Search

Objective: Volume status assessment of a patient by ultrasound (US) imaging of the inferior vena cava (IVC) is important for the diagnosis and prognosis of various clinical conditions. In order to improve the clinical investigation of IVC, which is mainly based on unidirectional US (in M-mode), automated processing of 2-D US scans (in B-mode) has enabled tissue movement tracking on the visualized plane and can average this in various directions. However, IVC geometry outside of the visualized plane is not under control and could result in errors that have not yet been evaluated., Methods: We used a method that integrates information from long- and short-axis IVC views (simultaneously acquired in the X-plane) to assess challenges in IVC diameter estimations using 2-D US scans in eight healthy subjects., Results: Relative movements between the US probe and IVC induced the following problems when assessing IVC diameter via 2-D view: a median error (i.e., absolute difference with respect to diameter measured in the X-plane) of 17% using 2-D US scans in the long-axis view of the IVC affected by medio-lateral displacements (median: 4 mm); and a median error of 7% and 9% when measuring the IVC diameter from a short-axis view in the presence of pitch angle (median: 0.12 radians) and cranio-caudal movement (median: 15 mm), respectively., Conclusion: Relative movements in the IVC that are out of view of B-mode scans cannot be detected, which results in challenges in IVC diameter estimation., Competing Interests: Conflict of interest An instrument implementing the 2-D algorithms used in this paper was patented by Politecnico di Torino and Università di Torino (patent number WO 2018/134726) and was taken in license by VIPER s.r.l., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Dendritic cells (DCs) are uniquely capable of transporting tumor antigens to tumor-draining lymph nodes (tdLNs) and interact with effector T cells in the tumor microenvironment (TME) itself, mediating both natural antitumor immunity and the response to checkpoint blockade immunotherapy. Using LIPSTIC (Labeling Immune Partnerships by SorTagging Intercellular Contacts)-based single-cell transcriptomics, we identified individual DCs capable of presenting antigen to CD4 + T cells in both the tdLN and TME. Our findings revealed that DCs with similar hyperactivated transcriptional phenotypes interact with helper T cells both in tumors and in the tdLN and that checkpoint blockade drugs enhance these interactions. These findings show that a relatively small fraction of DCs is responsible for most of the antigen presentation in the tdLN and TME to both CD4 + and CD8 + tumor-specific T cells and that classical checkpoint blockade enhances CD40-driven DC activation at both sites.

Re-exposure to an antigen generates abundant antibody responses and drives the formation of secondary germinal centers (GCs). Recall GCs in mice consist almost entirely of naïve B cells, whereas recall antibodies derive overwhelmingly from memory B cells. Here, we examine this division between cellular and serum compartments. After repeated immunization with the same antigen, tetramer analyses of recall GCs revealed a marked decrease in the ability of B cells in these structures to bind the antigen. Boosting with viral variant proteins restored antigen binding in recall GCs, as did genetic ablation of primary-derived antibody-secreting cells through conditional deletion of Prdm1, demonstrating suppression of GC recall responses by pre-existing antibodies. In hapten-carrier experiments in which B and T cell specificities were uncoupled, memory T cell help allowed B cells with undetectable antigen binding to access GCs. Thus, antibody-mediated feedback steers recall GC B cells away from previously targeted epitopes and enables specific targeting of variant epitopes, with implications for vaccination protocols., Competing Interests: Declaration of interests G.D.V. is a scientific advisor for Vaccine Company, Inc., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Sustained attention is pivotal for tasks like studying and working for which focus and low distractions are necessary for peak productivity. This study explores the effectiveness of adaptive transcranial direct current stimulation (tDCS) in either the frontal or parietal region to enhance sustained attention. The research involved ten healthy university students performing the Continuous Performance Task-AX (AX-CPT) while receiving either frontal or parietal tDCS. The study comprised three phases. First, we acquired the electroencephalography (EEG) signal to identify the most suitable metrics related to attention states. Among different spectral and complexity metrics computed on 3 s epochs of EEG, the Fuzzy Entropy and Multiscale Sample Entropy Index of frontal channels were selected. Secondly, we assessed how tDCS at a fixed 1.0 mA current affects attentional performance. Finally, a real-time experiment involving continuous metric monitoring allowed personalized dynamic optimization of the current amplitude and stimulation site (frontal or parietal). The findings reveal statistically significant improvements in mean accuracy (94.04 vs. 90.82%) and reaction times (262.93 vs. 302.03 ms) with the adaptive tDCS compared to a non-stimulation condition. Average reaction times were statistically shorter during adaptive stimulation compared to a fixed current amplitude condition (262.93 vs. 283.56 ms), while mean accuracy stayed similar (94.04 vs. 93.36%, improvement not statistically significant). Despite the limited number of subjects, this work points out the promising potential of adaptive tDCS as a tailored treatment for enhancing sustained attention.

Objective. The wide detection volume of surface electromyogram (EMG) makes it prone to crosstalk, i.e. the signal from other muscles than the target one. Removing this perturbation from bipolar recordings is an important open problem for many applications. Approach. An innovative nonlinear spatio-temporal filter is developed to estimate the EMG generated by the target muscle by processing noisy signals from two bipolar channels, placed over the target and the crosstalk muscle, respectively. The filter is trained on some calibration data and then can be applied on new signals. Tests are provided in simulations (considering different thicknesses of the subcutaneous tissue, inter-electrode distances, locations of the EMG channels, force levels) and experiments (from pronator teres and flexor carpi radialis of 8 healthy subjects). Main results. The proposed filter allows to reduce the effect of crosstalk in all investigated conditions, with a statistically significant reduction of its root mean squared of about 20%, both in simulated and experimental data. Its performances are also superior to those of a blind source separation method applied to the same data. Significance. The proposed filter is simple to be applied and feasible in applications in which single bipolar channels are placed over the muscles of interest. It can be useful in many fields, such as in gait analysis, tests of myoelectric fatigue, rehabilitation with EMG biofeedback, clinical studies, prosthesis control., (Creative Commons Attribution license.)

The inferior vena cava (IVC) is the largest vein in the body. It returns deoxygenated blood to the heart from the tissues placed under the diaphragm. The size and dynamics of the IVC depend on the blood volume and right atrial pressure, which are important indicators of a patient's hydration and reflect possible pathological conditions. Ultrasound (US) assessment of the IVC is a promising technique for evaluating these conditions, because it is fast, non-invasive, inexpensive, and without side effects. However, the standard M-mode approach for measuring IVC diameter is prone to errors due to the vein movements during respiration. B-mode US produces two-dimensional images that better capture the IVC shape and size. In this review, we discuss the pros and cons of current IVC segmentation techniques for B-mode longitudinal and transverse views. We also explored several scenarios where automated IVC segmentation could improve medical diagnosis and prognosis.

The aging process is a multifaceted phenomenon that affects cognitive-affective and physical functioning as well as interactions with the environment. Although subjective cognitive decline may be part of normal aging, negative changes objectified as cognitive impairment are present in neurocognitive disorders and functional abilities are most impaired in patients with dementia. Electroencephalography-based brain-machine interfaces (BMI) are being used to assist older people in their daily activities and to improve their quality of life with neuro-rehabilitative applications. This paper provides an overview of BMI used to assist older adults. Both technical issues (detection of signals, extraction of features, classification) and application-related aspects with respect to the users' needs are considered.

Understanding how different areas of the human brain communicate with each other is a crucial issue in neuroscience. The concepts of structural, functional and effective connectivity have been widely exploited to describe the human connectome, consisting of brain networks, their structural connections and functional interactions. Despite high-spatial-resolution imaging techniques such as functional magnetic resonance imaging (fMRI) being widely used to map this complex network of multiple interactions, electroencephalographic (EEG) recordings claim high temporal resolution and are thus perfectly suitable to describe either spatially distributed and temporally dynamic patterns of neural activation and connectivity. In this work, we provide a technical account and a categorization of the most-used data-driven approaches to assess brain-functional connectivity, intended as the study of the statistical dependencies between the recorded EEG signals. Different pairwise and multivariate, as well as directed and non-directed connectivity metrics are discussed with a pros-cons approach, in the time, frequency, and information-theoretic domains. The establishment of conceptual and mathematical relationships between metrics from these three frameworks, and the discussion of novel methodological approaches, will allow the reader to go deep into the problem of inferring functional connectivity in complex networks. Furthermore, emerging trends for the description of extended forms of connectivity (e.g., high-order interactions) are also discussed, along with graph-theory tools exploring the topological properties of the network of connections provided by the proposed metrics. Applications to EEG data are reviewed. In addition, the importance of source localization, and the impacts of signal acquisition and pre-processing techniques (e.g., filtering, source localization, and artifact rejection) on the connectivity estimates are recognized and discussed. By going through this review, the reader could delve deeply into the entire process of EEG pre-processing and analysis for the study of brain functional connectivity and learning, thereby exploiting novel methodologies and approaches to the problem of inferring connectivity within complex networks.

The protective efficacy of serum antibodies results from the interplay of antigen-specific B cell clones of different affinities and specificities. These cellular dynamics underlie serum-level phenomena such as original antigenic sin (OAS)-a proposed propensity of the immune system to rely repeatedly on the first cohort of B cells engaged by an antigenic stimulus when encountering related antigens, in detriment to the induction of de novo responses 1-5 . OAS-type suppression of new, variant-specific antibodies may pose a barrier to vaccination against rapidly evolving viruses such as influenza and SARS-CoV-2 6,7 . Precise measurement of OAS-type suppression is challenging because cellular and temporal origins cannot readily be ascribed to antibodies in circulation; its effect on subsequent antibody responses therefore remains unclear 5,8 . Here we introduce a molecular fate-mapping approach with which serum antibodies derived from specific cohorts of B cells can be differentially detected. We show that serum responses to sequential homologous boosting derive overwhelmingly from primary cohort B cells, while later induction of new antibody responses from naive B cells is strongly suppressed. Such 'primary addiction' decreases sharply as a function of antigenic distance, allowing reimmunization with divergent viral glycoproteins to produce de novo antibody responses targeting epitopes that are absent from the priming variant. Our findings have implications for the understanding of OAS and for the design and testing of vaccines against evolving pathogens., (© 2023. The Author(s), under exclusive licence to Springer Nature Limited.)