Diabetic retinopathy (DR) is the leading cause of preventable blindness worldwide. The risk of DR progression is highly variable among different individuals, making it difficult to predict risk and personalize screening intervals. We developed and validated a deep learning system (DeepDR Plus) to predict time to DR progression within 5 years solely from fundus images. First, we used 717,308 fundus images from 179,327 participants with diabetes to pretrain the system. Subsequently, we trained and validated the system with a multiethnic dataset comprising 118,868 images from 29,868 participants with diabetes. For predicting time to DR progression, the system achieved concordance indexes of 0.754–0.846 and integrated Brier scores of 0.153–0.241 for all times up to 5 years. Furthermore, we validated the system in real-world cohorts of participants with diabetes. The integration with clinical workflow could potentially extend the mean screening interval from 12 months to 31.97 months, and the percentage of participants recommended to be screened at 1–5 years was 30.62%, 20.00%, 19.63%, 11.85% and 17.89%, respectively, while delayed detection of progression to vision-threatening DR was 0.18%. Altogether, the DeepDR Plus system could predict individualized risk and time to DR progression over 5 years, potentially allowing personalized screening intervals.

Primary diabetes care and diabetic retinopathy (DR) screening persist as major public health challenges due to a shortage of trained primary care physicians (PCPs), particularly in low-resource settings. Here, to bridge the gaps, we developed an integrated image–language system (DeepDR-LLM), combining a large language model (LLM module) and image-based deep learning (DeepDR-Transformer), to provide individualized diabetes management recommendations to PCPs. In a retrospective evaluation, the LLM module demonstrated comparable performance to PCPs and endocrinology residents when tested in English and outperformed PCPs and had comparable performance to endocrinology residents in Chinese. For identifying referable DR, the average PCP’s accuracy was 81.0% unassisted and 92.3% assisted by DeepDR-Transformer. Furthermore, we performed a single-center real-world prospective study, deploying DeepDR-LLM. We compared diabetes management adherence of patients under the unassisted PCP arm (n = 397) with those under the PCP+DeepDR-LLM arm (n = 372). Patients with newly diagnosed diabetes in the PCP+DeepDR-LLM arm showed better self-management behaviors throughout follow-up (P < 0.05). For patients with referral DR, those in the PCP+DeepDR-LLM arm were more likely to adhere to DR referrals (P < 0.01). Additionally, DeepDR-LLM deployment improved the quality and empathy level of management recommendations. Given its multifaceted performance, DeepDR-LLM holds promise as a digital solution for enhancing primary diabetes care and DR screening.

As a progressive chronic disease, age-related macular degeneration (AMD) is the leading cause of irreversible vision impairment worldwide. Experimental and clinical evidence has demonstrated that vascular endothelial growth factor (VEGF) plays a vital role in the formation of choroidal neovascularization. Intravitreal injections of anti-VEGF agents have been recommended as a first-line treatment for neovascular AMD. However, persistent fluid or recurrent exudation still occurs despite standardized anti-VEGF therapy. Patients suffering from refractory or recurrent neovascular AMD may develop mechanisms of resistance to anti-VEGF therapy, which results in a diminished therapeutic effect. Until now, there has been no consensus on the definitions of refractory neovascular AMD and recurrent neovascular AMD. This article aims at clarifying these concepts to evaluate the efficacy of switching drugs, which contributes to making clinical decision more scientifically. Furthermore, insight into the causes of resistance to anti-VEGF therapy would be helpful for developing possible therapeutic approaches, such as combination therapy and multi-target treatment that can overcome this resistance.

Exosomes have a rapid development of bio-nanoparticles for drug delivery and confluent advances in next-generation diagnostics, monitoring the progression of several diseases, and accurate guidance for therapy. Based on their prominent stability, cargo-carriage properties, stable circulating capability, and favorable safety profile, exosomes have great potential to regulate cellular communication by carrying variable cargoes into specific site. However, the specific loading strategies and modification methods for engineered exosomes to enhance the targeting ability are unclear. The clinical application of exosomes is still limited. In this review, we discuss both original and modified exosomes for loading specific therapeutic molecules (proteins, nucleic acids, and small molecules) and the design strategies used to target specific cells. This review can be used as a reference for further loading and modification strategies as well as for the therapeutic applications of exosomes.

Prime editor (PE), which is developed by combining Cas9 nickase and an engineered reverse transcriptase, can mediate all twelve types of base substitutions and small insertions or deletions in living cells but its efficiency remains low. Here, we develop spegRNA by introducing same-sense mutations at proper positions in the reverse-transcription template of pegRNA to increase PE’s base-editing efficiency up-to 4,976-fold (on-average 353-fold). We also develop apegRNA by altering the pegRNA secondary structure to increase PE’s indel-editing efficiency up-to 10.6-fold (on-average 2.77-fold). The spegRNA and apegRNA can be combined to further enhance editing efficiency. When spegRNA and apegRNA are used in PE3 and PE5 systems, the efficiencies of sPE3, aPE3, sPE5 and aPE5 systems are all enhanced significantly. The strategies developed in this study realize highly efficient prime editing at certain previously uneditable sites.

Cell transplantation is a potential therapeutic strategy for retinal degenerative diseases involving the loss of photoreceptors. However, it faces challenges to clinical translation due to safety concerns and a limited supply of cells. Human retinal progenitor cells (hRPCs) from fetal neural retina are expandable in vitro and maintain an undifferentiated state. This study aimed to investigate the therapeutic potential of hRPCs transplanted into a Royal College of Surgeons (RCS) rat model of retinal degeneration. At 12 weeks, optokinetic response showed that hRPC-grafted eyes had significantly superior visual acuity compared with vehicle-treated eyes. Histological evaluation of outer nuclear layer (ONL) characteristics such as ONL thickness, spread distance, and cell count demonstrated a significantly greater preservation of the ONL in hRPC-treated eyes compared with both vehicle-treated and control eyes. The transplanted hRPCs arrested visual decline over time in the RCS rat and rescued retinal morphology, demonstrating their potential as a therapy for retinal diseases. We suggest that the preservation of visual acuity was likely achieved through host photoreceptor rescue. We found that hRPC transplantation into the subretinal space of RCS rats was well tolerated, with no adverse effects such as tumor formation noted at 12 weeks after treatment.