Citations

Background: Optic neuritis (ON) is often the presenting symptom in inflammatory central nervous system demyelinating disorders. Objective: To compare the frequency and pattern of optic chiasm involvement in patients with aquaporin-4-immunoglobulin G (AQP4-IgG)-associated ON to patients with myelin oligodendrocyte glycoprotein-immunoglobulin G (MOG-IgG)-associated ON. Methods: Retrospective review of all patients evaluated at Mayo Clinic, Stanford University and Ramathibodi Hospital who were found to have: (1) ON, (2) either MOG-IgG or AQP4-IgG by cell-based assay, and (3) magnetic resonance imaging (MRI) at the time of ON. MRI was reviewed for contrast enhancement of the optic chiasm and the pattern of involvement. Results: One hundred and fifty-four patients (74 AQP4-IgG and 80 MOG-IgG) were included. Among patients with AQP4-IgG-ON, 20% had chiasmal involvement, compared with 16% of patients with MOG-IgG-ON ( p = 0.66). In patients with chiasmal involvement, longitudinally extensive optic nerve enhancement (from orbit extending to chiasm) was identified in 54% of MOG-IgG-ON patients, compared with 7% of AQP4-IgG-ON patients ( p = 0.01). Conclusion: Chiasmal involvement of MOG-IgG-ON and AQP4-IgG-ON occur at more similar frequencies than previously reported. Furthermore, MOG-IgG-ON chiasmal involvement is more likely to be part of a longitudinally extensive optic nerve lesion.

Abstract B-cell non-Hodgkin lymphoma cell survival depends on poorly understood immune evasion mechanisms. In melanoma, the composition of the gut microbiota (GMB) is associated with immune system regulation and response to immunotherapy. We investigated the association of GMB composition and diversity with lymphoma biology and treatment outcome. Patients with diffuse large B-cell lymphoma (DLBCL), marginal zone (MZL), and follicular lymphoma (FL) were recruited at Mayo Clinic, Minnesota, and Perlmutter Cancer Center, NYU Langone Health. The pretreatment GMB was analyzed using 16S ribosomal RNA gene sequencing. We examined GMB compositions in 3 contexts: lymphoma patients (51) compared with healthy controls (58), aggressive (DLBCL) (8) compared with indolent (FL, MZL) (18), and the association of GMB with immunochemotherapy treatment outcomes (8 responders, 6 nonresponders). Respectively, we found that the pretreatment GMB in lymphoma patients had a distinct composition compared with healthy controls (P < .001); GMB compositions in DLBCL patients were significantly different than indolent patients (P = .01) with a trend toward reduced microbial diversity in DLBCL patients (P = .08); and pretreatment GMB diversity and composition were significant predictors of treatment responses (P = .01). The impact of these pilot results is limited by our small sample size, and should be considered a proof of principle. If validated, our results could lead toward improved treatment outcomes by improving medication stewardship and informing which GMB-targeted therapies should be tested to improve patient outcomes.

AbstractBackgroundTranslating recent advances in the diagnosis of Alzheimer’s disease (AD) and other dementias to clinical practice using biomarker‐based approaches requires accessible, affordable biomarkers. This protocol tested the performance of a Retinal Deep Phenotyping platform for the detection of likely positron‐emission tomography (PET) amyloid status (negative or positive) in human subjects. This platform generates data rich retinal reflectance images captured non‐invasively with a Metabolic Hyperspectral Retinal Camera (MHRC) yielding specific spatial‐spectral features which are analyzed by a machine learning algorithm relatively to a recognized gold standard biomarker (in this case PET amyloid imaging).Methods134 subjects (50 years and older) from 3 clinical sites were included, 94 with normal, 40 with abnormal cognition, imaged with the MHRC and amyloid PET (unanimous visual reads from a panel of 3 expert reviewers; 42 amyloid positive, 92 negative). Over 600 spatial‐spectral features were extracted from hyperspectral retinal images obtained at 450‐900 nm. After a feature selection based on F‐score ranking, a classifier was trained on the datasets from 87 patients (2/3 of the cohort) using the most discriminating features. The classification performance was then evaluated with the datasets from the remaining 47 patients.ResultsResubstituting the training set of 87 patients into the classifier yielded sensitivity and specificity values of 88% and 91%, respectively. The training misclassification rate was predicted using the k‐fold loss metric, with a predicted error rate of 19%. To verify this performance, the test set of 47 patients was classified by the model resulting in sensitivity and specificity values of 80% and 81%, respectively.ConclusionsThe Retinal Deep Phenotyping platform shows promise for detecting the likely cerebral amyloid PET status in human subjects and could serve as a screening tool to identify subjects in the AD continuum, for instance in a clinical or drug development context. The platform could also be used for the detection of other biomarkers involved in cognitive decline.