Fluorescence lifetime imaging ophthalmoscopy (FLIO) is a novel modality to investigate the human retina. This study aims to characterize the effects of age, pigmentation, and gender in FLIO.
 
  A total of 97 eyes from 97 healthy subjects (mean age 37 ± 18 years, range 9-85 years) were investigated in this study. This study included 47 (49%) females and 50 males. The pigmentation analysis was a substudy including 64 subjects aged 18 to 40 years (mean age 29 ± 6 years). These were categorized in groups A (darkly pigmented, 8), B (medium pigmented, 20), and C (lightly pigmented, 36). Subjects received Heidelberg Engineering FLIO and optical coherence tomography imaging. Retinal autofluorescence lifetimes were detected in two spectral channels (short spectral channel [SSC] 498-560 nm; long spectral channel [LSC] 560-720 nm), and amplitude-weighted mean fluorescence lifetimes (τ
  ) were calculated. Additionally, autofluorescence lifetimes of melanin were measured in a cuvette.
 
  Age significantly affected FLIO lifetimes, and age-related FLIO changes in the SSC start at approximately age 35 years, whereas the LSC shows a consistent prolongation with age from childhood. There were no gender- or pigmentation-specific significant differences of autofluorescence lifetimes.
 
  This study confirms age-effects in FLIO but shows that the two channels are affected differently.  https://www.selleckchem.com/products/elenestinib-phosphate.html  The LSC appears to show the lifelong accumulation of lipofuscin. Furthermore, it is important to know that neither gender nor pigmentation significantly affect FLIO lifetimes.
 
  This study helps to understand the FLIO technology better, which will aid in conducting future clinical studies.
 This study helps to understand the FLIO technology better, which will aid in conducting future clinical studies.
  Endothelin-1 (ET-1) is a potent vasoactive factor implicated in development of diabetic retinopathy, which is commonly associated with retinal edema and hyperglycemia. Although the vasomotor activity of venules contributes to the regulation of tissue fluid homeostasis, responses of human retinal venules to ET-1 under euglycemia and hyperglycemia remain unknown and the ET-1 receptor subtype corresponding to vasomotor function has not been determined. Herein, we addressed these issues by examining the reactivity of isolated human retinal venules to ET-1, and results from porcine retinal venules were compared.
 
  Retinal tissues were obtained from patients undergoing enucleation. Human and porcine retinal venules were isolated and pressurized to assess diameter changes in response to ET-1 after exposure to 5 mM control glucose or 25 mM high glucose for 2 hours.
 
  Both human and porcine retinal venules exposed to control glucose developed similar basal tone and constricted comparably to ET-1 in a concentration-dependent manner. ET-1-induced constrictions of human and porcine retinal venules were abolished by ET
  receptor antagonist BQ123. During high glucose exposure, basal tone of human and porcine retinal venules was unaltered but ET-1-induced vasoconstrictions were enhanced.
 
  ET-1 elicits comparable constriction of human and porcine retinal venules by activation of ET
  receptors. In vitro hyperglycemia augments human and porcine retinal venular responses to ET-1.
 
  Similarities in vasoconstriction to ET-1 between human and porcine retinal venules support the latter as an effective model of the human retinal microcirculation to help identify vascular targets for the treatment of retinal complications in patients with diabetes.
 Similarities in vasoconstriction to ET-1 between human and porcine retinal venules support the latter as an effective model of the human retinal microcirculation to help identify vascular targets for the treatment of retinal complications in patients with diabetes.
  To use machine learning in those with brain amyloid to predict thioflavin fluorescence (indicative of amyloid) of retinal deposits from their interactions with polarized light.
 
  We imaged 933 retinal deposits in 28 subjects with post mortem evidence of brain amyloid using thioflavin fluorescence and polarization sensitive microscopy. Means and standard deviations of 14 polarimetric properties were input to machine learning algorithms. Two oversampling strategies were applied to overcome data imbalance. Three machine learning algorithms linear discriminant analysis, supporting vector machine, and random forest (RF) were trained to predict thioflavin positive deposits. For each method; accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve were computed.
 
  For the polarimetric positive deposits, using 1 oversampling method, RF had the highest area under the receiver operating characteristic curve (0.986), which was not different from that with the second oversampling method. RF had 95% accuracy, 94% sensitivity, and 97% specificity. After including deposits with no polarimetric signals, polarimetry correctly predicted 93% of thioflavin positive deposits. Linear retardance and linear anisotropy were the dominant polarimetric properties in RF with 1 oversampling method, and no polarimetric properties were dominant in the second method.
 
  Thioflavin positivity of retinal amyloid deposits can be predicted from their images in polarized light. Polarimetry is a promising dye-free method of detecting amyloid deposits in 
  retinal tissue. Further testing is required for translation to live eye imaging.
 
  This dye-free method distinguishes retinal amyloid deposits, a promising biomarker of Alzheimer's disease, in human retinas imaged with polarimetry.
 This dye-free method distinguishes retinal amyloid deposits, a promising biomarker of Alzheimer's disease, in human retinas imaged with polarimetry.
  This study aimed to develop an automated system with artificial intelligence algorithms to comprehensively identify pathologic retinal cases and make urgent referrals.
 
  To build and test the intelligent system, this study obtained 28,664 optical coherence tomography (OCT) images from 2254 patients in the Eye and ENT Hospital of Fudan University (EENT Hospital) and Shanghai Tenth People's Hospital (TENTH Hospital). We applied a deep learning model with an adapted feature pyramid network to detect 15 categories of retinal pathologies from OCT images as common signs of various retinal diseases. Subsequently, the pathologies detected in the OCT images and thickness features extracted from retinal thickness measurements were combined for urgent referral using the random forest tool.
 
  The retinal pathologies detection model had a sensitivity of 96.39% and specificity of 98.91% from the EENT Hospital test dataset, whereas those from the TENTH Hospital test dataset were 94.89% and 98.76%, respectively. The urgent referral model achieved accuracies of 98.