Increased T- and B-cells associated with the phenotype of autoimmune limbic encephalitis with mainly memory dysfunction.

Background: Our goal is to investigate the autoantibodies' presence and immune cells in the bioprobes of autoimmune encephalitis (AE) patients with distinct phenotypes as a promising target in AE. Methods: We retrospectively analyzed immune cells via flow cytometry, serum and cerebrospinal fluid (CSF) autoantibodies, electroencephalography, magnetic resonance imaging in 94 AE patients with suspected temporal lobe epilepsy and classified neuropsychological phenotypes according to their occurrence. Results: We detected different phenotypes in 94 AE patients [10.6% with isolated memory dysfunction (MEM), 11.7% with mood-dysfunction, 12.7% with mood and memory dysfunction, 13.8% with memory and attention dysfunction, 18.1% with memory, mood and attention disturbances and 20.2% with no mood, memory or attention dysfunction]. We did discern a relevant association of phenotypes and CSF antibody-positivity on CSF CD4+ T-cells, CD8+T-cells and HLADR + CD8+T-cells in our patients with MEM presenting elevated CD8+T-cells and HLADR + CD8+T-cells. Furthermore, CSF CD19+B-cells differed significantly between phenotypes in patients with MEM. Discussion: Taken together, the phenotypes in combination with CSF antibody-positivity are biomarkers for stratifying patients. Furthermore, our results confirm the role of CD4+ T-cells, CD8+T-cells and CD19+B-cells in AE patients with a memory dysfunction, providing insights into AE pathogenesis. Our preliminary results should be confirmed by larger-scale investigations.

Potential Applications of Mobile and Wearable Devices for Psychological Support During the COVID-19 Pandemic: A Review.

The coronavirus disease 19 (COVID-19) pandemic that has been raging in 2020 does affect not only the physical state but also the mental health of the general population, particularly, that of the healthcare workers. Given the unprecedented large-scale impacts of the COVID-19 pandemic, digital technology has gained momentum as invaluable social interaction and health tracking tools in this time of great turmoil, in part due to the imposed state-wide mobilization limitations to mitigate the risk of infection that might arise from in-person socialization or hospitalization. Over the last five years, there has been a notable increase in the demand and usage of mobile and wearable devices as well as their adoption in studies of mental fitness. The purposes of this scoping review are to summarize evidence on the sweeping impact of COVID-19 on mental health as well as to evaluate the merits of the devices for remote psychological support. We conclude that the COVID-19 pandemic has inflicted a significant toll on the mental health of the population, leading to an upsurge in reports of pathological stress, depression, anxiety, and insomnia. It is also clear that mobile and wearable devices (e.g., smartwatches and fitness trackers) are well placed for identifying and targeting individuals with these psychological burdens in need of intervention. However, we found that most of the previous studies used research-grade wearable devices that are difficult to afford for the normal consumer due to their high cost. Thus, the possibility of replacing the research-grade wearable devices with the current smartwatch is also discussed.

Specific B- and T-cell populations are associated with cognition in patients with epilepsy and antibody positive and negative suspected limbic encephalitis.

OBJECTIVE: Neuropsychological impairments are major symptoms of autoimmune limbic encephalitis (LE) epilepsy patients. In LE epilepsy patients with an autoimmune response against intracellular antigens as well as in antibody-negative patients, the antibody findings and magnetic resonance imaging pathology correspond poorly to the clinical features. Here, we evaluated whether T- and B-cells are linked to cognitive impairment in these groups. METHODS: In this cross-sectional, observational, case-controlled study, we evaluated 106 patients with adult-onset epilepsies with a suspected autoimmune etiology. We assessed verbal and visual memory, executive function, and mood in relation to the presence or absence of known auto-antibodies, and regarding T- and B-cell activity as indicated by flow cytometry (fluorescence-activated cell sorting = FACS, peripheral blood = PB and cerebrospinal fluid = CSF). RESULTS: 56% of the patients were antibody-negative. In the other patients, auto-antibodies were directed against intracellular antigens (GAD65, paraneoplastic: 38%), or cellular surface antigens (LGI1/CASPR2/NMDA-R: 6%). Excluding LGI1/CASPR2/NMDA-R, the groups with and without antibodies did not differ in disease features, cognition, or mood. CD4+ T-cells and CD8+ T-cells in blood and CD4+ T-cells in CSF were prominent in the auto-antibody positive group. Regression analyses indicated the role education, drug load, amygdala and/or hippocampal pathology, and CD4+ T-cells play in verbal memory and executive function. Depressed mood revealed no relation to flow cytometry results. CONCLUSION: Our results indicate a link between T- and B-cell activity and cognition in epilepsy patients with suspected limbic encephalitis, thus suggesting that flow cytometry results can provide an understanding of cognitive impairment in LE patients with autoantibodies against intracellular antigens.

MetaSleepLearner: A Pilot Study on Fast Adaptation of Bio-Signals-Based Sleep Stage Classifier to New Individual Subject Using Meta-Learning.

Identifying bio-signals based-sleep stages requires time-consuming and tedious labor of skilled clinicians. Deep learning approaches have been introduced in order to challenge the automatic sleep stage classification conundrum. However, the difficulties can be posed in replacing the clinicians with the automatic system due to the differences in many aspects found in individual bio-signals, causing the inconsistency in the performance of the model on every incoming individual. Thus, we aim to explore the feasibility of using a novel approach, capable of assisting the clinicians and lessening the workload. We propose the transfer learning framework, entitled MetaSleepLearner, based on Model Agnostic Meta-Learning (MAML), in order to transfer the acquired sleep staging knowledge from a large dataset to new individual subjects (source code is available at https://github.com/IoBT-VISTEC/MetaSleepLearner). The framework was demonstrated to require the labelling of only a few sleep epochs by the clinicians and allow the remainder to be handled by the system. Layer-wise Relevance Propagation (LRP) was also applied to understand the learning course of our approach. In all acquired datasets, in comparison to the conventional approach, MetaSleepLearner achieved a range of 5.4% to 17.7% improvement with statistical difference in the mean of both approaches. The illustration of the model interpretation after the adaptation to each subject also confirmed that the performance was directed towards reasonable learning. MetaSleepLearner outperformed the conventional approaches as a result from the fine-tuning using the recordings of both healthy subjects and patients. This is the first work that investigated a non-conventional pre-training method, MAML, resulting in a possibility for human-machine collaboration in sleep stage classification and easing the burden of the clinicians in labelling the sleep stages through only several epochs rather than an entire recording.

SleepPoseNet: Multi-View Learning for Sleep Postural Transition Recognition Using UWB.

Recognizing movements during sleep is crucial for the monitoring of patients with sleep disorders, and the utilization of ultra-wideband (UWB) radar for the classification of human sleep postures has not been explored widely. This study investigates the performance of an off-the-shelf single antenna UWB in a novel application of sleep postural transition (SPT) recognition. The proposed Multi-View Learning, entitled SleepPoseNet or SPN, with time series data augmentation aims to classify four standard SPTs. SPN exhibits an ability to capture both time and frequency features, including the movement and direction of sleeping positions. The data recorded from 38 volunteers displayed that SPN with a mean accuracy of 73.7 ±0.8 % significantly outperformed the mean accuracy of 59.9 ±0.7 % obtained from deep convolution neural network (DCNN) in recent state-of-the-art work on human activity recognition using UWB. Apart from UWB system, SPN with the data augmentation can ultimately be adopted to learn and classify time series data in various applications.

CD19+ B-cells in autoantibody-negative limbic encephalitis.

PURPOSE: Flow cytometry helps to elucidate the cellular immune repertoire's mechanisms in patients with temporal lobe epilepsy (TLE) due to limbic encephalitis (LE) subcategories and carries potential significance for subtype-specific treatment. METHODS: We enrolled 62 patients with TLE due to LE associated with no autoantibodies (n = 40), neural autoantibodies (n = 22), as well as autoantibodies against intracellular antigens (n = 15/22). All patients underwent neuropsychological testing, brain magnetic resonance imaging (MRI), electroencephalography (EEG) recordings, and peripheral blood (PB) and cerebrospinal fluid (CSF) investigations including flow cytometry. RESULTS: CD19+ B-cells were increased in the PB and CSF of patients with antibody-negative LE compared with those associated with antibodies against intracellular antigens (Kruskal-Wallis one way analysis of variance (ANOVA) on ranks with Dunn's test, p < 0.05). There were no differences in CD138+ B-cells, CD4+ T-cells, human leukocyte antigen - DR isotype (HLA-DR+) CD4+ T-cells, CD8+ T-cells, and HLA-DR+ CD8+ T-cells in the CSF between groups with LE. The blood-brain barrier is more often impaired in patients with antibody-negative LE than in LE with antibodies against intracellular antigens (chi-square test, p < 0.05). In addition, we detected no correlations between immune cell subsets and clinical or paraclinical parameters in patients with antibody-negative and intracellular antibody-positive LE. CONCLUSIONS: The increase of CD19+ B-cells in the CSF and frequent signs of dysfunctional blood-brain barrier in patients with antibody-negative rather than intracellular antibody-positive LE suggest that CD19+ B-cells play a role in antibody-negative encephalitis although their pathogenic role in the central nervous system (CNS) immunity because of missing correlations between immune cells and clinical and paraclinical parameters remains unknown. Further studies are required to evaluate the usefulness of these B-cells as a biomarker for the stratification of treatment strategies.

Low CSF CD4/CD8+ T-cell proportions are associated with blood-CSF barrier dysfunction in limbic encephalitis.

PURPOSE: Investigating immune cells in autoimmune limbic encephalitis (LE) will contribute to our understanding of its pathophysiology and may help to develop appropriate therapies. The aim of the present study was to analyze immune cells to reveal underlying immune signatures in patients with temporal lobe epilepsy (TLE) with LE. METHODS: We investigated 68 patients with TLE with LE compared with 7 control patients with TLE with no signs of LE screened from 154 patients with suspected LE. From the patients with TLE-LE, we differentiated early seizure onset (<20 years, n = 9) and late seizure onset group (≥20 years, n = 59) of patients. Patients underwent neuropsychological assessment, electroencephalography (EEG), brain magnetic resonance imaging (MRI), and peripheral blood (PB) and cerebrospinal fluid (CSF) analysis including flow cytometry. RESULTS: We identified a higher CD4/8+ T-cell ratio in the PB in all patients with TLE-LE and in patients with late-onset TLE-LE each compared with controls (Kruskal-Wallis one-way ANOVA (analysis of variance) with Dunn's test, p < 0.05). Moreover, a lower CD4/CD8+ T-cell ratio is detected in all patients with TLE-LE with blood-CSF barrier dysfunction, unlike in those with none (Kruskal-Wallis one-way ANOVA with Dunn's test, p < 0.05). CONCLUSIONS: These findings suggest that the proportion of CD4+ and CD8+ T-cells in the CSF of patients with LE associated with blood-CSF barrier dysfunction plays a potential role in CNS (central nervous system) inflammation in these patients. Thus, flow cytometry as a methodology reveals novel insights into LE's genesis and symptomatology. The CD4/8+ T-cell ratio in PB as a biomarker for LE requires further investigation.