Alterations in B Cell and Follicular T-Helper Cell Subsets in Patients with Acute COVID-19 and COVID-19 Convalescents

Background. Humoral immunity requires interaction between B cell and T follicular helper cells (Tfh) to produce effective immune response, but the data regarding a role of B cells and Tfh in SARS-CoV-2 defense are still sparse. Methods. Blood samples from patients with acute COVID-19 (n = 64), convalescents patients who had specific IgG to SARS-CoV-2 N-protein (n = 55), and healthy donors with no detectable antibodies to any SARS-CoV-2 proteins (HC, n = 44) were analyses by multicolor flow cytometry. Results. Patients with acute COVID-19 showed decreased levels of memory B cells subsets and increased proportion plasma cell precursors compared to HC and COVID-19 convalescent patients, whereas for the latter the elevated numbers of virgin naïve, Bm2′ and “Bm3+Bm4” was found if compared with HC. During acute COVID-19 CXCR3+CCR6− Tfh1-like cells were decreased and the levels of CXCR3–CCR6+ Tfh17-like were increased then in HC and convalescent patients. Finally, COVID-19 convalescent patients had increased levels of Tfh2-, Tfh17- and DP Tfh-like cells while comparing their amount with HC. Conclusions. Our data indicate that COVID-19 can impact the humoral immunity in the long-term.

On the Complementarity of Sparse L0 and CEL0 Regularized Loss Landscapes for DOA Estimation

L0 sparse methods are not widespread in [AD]DOADirection-Of-Arrival (DOA) estimation yet, [AD]althoughdespite their potential superiority over classical methods in difficult scenarios. This comes from the difficulties encountered for [AD]theglobal optimization on hill-climbing error surfaces. In this paper, we explore the loss landscapes of L0 and [AD]CEL0Continuous Exact L0 (CEL0) regularized problems in order to design a new optimization scheme. As expected, we observe that the recently introduced CEL0 penalty leads to an error surface with less local minima than the L0 one. This property explains the good behavior of [AD]the CEL0-regularized sparse DOA estimation problem for well-separated sources. Unfortunately, CEL0-regularized landscape enlarges L0-basins in the middle of close sources, and CEL0 methods are thus unable to resolve two close sources. Consequently, we propose to alternate between both error surfaces to increase the probability of reaching the global solution. Experiments show that the proposed approach offers better performance than existing ones, and particularly an enhanced resolution limit.

Post-traumatic Anton’s Syndrome and Its Rehabilitation Challenges: A Case Report

Objectives: Anton’s syndrome is a rare clinical phenomenon characterized by acquired cortical blindness without the self-awareness of blindness. Patients with this syndrome tend to deny their visual deficits. The literature on the rehabilitation of individuals with Anton’s syndrome is sparse. Methods: We describe a 26-year-old patient who developed Anton’s syndrome in the background of anterior visual tract damage and multiple intracranial bleedings after traumatic brain injury. He presented with visual agnosia and confabulations that were only detected 2 months after trauma. He received 4 months of multidisciplinary rehabilitation care as an outpatient. His comprehensive program consisted of (a) compensatory strategies, (b) restorative strategies, and (c) counseling sessions. Although he showed significant improvement in terms of functional gain with rehabilitation, he still had visual deficits with occasional visual anosognosia that required 24-h supervision. Conclusion: Anton’s syndrome in traumatic brain injury should not be overlooked. A prompt diagnosis is vital to initiate appropriate rehabilitation measures.

Persisting Salivary IgG against SARS-CoV-2 at 9 Months After Mild COVID-19: A Complementary Approach to Population Surveys

Background: Declining humoral immunity in COVID-19 patients and possibility of reinfections has raised concern. Mucosal immunity particularly salivary antibodies could be short-lived. However, long-term studies are sparse. Methods: Using a multiplex bead-based array platform, we investigated antibodies specific to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) proteins in 256 saliva samples from convalescent patients 1-9 months after symptomatic COVID-19 (n=74, Cohort 1), undiagnosed individuals with self-reported questionnaires (n=147, Cohort 2), and individuals sampled pre-pandemic time (n= 35, Cohort 3). Results: Salivary IgG antibody responses in Cohort 1 (mainly mild COVID-19) were detectable up to 9 month recovery, with high correlations between spike and nucleocapsid specificity. At 9 months, IgG remained in saliva in majority as seen in blood serology. Salivary IgA was rarely detected at this timepoint. In Cohort 2, salivary IgG and IgA responses were significantly associated with recent history of COVID-19 like symptoms. Salivary IgG also tolerated temperature and detergent pre-treatments. Conclusions: Unlike SARS-CoV-2 salivary IgA that appeared short-lived, the specific IgG in saliva appears stable even after mild COVID-19 as noted for blood serology. The non-invasive saliva-based SARS-Cov-2 antibody testing with self-collection at homes may thus serve as a complementary alternative to conventional blood serology.

A Framework for Generating Extractive Summary from Multiple Malayalam Documents

Automatic extractive text summarization retrieves a subset of data that represents most notable sentences in the entire document. In the era of digital explosion, which is mostly unstructured textual data, there is a demand for users to understand the huge amount of text in a short time; this demands the need for an automatic text summarizer. From summaries, the users get the idea of the entire content of the document and can decide whether to read the entire document or not. This work mainly focuses on generating a summary from multiple news documents. In this case, the summary helps to reduce the redundant news from the different newspapers. A multi-document summary is more challenging than a single-document summary since it has to solve the problem of overlapping information among sentences from different documents. Extractive text summarization yields the sensitive part of the document by neglecting the irrelevant and redundant sentences. In this paper, we propose a framework for extracting a summary from multiple documents in the Malayalam Language. Also, since the multi-document summarization data set is sparse, methods based on deep learning are difficult to apply. The proposed work discusses the performance of existing standard algorithms in multi-document summarization of the Malayalam Language. We propose a sentence extraction algorithm that selects the top ranked sentences with maximum diversity. The system is found to perform well in terms of precision, recall, and F-measure on multiple input documents.

Variable Selection in Untargeted Metabolomics and the Danger of Sparsity

The goal of metabolomics is to measure as many metabolites as possible in order to capture biomarkers that may indicate disease mechanisms. Variable selection in chemometric methods can be divided into the following two groups: (1) sparse methods that find the minimal set of variables to discriminate between groups and (2) methods that find all variables important for discrimination. Such important variables can be summarized into metabolic pathways using pathway analysis tools like Mummichog. As a test case, we studied the metabolic effects of treatment with nicotinamide riboside, a form of vitamin B3, in a cohort of patients with ataxia–telangiectasia. Vitamin B3 is an important co-factor for many enzymatic reactions in the human body. Thus, the variable selection method was expected to find vitamin B3 metabolites and also other secondary metabolic changes during treatment. However, sparse methods did not select any vitamin B3 metabolites despite the fact that these metabolites showed a large difference when comparing intensity before and during treatment. Univariate analysis or significance multivariate correlation (sMC) in combination with pathway analysis using Mummichog were able to select vitamin B3 metabolites. Moreover, sMC analysis found additional metabolites. Therefore, in our comparative study, sMC displayed the best performance for selection of relevant variables.

Seabuckthorn Berries Attenuate Pulmonary Vascular Hyperpermeability in Lipopolysaccharide-induced Acute Lung Injury in Mice

Background Acute lung injury is featured by pulmonary vascular hyperpermeability, resulting in high short-term mortality. Currently pharmacological therapies are still sparse. Methods In the mice model of acute lung injury induced by Lipopolysaccharide, the effect of seabuckthorn berries extract on pulmonary vascular hyperpermeability was evaluated by histopathologic observation and transvascular leakage determination. The key factors involved in alveolar-capillary barrier lesion were assessed. Results The findings indicated that treatment of seabuckthorn berries alleviated morphological lesion as well as water, Evans blue and total proteins leakage in lung tissue, suppressed the release of TNF-α and IL-6, decreased accumulation of neutrophils, inhibited the activation of NF-κB and down-regulated the expression of ICAM-1 and CD62E. Conclusions These results demonstrated seabuckthorn berries help maintaining alveolar-capillary barrier integrity under endotoxin challenge in mice by suppressing the key factors in the pathogenesis of acute lung injury.