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.

To increase the in vivo stability of cationic gene carriers and avoid the adverse effects of their positive charge, we synthesized a new shielding material by conjugating low molecular weight polyethylene glycol (PEG) to a hyaluronic acid (HA) core. The HA-PEG conjugate assembled with the positively charged complex, forming a protective layer through electrostatic interactions. DNA/polyetherimide/HA-PEG (DNA/PEI/HA-PEG) nanoparticles had higher stability than both DNA/polyethyleneimine (DNA/PEI) and DNA/PEI/HA complexes. Furthermore, DNA/PEI/HA-PEG nanoparticles also showed a diminished nonspecific response toward serum proteins in vivo. The in vivo transfection efficiency was also enhanced by the low cytotoxicity and the improved stability; therefore, this material might be promising for use in gene delivery applications.

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.

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Orbital and eyelid disorders affect normal visual functions and facial appearance, and precise oculoplastic and reconstructive surgeries are crucial. Artificial intelligence (AI) network models exhibit a remarkable ability to analyze large sets of medical images to locate lesions. Currently, AI-based technology can automatically diagnose and grade orbital and eyelid diseases, such as thyroid-associated ophthalmopathy (TAO), as well as measure eyelid morphological parameters based on external ocular photographs to assist surgical strategies. The various types of imaging data for orbital and eyelid diseases provide a large amount of training data for network models, which might be the next breakthrough in AI-related research. This paper retrospectively summarizes different imaging data aspects addressed in AI-related research on orbital and eyelid diseases, and discusses the advantages and limitations of this research field.