Neurosarcoidosis Presenting as Young-Onset Chronic Headache: A Case Report.

Sarcoidosis causes granulomatous inflammation in multiple organ systems. Lungs are the most commonly affected organs. In 50% of the cases with lung involvement, other organs are also involved. The central nervous system is one of the rarer organ systems involved - comprising 5-15% of all the cases. Neurological involvement, when present, can affect virtually every part of the central and peripheral nervous system. We present an interesting case of a 30-year-old nonalcoholic, nonsmoker man who presented with a chronic headache of 6 years. He had a recent swaying of the body for 6 months and mild forgetfulness for 4 months. The patient had 2 episodes of facial nerve palsy in the past - which had responded to the treatment of Bell's palsy; however, the patient had some residual findings on presentation. While CSF findings suggested meningeal tuberculosis, the radiological evidence and biopsy confirmed the diagnosis of neurosarcoidosis. On follow-up after 6 weeks of steroid therapy, the patient displayed a marked improvement in headache, cognitive function, and a good response on imaging studies. Our case emphasizes that neurosarcoidosis should be considered in the differential diagnosis of chronic headaches and recurrent facial nerve palsy despite being very rare.

Retino-choroidal changes in patients with acute pancreatitis: A prospective analysis of a novel biomarker.

BACKGROUND: There is paucity of data on ocular changes in acute Pancreatitis (AP). Moreover, subclinical alterations in retina & choroid have not been studied in AP. OBJECTIVE: To prospectively study retino-choroidal changes in AP. METHODS: Sixty patients (mean age 39.07 years; 41 males) with AP were followed up till recovery/death. Baseline slit-lamp examination, choroidal thickness (CT), retinal thickness (RT), choroidal vascularity index (CVI), retinal capillary density index (CDI) and arteriovenous ratio (AVR) were recorded. The patients were divided into two groups - mild (Group A; 5 patients) and moderately severe/severe (Group B; 55 patients) as per revised Atlanta classification. RESULTS: Fundus examination showed mild optic disc edema with retinal hemorrhages in 6 (10%) patients in group B as compared to none in group A (p = 1.00). None of the patients had Purtscher retinopathy. Mean CT (317 ± 56.29 µm) was increased as compared to normal subjects (278.90 ± 57.84 µm, p = 0.003). The mean CVI (0.62 ± 0.04) was decreased as compared to normal (0.66 ± 0.01, p < 0.0001) as was the mean AVR (0.67 ± 0.03 vs. 0.7 ± 0.02, p < 0.0001). However, the mean RT of subjects with AP (239.68 ± 33.76 µm) was not significantly different compared to the normal subjects 253.17 ± 33.67 µm (p=NS). The mean CDI of superficial and deep plexus were comparable between normal and patients with AP. CT, RT, CVI, AVR and CDI were comparable between group A and group B as well as survivors and non-survivors. CONCLUSIONS: Clinically significant ocular changes are seen infrequently in AP. However, subclinical changes in CT, CVI and AVR are observed in patients with AP compared to normal individuals.

Collinearity in ecological niche modeling: Confusions and challenges.

Ecological niche models are widely used in ecology and biogeography. Maxent is one of the most frequently used niche modeling tools, and many studies have aimed to optimize its performance. However, scholars have conflicting views on the treatment of predictor collinearity in Maxent modeling. Despite this lack of consensus, quantitative examinations of the effects of collinearity on Maxent modeling, especially in model transfer scenarios, are lacking. To address this knowledge gap, here we quantify the effects of collinearity under different scenarios of Maxent model training and projection. We separately examine the effects of predictor collinearity, collinearity shifts between training and testing data, and environmental novelty on model performance. We demonstrate that excluding highly correlated predictor variables does not significantly influence model performance. However, we find that collinearity shift and environmental novelty have significant negative effects on the performance of model transfer. We thus conclude that (a) Maxent is robust to predictor collinearity in model training; (b) the strategy of excluding highly correlated variables has little impact because Maxent accounts for redundant variables; and (c) collinearity shift and environmental novelty can negatively affect Maxent model transferability. We therefore recommend to quantify and report collinearity shift and environmental novelty to better infer model accuracy when models are spatially and/or temporally transferred.