The retinal pigment epithelium (RPE) is a highly specialized and polarized epithelial cell layer that plays an important role in sustaining the structural and functional integrity of photoreceptors. However, the death of RPE is a common pathological feature in various retinal diseases, especially in age-related macular degeneration (AMD) and diabetic retinopathy (DR). Mitophagy, as a programmed self-degradation of dysfunctional mitochondria, is crucial for maintaining cellular homeostasis and cell survival under stress. RPE contains a high density of mitochondria necessary for it to meet energy demands, so severe stimuli can cause mitochondrial dysfunction and the excess generation of intracellular reactive oxygen species (ROS), which can further trigger oxidative stress-involved mitophagy. In this review, we summarize the classical pathways of oxidative stress-involved mitophagy in RPE and investigate its role in the progression of retinal diseases, aiming to provide a new therapeutic strategy for treating retinal degenerative diseases. Graphical Abstract The role of mitophagy in AMD and DR. In AMD, excessive ROS production promotes mitophagy in the RPE by activating the Nrf2/p62 pathway, while in DR, ROS may suppress mitophagy by the FOXO3-PINK1/parkin signaling pathway or the TXNIP-mitochondria-lysosome-mediated mitophagy.

Age-related macular degeneration (AMD) is a leading cause of severe visual loss and irreversible blindness in the elderly population worldwide. Retinal pigment epithelial (RPE) cells are the major site of pathological alterations in AMD. They are responsible for the phagocytosis of shed photoreceptor outer segments (POSs) and clearance of cellular waste under physiological conditions. Age-related, cumulative oxidative stimuli contribute to the pathogenesis of AMD. Excessive oxidative stress induces RPE cell degeneration and incomplete digestion of POSs, leading to the continuous accumulation of cellular waste (such as lipofuscin). Autophagy is a major system of degradation of damaged or unnecessary proteins. However, degenerative RPE cells in AMD patients cannot perform autophagy sufficiently to resist oxidative damage. Increasing evidence supports the idea that enhancing the autophagic process can properly alleviate oxidative injury in AMD and protect RPE and photoreceptor cells from degeneration and death, although overactivated autophagy may lead to cell death at early stages of retinal degenerative diseases. The crosstalk among the NFE2L2, PGC-1, p62, AMPK, and PI3K/Akt/mTOR pathways may play a crucial role in improving disturbed autophagy and mitigating the progression of AMD. In this review, we discuss how autophagy prevents oxidative damage in AMD, summarize potential neuroprotective strategies for therapeutic interventions, and provide an overview of these neuroprotective mechanisms.

Background: Excessive light exposure is a detrimental environmental factor that plays a critical role in the pathogenesis of retinal degeneration. However, the mechanism of light-induced death of retina/photoreceptor cells remains unclear. The mammalian/mechanistic target of rapamycin (mTOR) and Poly (ADP-ribose) polymerase-1 (PARP-1) have become the primary targets for treating many neurodegenerative disorders. The aim of this study was to elucidate the mechanisms underlying light-induced photoreceptor cell death and whether the neuroprotective effects of mTOR and PARP-1 inhibition against death are mediated through apoptosis-inducing factor (AIF). Methods: Propidium iodide (PI)/Hoechst staining, lentiviral-mediated short hairpin RNA (shRNA), Western blot analysis, cellular fraction separation, plasmid transient transfection, laser confocal microscopy, a mice model, electroretinography (ERG), and hematoxylin-eosin (H & E) staining were employed to explore the mechanisms by which rapamycin/3-Aminobenzamide (3AB) exert neuroprotective effects of mTOR/PARP-1 inhibition in light-injured retinas. Results: A parthanatos-like death mechanism was evaluated in light-injured 661 W cells that are an immortalized photoreceptor-like cell line that exhibit cellular and biochemical feature characteristics of cone photoreceptor cells. The death process featured over-activation of PARP-1 and AIF nuclear translocation. Either PARP-1 or AIF knockdown played a significantly protective role for light-damaged photoreceptors. More importantly, crosstalk was observed between mTOR and PARP-1 signaling and mTOR could have regulated parthanatos via the intermediate factor sirtuin 1 (SIRT1). The parthanatos-like injury was also verified in vivo, wherein either PARP-1 or mTOR inhibition provided significant neuroprotection against light-induced injury, which is evinced by both structural and functional retinal analysis. Overall, these results elucidate the mTOR-regulated parthanatos death mechanism in light-injured photoreceptors/retinas and may facilitate the development of novel neuroprotective therapies for retinal degeneration diseases. Conclusions: Our results demonstrate that inhibition of the mTOR/PARP-1 axis exerts protective effects on photoreceptors against visible-light–induced parthanatos. These protective effects are conducted by regulating the downstream factors of AIF, while mTOR possibly interacts with PARP-1 via SIRT1 to regulate parthanatos. Graphical Schematic diagram of mTOR interacting with PARP-1 to regulate visible light-induced parthanatos. Increased ROS caused by light exposure penetrates the nuclear membrane and causes nuclear DNA strand breaks. PARP-1 detects DNA breaks and synthesizes PAR polymers to initiate the DNA repair system that consumes a large amount of cellular NAD+. Over-production of PAR polymers prompts the release of AIF from the mitochondria and translocation to the nucleus, which leads to parthanatos. Activated mTOR may interact with PARP-1 via SIRT1 to regulate visible light-induced parthanatos.

Natural visible light is an electromagnetic wave composed of a spectrum of monochromatic wavelengths, each with a characteristic color. Photons are the basic units of light, and their wavelength correlates to the energy of light; short‐wavelength photons carry high energy. The retina is a fragile neuronal tissue that senses light and generates visual signals conducted to the brain. However, excessive and intensive light exposure will cause retinal light damage. Within the visible spectrum, short‐wavelength light, such as blue light, carries higher energy, and thus the retinal injury, is more significant when exposed to these wavelengths. The damage mechanism triggered by different short‐wavelength light varies due to photons carrying different energy and being absorbed by different photosensitive molecules in the retinal neurons. However, photooxidation might be a common molecular step to initiate cell death. Herein, we summarize the historical understanding of light, the key molecular steps related to retinal light injury, and the death pathways of photoreceptors to further decipher the molecular mechanism of retinal light injury and explore potential neuroprotective strategies.

Purpose: The incidence of orbital blowout fractures (OBFs) is gradually increasing due to traffic accidents, sports injuries, and ocular trauma. Orbital computed tomography (CT) is crucial for accurate clinical diagnosis. In this study, we built an artificial intelligence (AI) system based on two available deep learning networks (DenseNet-169 and UNet) for fracture identification, fracture side distinguishment, and fracture area segmentation. Methods: We established a database of orbital CT images and manually annotated the fracture areas. DenseNet-169 was trained and evaluated on the identification of CT images with OBFs. We also trained and evaluated DenseNet-169 and UNet for fracture side distinguishment and fracture area segmentation. We used cross-validation to evaluate the performance of the AI algorithm after training. Results: For fracture identification, DenseNet-169 achieved an area under the receiver operating characteristic curve (AUC) of 0.9920 ± 0.0021, with an accuracy, sensitivity, and specificity of 0.9693 ± 0.0028, 0.9717 ± 0.0143, and 0.9596 ± 0.0330, respectively. DenseNet-169 realized the distinguishment of the fracture side with accuracy, sensitivity, specificity, and AUC of 0.9859 ± 0.0059, 0.9743 ± 0.0101, 0.9980 ± 0.0041, and 0.9923 ± 0.0008, respectively. The intersection over union (IoU) and Dice coefficient of UNet for fracture area segmentation were 0.8180 ± 0.0093 and 0.8849 ± 0.0090, respectively, showing a high agreement with manual segmentation. Conclusions: The trained AI system could realize the automatic identification and segmentation of OBFs, which might be a new tool for smart diagnoses and improved efficiencies of three-dimensional (3D) printing-assisted surgical repair of OBFs. Translational Relevance: Our AI system, based on two available deep learning network models, could help in precise diagnoses and accurate surgical repairs.