group of eye- and vision-related problems that result from prolonged computer, tablet, e-reader and cell phone use". We aimed to create a well-structured, valid, and reliable questionnaire to determine the prevalence of CVS, and to analyze the visual, ocular surface, and extraocular sequelae of CVS using a novel and smart self-assessment questionnaire. Methods: This multicenter, observational, cross-sectional, descriptive, survey-based, online study included 6853 complete online responses of medical students from 15 universities. All participants responded to the updated, online, fourth version of the CVS questionnaire (CVS-F4), which has high validity and reliability. CVS was diagnosed according to five basic diagnostic criteria (5DC) derived from the CVS-F4. Respondents who fulfilled the 5DC were considered CVS cases. The 5DC were then converted into a novel five-question self-assessment questionnaire designated as the CVS-Smart. Results: Of 10 000 invited medical students, 8006 responded to the CVS-F4 survey (80% response rate), while 6853 of the 8006 respondents provided complete online responses (85.6% completion rate). The overall CVS prevalence was 58.78% (n = 4028) among the study respondents; CVS prevalence was higher among women (65.87%) than among men (48.06%). Within the CVS group, the most common visual, ocular surface, and extraocular complaints were eye strain, dry eye, and neck/shoulder/back pain in 74.50% (n = 3001), 58.27% (n = 2347), and 80.52% (n = 3244) of CVS cases, respectively. Notably, 75.92% (3058/4028) of CVS cases were involved in the Mandated Computer System Use Program. Multivariate logistic regression analysis revealed that the two most statistically significant diagnostic criteria of the 5DC were ≥2 symptoms/attacks per month over the last 12 months (odds ratio [OR] = 204177.2; P <0.0001) and symptoms/attacks associated with screen use (OR = 16047.34; P <0.0001). The CVS-Smart demonstrated a Cronbach's alpha reliability coefficient of 0.860, Guttman split-half coefficient of 0.805, with perfect content and construct validity. A CVS-Smart score of 7-10 points indicated the presence of CVS. Conclusions: The visual, ocular surface, and extraocular diagnostic criteria for CVS constituted the basic components of CVS-Smart. CVS-Smart is a novel, valid, reliable, subjective instrument for determining CVS diagnosis and prevalence and may provide a tool for rapid periodic assessment and prognostication. Individuals with positive CVS-Smart results should consider modifying their lifestyles and screen styles and seeking the help of ophthalmologists and/or optometrists. Higher institutional authorities should consider revising the Mandated Computer System Use Program to avoid the long-term consequences of CVS among university students. Further research must compare CVS-Smart with other available metrics for CVS, such as the CVS questionnaire, to determine its test-retest reliability and to justify its widespread use.

Aflibercept or ranibizumab for diabetic macular edema.

Background: Vascular endothelial growth factor (VEGF) is the primary substance involved in retinal barrier breach. VEGF overexpression may cause diabetic macular edema (DME). Laser photocoagulation of the macula is the standard treatment for DME; however, recently, intravitreal anti-VEGF injections have surpassed laser treatment. Our aim was to evaluate the efficacy of intravitreal injections of aflibercept or ranibizumab for managing treatment-naive DME. Methods: This single-center, retrospective, interventional, comparative study included eyes with visual impairment due to treatment-naive DME that underwent intravitreal injection of either aflibercept 2 mg/0.05 mL or ranibizumab 0.5 mg/0.05 mL at Al-Azhar University Hospitals, Egypt between March 2023 and January 2024. Demographic data and full ophthalmological examination results at baseline and 1, 3, and 6 months post-injection were collected, including the best-corrected distance visual acuity (BCDVA) in logarithm of the minimum angle of resolution (logMAR) notation, slit-lamp biomicroscopy, dilated fundoscopy, and central subfield thickness (CST) measured using spectral-domain optical coherence tomography. Results: Overall, the 96 eyes of 96 patients with a median (interquartile range [IQR]) age of 57 (10) (range: 20-74) years and a male-to-female ratio of 1:2.7 were allocated to one of two groups with comparable age, sex, diabetes mellitus duration, and presence of other comorbidities (all P >0.05). There was no statistically significant difference in baseline diabetic retinopathy status or DME type between groups (both P >0.05). In both groups, the median (IQR) BCDVA significantly improved from 0.7 (0.8) logMAR at baseline to 0.4 (0.1) logMAR at 6 months post-injection (both P = 0.001), with no statistically significant difference between groups at all follow-up visits (all P >0.05). The median (IQR) CST significantly decreased in the aflibercept group from 347 (166) µm at baseline to 180 (233) µm at 6 months post-injection, and it decreased in the ranibizumab group from 360 (180) µm at baseline to 190 (224) µm at 6 months post-injection (both P = 0.001), with no statistically significant differences between groups at all follow-up visits (all P >0.05). No serious adverse effects were documented in either group. Conclusions: Ranibizumab and aflibercept were equally effective in achieving the desired anatomical and functional results in patients with treatment-naïve DME in short-term follow-up without significant differences in injection counts between both drugs. Larger prospective, randomized, double-blinded trials with longer follow-up periods are needed to confirm our preliminary results.

Fenofibrate and diabetic retinopathy.

Background: Diabetic retinopathy (DR), a sight-threatening ocular complication of diabetes mellitus, is one of the main causes of blindness in the working-age population. Dyslipidemia is a potential risk factor for the development or worsening of DR, with conflicting evidence in epidemiological studies. Fenofibrate, an antihyperlipidemic agent, has lipid-modifying and pleiotropic (non-lipid) effects that may lessen the incidence of microvascular events. Methods: Relevant studies were identified through a PubMed/MEDLINE search spanning the last 20 years, using the broad term "diabetic retinopathy" and specific terms "fenofibrate" and "dyslipidemia". References cited in these studies were further examined to compile this mini-review. These pivotal investigations underwent meticulous scrutiny and synthesis, focusing on methodological approaches and clinical outcomes. Furthermore, we provided the main findings of the seminal studies in a table to enhance comprehension and comparison. Results: Growing evidence indicates that fenofibrate treatment slows DR advancement owing to its possible protective effects on the blood-retinal barrier. The protective attributes of fenofibrate against DR progression and development can be broadly classified into two categories: lipid-modifying effects and non-lipid-related (pleiotropic) effects. The lipid-modifying effect is mediated through peroxisome proliferator-activated receptor-α activation, while the pleiotropic effects involve the reduction in serum levels of C-reactive protein, fibrinogen, and pro-inflammatory markers, and improvement in flow-mediated dilatation. In patients with DR, the lipid-modifying effects of fenofibrate primarily involve a reduction in lipoprotein-associated phospholipase A2 levels and the upregulation of apolipoprotein A1 levels. These changes contribute to the anti-inflammatory and anti-angiogenic effects of fenofibrate. Fenofibrate elicits a diverse array of pleiotropic effects, including anti-apoptotic, antioxidant, anti-inflammatory, and anti-angiogenic properties, along with the indirect consequences of these effects. Two randomized controlled trials-the Fenofibrate Intervention and Event Lowering in Diabetes and Action to Control Cardiovascular Risk in Diabetes studies-noted that fenofibrate treatment protected against DR progression, independent of serum lipid levels. Conclusions: Fenofibrate, an oral antihyperlipidemic agent that is effective in decreasing DR progression, may reduce the number of patients who develop vision-threatening complications and require invasive treatment. Despite its proven protection against DR progression, fenofibrate treatment has not yet gained wide clinical acceptance in DR management. Ongoing and future clinical trials may clarify the role of fenofibrate treatment in DR management.

RDH12 allows cone photoreceptors to regenerate opsin visual pigments from a chromophore precursor to escape competition with rods.

Capture of a photon by an opsin visual pigment isomerizes its 11-cis-retinaldehyde (11cRAL) chromophore to all-trans-retinaldehyde (atRAL), which subsequently dissociates. To restore light sensitivity, the unliganded apo-opsin combines with another 11cRAL to make a new visual pigment. Two enzyme pathways supply chromophore to photoreceptors. The canonical visual cycle in retinal pigment epithelial cells supplies 11cRAL at low rates. The photic visual cycle in Müller cells supplies cones with 11-cis-retinol (11cROL) chromophore precursor at high rates. Although rods can only use 11cRAL to regenerate rhodopsin, cones can use 11cRAL or 11cROL to regenerate cone visual pigments. We performed a screen in zebrafish retinas and identified ZCRDH as a candidate for the enzyme that converts 11cROL to 11cRAL in cone inner segments. Retinoid analysis of eyes from Zcrdh-mutant zebrafish showed reduced 11cRAL and increased 11cROL levels, suggesting impaired conversion of 11cROL to 11cRAL. By microspectrophotometry, isolated Zcrdh-mutant cones lost the capacity to regenerate visual pigments from 11cROL. ZCRDH therefore possesses all predicted properties of the cone 11cROL dehydrogenase. The human protein most similar to ZCRDH is RDH12. By immunocytochemistry, ZCRDH was abundantly present in cone inner segments, similar to the reported distribution of RDH12. Finally, RDH12 was the only mammalian candidate protein to exhibit 11cROL-oxidase catalytic activity. These observations suggest that RDH12 in mammals is the functional ortholog of ZCRDH, which allows cones, but not rods, to regenerate visual pigments from 11cROL provided by Müller cells. This capacity permits cones to escape competition from rods for visual chromophore in daylight-exposed retinas.