Retinitis pigmentosa (RP) is a heterogeneous group of hereditary diseases characterized by progressive degeneration of retinal photoreceptors leading to progressive visual decline. It is the most common type of inherited retinal dystrophy and has a high burden on both patients and society. This condition causes gradual loss of vision, with its typical manifestations including nyctalopia, concentric visual field loss, and ultimately bilateral central vision loss. It is one of the leading causes of visual disability and blindness in people under 60 years old and affects over 1.5 million people worldwide. There is currently no curative treatment for people with RP, and only a small group of patients with confirmed RPE65 mutations are eligible to receive the only gene therapy on the market: voretigene neparvovec. The current therapeutic armamentarium is limited to retinoids, vitamin A supplements, protection from sunlight, visual aids, and medical and surgical interventions to treat ophthalmic comorbidities, which only aim to slow down the progression of the disease. Considering such a limited therapeutic landscape, there is an urgent need for developing new and individualized therapeutic modalities targeting retinal degeneration. Although the heterogeneity of gene mutations involved in RP makes its target treatment development difficult, recent fundamental studies showed promising progress in elucidation of the photoreceptor degeneration mechanism. The discovery of novel molecule therapeutics that can selectively target specific receptors or specific pathways will serve as a solid foundation for advanced drug development. This article is a review of recent progress in novel treatment of RP focusing on preclinical stage fundamental research on molecular targets, which will serve as a starting point for advanced drug development. We will review the alterations in the molecular pathways involved in the development of RP, mainly those regarding endoplasmic reticulum (ER) stress and apoptotic pathways, maintenance of the redox balance, and genomic stability. We will then discuss the therapeutic approaches under development, such as gene and cell therapy, as well as the recent literature identifying novel potential drug targets for RP.

Objective: To determine whether interictal epileptiform discharges (IEDs) on routine electroencephalography (EEG) predict seizure recurrence in adults with established epilepsy. Methods: We conducted a retrospective survival analysis of consecutive adults with epilepsy undergoing routine EEG at a tertiary center between 2018 and 2019. Using multivariate Cox proportional hazards models guided by a directed acyclic graph and adjusted for confounders including past seizure frequency and duration of epilepsy, we estimated the association between the presence of IEDs and time to next seizure, stratified by epilepsy type. Results: We included 488 consecutive routine EEG studies from 438 patients. Over a median follow‐up of 124.5 weeks, seizures recurred in 50.4% of cases. The presence of IEDs was associated with increased seizure risk in both focal (adjusted hazard ratio [aHR] = 1.47, 95% confidence interval [CI]: 1.01–2.15, p = .043) and generalized epilepsy (aHR = 1.82, 95% CI: 1.08–3.06, p = .024). Significance: IEDs on routine EEG independently predict increased seizure risk in adults with epilepsy, with a stronger effect in generalized epilepsies. This suggests that routine EEG may have prognostic value during epilepsy follow‐up and warrant further investigations as a potential prognostic biomarker to help inform clinical decision‐making.

Background and Objectives The diagnostic yield of routine EEG in epilepsy is limited by low sensitivity and the potential for misinterpretation of interictal epileptiform discharges (IEDs). Our objective is to develop, train, and validate a deep learning model that can identify epilepsy from routine EEG recordings, complementing traditional IED-based interpretation. Methods This is a retrospective cohort study of diagnostic accuracy. All consecutive patients undergoing routine EEG at our tertiary care center between January 2018 and September 2019 were included. EEGs recorded between July 2019 and September 2019 constituted a temporally shifted testing cohort. The diagnosis of epilepsy was established by the treating neurologist at the end of the available follow-up period, based on clinical file review. Original EEG reports were reviewed for IEDs. We developed seven novel deep learning models based on Vision Transformers (ViT) and Convolutional Neural Networks (CNN), training them to classify raw EEG recordings. We compared their performance to IED-based interpretation and two previously proposed machine learning methods. Results The study included 948 EEGs from 846 patients (820 EEGs/728 patients in training/validation, 128 EEGs/118 patients in testing). Median follow-up was 2.2 years and 1.7 years in each cohort, respectively. Our flagship ViT model, DeepEpilepsy, achieved an area under the receiver operating characteristic curve (AUROC) of 0.76 (95% CI: 0.69–0.83), outperforming IED-based interpretation (0.69; 0.64–0.73) and previous methods. Combining DeepEpilepsy with IEDs increased the AUROC to 0.83 (0.77–0.89). Discussion DeepEpilepsy can identify epilepsy on routine EEG independently of IEDs, suggesting that deep learning can detect novel EEG patterns relevant to epilepsy diagnosis. Further research is needed to understand the exact nature of these patterns and evaluate the clinical impact of this increased diagnostic yield in specific settings.

This paper initiates the study of fractional eternal domination in graphs, a natural relaxation of the well-studied eternal domination problem. We study the connections to flows and linear programming in order to obtain results on the complexity of determining the fractional eternal domination number of a graph $G$, which we denote $γ_{\,\textit{f}}^{\infty}(G)$. We study the behaviour of $γ_{\,\textit{f}}^{\infty}(G)$ as it relates to other domination parameters. We also determine bounds on, and in some cases exact values for, $γ_{\,\textit{f}}^{\infty}(G)$ when $G$ is a member of one of a variety of important graph classes, including trees, split graphs, strongly chordal graphs, Kneser graphs, abelian Cayley graphs, and graph products.