Epigenetic annotation studies of genetic risk variants for multiple sclerosis (MS) implicate dysfunctional lymphocytes in MS susceptibility; however, the role of central nervous system (CNS) cells remains unclear. We investigated the effect of the risk variant, rs7665090G, located near NFKB1, on astrocytes. We demonstrated that chromatin is accessible at the risk locus, a prerequisite for its impact on astroglial function. The risk variant was associated with increased NF-κB signaling and target gene expression, driving lymphocyte recruitment, in cultured human astrocytes and astrocytes within MS lesions, and with increased lesional lymphocytic infiltrates and lesion sizes. Thus, our study establishes a link between genetic risk for MS (rs7665090G) and dysfunctional astrocyte responses associated with increased CNS access for peripheral immune cells. MS may therefore result from variant-driven dysregulation of the peripheral immune system and of the CNS, where perturbed CNS cell function aids in establishing local autoimmune inflammation.

Epigenetic annotation studies of genetic risk variants for multiple sclerosis (MS) implicate dysfunctional lymphocytes in MS susceptibility; however, the role of central nervous system (CNS) cells remains unclear. We investigated the effect of the risk variant, rs7665090 G , located near NFKB1, on astrocytes. We demonstrated that chromatin is accessible at the risk locus, a prerequisite for its impact on astroglial function. The risk variant was associated with increased NF-κB signaling and target gene expression driving lymphocyte recruitment in cultured human astrocytes and astrocytes within MS lesions, and with increased lesional lymphocytic infiltrates.In MS patients, the risk genotype was associated with increased lesion volumes on MRI. Thus, we established that the rs7665090 G variant perturbs astrocyte function resulting in increased CNS access for peripheral immune cells. MS may thus result from variant-driven dysregulation of the peripheral immune system and the CNS, where perturbed CNS cell function aids in establishing local autoimmune inflammation.

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Cefepime is a fourth generation cephalosporin which is bactericidal for broad spectrum of organisms. This is a case-series of three patients who presented to our hospital with confusion secondary to cefepime use to treat urinary tract infection (UTI) and health care associated pneumonia (HCAP), after excluding other common etiologies of altered mental status (AMS). Of these three patients, one had progressive expressive aphasia and the other two demonstrated asynchronous myoclonic activity of the limbs. The symptoms were seen within four to five days of initiating the treatment and resolved within three days of discontinuation of cefepime. Acute structural abnormalities were excluded by computed tomography (CT) and magnetic resonance imaging (MRI) of the brain. Electroencephalogram (EEG) showed diffuse slowing activity with triphasic waves consistent with encephalopathy. In one patient, renal function was within normal limits, whereas it was abnormal in two patients. To our knowledge, this is the first report of cefepime induced asynchronous myoclonus and expressive aphasia in a patient with normal kidney function.

Background: Immune checkpoint inhibitors (ICIs) have become an increasingly important tool in cancer treatment, revealing durable responses in several different types of tumors, including NSCLCs. Nevertheless, ICIs carry a risk of immune-mediated toxicities. There is a paucity of data for concurrent use of these agents in patients with autoimmune disorders, such as multiple sclerosis (MS).Case Presentation: We report a case of a man with a history of MS and metastatic NSCLC with brain metastases who had cancer progression after receiving chemotherapy, whole-brain radiation therapy, and stereotactic radiosurgery to brain lesions and was treated with the programmed death-ligand 1 inhibitor, atezolizumab. He had dramatic clinical and radiographic benefit but developed a severe MS flare and neurologic decline precluding further treatment. Considerable growth of a previously radiated brain lesion prompted resection, with pathologic findings consistent with radiation necrosis and demyelination without viable tumor cells.Conclusions: Although patients with preexisting autoimmune diseases, including MS, might be at an increased risk of developing immune-related adverse events with ICIs, they may also experience anticancer benefit. Intracranial disease can be challenging to accurately diagnose in a patient with MS who previously underwent radiation, as progressing lesions can be tumor growth, MS flare, or radiation necrosis.

Background: Magnetic resonance (MR) scans are routine clinical procedures for monitoring people with multiple sclerosis (PwMS). Patient discomfort, timely scheduling, and financial burden motivate the need to accelerate MR scan time. We examined the clinical application of a deep learning (DL) model in restoring the image quality of accelerated routine clinical brain MR scans for PwMS.Methods: We acquired fast 3D T1w BRAVO and fast 3D T2w FLAIR MRI sequences (half the phase encodes and half the number of slices) in parallel to conventional parameters. Using a subset of the scans, we trained a DL model to generate images from fast scans with quality similar to the conventional scans and then applied the model to the remaining scans. We calculated clinically relevant T1w volumetrics (normalized whole brain, thalamic, gray matter, and white matter volume) for all scans and T2 lesion volume in a sub-analysis. We performed paired t-tests comparing conventional, fast, and fast with DL for these volumetrics, and fit repeated measures mixed-effects models to test for differences in correlations between volumetrics and clinically relevant patient-reported outcomes (PRO).Results: We found statistically significant but small differences between conventional and fast scans with DL for all T1w volumetrics. There was no difference in the extent to which the key T1w volumetrics correlated with clinically relevant PROs of MS symptom burden and neurological disability.Conclusion: A deep learning model that improves the image quality of the accelerated routine clinical brain MR scans has the potential to inform clinically relevant outcomes in MS.

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