Antinuclear antibodies in COVID 19

We appreciated very much the interesting study by Chang et al. on the presence of antinuclear antibodies (ANAs) in patients with moderate/critical coronavirus disease 2019 (COVID 19). Both we and Chang and collaborators described the presence and significance of ANAs in patients with COVID‐19. The two experiences can be compared because Chang et al. studied a number of cases only slightly larger than us. In our opinion, the most important finding is represented by the presence of the nucleolar ANA reactivity, which, in the study by Chang et al., as in ours, is the most frequently detected among the different ANA patterns. In this regard, it is worth mentioning that the nucleolar ANA pattern is one of the several ANA pattern detectable by Indirect immunofluorescence, together with other patterns, such as speckled, homogenous, multiple nuclear dots, and rim like membranous; this pattern can be the serological marker of systemic sclerosis and its antigenic target is the topoisomerase I protein (or scl70). Interestingly, it is of major relevance to note that among the clinical manifestations of systemic sclerosis, it includes pulmonary involvement in the form of a restrictive syndrome secondary to interstitial pneumopathy resembling COVID‐19 interstitial pneumonia

Decompensated cirrhosis as presentation of LKM1/LC1 positive type 2 autoimmune hepatitis in adulthood. A rare clinical entity of difficult management

Background: Autoimmune hepatitis (AIH) is a chronic and aggressive liver disease that rapidly evolves into cirrhosis and end-stage liver disease if not timely diagnosed and treated with immunosuppressive therapy. AIH is classified into type 1 and type 2 according to the autoantibody pattern, with smooth muscle antibodies and/or antinuclear antibodies as serological markers of AIH-1, while antiliver cytosol antibody type 1 and/or antiliver/kidney microsomal antibody type 1 characterize type 2 AIH, which mainly affects children, including infants, and adolescents. Case Summary: We describe a case of type 2 AIH, clinically onset in a 34-year-old woman with decompensated cirrhosis. Only a thorough analysis of the autoantibody profile allowed for a diagnosis of an AIH-2 evolved into cirrhosis. The patient received a moderate corticosteroid therapy without achieving optimal disease control. We discuss the controversial decision of whether or not to treat the patient with immunosuppressive therapy, which should be balanced with the potential risk of infectious and other complications. A review of the literature on the management of patients with autoimmune cirrhosis is also presented. Conclusions: AIH-2 can be clinically onset in adult patients with cirrhosis and its complications, without being preceded by major clinical signs. Due to the difficult management of cirrhosis with immunosuppressive treatments, a patient-tailored strategy with a case-by-case approach is needed to prevent major complications such as infections, potentially precluding liver transplantation the only curative therapy

On non-L2L^2 solutions to the Seiberg-Witten equations

We show that a previous paper of Freund describing a solution to the Seiberg-Witten equations has a sign error rendering it a solution to a related but different set of equations. The non-$L^2$ nature of Freund's solution is discussed and clarified and we also construct a whole class of solutions to the Seiberg-Witten equations.Comment: 8 pages, Te

Evaluation of altered functional connections in male children with autism spectrum disorders on multiple-site data optimized with machine learning

No univocal and reliable brain-based biomarkers have been detected to date in Autism Spectrum Disorders (ASD). Neuroimaging studies have consistently revealed alterations in brain structure and function of individuals with ASD; however, it remains difficult to ascertain the extent and localization of affected brain networks. In this context, the application of Machine Learning (ML) classification methods to neuroimaging data has the potential to contribute to a better distinction between subjects with ASD and typical development controls (TD). This study is focused on the analysis of resting-state fMRI data of individuals with ASD and matched TD, available within the ABIDE collection. To reduce the multiple sources of heterogeneity that impact on understanding the neural underpinnings of autistic condition, we selected a subgroup of 190 subjects (102 with ASD and 88 TD) according to the following criteria: male children (age range: 6.5–13 years); rs-fMRI data acquired with open eyes; data from the University sites that provided the largest number of scans (KKI, NYU, UCLA, UM). Connectivity values were evaluated as the linear correlation between pairs of time series of brain areas; then, a Linear kernel Support Vector Machine (L-SVM) classification, with an inter-site cross-validation scheme, was carried out. A permutation test was conducted to identify over-connectivity and under-connectivity alterations in the ASD group. The mean L-SVM classification performance, in terms of the area under the ROC curve (AUC), was 0.75 ± 0.05. The highest performance was obtained using data from KKI, NYU and UCLA sites in training and data from UM as testing set (AUC = 0.83). Specifically, stronger functional connectivity (FC) in ASD with respect to TD involve (p < 0.001) the angular gyrus with the precuneus in the right (R) hemisphere, and the R frontal operculum cortex with the pars opercularis of the left (L) inferior frontal gyrus. Weaker connections in ASD group with respect to TD are the intra-hemispheric R temporal fusiform cortex with the R hippocampus, and the L supramarginal gyrus with L planum polare. The results indicate that both under-and over-FC occurred in a selected cohort of ASD children relative to TD controls, and that these functional alterations are spread in different brain networks