Deep learning-based normative database of anterior chamber dimensions for angle closure assessment: the Singapore Chinese Eye Study.

BACKGROUND/ AIMS: The lack of context for anterior segment optical coherence tomography (ASOCT) measurements impedes its clinical utility. We established the normative distribution of anterior chamber depth (ACD), area (ACA) and width (ACW) and lens vault (LV), and applied percentile cut-offs to detect primary angle closure disease (PACD; ≥180° posterior trabecular meshwork occluded). METHODS: We included subjects from the Singapore Chinese Eye Study with ASOCT scans. Eyes with ocular surgery or laser procedures, and ocular trauma were excluded. A deep-learning algorithm was used to obtain Visante ASOCT (Carl Zeiss Meditec, USA) measurements. Normative distribution was established using 80% of eyes with open angles. Multivariable logistic regression was performed on 80% open and 80% angle closure eyes. Diagnostic performance was evaluated using 20% open and 20% angle closure eyes. RESULTS: We included 2157 eyes (1853 open angles; 304 angle closure) for analysis. ACD, ACA and ACW decreased with age and were smaller in females, and vice versa for LV (all p<0.022). ACD 20th percentile and LV 85th percentile had a balanced accuracy of 84.4% and 84.2% in detecting PACD, respectively. When combined, ACD 20th and LV 85th percentile had 88.68% sensitivity and 88.85% specificity in detecting PACD as compared with a multivariable regression model (ACA, angle opening distance, LV, iris area) with 88.33% sensitivity and 83.75% specificity. CONCLUSION: Anterior chamber parameters varied with age and gender. The ACD 20th and LV 85th percentile values may be used in silos or in combination to detect PACD in the absence of more sophisticated classification algorithms.

Photosensitizing CNTs by organotin(IV) compounds: generation of reactive oxygen species and degradation of amoxicillin.

This work is based on probing photosensitization in carbon nanotubes (CNTs) by organotin(IV) compounds to fabricate a hybrid material with excellent photocatalytic activity and generation of reactive oxygen species. Two organotin(IV) compounds (compounds 1 and 2) were synthesized and characterized by spectroscopic and spectrometric studies, elemental analysis and single crystal X-ray diffraction followed by their impregnation inside the CNTs. The so obtained hybrid materials (1@CNT and 2@CNT) were characterized by FTIR, TGA, FE-SEM, HR-TEM, PXRD and XPS analysis, and assessed for photosensitization and generation of reactive oxygen species. The enhanced photocatalytic activity of the fabricated materials in comparison to bare CNTs is attributed to the reduction of band gap and suppression of rapid recombination rates due to the encapsulation of photogenerated electrons. The generation of reactive species in photocatalyst 1@CNT was validated by the degradation of Amoxicillin (AMX) under optimized conditions for catalytic dosage, H2O2 concentration, response time and pH. The material 1@CNT could degrade ca. 83% of AMX by generating free radicals (˙OH and ˙O2-) under visible light irradiation at pH 6 as investigated by UV-visible spectroscopy and supported by EPR and DFT studies. Furthermore, the structural stability and sustained photocatalytic properties of 1@CNT over four cycles highlight its potential as an eco-friendly solution for degrading environmental toxins.

Standalone Highly Efficient Graphene Oxide as an Emerging Visible Light-Driven Photocatalyst and Recyclable Adsorbent for the Sustainable Removal of Organic Pollutants.

Carbon-based nanostructures are promising eco-friendly multifunctional nanomaterials because of their tunable surface and optoelectronic properties for a variety of energy and environmental applications. The present study focuses on the synthesis of graphene oxide (GO) with particular emphasis on engineering its surface and optical properties for making it an excellent adsorbent as well as a visible light-active photocatalyst. It was achieved by modifying the improved Hummers method through optimizing the synthesis parameters involved in the oxidation process. This controlled synthesis allows for systematic tailoring of structural, optical, and surface functionality, leading to improved adsorption and photocatalytic properties for the sustainable removal of organic pollutants in water treatment. Several spectroscopic and microscopic characterization techniques, such as XRD, SEM, Raman, UV-visible, FTIR, TEM, XPS, BET, etc. were employed to analyze the degree of oxidation, surface chemistry/functionalization, morphological, optical, and structural properties of the synthesized GO nanostructures. The analyses showed excellent surface functionality with surface active sites for better adsorptive removal and a tunable band gap from 2.51 to 2.76 eV exhibiting excellent natural sunlight activity (>99%) for photocatalytic removal of the organic pollutant. Various adsorption isotherms have been studied with excellent adsorption capability (Qmax = 454.54 mg/g) as compared to the literature. The study introduces GO both as a proficient stand-alone (sole) nanoadsorbent as well as a nanophotocatalyst for the efficient removal of organic dye pollutants in water treatment. Additionally, the article highlights the sustainable solar light-induced green chemistry aspects of GO as an excellent recyclable adsorbent as a result of its self-cleaning ability under natural sunlight, demonstrating its potential in real eco-friendly environmental and practical applications.

Prognostic potentials of AI in ophthalmology: systemic disease forecasting via retinal imaging.

BACKGROUND: Artificial intelligence (AI) that utilizes deep learning (DL) has potential for systemic disease prediction using retinal imaging. The retina's unique features enable non-invasive visualization of the central nervous system and microvascular circulation, aiding early detection and personalized treatment plans for personalized care. This review explores the value of retinal assessment, AI-based retinal biomarkers, and the importance of longitudinal prediction models in personalized care. MAIN TEXT: This narrative review extensively surveys the literature for relevant studies in PubMed and Google Scholar, investigating the application of AI-based retina biomarkers in predicting systemic diseases using retinal fundus photography. The study settings, sample sizes, utilized AI models and corresponding results were extracted and analysed. This review highlights the substantial potential of AI-based retinal biomarkers in predicting neurodegenerative, cardiovascular, and chronic kidney diseases. Notably, DL algorithms have demonstrated effectiveness in identifying retinal image features associated with cognitive decline, dementia, Parkinson's disease, and cardiovascular risk factors. Furthermore, longitudinal prediction models leveraging retinal images have shown potential in continuous disease risk assessment and early detection. AI-based retinal biomarkers are non-invasive, accurate, and efficient for disease forecasting and personalized care. CONCLUSION: AI-based retinal imaging hold promise in transforming primary care and systemic disease management. Together, the retina's unique features and the power of AI enable early detection, risk stratification, and help revolutionizing disease management plans. However, to fully realize the potential of AI in this domain, further research and validation in real-world settings are essential.

Selective laser trabeculoplasty: An updated narrative review.

Selective laser trabeculoplasty (SLT) has experienced a resurgence in interest, primarily driven by promising findings from the Laser in Glaucoma and Ocular Hypertension Trial. By offering SLT as an initial drug-free treatment option, we may be able to thwart issues such as adherence and persistence that plague our current medical management protocols. In this comprehensive narrative review, we delve into the current body of literature that explores the utility of SLT across a wide spectrum of scenarios and glaucoma subtypes. We present evidence that provides valuable insight into the efficacy and benefits of SLT, positioning it as a viable option in the management of glaucoma. Careful consideration of the associated risks and challenges is also necessary for successful adoption into clinical practice. Despite the ample evidence supporting SLT's efficacy, some questions remain regarding its long-term effects and the potential need for retreatment. This review aims to shed light on these aspects to guide clinicians in making informed decisions and tailoring treatment plans to individual patient needs. This review also provides the readers with a bird's eye view of the potential impact of SLT and adds clarity to the various therapeutic protocols that one can follow to ensure optimal clinical outcomes for our patients.

Graphene oxide as an emerging sole adsorbent and photocatalyst: Chemistry of synthesis and tailoring properties for removal of emerging contaminants.

Contaminants of emerging concern (CEC) contain a wide range of compounds, such as pharmaceutical waste, pesticides, herbicides, industrial chemicals, organic dyes, etc. Their presence in the surrounding has extensive and multifaceted effects on human health as they have the potential to persist in the environment, accumulate in biota, and disrupt ecosystems. In this regard, various remediation methods involving different kind of functional nanomaterials with unique properties have been developed. The functional nanomaterials can provide several mechanisms for water pollutant removal, such as adsorption, catalysis, and disinfection, in a single platform. Graphene oxide (GO) is a two-dimensional carbon-based material that has an extremely large surface area and a large number of active sites. Recent advances in synthesising GO have shown great progress in tailoring its various physiochemical, optical, surface, structural properties etc., making it better adsorbent and photocatalysts. In this review, sole adsorbent and standalone photocatalytic performances of GO for the removal of CEC have been discussed in light of tailoring its adsorption and photocatalytic properties through novel synthesis routes and optimizing synthesis parameters. This review also examines various models describing the structure of GO and its surface/structural modifications for improved adsorption and photocatalytic properties. The article provides valuable information for the production of efficient and cost-effective GO-based sole adsorbents and photocatalysts as compared to the traditional materials. Furthermore, future prospective and challenges for sole GO nanostructures to compete with traditional adsorbents and photocatalysts have been discussed providing interesting avenues for future research.

Six-Year Incidence and Risk Factors for Primary Open-Angle Glaucoma and Ocular Hypertension: The Singapore Epidemiology of Eye Diseases Study.

OBJECTIVE: To determine the incidence and risk factors for primary open-angle glaucoma (POAG) and ocular hypertension (OHT) in a multiethnic Asian population. DESIGN: Population-based cohort study. PARTICIPANTS: The Singapore Epidemiology of Eye Diseases study included 10 033 participants in the baseline examination between 2004 and 2011. Of those, 6762 (response rate = 78.8%) participated in the 6-year follow-up visit between 2011 and 2017. METHODS: Standardized examination and investigations were performed, including slit lamp biomicroscopy, intraocular pressure (IOP) measurement, pachymetry, gonioscopy, optic disc examination and static automated perimetry. Glaucoma was defined according to a combination of clinical evaluation, ocular imaging (fundus photo, visual field, and OCT) and criteria given by International Society of Geographical and Epidemiological Ophthalmology. OHT was defined on the basis of elevated IOP over the upper limit of normal; i.e., 20.4 mmHg, 21.5 mmHg, and 22.6 mmHg for the Chinese, Indian, and Malay cohort respectively, without glaucomatous optic disc change. MAIN OUTCOME MEASURES: Incidence of POAG, OHT, and OHT progression. RESULTS: The overall 6-year age-adjusted incidences of POAG and OHT were 1.31% (95% confidence interval [CI], 1.04-1.62) and 0.47% (95% CI, 0.30-0.70). The rate of progression of baseline OHT to POAG at 6 years was 5.32%. Primary open-angle glaucoma incidence was similar (1.37%) in Chinese and Indians and lower (0.80%) in Malays. Malays had higher incidence (0.79%) of OHT than Indians (0.38%) and Chinese (0.37%). Baseline parameters associated with higher risk of POAG were older age (per decade: odds ratio [OR], 1.90; 95% CI, 1.54-2.35; P < 0.001), higher baseline IOP (per mmHg: OR, 1.20; 95% CI, 1.12-1.29; P < 0.001) and longer axial length (per mm: OR, 1.22; 95% CI, 1.07-1.40, P = 0.004). CONCLUSION: Six-year incidence of POAG was 1.31% in a multiethnic Asian population. Older age, higher IOP, and longer axial length were associated with higher risk of POAG. These findings can help in future projections and guide public healthcare policy decisions for screening at-risk individuals. FINANCIAL DISCLOSURE(S): The authors have no proprietary or commercial interest in any materials discussed in this article.

Cardiovascular disease and thinning of retinal nerve fiber layer in a multi-ethnic Asian population: the Singapore epidemiology of eye diseases study.

Introduction: Our study aimed to examine the relationship between cardiovascular diseases (CVD) with peripapillary retinal fiber layer (RNFL) and macular ganglion cell-inner plexiform layer (GCIPL) thickness profiles in a large multi-ethnic Asian population study. Methods: 6,024 Asian subjects were analyzed in this study. All participants underwent standardized examinations, including spectral domain OCT imaging (Cirrus HD-OCT; Carl Zeiss Meditec). In total, 9,188 eyes were included for peripapillary RNFL analysis (2,417 Malays; 3,240 Indians; 3,531 Chinese), and 9,270 eyes (2,449 Malays, 3,271 Indians, 3,550 Chinese) for GCIPL analysis. History of CVD was defined as a self-reported clinical history of stroke, myocardial infarction, or angina. Multivariable linear regression models with generalized estimating equations were performed, adjusting for age, gender, ethnicity, diabetes, hypertension, hyperlipidaemia, chronic kidney disease, body mass index, current smoking status, and intraocular pressure. Results: We observed a significant association between CVD history and thinner average RNFL (ß = -1.63; 95% CI, -2.70 to -0.56; p = 0.003). This association was consistent for superior (ß = -1.79, 95% CI, -3.48 to -0.10; p = 0.038) and inferior RNFL quadrant (ß = -2.14, 95% CI, -3.96 to -0.32; p = 0.021). Of the CVD types, myocardial infarction particularly showed significant association with average (ß = -1.75, 95% CI, -3.08 to -0.42; p = 0.010), superior (ß = -2.22, 95% CI, -4.36 to -0.09; p = 0.041) and inferior (ß = -2.42, 95% CI, -4.64 to -0.20; p = 0.033) RNFL thinning. Among ethnic groups, the association between CVD and average RNFL was particularly prominent in Indian eyes (ß = -1.92, 95% CI, -3.52 to -0.33; p = 0.018). CVD was not significantly associated with average GCIPL thickness, albeit a consistent negative direction of association was observed (ß = -0.22, 95% CI, -1.15 to 0.71; p = 0.641). Discussion: In this large multi-ethnic Asian population study, we observed significant association between CVD history and RNFL thinning. This finding further validates the impact of impaired systemic circulation on RNFL thickness.

Biomass-Derived Core-Shell Carbon Dots with Embedded Tripodal Receptors for the Selective Recognition of Mefenamic Acid in Pharmaceutical Formulations and Urine.

A tripodal amine (TPA) with -OH, N, and S donors is synthesized to functionalize a core-shell carbon dot composite (FCDs@SiO2-TPA) for sensing application. The TPA is characterized by spectroscopic and spectrometric techniques, and the composite is characterized by Fourier transform infrared spectroscopy (FT-IR), thermogravimetric analysis (TGA), Brunauer-Emmett-Teller (BET), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and energy-dispersive X-ray spectra (EDS) techniques. The composite has the ability to recognize mefenamic acid (MFA) selectively even in the presence of other drugs like ibuprofen sodium, acetylsalicylic acid, naproxen sodium, diclofenac sodium, and ketoprofen. It can also be used for the quantification of MFA by recording the emission quenching response of the sample at λexc. = 350 nm and λems. = 460 nm (linear range = 1-8 µM and LOD = 197 nM). The density functional theory calculations and 1H NMR titration suggest quenching of the emission signal due to photoinduced electron transfer via hydrogen bonding between the probe and MFA. The composite FCDs@SiO2-TPA has been demonstrated as a reliable and cost-effective sensing probe for the detection of MFA in pharmaceutical formulations, water samples, and cow urine samples.

Use of artificial intelligence in forecasting glaucoma progression.

Artificial intelligence (AI) has been widely used in ophthalmology for disease detection and monitoring progression. For glaucoma research, AI has been used to understand progression patterns and forecast disease trajectory based on analysis of clinical and imaging data. Techniques such as machine learning, natural language processing, and deep learning have been employed for this purpose. The results from studies using AI for forecasting glaucoma progression however vary considerably due to dataset constraints, lack of a standard progression definition and differences in methodology and approach. While glaucoma detection and screening have been the focus of most research that has been published in the last few years, in this narrative review we focus on studies that specifically address glaucoma progression. We also summarize the current evidence, highlight studies that have translational potential, and provide suggestions on how future research that addresses glaucoma progression can be improved.

Assessment of anxiety and depression in patients with Posner-Schlossman syndrome.

BACKGROUND: To assess the anxiety and depression levels in patients with Posner-Schlossman syndrome (PSS) and to determine the potential risk factors. METHODS: In this cross-sectional study, a total of 195 participants, including 93 PSS patients and 102 healthy controls were recruited. Sociodemographic and clinical information were collected for all participants. Hospital Anxiety and Depression scale (HADS) was administered to evaluate the anxiety and depression levels. Visual function (VF) and quality-of-life (QOL) questionnaires were administered to assess variables potentially associated with anxiety and depression. RESULTS: Increased anxiety level was observed in 22 (23.7%) PSS patients as compared to 10 (9.8%) of controls (P = 0.009). While the frequency of depression between the two groups was not significantly different (P = 0.349). The mean anxiety and depression scores were 6.98 ± 4.20 and 6.44 ± 3.66 in PSS patients as compared to 6.67 ± 3.21 (P = 0.564) and 5.96 ± 2.93 (P = 0.311) in controls. Logistic regression analysis showed mental well-being was significantly associated with anxiety (odds ratio [OR] = 0.920, 95% confidence interval [CI] = 0.881-0.962, P < 0.001) and depression (OR = 0.959, CI = 0.926-0.994, P = 0.023) in PSS patients. CONCLUSION: More patients with PSS may experience anxiety as compared to healthy controls. Mental well-being is an independent risk factor for anxiety and depression. It is important for ophthalmologists to be aware of these factors and should pay more attention on mental health when PSS is managed in clinic.

Selective quantification of diclofenac from groundwater and pharmaceutical samples by magnetic molecularly imprinted polymer-based sorbent coupled with the HPLC-PDA detection.

Diclofenac (DCF) is a pharmaceutical contaminant of water bodies and therefore, improvement of analytical techniques for its removal and quantitation is one of the current interests of analysts. Herein, DCF selective magnetic molecularly imprinted polymer (MMIP) has been fabricated and characterized by Fourier transform-infrared spectroscopy, thermogravimetric analysis, vibrating scanning magnetometer, scanning electron microscopy, high-resolution transmission electron microscope, energy-dispersive X-ray spectroscopy, and Brunauer-Emmett-Teller analyzer. Furthermore, the protocol for the quantification of DCF using MMIP-HPLC-PDA combo has been optimized by investigating the effect of the amount of MMIP, type and volume of eluent, and variation of pH. The optimized protocol suggested a method detection limit of 0.042 ng mL-1 and linearity of results in the range 0.1-100 ng mL-1 (R2 = 0.99). The fabricated material offered recovery of DCF up to 96.38-99.46% from groundwater and pharmaceutical samples with a relative standard deviation of <4%. In addition, the material was found selective and sensitive for DCF among its analogous drugs like mefenamic acid, ketoprofen, fenofibrate, aspirin, ibuprofen, and naproxen.

Synthesis of a trinuclear zinc(II) cluster composed of [4.4.3.01,5]tridecane cages: a rapid detection and degradation probe for the chemical warfare agent simulant diethyl cyanophosphonate in protein-rich food products.

Diethyl cyanophosphonate (DCNP), a simulant of Tabun, is a common pollutant in pharmaceutical waste and poses a high risk to living organisms. Herein, we demonstrate a compartmental ligand-derived trinuclear zinc(II) cluster [Zn3(LH)2(CH3COO)2] as a probe for the selective detection and degradation of DCNP. It consists of two pentacoordinated Zn(II) [4.4.3.01,5]tridecane cages bridged through a hexacoordinated Zn(II) acetate unit. The structure of the cluster has been elucidated by spectrometric, spectroscopic, and single-crystal X-ray diffraction studies. The cluster shows a two-fold increased emission as compared to the compartmental ligand (at λexc = 370 nm and λem = 463 nm) due to the chelation-enhanced fluorescence effect and acts as a turn-off signal in the presence of DCNP. It can detect DCNP at nano levels up to 186 nM (LOD). The direct bond formation between DCNP and Zn(II) via the -CN group degrades it to inorganic phosphates. The mechanism of the interaction and degradation is supported by spectrofluorimetric experiments, NMR titration (1H and 31P), time of flight mass spectrometry and density functional theory calculations. The applicability of the probe has been further tested by the bio-imaging of zebrafish larvae, analysis of high-protein food products (meat and fish) and vapour phase detection by paper strips.

Engineered organotin(IV) and vanadium(V) derivatives with distinct coordination modes and luminescent properties for the efficient detection and quantification of permanganate ions.

The distinction in coordination modes of metal complexes leads to their versatile structural features and unique properties. Here, we report two tetradentate Schiff base ligands (H2L1 and H2L2) bearing N2O2 donor sets, tactically selected to provide distinct coordination modes with different metal ions. The ligands were utilized to synthesize their organotin(IV) (1-4) and vanadium(V) (5) derivatives. The synthesized compounds were characterized using elemental analysis, FT-IR spectroscopy, multi-nuclei NMR (1H, 13C, and 119Sn) spectroscopy, mass spectrometry, and single-crystal X-ray diffraction. The organotin(IV) derivatives (1-4) displayed hepta-coordination around both the Sn centres as they were achieved in their dimeric form. Contrariwise, the vanadium(V) compound (5) was isolated as a mononuclear entity exhibiting penta-coordinated geometry around the vanadium centre. The variation in the coordination modes was evident in their UV-vis and fluorescence spectra. The organotin(IV) compounds (1-4) exhibited a strong emission band centred at 468 nm when excited at a wavelength of 360 nm whereas the vanadium(V) (5) derivative displayed poor fluorogenic response. Compound 1 was further explored for the fluorogenic chemo-sensing of permanganate ions (MnO4-) amongst various anions by quenching response. A detailed investigation of the recognition of permanganate ions was accomplished by spectrofluorometric, spectroscopic (119Sn NMR), mass spectrometric, and computational studies.

Validation of a deep-learning-based retinal biomarker (Reti-CVD) in the prediction of cardiovascular disease: data from UK Biobank.

BACKGROUND: Currently in the United Kingdom, cardiovascular disease (CVD) risk assessment is based on the QRISK3 score, in which 10% 10-year CVD risk indicates clinical intervention. However, this benchmark has limited efficacy in clinical practice and the need for a more simple, non-invasive risk stratification tool is necessary. Retinal photography is becoming increasingly acceptable as a non-invasive imaging tool for CVD. Previously, we developed a novel CVD risk stratification system based on retinal photographs predicting future CVD risk. This study aims to further validate our biomarker, Reti-CVD, (1) to detect risk group of ≥ 10% in 10-year CVD risk and (2) enhance risk assessment in individuals with QRISK3 of 7.5-10% (termed as borderline-QRISK3 group) using the UK Biobank. METHODS: Reti-CVD scores were calculated and stratified into three risk groups based on optimized cut-off values from the UK Biobank. We used Cox proportional-hazards models to evaluate the ability of Reti-CVD to predict CVD events in the general population. C-statistics was used to assess the prognostic value of adding Reti-CVD to QRISK3 in borderline-QRISK3 group and three vulnerable subgroups. RESULTS: Among 48,260 participants with no history of CVD, 6.3% had CVD events during the 11-year follow-up. Reti-CVD was associated with an increased risk of CVD (adjusted hazard ratio [HR] 1.41; 95% confidence interval [CI], 1.30-1.52) with a 13.1% (95% CI, 11.7-14.6%) 10-year CVD risk in Reti-CVD-high-risk group. The 10-year CVD risk of the borderline-QRISK3 group was greater than 10% in Reti-CVD-high-risk group (11.5% in non-statin cohort [n = 45,473], 11.5% in stage 1 hypertension cohort [n = 11,966], and 14.2% in middle-aged cohort [n = 38,941]). C statistics increased by 0.014 (0.010-0.017) in non-statin cohort, 0.013 (0.007-0.019) in stage 1 hypertension cohort, and 0.023 (0.018-0.029) in middle-aged cohort for CVD event prediction after adding Reti-CVD to QRISK3. CONCLUSIONS: Reti-CVD has the potential to identify individuals with ≥ 10% 10-year CVD risk who are likely to benefit from earlier preventative CVD interventions. For borderline-QRISK3 individuals with 10-year CVD risk between 7.5 and 10%, Reti-CVD could be used as a risk enhancer tool to help improve discernment accuracy, especially in adult groups that may be pre-disposed to CVD.

Machine learning identifying peripheral circulating metabolites associated with intraocular pressure alterations.

AIMS: To identify blood metabolite markers associated with intraocular pressure (IOP) in a population-based cross-sectional study. METHODS: This study was conducted in a multiethnic Asian population (Chinese, n=2805; Indians, n=3045; Malays, n=3041 aged 40-80 years) in Singapore. All subjects underwent standardised systemic and ocular examinations, and biosamples were collected. Selected metabolites (n=228) in either serum or plasma were analysed and quantified using nuclear magnetic resonance spectroscopy. Least absolute shrinkage and selection operator regression was used for metabolites selection. Multivariable linear regression was used to evaluate the relationship between metabolites and IOP in each of the three ethnic groups, followed by a meta-analysis combining the three cohorts. RESULTS: Six metabolites, including albumin, glucose, lactate, glutamine, ratio of saturated fatty acids to total fatty acids (SFAFA) and cholesterol esters in very large high-density lipoprotein (HDL), were significantly associated with IOP in all three cohorts. Higher levels of albumin (per SD, beta=0.24, p=0.002), lactate (per SD, beta=0.27, p=0.008), glucose (per SD, beta=0.11, p=0.010) and cholesterol esters in very large HDL (per SD, beta=0.47, p=0.006), along with lower levels of glutamine (per SD, beta=0.17, p<0.001) and SFAFA (per SD, beta=0.21, p=0.008) were associated with higher IOP levels. CONCLUSION: We identify several novel blood metabolites associated with IOP. These findings may provide insight into the physiological and pathological processes underlying IOP control.

Predictive, preventive, and personalized management of retinal fluid via computer-aided detection app for optical coherence tomography scans.

Aims: Computer-aided detection systems for retinal fluid could be beneficial for disease monitoring and management by chronic age-related macular degeneration (AMD) and diabetic retinopathy (DR) patients, to assist in disease prevention via early detection before the disease progresses to a "wet AMD" pathology or diabetic macular edema (DME), requiring treatment. We propose a proof-of-concept AI-based app to help predict fluid via a "fluid score", prevent fluid progression, and provide personalized, serial monitoring, in the context of predictive, preventive, and personalized medicine (PPPM) for patients at risk of retinal fluid complications. Methods: The app comprises a convolutional neural network-Vision Transformer (CNN-ViT)-based segmentation deep learning (DL) network, trained on a small dataset of 100 training images (augmented to 992 images) from the Singapore Epidemiology of Eye Diseases (SEED) study, together with a CNN-based classification network trained on 8497 images, that can detect fluid vs. non-fluid optical coherence tomography (OCT) scans. Both networks are validated on external datasets. Results: Internal testing for our segmentation network produced an IoU score of 83.0% (95% CI = 76.7-89.3%) and a DICE score of 90.4% (86.3-94.4%); for external testing, we obtained an IoU score of 66.7% (63.5-70.0%) and a DICE score of 78.7% (76.0-81.4%). Internal testing of our classification network produced an area under the receiver operating characteristics curve (AUC) of 99.18%, and a Youden index threshold of 0.3806; for external testing, we obtained an AUC of 94.55%, and an accuracy of 94.98% and an F1 score of 85.73% with Youden index. Conclusion: We have developed an AI-based app with an alternative transformer-based segmentation algorithm that could potentially be applied in the clinic with a PPPM approach for serial monitoring, and could allow for the generation of retrospective data to research into the varied use of treatments for AMD and DR. The modular system of our app can be scaled to add more iterative features based on user feedback for more efficient monitoring. Further study and scaling up of the algorithm dataset could potentially boost its usability in a real-world clinical setting. Supplementary information: The online version contains supplementary material available at 10.1007/s13167-022-00301-5.

Retinal photograph-based deep learning predicts biological age, and stratifies morbidity and mortality risk.

BACKGROUND: ageing is an important risk factor for a variety of human pathologies. Biological age (BA) may better capture ageing-related physiological changes compared with chronological age (CA). OBJECTIVE: we developed a deep learning (DL) algorithm to predict BA based on retinal photographs and evaluated the performance of our new ageing marker in the risk stratification of mortality and major morbidity in general populations. METHODS: we first trained a DL algorithm using 129,236 retinal photographs from 40,480 participants in the Korean Health Screening study to predict the probability of age being ≥65 years ('RetiAGE') and then evaluated the ability of RetiAGE to stratify the risk of mortality and major morbidity among 56,301 participants in the UK Biobank. Cox proportional hazards model was used to estimate the hazard ratios (HRs). RESULTS: in the UK Biobank, over a 10-year follow up, 2,236 (4.0%) died; of them, 636 (28.4%) were due to cardiovascular diseases (CVDs) and 1,276 (57.1%) due to cancers. Compared with the participants in the RetiAGE first quartile, those in the RetiAGE fourth quartile had a 67% higher risk of 10-year all-cause mortality (HR = 1.67 [1.42-1.95]), a 142% higher risk of CVD mortality (HR = 2.42 [1.69-3.48]) and a 60% higher risk of cancer mortality (HR = 1.60 [1.31-1.96]), independent of CA and established ageing phenotypic biomarkers. Likewise, compared with the first quartile group, the risk of CVD and cancer events in the fourth quartile group increased by 39% (HR = 1.39 [1.14-1.69]) and 18% (HR = 1.18 [1.10-1.26]), respectively. The best discrimination ability for RetiAGE alone was found for CVD mortality (c-index = 0.70, sensitivity = 0.76, specificity = 0.55). Furthermore, adding RetiAGE increased the discrimination ability of the model beyond CA and phenotypic biomarkers (increment in c-index between 1 and 2%). CONCLUSIONS: the DL-derived RetiAGE provides a novel, alternative approach to measure ageing.