Interpretable Diabetic Retinopathy Diagnosis Based on Biomarker Activation Map

OBJECTIVE

Deep learning classifiers provide the most accurate means of automatically diagnosing diabetic retinopathy (DR) based on optical coherence tomography (OCT) and its angiography (OCTA). The power of these models is attributable in part to the inclusion of hidden layers that provide the complexity required to achieve a desired task. However, hidden layers also render algorithm outputs difficult to interpret. Here we introduce a novel biomarker activation map (BAM) framework based on generative adversarial learning that allows clinicians to verify and understand classifiers' decision-making.

METHODS

A data set including 456 macular scans were graded as non-referable or referable DR based on current clinical standards. A DR classifier that was used to evaluate our BAM was first trained based on this data set. The BAM generation framework was designed by combing two U-shaped generators to provide meaningful interpretability to this classifier. The main generator was trained to take referable scans as input and produce an output that would be classified by the classifier as non-referable. The BAM is then constructed as the difference image between the output and input of the main generator. To ensure that the BAM only highlights classifier-utilized biomarkers an assistant generator was trained to do the opposite, producing scans that would be classified as referable by the classifier from non-referable scans.

RESULTS

The generated BAMs highlighted known pathologic features including nonperfusion area and retinal fluid.

CONCLUSION/SIGNIFICANCE

A fully interpretable classifier based on these highlights could help clinicians better utilize and verify automated DR diagnosis.

Deep-Learning-Aided Diagnosis of Diabetic Retinopathy, Age-Related Macular Degeneration, and Glaucoma Based on Structural and Angiographic OCT

Purpose

Timely diagnosis of eye diseases is paramount to obtaining the best treatment outcomes. OCT and OCT angiography (OCTA) have several advantages that lend themselves to early detection of ocular pathology; furthermore, the techniques produce large, feature-rich data volumes. However, the full clinical potential of both OCT and OCTA is stymied when complex data acquired using the techniques must be manually processed. Here, we propose an automated diagnostic framework based on structural OCT and OCTA data volumes that could substantially support the clinical application of these technologies.

Design

Cross sectional study.

Participants

Five hundred twenty-six OCT and OCTA volumes were scanned from the eyes of 91 healthy participants, 161 patients with diabetic retinopathy (DR), 95 patients with age-related macular degeneration (AMD), and 108 patients with glaucoma.

Methods

The diagnosis framework was constructed based on semisequential 3-dimensional (3D) convolutional neural networks. The trained framework classifies combined structural OCT and OCTA scans as normal, DR, AMD, or glaucoma. Fivefold cross-validation was performed, with 60% of the data reserved for training, 20% for validation, and 20% for testing. The training, validation, and test data sets were independent, with no shared patients. For scans diagnosed as DR, AMD, or glaucoma, 3D class activation maps were generated to highlight subregions that were considered important by the framework for automated diagnosis.

Main Outcome Measures

The area under the curve (AUC) of the receiver operating characteristic curve and quadratic-weighted kappa were used to quantify the diagnostic performance of the framework.

Results

For the diagnosis of DR, the framework achieved an AUC of 0.95 ± 0.01. For the diagnosis of AMD, the framework achieved an AUC of 0.98 ± 0.01. For the diagnosis of glaucoma, the framework achieved an AUC of 0.91 ± 0.02.

Conclusions

Deep learning frameworks can provide reliable, sensitive, interpretable, and fully automated diagnosis of eye diseases.

Financial Disclosures

Proprietary or commercial disclosure may be found after the references.

A Diabetic Retinopathy Classification Framework Based on Deep-Learning Analysis of OCT Angiography

Purpose

Reliable classification of referable and vision threatening diabetic retinopathy (DR) is essential for patients with diabetes to prevent blindness. Optical coherence tomography (OCT) and its angiography (OCTA) have several advantages over fundus photographs. We evaluated a deep-learning-aided DR classification framework using volumetric OCT and OCTA.

Methods

Four hundred fifty-six OCT and OCTA volumes were scanned from eyes of 50 healthy participants and 305 patients with diabetes. Retina specialists labeled the eyes as non-referable (nrDR), referable (rDR), or vision threatening DR (vtDR). Each eye underwent a 3 × 3-mm scan using a commercial 70 kHz spectral-domain OCT system. We developed a DR classification framework and trained it using volumetric OCT and OCTA to classify eyes into rDR and vtDR. For the scans identified as rDR or vtDR, 3D class activation maps were generated to highlight the subregions which were considered important by the framework for DR classification.

Results

For rDR classification, the framework achieved a 0.96 ± 0.01 area under the receiver operating characteristic curve (AUC) and 0.83 ± 0.04 quadratic-weighted kappa. For vtDR classification, the framework achieved a 0.92 ± 0.02 AUC and 0.73 ± 0.04 quadratic-weighted kappa. In addition, the multiple DR classification (non-rDR, rDR but non-vtDR, or vtDR) achieved a 0.83 ± 0.03 quadratic-weighted kappa.

Conclusions

A deep learning framework only based on OCT and OCTA can provide specialist-level DR classification using only a single imaging modality.

Translational Relevance

The proposed framework can be used to develop clinically valuable automated DR diagnosis system because of the specialist-level performance showed in this study.

Determination of the ^{27}Al Neutron Distribution Radius from a Parity-Violating Electron Scattering Measurement

We report the first measurement of the parity-violating elastic electron scattering asymmetry on ^{27}Al. The ^{27}Al elastic asymmetry is A_{PV}=2.16±0.11(stat)±0.16(syst)  ppm, and was measured at ⟨Q^{2}⟩=0.02357±0.00010  GeV^{2}, ⟨θ_{lab}⟩=7.61°±0.02°, and ⟨E_{lab}⟩=1.157  GeV with the Q_{weak} apparatus at Jefferson Lab. Predictions using a simple Born approximation as well as more sophisticated distorted-wave calculations are in good agreement with this result. From this asymmetry the ^{27}Al neutron radius R_{n}=2.89±0.12  fm was determined using a many-models correlation technique. The corresponding neutron skin thickness R_{n}-R_{p}=-0.04±0.12  fm is small, as expected for a light nucleus with a neutron excess of only 1. This result thus serves as a successful benchmark for electroweak determinations of neutron radii on heavier nuclei. A tree-level approach was used to extract the ^{27}Al weak radius R_{w}=3.00±0.15  fm, and the weak skin thickness R_{wk}-R_{ch}=-0.04±0.15  fm. The weak form factor at this Q^{2} is F_{wk}=0.39±0.04.

Associations between gut microbiota and thyroidal function status in Chinese patients with Graves' disease

OBJECTIVE

The imbalance of gut microbiota has been linked to manifold endocrine diseases, but the association with Graves' disease (GD) is still unclear. The purpose of this study was to investigate the correlation between human gut microbiota and clinical characteristics and thyroidal functional status of GD.

METHODS

14 healthy volunteers (CG) and 15 patients with primary GD (HG) were recruited as subjects. 16SrDNA high-throughput sequencing was performed on IlluminaMiSeq platform to analyze the characteristics of gut microbiota in patients with GD. Among them, the thyroid function of 13 patients basically recovered after treatment with anti-thyroid drugs (oral administration of Methimazole for 3-5 months). The fecal samples of patients after treatment (TG) were sequenced again, to further explore and investigate the potential relationship between dysbacteriosis and GD.

RESULTS

In terms of alpha diversity index, the observed OTUs, Simpson and Shannon indices of gut microbiota in patients with GD were significantly lower than those in healthy volunteers (P < 0.05).The difference of bacteria species was mainly reflected in the genus level, in which the relative abundance of Lactobacillus, Veillonella and Streptococcus increased significantly in GD. After the improvement of thyroid function, a significant reduction at the genus level were Blautia, Corynebacter, Ruminococcus and Streptococcus, while Phascolarctobacterium increased significantly (P < 0.05). According to Spearman correlation analysis, the correlation between the level of thyrotropin receptor antibody (TRAb) and the relative abundance of Lactobacillus and Ruminococcus was positive, while Synergistetes and Phascolarctobacterium showed a negative correlation with TRAb. Besides, there were highly significant negative correlation between Synergistetes and clinical variables of TRAb, TPOAb and TGAb (P < 0.05, R <  - 0.6).

CONCLUSIONS

This study revealed that functional status and TRAb level in GD were associated with composition and biological function in the gut microbiota, with Synergistetes and Phascolarctobacterium protecting the thyroid probably, while Ruminococcus and Lactobacillus may be novel biomarkers of GD.

DcardNet: Diabetic Retinopathy Classification at Multiple Levels Based on Structural and Angiographic Optical Coherence Tomography

OBJECTIVE

Optical coherence tomography (OCT) and its angiography (OCTA) have several advantages for the early detection and diagnosis of diabetic retinopathy (DR). However, automated, complete DR classification frameworks based on both OCT and OCTA data have not been proposed. In this study, a convolutional neural network (CNN) based method is proposed to fulfill a DR classification framework using en face OCT and OCTA.

METHODS

A densely and continuously connected neural network with adaptive rate dropout (DcardNet) is designed for the DR classification. In addition, adaptive label smoothing was proposed and used to suppress overfitting. Three separate classification levels are generated for each case based on the International Clinical Diabetic Retinopathy scale. At the highest level the network classifies scans as referable or non-referable for DR. The second level classifies the eye as non-DR, non-proliferative DR (NPDR), or proliferative DR (PDR). The last level classifies the case as no DR, mild and moderate NPDR, severe NPDR, and PDR.

RESULTS

We used 10-fold cross-validation with 10% of the data to assess the network's performance. The overall classification accuracies of the three levels were 95.7%, 85.0%, and 71.0% respectively.

CONCLUSION/SIGNIFICANCE

A reliable, sensitive and specific automated classification framework for referral to an ophthalmologist can be a key technology for reducing vision loss related to DR.

Precision Measurement of the Beam-Normal Single-Spin Asymmetry in Forward-Angle Elastic Electron-Proton Scattering

A beam-normal single-spin asymmetry generated in the scattering of transversely polarized electrons from unpolarized nucleons is an observable related to the imaginary part of the two-photon exchange process. We report a 2% precision measurement of the beam-normal single-spin asymmetry in elastic electron-proton scattering with a mean scattering angle of θ_{lab}=7.9° and a mean energy of 1.149 GeV. The asymmetry result is B_{n}=-5.194±0.067(stat)±0.082 (syst) ppm. This is the most precise measurement of this quantity available to date and therefore provides a stringent test of two-photon exchange models at far-forward scattering angles (θ_{lab}→0) where they should be most reliable.

Sensorless adaptive-optics optical coherence tomographic angiography

Optical coherence tomographic angiography (OCTA) can image the retinal blood flow but visualization of the capillary caliber is limited by the low lateral resolution. Adaptive optics (AO) can be used to compensate ocular aberrations when using high numerical aperture (NA), and thus improve image resolution. However, previously reported AO-OCTA instruments were large and complex, and have a small sub-millimeter field of view (FOV) that hinders the extraction of biomarkers with clinical relevance. In this manuscript, we developed a sensorless AO-OCTA prototype with an intermediate numerical aperture to produce depth-resolved angiograms with high resolution and signal-to-noise ratio over a 2 × 2 mm FOV, with a focal spot diameter of 6 µm, which is about 3 times finer than typical commercial OCT systems. We believe these parameters may represent a better tradeoff between resolution and FOV compared to large-NA AO systems, since the spot size matches better that of capillaries. The prototype corrects defocus, astigmatism, and coma using a figure of merit based on the mean reflectance projection of a slab defined with real-time segmentation of retinal layers. AO correction with the ability to optimize focusing in arbitrary retinal depths - particularly the plexuses in the inner retina - could be achieved in 1.35 seconds. The AO-OCTA images showed greater flow signal, signal-to-noise ratio, and finer capillary caliber compared to commercial OCTA. Projection artifacts were also reduced in the intermediate and deep capillary plexuses. The instrument reported here improves OCTA image quality without excessive sacrifice in FOV and device complexity, and thus may have potential for clinical translation.

AB0152 LENABASUM, A CB2 AGONIST, INHIBITS INFLAMMASOME ACTIVATION

Background:

Upregulation of the innate immune response via the activity of Toll-like receptors and the NLRP3 inflammasome have been suggested as initiating events that can drive fibrosis in systemic sclerosis (SSc) (Pharmacol Ther. 2018;192:163). Lenabasum, a cannabinoid receptor type 2 agonist, is known to activate the resolution phase of acute human innate immune responses triggered through Toll-like receptor activation, favoring production of pro-resolving lipid mediators, reducing inflammatory infiltrates, and increasing bacterial clearance (Clin Pharmacol Ther. 2018;104:675). Given the potential importance of inflammasome activation in the pathogenesis of SSc, the question remained whether lenabasum inhibits inflammasome activation.

Objectives:

Assess effects of lenabasum on IL-1β and IL-18 production induced by inflammasome activation.

Methods:

Primary human macrophages were derived from monocytes, stimulated with LPS and ATP to active inflammasomes and cultured with lenabasum. Levels of IL-1β and IL-18 were measured in cell supernatants by ELISA. Separately, human PBMC were activated with 0.1 µg/ml LPS ± 10 µM lenabasum for 24 hours, and effects of lenabasum on the levels of IL-1β and other pro-inflammatory cytokines were measured.

Results:

Lenabasum significantly inhibited IL-1β and IL-18 secretion by monocyte-derived macrophages, with IC50= 66.73 ± 3.92 nM and 349.23 ± 21.27 nM, respectively. A control inflammasome activation inhibitor, MCC950, which showed IC50= 18.33 ± 1.22 nM for IL-1 β inhibition and IC50= 21.43 ± 0.81 nM for IL-8 inhibition.

Conclusion:

Lenabasum inhibits inflammasome activation, which could contribute to potential therapeutic efficacy in SSc and other autoimmune diseases.

References:

[1]Henderson, J., S. Bhattacharyya, J. Varga, and S. O’Reilly. 2018. ‘Targeting TLRs and the inflammasome in systemic sclerosis’, Pharmacol Ther, 192: 163-69.

[2]Motwani, M. P., F. Bennett, P. C. Norris, A. A. Maini, M. J. George, J. Newson, A. Henderson, A. J. Hobbs, M. Tepper, B. White, C. N. Serhan, R. MacAllister, and D. W. Gilroy. 2018. ‘Potent Anti-Inflammatory and Pro-Resolving Effects of Anabasum in a Human Model of Self-Resolving Acute Inflammation’, Clin Pharmacol Ther, 104: 675-86.

Disclosure of Interests:

Subba Chintalacharuvu Shareholder of: Corbus Pharmaceuticals, Inc., Employee of: Corbus Pharmaceuticals, Inc., Ping Zang Employee of: Corbus Pharmaceuticals, Inc., Barbara White Shareholder of: Corbus Pharmaceuticals, Inc., Employee of: Corbus Pharmaceuticals, Inc., Sergei Atamas Shareholder of: Corbus Pharmaceuticals, Inc., Employee of: Corbus Pharmaceuticals, Inc.

Automated diagnosis and segmentation of choroidal neovascularization in OCT angiography using deep learning

Accurate identification and segmentation of choroidal neovascularization (CNV) is essential for the diagnosis and management of exudative age-related macular degeneration (AMD). Projection-resolved optical coherence tomographic angiography (PR-OCTA) enables both cross-sectional and en face visualization of CNV. However, CNV identification and segmentation remains difficult even with PR-OCTA due to the presence of residual artifacts. In this paper, a fully automated CNV diagnosis and segmentation algorithm using convolutional neural networks (CNNs) is described. This study used a clinical dataset, including both scans with and without CNV, and scans of eyes with different pathologies. Furthermore, no scans were excluded due to image quality. In testing, all CNV cases were diagnosed from non-CNV controls with 100% sensitivity and 95% specificity. The mean intersection over union of CNV membrane segmentation was as high as 0.88. By enabling fully automated categorization and segmentation, the proposed algorithm should offer benefits for CNV diagnosis, visualization monitoring.

[Correlation between serum bilirubin and cardiovascular autonomic neuropathy in patients with type 2 diabetes]

Objective: To investigate the correlation between serum bilirubin and cardiovascular autonomic neuropathy (CAN) in type 2 diabetes mellitus patients. Methods: A total of 369 patients with type 2 diabetes mellitus who were hospitalized at the Department of Endocrinology, Nanjing Jinling Hospital from April 2017 to October 2018 were enrolled, including 226 males and 143 females, with an average age of (54.6±12.1) years. According to cardiovascular reflex tests (CARTs), all the patients were divided into Non CAN group(149 patients without CAN) and CAN group (220 patients complicated with CAN). The difference of serum bilirubin levels between the two groups was compared. The differences of CARTs and the incidence of CAN were compared by tertiles of serum bilirubin levels. The binary logistic regression was used to analyze the risk factors for diabetic cardiovascular autonomic neuropathy. Results: The serum total bilirubin [(9.28±2.74) μmol/L vs (11.08±2.98) μmol/L, P<0.001], direct bilirubin [(3.17±1.20) μmol/L vs (3.71±1.24) μmol/L, P<0.001] and indirect bilirubin levels [(6.11±1.89) μmol/L vs (7.37±2.10) μmol/L, P<0.001] in CAN group were significantly lower than that in Non CAN group. With the increase of serum bilirubin, the incidence of CAN decreased (P<0.01). Multivariate Logistic regression analysis showed that serum total bilirubin (OR=0.819, 95%CI: 0.744-0.901, P<0.001), direct bilirubin (OR=0.739, 95%CI: 0.601-0.908, P=0.004) and indirect bilirubin (OR=0.749, 95%CI: 0.653-0.860, P<0.001) were inversely correlated with the incidence of CAN. Conclusions: Within the physiological range, lower level of serum bilirubin is inversely correlated with the incidence of CAN. It is noteworthy to screen diabetic cardiovascular autonomic neuropathy in patients with type 2 diabetes mellitus who had a lower serum bilirubin level.

Automated segmentation of peripapillary retinal boundaries in OCT combining a convolutional neural network and a multi-weights graph search

Quantitative analysis of the peripapillary retinal layers and capillary plexuses from optical coherence tomography (OCT) and OCT angiography images depend on two segmentation tasks - delineating the boundary of the optic disc and delineating the boundaries between retinal layers. Here, we present a method combining a neural network and graph search to perform these two tasks. A comparison of this novel method's segmentation of the disc boundary showed good agreement with the ground truth, achieving an overall Dice similarity coefficient of 0.91 ± 0.04 in healthy and glaucomatous eyes. The absolute error of retinal layer boundaries segmentation in the same cases was 4.10 ± 1.25 µm.

Extended axial imaging range, widefield swept source optical coherence tomography angiography

We developed a high-speed, swept source OCT system for widefield OCT angiography (OCTA) imaging. The system has an extended axial imaging range of 6.6 mm. An electrical lens is used for fast, automatic focusing. The recently developed split-spectrum amplitude and phase-gradient angiography allow high-resolution OCTA imaging with only two B-scan repetitions. An improved post-processing algorithm effectively removed trigger jitter artifacts and reduced noise in the flow signal. We demonstrated high contrast 3 mm×3 mm OCTA image with 400×400 pixels acquired in 3 seconds and high-definition 8 mm×6 mm and 12 mm×6 mm OCTA images with 850×400 pixels obtained in 4 seconds. A widefield 8 mm×11 mm OCTA image is produced by montaging two 8 mm×6 mm scans. An ultra-widefield (with a maximum of 22 mm along both vertical and horizontal directions) capillary-resolution OCTA image is obtained by montaging six 12 mm×6 mm scans.

Automated boundary detection of the optic disc and layer segmentation of the peripapillary retina in volumetric structural and angiographic optical coherence tomography

To improve optic disc boundary detection and peripapillary retinal layer segmentation, we propose an automated approach for structural and angiographic optical coherence tomography. The algorithm was performed on radial cross-sectional B-scans. The disc boundary was detected by searching for the position of Bruch's membrane opening, and retinal layer boundaries were detected using a dynamic programming-based graph search algorithm on each B-scan without the disc region. A comparison of the disc boundary using our method with that determined by manual delineation showed good accuracy, with an average Dice similarity coefficient ≥0.90 in healthy eyes and eyes with diabetic retinopathy and glaucoma. The layer segmentation accuracy in the same cases was on average less than one pixel (3.13 μm).

Automated three-dimensional registration and volume rebuilding for wide-field angiographic and structural optical coherence tomography

We propose a three-dimensional (3-D) registration method to correct motion artifacts and construct the volume structure for angiographic and structural optical coherence tomography (OCT). This algorithm is particularly suitable for the nonorthogonal wide-field OCT scan acquired by a ultrahigh-speed swept-source system ( > 200 ?? kHz A-scan rate). First, the transverse motion artifacts are corrected by the between-frame registration based on en face OCT angiography (OCTA). After A-scan transverse translation between B-frames, the axial motions are corrected based on the rebuilt boundary of inner limiting membrane. Finally, a within-frame registration is performed for local optimization based on cross-sectional OCTA. We evaluated this algorithm on retinal volumes of six normal subjects. The results showed significantly improved retinal smoothness in 3-D-registered structural OCT and image contrast on en face OCTA.

Automatic multiorgan segmentation in CT images of the male pelvis using region-specific hierarchical appearance cluster models

PURPOSE

Accurate segmentation of pelvic organs in CT images is of great importance in external beam radiotherapy for prostate cancer. The aim of this studying is to develop a novel method for automatic, multiorgan segmentation of the male pelvis.

METHODS

The authors' segmentation method consists of several stages. First, a pretreatment includes parameterization, principal component analysis (PCA), and an established process of region-specific hierarchical appearance cluster (RSHAC) model which was executed on the training dataset. After the preprocessing, online automatic segmentation of new CT images is achieved by combining the RSHAC model with the PCA-based point distribution model. Fifty pelvic CT from eight prostate cancer patients were used as the training dataset. From another 20 prostate cancer patients, 210 CT images were used for independent validation of the segmentation method.

RESULTS

In the training dataset, 15 PCA modes were needed to represent 95% of shape variations of pelvic organs. When tested on the validation dataset, the authors' segmentation method had an average Dice similarity coefficient and mean absolute distance of 0.751 and 0.371 cm, 0.783 and 0.303 cm, 0.573 and 0.604 cm for prostate, bladder, and rectum, respectively. The automated segmentation process took on average 5 min on a personal computer equipped with Core 2 Duo CPU of 2.8 GHz and 8 GB RAM.

CONCLUSIONS

The authors have developed an efficient and reliable method for automatic segmentation of multiple organs in the male pelvis. This method should be useful for treatment planning and adaptive replanning for prostate cancer radiotherapy. With this method, the physicist can improve the work efficiency and stability.

Automated motion correction using parallel-strip registration for wide-field en face OCT angiogram

We propose an innovative registration method to correct motion artifacts for wide-field optical coherence tomography angiography (OCTA) acquired by ultrahigh-speed swept-source OCT (>200 kHz A-scan rate). Considering that the number of A-scans along the fast axis is much higher than the number of positions along slow axis in the wide-field OCTA scan, a non-orthogonal scheme is introduced. Two en face angiograms in the vertical priority (2 y-fast) are divided into microsaccade-free parallel strips. A gross registration based on large vessels and a fine registration based on small vessels are sequentially applied to register parallel strips into a composite image. This technique is extended to automatically montage individual registered, motion-free angiograms into an ultrawide-field view.

Astrocytic Adrenoceptors: A Major Drug Target in Neurological and Psychiatric Disorders?

Considerable attention has recently been paid to astrocyte functions, which are briefly summarized. A large amount of data is available about adrenoceptor expression and function in astrocytes, some of it dating back to the 1970's and some of it very recent. This material is reviewed in the present paper. The brain is innervated by noradrenergic fibers extending from locus coeruleus in the brain stem, which in turn is connected to a network of adrenergic and noradrenergic nuclei in the medulla and pons, contributing to the control of (nor)adrenergic, serotonergic, dopaminergic and cholinergic function, both in the central nervous system (CNS) and in the periphery. In the CNS astrocytes constitute a major target for noradrenergic innervation, which regulates morphological plasticity, energy metabolism, membrane transport, gap junction permeability and immunological responses in these cells. Noradrenergic effects on astrocytes are essential during consolidation of episodic, long-term memory, which is reinforced by beta-adrenergic activation. Glycogenolysis and synthesis of glutamate and glutamine from glucose, both of which are metabolic processes restricted to astrocytes, occur at several time-specific stages during the consolidation. Astrocytic abnormalities are almost certainly important in the pathogenesis of multiple sclerosis and in all probability contribute essentially to inflammation and malfunction in Alzheimer's disease and to mood disturbances in affective disorders. Noradrenergic function in astrocytes is severely disturbed by chronic exposure to cocaine, which also changes astrocyte morphology. Development of drugs modifying noradrenergic receptor activity and/or down-stream signaling is advocated for treatment of several neurological/psychiatric disorders and for neuroprotection. Astrocytic preparations are suggested for study of mechanism(s) of action of antidepressant drugs and pathophysiology of mood disorders.

[Determination and significance of catecholamines in aqueous humor, plasma and 24 hour urine of patients with acute angle-closure glaucoma]

OBJECTIVE

To study the functional state of ocular and systemic sympathetic nervous system and the effects of its related neurohumoral factor, catecholamines (CA), on intraocular pressure (IOP) in patients with acute angle-closure glaucoma (AACG).

METHODS

The levels of CA in the aqueous humor, plasma and the total amount of CA in 24 hour urine of cases with AACG during attack stage were determined with fluorometry, and the results were compared to that of patients with senile cataract and normal adults.

RESULTS

The CA levels within aqueous humor and plasma and in 24 hour urine of the patients with AACG during attack stage were elevated obviously as compared with that of the control subjects (P < 0.001), and positively correlated with the level of IOP.

CONCLUSION

During attack stage of AACG, the ocular sympathetic nervous system is highly excited and a large amount of CA is released into the aqueous humor, in the meantime the systemic sympathetic nervous system is also excited and CA is released into the blood circulation, which may play a certain role in the regulation of IOP. Possibly, CA is an important supplemental factor within the attack stage of AACG.