An Unusual Presentation of Neurosarcoidosis in a 64-Year-Old Man: A Case Report.

Sarcoidosis is a multisystem granulomatous disorder with an unknown etiology that typically involves the lungs, skin, and lymph nodes, with neurological involvement being relatively rare. We discuss a case of neurosarcoidosis in a 64-year-old man who initially presented with unexplained cognitive impairment, insomnia, hyponatremia, paresthesias, and weight loss and later developed uveitis, diplopia, and dysphagia. Ultimately, findings of hilar and mediastinal lymphadenopathy on chest computed tomography (CT) resulted in bronchoscopy, which led to the diagnosis. This case highlights a rare presentation of sarcoidosis with an unusual constellation of symptoms. We discuss the difficulty involved in diagnosing this disorder as well as its highly variable course.

The Clinical Course of a Severe Case of Malignant Catatonia.

Catatonia is a psychomotor syndrome predominantly associated with mental illness disorders, most commonly bipolar disorder and schizophrenia. Catatonia is classified as malignant when, in addition to catatonic symptoms, dysautonomia is present. Autonomic abnormalities can include changes in temperature, labile blood pressure, and changes in heart and respiratory rates. Because malignant catatonia is life-threatening, prompt recognition and management are essential to prevent mortality. We present a severe case of catatonia with malignant features that highlight the importance of early diagnosis and treatment.

Real-Time Mobile Teleophthalmology for the Detection of Eye Disease in Minorities and Low Socioeconomics At-Risk Populations.

PURPOSE: To examine the benefits and feasibility of a mobile, real-time, community-based, teleophthalmology program for detecting eye diseases in the New York metro area. DESIGN: Single site, nonrandomized, cross-sectional, teleophthalmologic study. METHODS: Participants underwent a comprehensive evaluation in a Wi-Fi-equipped teleophthalmology mobile unit. The evaluation consisted of a basic anamnesis with a questionnaire form, brief systemic evaluations and an ophthalmologic evaluation that included visual field, intraocular pressure, pachymetry, anterior segment optical coherence tomography, posterior segment optical coherence tomography, and nonmydriatic fundus photography. The results were evaluated in real-time and follow-up calls were scheduled to complete a secondary questionnaire form. Risk factors were calculated for different types of ophthalmological referrals. RESULTS: A total of 957 participants were screened. Out of 458 (48%) participants that have been referred, 305 (32%) had glaucoma, 136 (14%) had narrow-angle, 124 (13%) had cataract, 29 had (3%) diabetic retinopathy, 9 (1%) had macular degeneration, and 97 (10%) had other eye disease findings. Significant risk factors for ophthalmological referral consisted of older age, history of high blood pressure, diabetes mellitus, Hemoglobin A1c measurement of ≥6.5, and stage 2 hypertension. As for the ocular parameters, all but central corneal thickness were found to be significant, including having an intraocular pressure >21 mm Hg, vertical cup-to-disc ratio ≥0.5, visual field abnormalities, and retinal nerve fiber layer thinning. CONCLUSIONS: Mobile, real-time teleophthalmology is both workable and effective in increasing access to care and identifying the most common causes of blindness and their risk factors.

Evaluation of a Deep Learning System For Identifying Glaucomatous Optic Neuropathy Based on Color Fundus Photographs.

PRECIS: Pegasus outperformed 5 of the 6 ophthalmologists in terms of diagnostic performance, and there was no statistically significant difference between the deep learning system and the "best case" consensus between the ophthalmologists. The agreement between Pegasus and gold standard was 0.715, whereas the highest ophthalmologist agreement with the gold standard was 0.613. Furthermore, the high sensitivity of Pegasus makes it a valuable tool for screening patients with glaucomatous optic neuropathy. PURPOSE: The purpose of this study was to evaluate the performance of a deep learning system for the identification of glaucomatous optic neuropathy. MATERIALS AND METHODS: Six ophthalmologists and the deep learning system, Pegasus, graded 110 color fundus photographs in this retrospective single-center study. Patient images were randomly sampled from the Singapore Malay Eye Study. Ophthalmologists and Pegasus were compared with each other and to the original clinical diagnosis given by the Singapore Malay Eye Study, which was defined as the gold standard. Pegasus' performance was compared with the "best case" consensus scenario, which was the combination of ophthalmologists whose consensus opinion most closely matched the gold standard. The performance of the ophthalmologists and Pegasus, at the binary classification of nonglaucoma versus glaucoma from fundus photographs, was assessed in terms of sensitivity, specificity and the area under the receiver operating characteristic curve (AUROC), and the intraobserver and interobserver agreements were determined. RESULTS: Pegasus achieved an AUROC of 92.6% compared with ophthalmologist AUROCs that ranged from 69.6% to 84.9% and the "best case" consensus scenario AUROC of 89.1%. Pegasus had a sensitivity of 83.7% and a specificity of 88.2%, whereas the ophthalmologists' sensitivity ranged from 61.3% to 81.6% and specificity ranged from 80.0% to 94.1%. The agreement between Pegasus and gold standard was 0.715, whereas the highest ophthalmologist agreement with the gold standard was 0.613. Intraobserver agreement ranged from 0.62 to 0.97 for ophthalmologists and was perfect (1.00) for Pegasus. The deep learning system took ∼10% of the time of the ophthalmologists in determining classification. CONCLUSIONS: Pegasus outperformed 5 of the 6 ophthalmologists in terms of diagnostic performance, and there was no statistically significant difference between the deep learning system and the "best case" consensus between the ophthalmologists. The high sensitivity of Pegasus makes it a valuable tool for screening patients with glaucomatous optic neuropathy. Future work will extend this study to a larger sample of patients.

T-Lymphocyte Subset Distribution and Activity in Patients With Glaucoma.

Purpose: Besides glia-driven neuroinflammation, growing evidence from analysis of human blood samples, isolated autoantibodies, and postmortem tissues also support systemic immune responses during neurodegeneration in glaucoma patients. To explore the T-cell-mediated component of systemic immunity, this study analyzed T lymphocytes in patients' blood. Methods: Blood samples were collected from 32 patients with glaucoma and 21 nonglaucomatous controls, and mononuclear cells were isolated by Histopaque density gradient centrifugation. T-cell subset distribution was analyzed by multicolor flow cytometry after helper (Th) and cytotoxic fractions, and Th subpopulations, were stained with antibodies to CD4, CD8, or distinctive markers, such as IFN-γ (for Th1), IL-4 (for Th2), IL-17A (for Th17), and CD25/FoxP3 (for T regulatory cells [Tregs]). In addition, proliferative activity and cytokine secretion of T cells were analyzed after in vitro stimulation. Results: Analysis of T-cell subset distribution detected a glaucoma-related shift. Despite similar frequencies of CD4+ or CD8+ T cells, or Th1, Th2, or Th17 subsets in glaucoma and control groups, glaucomatous samples exhibited a trend toward decreased frequency of CD4+ (or CD8+)/CD25+/FoxP3+ Tregs within the entire CD4+ (or CD8+) population (P < 0.001). Furthermore, CD4+ T cells in glaucomatous samples presented a greater stimulation response (∼3-fold) as characterized by increased proliferation and proinflammatory cytokine secretion (P < 0.05). Conclusions: These findings suggest that the immunity activated in glaucoma may not be counterbalanced by an efficient immune suppression. More work is encouraged to determine whether shifted T-cell homeostasis may contribute to neurodegeneration in glaucoma, and/or whether T-cell subset imbalance may serve as a biomarker of autoimmune susceptibility.