LensAge index as a deep learning-based biological age for self-monitoring the risks of age-related diseases and mortality.

Age is closely related to human health and disease risks. However, chronologically defined age often disagrees with biological age, primarily due to genetic and environmental variables. Identifying effective indicators for biological age in clinical practice and self-monitoring is important but currently lacking. The human lens accumulates age-related changes that are amenable to rapid and objective assessment. Here, using lens photographs from 20 to 96-year-olds, we develop LensAge to reflect lens aging via deep learning. LensAge is closely correlated with chronological age of relatively healthy individuals (R2 > 0.80, mean absolute errors of 4.25 to 4.82 years). Among the general population, we calculate the LensAge index by contrasting LensAge and chronological age to reflect the aging rate relative to peers. The LensAge index effectively reveals the risks of age-related eye and systemic disease occurrence, as well as all-cause mortality. It outperforms chronological age in reflecting age-related disease risks (p < 0.001). More importantly, our models can conveniently work based on smartphone photographs, suggesting suitability for routine self-examination of aging status. Overall, our study demonstrates that the LensAge index may serve as an ideal quantitative indicator for clinically assessing and self-monitoring biological age in humans.

Application of Artificial Intelligence in Precision Medicine for Diabetic Macular Edema.

Diabetic macular edema (DME) is the primary cause of central vision impairment in patients with diabetes and the leading cause of preventable blindness in working-age people. With the advent of optical coherence tomography and antivascular endothelial growth factor (anti-VEGF) therapy, the diagnosis, evaluation, and treatment of DME were greatly revolutionized in the last decade. However, there is tremendous heterogeneity among DME patients, and 30%-50% of DME patients do not respond well to anti-VEGF agents. In addition, there is no evidence-based and universally accepted administration regimen. The identification of DME patients not responding to anti-VEGF agents and the determination of the optimal administration interval are the 2 major challenges of DME, which are difficult to achieve with the coarse granularity of conventional health care modality. Therefore, more and more retina specialists have pointed out the necessity of introducing precision medicine into the management of DME and have conducted related studies in recent years. One of the most frontier methods is the targeted extraction of individualized disease features from optical coherence tomography images based on artificial intelligence technology, which provides precise evaluation and risk classification of DME. This review aims to provide an overview of the progress of artificial intelligence-enabled precision medicine in automated screening, precise evaluation, prognosis prediction, and follow-up monitoring of DME. Further, the challenges ahead of real-world applications and the future development of precision medicine in DME will be discussed.

UFL1 alleviates ER stress and apoptosis stimulated by LPS via blocking the ferroptosis pathway in human granulosa-like cells.

Ubiquitin-like modifier 1 ligating enzyme 1 (UFL1) is a unique E3 ligase of the UFMylation system. Recent studies have shown that this enzyme plays a crucial role in the processes of endoplasmic reticulum stress (ER stress) and apoptosis. Lipopolysaccharide (LPS) can cause injury to ovarian granule cells and hinder follicular development by triggering ER stress and apoptosis. Our study aimed to investigate the mechanism by which UFL1 alleviates ER stress and apoptosis caused by LPS in human granulosa-like cells (KGNs). In this study, we found that the protein levels of UFL1 were increased obviously under LPS stimulation in KGNs and that ER stress and apoptosis were further aggravated when UFL1 was knocked down; in contrast, these events were rescued when UFL1 was overexpressed. Next, we showed that the levels of ferroptosis-related proteins were relatively altered, accompanied by the accumulation of reactive oxygen species (ROS) and Fe2+, following the inhibition of UFL1 expression. In contrast, the overexpression of UFL1 reversed the ferroptosis process by regulating the P53/SLC7A11 (solute carrier family 7, member 11, SLC7A11) system and autophagy in response to LPS stimulation. Furthermore, apoptosis and ER stress in KGNs are rescued by the administration of the ferroptosis inhibitor ferrostatin-1 (Fer-1). Collectively, our research demonstrated a new mechanism for UFL1 that can alleviate ER stress and apoptosis stimulated by LPS; this occurred via the regulation of the ferroptosis pathway in KGNs and may provide a new strategy for research in the field of reproduction.