The Scope and Impact of Viral Infections in Common Variable Immunodeficiency (CVID) and CVID-like Disorders: A Literature Review.

Common Variable Immunodeficiency (CVID) is a heterogeneous primary immunodeficiency disorder characterised by impaired antibody production, leading to recurrent infections and an increased susceptibility to viral pathogens. This literature review aims to provide a comprehensive overview of CVID's relationship with viral infections, encompassing disease pathogenesis, key presenting features, specific monogenic susceptibilities, the impact of COVID-19, and existing treatment options. The pathogenesis of CVID involves complex immunological dysregulation, including defects in B cell development, antibody class switching, and plasma cell differentiation. These abnormalities contribute to an impaired humoral immune response against viral agents, predisposing individuals with CVID to a broad range of viral infections. Genetic factors play a prominent role in CVID, and monogenic drivers of CVID-like disease are increasingly identified through advanced genomic studies. Some monogenic causes of the CVID-like phenotype appear to cause specific viral susceptibilities, and these are explored in the review. The emergence of the COVID-19 pandemic highlighted CVID patients' heightened predisposition to severe outcomes with viral infections. This review explores the clinical manifestations, outcomes, and potential therapeutic approaches for COVID-19 in CVID patients. It assesses the efficacy of prophylactic measures for COVID-19, including vaccination and immunoglobulin replacement therapy, as well as trialled therapies.

Pre-acquired Functional Connectivity Predicts Choice Inconsistency.

Economic choice theories usually assume that humans maximize utility in their choices. However, studies have shown that humans make inconsistent choices, leading to suboptimal behavior, even without context-dependent manipulations. Previous studies showed that activation in value and motor networks are associated with inconsistent choices at the moment of choice. Here, we investigated if the neural predispositions, measured before a choice task, can predict choice inconsistency in a later risky choice task. Using functional connectivity (FC) measures from resting-state functional magnetic resonance imaging (rsfMRI), derived before any choice was made, we aimed to predict subjects' inconsistency levels in a later-performed choice task. We hypothesized that rsfMRI FC measures extracted from value and motor brain areas would predict inconsistency. Forty subjects (21 females) completed a rsfMRI scan before performing a risky choice task. We compared models that were trained on FC that included only hypothesized value and motor regions with models trained on whole-brain FC. We found that both model types significantly predicted inconsistency levels. Moreover, even the whole-brain models relied mostly on FC between value and motor areas. For external validation, we used a neural network pretrained on FC matrices of 37,000 subjects and fine-tuned it on our data and again showed significant predictions. Together, this shows that the tendency for choice inconsistency is predicted by predispositions of the nervous system and that synchrony between the motor and value networks plays a crucial role in this tendency.

DeePay: deep learning decodes EEG to predict consumer's willingness to pay for neuromarketing.

There is an increasing demand within consumer-neuroscience (or neuromarketing) for objective neural measures to quantify consumers' subjective valuations and predict responses to marketing campaigns. However, the properties of EEG raise difficulties for these aims: small datasets, high dimensionality, elaborate manual feature extraction, intrinsic noise, and between-subject variations. We aimed to overcome these limitations by combining unique techniques of Deep Learning Networks (DLNs), while providing interpretable results for neuroscientific and decision-making insight. In this study, we developed a DLN to predict subjects' willingness to pay (WTP) based on their EEG data. In each trial, 213 subjects observed a product's image, from 72 possible products, and then reported their WTP for the product. The DLN employed EEG recordings from product observation to predict the corresponding reported WTP values. Our results showed 0.276 test root-mean-square-error and 75.09% test accuracy in predicting high vs. low WTP, surpassing other models and a manual feature extraction approach. Network visualizations provided the predictive frequencies of neural activity, their scalp distributions, and critical timepoints, shedding light on the neural mechanisms involved with evaluation. In conclusion, we show that DLNs may be the superior method to perform EEG-based predictions, to the benefit of decision-making researchers and marketing practitioners alike.

An update on VEXAS syndrome.

INTRODUCTION: VEXAS (vacuoles, E1 enzyme, X-linked, autoinflammatory, somatic) syndrome is a recently described, late-onset, acquired autoinflammatory disorder caused by mutations in the UBA1 gene. The various clinical manifestations of VEXAS broadly divided into inflammatory or haematological. VEXAS defines a new disease category - the hematoinflammatory disorders triggered by somatic mutations restricted to blood but causing systemic inflammation with multi-organ involvement and associated with aberrant bone marrow status. VEXAS causes significant morbidity and reduced life expectancy, but the optimum standard of care remains undefined. AREAS COVERED: This review describes the discovery of VEXAS, relevant genetic causes and immunopathology of the disease. A detailed account of its various clinical manifestations and disease mimics is provided. Current treatment and management options are discussed. EXPERT OPINION: New rare variants in UBA1 and VEXAS-like UBA1 negative cases are reported. Consensus diagnostic criteria might be required to define VEXAS and its related disorders. Investigation of sporadic, VEXAS-like cases will require the application of deep sequencing using DNA obtained from various cellular or tissue locations. Prospective studies are needed to define the optimal supportive and treatment options for patients with varying disease severity and prognosis. VEXAS-specific hematopoietic stem cell transplant selection criteria also require development.

Improving Diagnosis and Clinical Management of Acquired Systemic Autoinflammatory Diseases.

Systemic autoinflammatory diseases (SAID) are conditions caused by dysregulation or disturbance of the innate immune system, with neutrophils and macrophages the main effector cells. Although there are now more than 40 distinct, genetically defined SAIDs, the genetic/molecular diagnosis remains unknown for a significant proportion of patients with the disease onset in adulthood. This review focuses on new developments related to acquired/late onset SAID, including phenocopies of monogenic disorders, Schnitzler's syndrome, Adult onset Still's disease, VEXAS syndrome, and autoinflammatory complications associated with myelodysplastic syndrome.

Impact of vaccination on hospitalization and mortality from COVID-19 in patients with primary and secondary immunodeficiency: The United Kingdom experience.

Background: Individuals with primary and secondary immunodeficiency (PID/SID) were shown to be at risk of poor outcomes during the early stages of the SARS-CoV-2 pandemic. SARS-CoV-2 vaccines demonstrate reduced immunogenicity in these patients. Objectives: To understand whether the risk of severe COVID-19 in individuals with PID or SID has changed following the deployment of vaccination and therapeutics in the context of the emergence of novel viral variants of concern. Methods: The outcomes of two cohorts of patients with PID and SID were compared: the first, infected between March and July 2020, prior to vaccination and treatments, the second after these intervention became available between January 2021 and April 2022. Results: 22.7% of immunodeficient patients have been infected at least once with SARS-CoV-2 since the start of the pandemic, compared to over 70% of the general population. Immunodeficient patients were typically infected later in the pandemic when the B.1.1.529 (Omicron) variant was dominant. This delay was associated with receipt of more vaccine doses and higher pre-infection seroprevalence. Compared to March-July 2020, hospitalization rates (53.3% vs 17.9%, p<0.0001) and mortality (Infection fatality rate 20.0% vs 3.4%, p=0.0003) have significantly reduced for patients with PID but remain elevated compared to the general population. The presence of a serological response to vaccination was associated with a reduced duration of viral detection by PCR in the nasopharynx. Early outpatient treatment with antivirals or monoclonal antibodies reduced hospitalization during the Omicron wave. Conclusions: Most individuals with immunodeficiency in the United Kingdom remain SARS-CoV-2 infection naïve. Vaccination, widespread availability of outpatient treatments and, possibly, the emergence of the B.1.1.529 variant have led to significant improvements in morbidity and mortality followings SARS-CoV-2 infection since the start of the pandemic. However, individuals with PID and SID remain at significantly increased risk of poor outcomes compared to the general population; mitigation, vaccination and treatment strategies must be optimized to minimize the ongoing burden of the pandemic in these vulnerable cohorts.

A gateway to consumers' minds: Achievements, caveats, and prospects of electroencephalography-based prediction in neuromarketing.

In the last decade, the field of consumer neuroscience, or neuromarketing, has been flourishing, with numerous publications, academic programs, initiatives, and companies. The demand for objective neural measures to quantify consumers' preferences and predict responses to marketing campaigns is ever on the rise, particularly due to the limitations of traditional marketing techniques, such as questionnaires, focus groups, and interviews. However, research has yet to converge on a unified methodology or conclusive results that can be applied in the industry. In this review, we present the potential of electroencephalography (EEG)-based preference prediction. We summarize previous EEG research and propose features which have shown promise in capturing the consumers' evaluation process, including components acquired from an event-related potential design, inter-subject correlations, hemispheric asymmetry, and various spectral band powers. Next, we review the latest findings on attempts to predict preferences based on various features of the EEG signal. Finally, we conclude with several recommended guidelines for prediction. Chiefly, we stress the need to demonstrate that neural measures contribute to preference prediction beyond what traditional measures already provide. Second, prediction studies in neuromarketing should adopt the standard practices and methodology used in data science and prediction modeling that is common in other fields such as computer science and engineering. This article is categorized under: Economics > Interactive Decision-Making Economics > Individual Decision-Making Psychology > Prediction Neuroscience > Cognition.

Affect dynamics of facial EMG during continuous emotional experiences.

Emotional experiences are complex, multi-component processes that unfold over time. Accordingly, it is crucial to understand the temporal dynamics of these constituent components. Here we studied the dynamics of one of the core emotional systems, i.e. facial muscle activity, during continuous emotional experiences, elicited by movies. We found that transient zygomatic fluctuations were narrowly tuned to a positive emotional experience. During a positive but not a negative movie, zygomatic response patterns were consistent across participants, tracked with subjective ratings and co-varied with emotional dynamics. Corrugator activity evidenced a broader affective profile and larger individual variability. It was coordinated with tonic changes in emotional negativity and inversely coupled with transient changes in positive affect. Taken together, our results confirmed previous findings on the affective profiles of facial responses and extended them to temporal dynamics. They further uncovered important differences in temporal response characteristics of zygomatic and corrugator measures.

WorMachine: machine learning-based phenotypic analysis tool for worms.

BACKGROUND: Caenorhabditis elegans nematodes are powerful model organisms, yet quantification of visible phenotypes is still often labor-intensive, biased, and error-prone. We developed WorMachine, a three-step MATLAB-based image analysis software that allows (1) automated identification of C. elegans worms, (2) extraction of morphological features and quantification of fluorescent signals, and (3) machine learning techniques for high-level analysis. RESULTS: We examined the power of WorMachine using five separate representative assays: supervised classification of binary-sex phenotype, scoring continuous-sexual phenotypes, quantifying the effects of two different RNA interference treatments, and measuring intracellular protein aggregation. CONCLUSIONS: WorMachine is suitable for analysis of a variety of biological questions and provides an accurate and reproducible analysis tool for measuring diverse phenotypes. It serves as a "quick and easy," convenient, high-throughput, and automated solution for nematode research.