Isolated Intracranial Hypertensions as Onset of Myelin Oligodendrocyte Glycoprotein Antibody Disease

Myelin oligodendrocyte glycoprotein antibody disease (MOGAD) is characterized by multiple phenotypic conditions such as acute disseminated encephalomyelitis, optic neuritis, and myelitis. MOGAD's spectrum is expanding, with potential symptoms of increased intracranial pressure that are similar to idiopathic intracranial hypertension (IIH). We report a boy with new-onset continuous headache and a brain MRI at onset suggesting idiopathic intracranial hypertension (IIH). The patient showed resistance to treatment with acetazolamide and, after one month, developed optic neuritis in the left eye. Laboratory tests documented positive MOG antibodies (anti-MOG) in the serum. The final diagnosis was MOGAD, with the initial symptoms resembling IIH

Risk of hospitalization for heart failure in patients with type 2 diabetes newly treated with DPP-4 inhibitors or other oral glucose-lowering medications: A retrospective registry study on 127,555 patients from the Nationwide OsMed Health-DB Database

Aims Oral glucose-lowering medications are associated with excess risk of heart failure (HF). Given the absence of comparative data among drug classes, we performed a retrospective study in 32 Health Services of 16 Italian regions accounting for a population of 18 million individuals, to assess the association between HF risk and use of sulphonylureas, DPP-4i, and glitazones. Methods and results We extracted data on patients with type 2 diabetes who initiated treatment with DPP-4i, thiazolidinediones, or sulphonylureas alone or in combination with metformin during an accrual time of 2 years. The endpoint was hospitalization for HF (HHF) occurring after the first 6 months of therapy, and the observation was extended for up to 4 years. A total of 127 555 patients were included, of whom 14.3% were on DPP-4i, 72.5% on sulphonylurea, 13.2% on thiazolidinediones, with average 70.7% being on metformin as combination therapy. Patients in the three groups differed significantly for baseline characteristics: age, sex, Charlson index, concurrent medications, and previous cardiovascular events. During an average 2.6-year follow-up, after adjusting for measured confounders, use of DPP-4i was associated with a reduced risk of HHF compared with sulphonylureas [hazard ratio (HR) 0.78; 95% confidence interval (CI) 0.62-0.97; P = 0.026]. After propensity matching, the analysis was restricted to 39 465 patients, and the use of DPP-4i was still associated with a lower risk of HHF (HR 0.70; 95% CI 0.52-0.94; P = 0.018). Conclusion In a very large observational study, the use of DPP-4i was associated with a reduced risk of HHF when compared with sulphonylureas

Cerebral Venous Thrombosis. A Challenging Diagnosis; A New Nonenhanced Computed Tomography Standardized Semi-Quantitative Method

Cerebral venous sinus thrombosis (CVST) on non-contrast CT (NCCT) is often challenging to detect. We retrospectively selected 41 children and 36 adults with confirmed CVST and two age-matched control groups with comparable initial symptoms. We evaluated NCCT placing four small circular ROIs in standardized regions of the cerebral dural venous system. The mean and maximum HU values were considered from each ROI, and the relative percentage variations were calculated (mean % variation and maximum % variation). We compared the highest measured value to the remaining three HU values through an ad-hoc formula based on the assumption that the thrombosed sinus has higher attenuation compared with the healthy sinuses. Percentage variations were employed to reflect how the attenuation of the thrombosed sinus deviates from the unaffected counterparts. The attenuation of the affected sinus was increased in patients with CVST, and consequently both the mean % and maximum % variations were increased. A mean % variation value of 12.97 and a maximum % variation value of 10.14 were found to be useful to distinguish patients with CVST from healthy subjects, with high sensitivity and specificity. Increased densitometric values were present in the site of venous thrombosis. A systematic, blind evaluation of the brain venous system can assist radiologists in identifying patients who need or do not need further imaging

Non-congenital viral infections of the central nervous system. from the immunocompetent to the immunocompromised child

Non-congenital viral infections of the central nervous system in children can represent a severe clinical condition that needs a prompt diagnosis and management. However, the aetiological diagnosis can be challenging because symptoms are often nonspecific and cerebrospinal fluid analysis is not always diagnostic. In this context, neuroimaging represents a helpful tool, even though radiologic patterns sometimes overlap. The purpose of this pictorial essay is to suggest a schematic representation of different radiologic patterns of non-congenital viral encephalomyelitis based on the predominant viral tropism and vulnerability of specific regions: cortical grey matter, deep grey matter, white matter, brainstem, cerebellum and spine

Deep learning can differentiate idh-mutant from idh-wild gbm

Isocitrate dehydrogenase (IDH) mutant and wildtype glioblastoma multiforme (GBM) often show overlapping features on magnetic resonance imaging (MRI), representing a diagnostic challenge. Deep learning showed promising results for IDH identification in mixed low/high grade glioma populations; however, a GBM-specific model is still lacking in the literature. Our aim was to develop a GBM-tailored deep-learning model for IDH prediction by applying convoluted neural networks (CNN) on multiparametric MRI. We selected 100 adult patients with pathologically demonstrated WHO grade IV gliomas and IDH testing. MRI sequences included: MPRAGE, T1, T2, FLAIR, rCBV and ADC. The model consisted of a 4-block 2D CNN, applied to each MRI sequence. Probability of IDH mutation was obtained from the last dense layer of a softmax activation function. Model performance was evaluated in the test cohort considering categorical cross-entropy loss (CCEL) and accuracy. Calculated performance was: rCBV (accuracy 83%, CCEL 0.64), T1 (accuracy 77%, CCEL 1.4), FLAIR (accuracy 77%, CCEL 1.98), T2 (accuracy 67%, CCEL 2.41), MPRAGE (accuracy 66%, CCEL 2.55). Lower performance was achieved on ADC maps. We present a GBM-specific deep-learning model for IDH mutation prediction, with a maximal accuracy of 83% on rCBV maps. Highest predictivity achieved on perfusion images possibly reflects the known link between IDH and neoangiogenesis through the hypoxia inducible factor