Nearly all organisms rely on natural fluctuations of light as cues for synchronizing physiological processes and behavioural actions associated with foraging, growth, sleep and rest, reproduction, and migration. Consequently, although artificial lighting sources have provided a plethora of benefits for humans, they can lead to disruptions for wild organisms. With one quarter of the human population living within 100 km of coastlines, there is great potential for artificial light at night (ALAN) to influence the physiology, behaviour and fitness of fishes. Through a review of the literature (n = 584 publications focused on the effects of ALAN on individual organisms or ecosystems), we illustrate that most papers have concentrated on terrestrial species (59%) compared with aquatic species (20%) or a mixed approach (21%). Fishes have been underrepresented in comparison with many other taxa such as birds, insects and mammals, representing the focus of less than 8% of taxa‐specific publications. While the number of publications per year focusing on fishes has generally been increasing since the mid‐2000s, there has been a downturn in publication rate in the last few years. To understand where research related to ALAN in fishes has been focused, we partitioned studies into categories and found that publications have mostly concerned behaviour (41.0%), abundance and community structure (24.4%), and physiology (22.8%), while the longer‐term effects on fitness (6.9%) are lacking. We synthesize the research completed in fishes and outline future priorities that will help ascertain the short‐ and long‐term consequences of this relatively novel stressor for fish health and persistence.

Purpose This study aims to identify the available literature describing the utilization of artificial intelligence (AI) as a clinical tool in uveal diseases. Methods A comprehensive literature search was conducted in 5 electronic databases, finding studies relating to AI and uveal diseases. Results After screening 10,258 studies,18 studies met the inclusion criteria. Uveal melanoma (44%) and uveitis (56%) were the two uveal diseases examined. Ten studies (56%) used complex AI, while 13 studies (72%) used regression methods. Lactate dehydrogenase (LDH), found in 50% of studies concerning uveal melanoma, was the only biomarker that overlapped in multiple studies. However, 94% of studies highlighted that the biomarkers of interest were significant. Conclusion This study highlights the value of using complex and simple AI tools as a clinical tool in uveal diseases. Particularly, complex AI methods can be used to weigh the merit of significant biomarkers, such as LDH, in order to create staging tools and predict treatment outcomes.

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Purpose This review focuses on utility of artificial intelligence (AI) in analysis of biofluid markers in glaucoma. We detail the accuracy and validity of AI in the exploration of biomarkers to provide insight into glaucoma pathogenesis. Methods A comprehensive search was conducted across five electronic databases including Embase, Medline, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, and Web of Science. Studies pertaining to biofluid marker analysis using AI or bioinformatics in glaucoma were included. Identified studies were critically appraised and assessed for risk of bias using the Joanna Briggs Institute Critical Appraisal tools. Results A total of 10,258 studies were screened and 39 studies met the inclusion criteria, including 23 cross-sectional studies (59%), nine prospective cohort studies (23%), six retrospective cohort studies (15%), and one case-control study (3%). Primary open angle glaucoma (POAG) was the most commonly studied subtype (55% of included studies). Twenty-four studies examined disease characteristics, 10 explored treatment decisions, and 5 provided diagnostic clarification. While studies examined at entire metabolomic or proteomic profiles to determine changes in POAG, there was heterogeneity in the data with over 175 unique, differentially expressed biomarkers reported. Discriminant analysis and artificial neural network predictive models displayed strong differentiating ability between glaucoma patients and controls, although these tools were untested in a clinical context. Conclusion The use of AI models could inform glaucoma diagnosis with high sensitivity and specificity. While insight into differentially expressed biomarkers is valuable in pathogenic exploration, no clear pathogenic mechanism in glaucoma has emerged.

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