Computational investigation of blood cell transport in retinal microaneurysms

Microaneurysms (MAs) are one of the earliest clinically visible signs of diabetic retinopathy (DR). MA leakage or rupture may precipitate local pathology in the surrounding neural retina that impacts visual function. Thrombosis in MAs may affect their turnover time, an indicator associated with visual and anatomic outcomes in the diabetic eyes. In this work, we perform computational modeling of blood flow in microchannels containing various MAs to investigate the pathologies of MAs in DR. The particle-based model employed in this study can explicitly represent red blood cells (RBCs) and platelets as well as their interaction in the blood flow, a process that is very difficult to observe in vivo. Our simulations illustrate that while the main blood flow from the parent vessels can perfuse the entire lumen of MAs with small body-to-neck ratio (BNR), it can only perfuse part of the lumen in MAs with large BNR, particularly at a low hematocrit level, leading to possible hypoxic conditions inside MAs. We also quantify the impacts of the size of MAs, blood flow velocity, hematocrit and RBC stiffness and adhesion on the likelihood of platelets entering MAs as well as their residence time inside, two factors that are thought to be associated with thrombus formation in MAs. Our results show that enlarged MA size, increased blood velocity and hematocrit in the parent vessel of MAs as well as the RBC-RBC adhesion promote the migration of platelets into MAs and also prolong their residence time, thereby increasing the propensity of thrombosis within MAs. Overall, our work suggests that computational simulations using particle-based models can help to understand the microvascular pathology pertaining to MAs in DR and provide insights to stimulate and steer new experimental and computational studies in this area.

Artificial intelligence velocimetry and microaneurysm-on-a-chip for three-dimensional analysis of blood flow in physiology and disease

Understanding the mechanics of blood flow is necessary for developing insights into mechanisms of physiology and vascular diseases in microcirculation. Given the limitations of technologies available for assessing in vivo flow fields, in vitro methods based on traditional microfluidic platforms have been developed to mimic physiological conditions. However, existing methods lack the capability to provide accurate assessment of these flow fields, particularly in vessels with complex geometries. Conventional approaches to quantify flow fields rely either on analyzing only visual images or on enforcing underlying physics without considering visualization data, which could compromise accuracy of predictions. Here, we present artificial-intelligence velocimetry (AIV) to quantify velocity and stress fields of blood flow by integrating the imaging data with underlying physics using physics-informed neural networks. We demonstrate the capability of AIV by quantifying hemodynamics in microchannels designed to mimic saccular-shaped microaneurysms (microaneurysm-on-a-chip, or MAOAC), which signify common manifestations of diabetic retinopathy, a leading cause of vision loss from blood-vessel damage in the retina in diabetic patients. We show that AIV can, without any a priori knowledge of the inlet and outlet boundary conditions, infer the two-dimensional (2D) flow fields from a sequence of 2D images of blood flow in MAOAC, but also can infer three-dimensional (3D) flow fields using only 2D images, thanks to the encoded physics laws. AIV provides a unique paradigm that seamlessly integrates images, experimental data, and underlying physics using neural networks to automatically analyze experimental data and infer key hemodynamic indicators that assess vascular injury.

Quantitative Ultra-Widefield Angiography and Diabetic Retinopathy Severity: An Assessment of Panretinal Leakage Index, Ischemic Index and Microaneurysm Count

PURPOSE

To investigate the relationship between the diabetic retinopathy (DR) severity and quantitative ultra-widefield angiographic metrics, including leakage index, ischemic index, and microaneurysm count.

DESIGN

Retrospective image analysis study.

METHODS

Eyes with DR that had undergone ultra-widefield fluorescein angiography (UWFA) with associated color photography were identified. All eyes were laser-naive and had not received any intravitreal pharmacotherapy within 6 months of UWFA. Each eye was graded for DR severity. Quantitative angiographic parameters were evaluated with a semiautomated analysis platform with expert reader correction, as needed. Angiographic parameters included panretinal leakage index, ischemic index, and microaneurysm count. Clinical characteristics analyzed included age, gender, race, hemoglobin A1C level, hypertension, systolic blood pressure, diastolic blood pressure, and smoking history.

MAIN OUTCOME MEASURES

Association of DR severity with panretinal leakage index, ischemic index, and microaneurysm count.

RESULTS

Three hundred thirty-nine eyes were included with mean age of 62±13 years. Forty-two percent of eyes were from women and 57.5% were from men. Distribution of DR severity was as follows: mild NPDR in 11.2%, moderate NPDR in 23.9%, severe NPDR in 40.1%, and PDR with 24.8%. Panretinal leakage index [mild NPDR (mean = 0.51%), moderate NPDR mean = 1.20%, severe NPDR (mean = 2.75%), and PDR (mean = 5.84%); P<2×10-16], panretinal ischemic index [mild NPDR (mean = 0.95%, moderate NPDR (mean = 1.37%), severe NPDR (mean = 2.80%), and PDR (mean = 9.53%); P<2×10-16], and panretinal microaneurysm count [mild NPDR (mean = 36), moderate NPDR (mean = 129), severe NPDR (mean = 203), and PDR (mean = 254); P<5×10-7] were strongly associated with DR severity. Multivariate analysis demonstrated that ischemic index and leakage index were the parameters associated most strongly with level of DR severity.

CONCLUSIONS

Panretinal leakage index, panretinal ischemic index, and panretinal microaneurysm count are associated with DR severity. Additional research is needed to understand the clinical implications of these parameters related to progression risk, prognosis, and implications for therapeutic response.

Higher skin autofluorescence in young people with Type 1 diabetes and microvascular complications

AIM

To test the hypothesis that non-invasive skin autofluorescence, a measure of advanced glycation end products, would provide a surrogate measure of long-term glycaemia and be associated with early markers of microvascular complications in adolescents with Type 1 diabetes.

METHODS

Forearm skin autofluorescence (arbitrary units) was measured in a cross-sectional study of 135 adolescents with Type 1 diabetes [mean ± sd age 15.6 ± 2.1 years, diabetes duration 8.7 ± 3.5 years, HbA_1c 72 ± 16 mmol/mol (8.7 ± 1.5%)]. Retinopathy, assessed using seven-field stereoscopic fundal photography, was defined as ≥1 microaneurysm or haemorrhage. Cardiac autonomic function was measured by standard deviation of consecutive RR intervals on a 10-min continuous electrocardiogram recording, as a measure of heart rate variability.

RESULTS

Skin autofluorescence was significantly associated with age (R² = 0.15; P < 0.001). Age- and gender-adjusted skin autofluorescence was associated with concurrent HbA_1c (R² = 0.32; P < 0.001) and HbA_1c over the previous 2.5-10 years (R² = 0.34-0.43; P < 0.002). Age- and gender-adjusted mean skin autofluorescence was higher in adolescents with retinopathy vs those without retinopathy [mean 1.38 (95% CI 1.29, 1.48) vs 1.22 (95% CI 1.17, 1.26) arbitrary units; P = 0.002]. In multivariable analysis, retinopathy was significantly associated with skin autofluorescence, adjusted for duration (R² = 0.19; P = 0.03). Cardiac autonomic dysfunction was also independently associated with skin autofluorescence (R² = 0.11; P = 0.006).

CONCLUSIONS

Higher skin autofluorescence is associated with retinopathy and cardiac autonomic dysfunction in adolescents with Type 1 diabetes. The relationship between skin autofluorescence and previous glycaemia may provide insight into metabolic memory. Longitudinal studies will determine the utility of skin autofluorescence as a non-invasive screening tool to predict future microvascular complications.

Retinal Image Enhancement Using Robust Inverse Diffusion Equation and Self-Similarity Filtering

As a common ocular complication for diabetic patients, diabetic retinopathy has become an important public health problem in the world. Early diagnosis and early treatment with the help of fundus imaging technology is an effective control method. In this paper, a robust inverse diffusion equation combining a self-similarity filtering is presented to detect and evaluate diabetic retinopathy using retinal image enhancement. A flux corrected transport technique is used to control diffusion flux adaptively, which eliminates overshoots inherent in the Laplacian operation. Feature preserving denoising by the self-similarity filtering ensures a robust enhancement of noisy and blurry retinal images. Experimental results demonstrate that this algorithm can enhance important details of retinal image data effectively, affording an opportunity for better medical interpretation and subsequent processing.