Immune responses and clinical outcomes after COVID-19 vaccination in patients with liver disease and liver transplant recipients.

BACKGROUND & AIMS: Comparative assessments of immunogenicity following different COVID-19 vaccines in patients with distinct liver diseases are lacking. SARS-CoV-2-specific T-cell and antibody responses were evaluated longitudinally after one to three vaccine doses, with long-term follow-up for COVID-19-related clinical outcomes. METHODS: A total of 849 participants (355 with cirrhosis, 74 with autoimmune hepatitis [AIH], 36 with vascular liver disease [VLD], 257 liver transplant recipients [LTRs] and 127 healthy controls [HCs]) were recruited from four countries. Standardised immune assays were performed pre and post three vaccine doses (V1-3). RESULTS: In the total cohort, there were incremental increases in antibody titres after each vaccine dose (p <0.0001). Factors associated with reduced antibody responses were age and LT, whereas heterologous vaccination, prior COVID-19 and mRNA platforms were associated with greater responses. Although antibody titres decreased between post-V2 and pre-V3 (p = 0.012), patients with AIH, VLD, and cirrhosis had equivalent antibody responses to HCs post-V3. LTRs had lower and more heterogenous antibody titres than other groups, including post-V3 where 9% had no detectable antibodies; this was heavily influenced by intensity of immunosuppression. Vaccination increased T-cell IFNγ responses in all groups except LTRs. Patients with liver disease had lower functional antibody responses against nine Omicron subvariants and reduced T-cell responses to Omicron BA.1-specific peptides compared to wild-type. 122 cases of breakthrough COVID-19 were reported of which 5/122 (4%) were severe. Of the severe cases, 4/5 (80%) occurred in LTRs and 2/5 (40%) had no serological response post-V2. CONCLUSION: After three COVID-19 vaccines, patients with liver disease generally develop robust antibody and T-cell responses to vaccination and have mild COVID-19. However, LTRs have sustained no/low antibody titres and appear most vulnerable to severe disease. IMPACT AND IMPLICATIONS: Standardised assessments of the immune response to different COVID-19 vaccines in patients with liver disease are lacking. We performed antibody and T-cell assays at multiple timepoints following up to three vaccine doses in a large cohort of patients with a range of liver conditions. Overall, the three most widely available vaccine platforms were immunogenic and appeared to protect against severe breakthrough COVID-19. This will provide reassurance to patients with chronic liver disease who were deemed at high risk of severe COVID-19 during the pre-vaccination era, however, liver transplant recipients had the lowest antibody titres and remained vulnerable to severe breakthrough infection. We also characterise the immune response to multiple SARS-CoV-2 variants and describe the interaction between disease type, severity, and vaccine platform. These insights may prove useful in the event of future viral infections which also require rapid vaccine development and delivery to patients with liver disease.

The impact of cumulative obstetric complications and childhood trauma on brain volume in young people with psychotic experiences.

Psychotic experiences (PEs) occur in 5-10% of the general population and are associated with exposure to childhood trauma and obstetric complications. However, the neurobiological mechanisms underlying these associations are unclear. Using the Avon Longitudinal Study of Parents and Children (ALSPAC), we studied 138 young people aged 20 with PEs (n = 49 suspected, n = 53 definite, n = 36 psychotic disorder) and 275 controls. Voxel-based morphometry assessed whether MRI measures of grey matter volume were associated with (i) PEs, (ii) cumulative childhood psychological trauma (weighted summary score of 6 trauma types), (iii) cumulative pre/peri-natal risk factors for psychosis (weighted summary score of 16 risk factors), and (iv) the interaction between PEs and cumulative trauma or pre/peri-natal risk. PEs were associated with smaller left posterior cingulate (pFWE < 0.001, Z = 4.19) and thalamus volumes (pFWE = 0.006, Z = 3.91). Cumulative pre/perinatal risk was associated with smaller left subgenual cingulate volume (pFWE < 0.001, Z = 4.54). A significant interaction between PEs and cumulative pre/perinatal risk found larger striatum (pFWE = 0.04, Z = 3.89) and smaller right insula volume extending into the supramarginal gyrus and superior temporal gyrus (pFWE = 0.002, Z = 4.79), specifically in those with definite PEs and psychotic disorder. Cumulative childhood trauma was associated with larger left dorsal striatum (pFWE = 0.002, Z = 3.65), right prefrontal cortex (pFWE < 0.001, Z = 4.63) and smaller left insula volume in all participants (pFWE = 0.03, Z = 3.60), and there was no interaction with PEs group. In summary, pre/peri-natal risk factors and childhood psychological trauma impact similar brain pathways, namely smaller insula and larger striatum volumes. The effect of pre/perinatal risk was greatest in those with more severe PEs, whereas effects of trauma were seen in all participants. In conclusion, environmental risk factors affect brain networks implicated in schizophrenia, which may increase an individual's propensity to develop later psychotic disorders.

How we achieve satisfaction in training - A German-wide survey on preferred training conditions among trainers and trainees for board certification in gastroenterology.

BACKGROUND: a majority of resident physicians in Germany are not satisfied with their training conditions. However, training satisfaction is important for physician retention and patient care. Although federal and state laws define the general training regulations and conditions, considerable variability still exists concerning their implementation in the healthcare units. Little is known about the expectations concerning training for gastroenterology board certification by trainers and trainees in Germany. This lack of data hinders discussion on and improvement of training in gastroenterology in Germany. AIM: assessment of preferred training conditions among trainers and trainees for board certification in gastroenterology in Germany. METHODS: an anonymous, voluntary survey consisting of single- and multiple-choice questions utilizing the Likert scale and fill-in responses was circulated to all members of the German Society for Digestive and Metabolic Diseases (DGVS - Deutsche Gesellschaft für Gastroenterologie, Verdauungs und Stoffwechselerkrankungen), as well as through the student council mailing lists of all German medical schools. The survey aimed to assess the consent regarding the ideal implementation of training regulations for gastroenterology board certification. Department heads, senior physicians, board-certified physicians, and outpatient-care physicians were classified as trainers and residents and students as trainees. Subgroups defined by place of work, age, gender, professional position, employment status, and parental status were investigated. RESULTS: 958 responses were included in the final analysis. We found a broad consensus among trainers and trainees on most aspects of our survey. Considerable differences were seen in items on part-time work, overtime, protected time for research, and advanced endoscopy training. CONCLUSION: the broad consensus seen in this survey is indicative of a shared vision for training conditions among trainers and trainees. However, the areas of dissent identified in this survey may assist trainers to better understand the expectations of trainees. Furthermore, this survey creates a sound basis upon which training conditions for board certification in gastroenterology in Germany can be discussed and improved.

Multiplexity of human brain oscillations as a personal brain signature.

Human individuality is likely underpinned by the constitution of functional brain networks that ensure consistency of each person's cognitive and behavioral profile. These functional networks should, in principle, be detectable by noninvasive neurophysiology. We use a method that enables the detection of dominant frequencies of the interaction between every pair of brain areas at every temporal segment of the recording period, the dominant coupling modes (DoCM). We apply this method to brain oscillations, measured with magnetoencephalography (MEG) at rest in two independent datasets, and show that the spatiotemporal evolution of DoCMs constitutes an individualized brain fingerprint. Based on this successful fingerprinting we suggest that DoCMs are important targets for the investigation of neural correlates of individual psychological parameters and can provide mechanistic insight into the underlying neurophysiological processes, as well as their disturbance in brain diseases.

A transformer-based multi-features fusion model for prediction of conversion in mild cognitive impairment.

Mild cognitive impairment (MCI) is usually considered the early stage of Alzheimer's disease (AD). Therefore, the accurate identification of MCI individuals with high risk in converting to AD is essential for the potential prevention and treatment of AD. Recently, the great success of deep learning has sparked interest in applying deep learning to neuroimaging field. However, deep learning techniques are prone to overfitting since available neuroimaging datasets are not sufficiently large. Therefore, we proposed a deep learning model fusing cortical features to address the issue of fusion and classification blocks. To validate the effectiveness of the proposed model, we compared seven different models on the same dataset in the literature. The results show that our proposed model outperformed the competing models in the prediction of MCI conversion with an accuracy of 83.3% in the testing dataset. Subsequently, we used deep learning to characterize the contribution of brain regions and different cortical features to MCI progression. The results revealed that the caudal anterior cingulate and pars orbitalis contributed most to the classification task, and our model pays more attention to volume features and cortical thickness features.

Dyconnmap: Dynamic connectome mapping-A neuroimaging python module.

Despite recent progress in the analysis of neuroimaging data sets, our comprehension of the main mechanisms and principles which govern human brain cognition and function remains incomplete. Network neuroscience makes substantial efforts to manipulate these challenges and provide real answers. For the last decade, researchers have been modelling brain structure and function via a graph or network that comprises brain regions that are either anatomically connected via tracts or functionally via a more extensive repertoire of functional associations. Network neuroscience is a relatively new multidisciplinary scientific avenue of the study of complex systems by pursuing novel ways to analyze, map, store and model the essential elements and their interactions in complex neurobiological systems, particularly the human brain, the most complex system in nature. Due to a rapid expansion of neuroimaging data sets' size and complexity, it is essential to propose and adopt new empirical tools to track dynamic patterns between neurons and brain areas and create comprehensive maps. In recent years, there is a rapid growth of scientific interest in moving functional neuroimaging analysis beyond simplified group or time-averaged approaches and sophisticated algorithms that can capture the time-varying properties of functional connectivity. We describe algorithms and network metrics that can capture the dynamic evolution of functional connectivity under this perspective. We adopt the word 'chronnectome' (integration of the Greek word 'Chronos', which means time, and connectome) to describe this specific branch of network neuroscience that explores how mutually informed brain activity correlates across time and brain space in a functional way. We also describe how good temporal mining of temporally evolved dynamic functional networks could give rise to the detection of specific brain states over which our brain evolved. This characteristic supports our complex human mind. The temporal evolution of these brain states and well-known network metrics could give rise to new analytic trends. Functional brain networks could also increase the multi-faced nature of the dynamic networks revealing complementary information. Finally, we describe a python module (https://github.com/makism/dyconnmap) which accompanies this article and contains a collection of dynamic complex network analytics and measures and demonstrates its great promise for the study of a healthy subject's repeated fMRI scans.

The impact of graph construction scheme and community detection algorithm on the repeatability of community and hub identification in structural brain networks.

A critical question in network neuroscience is how nodes cluster together to form communities, to form the mesoscale organisation of the brain. Various algorithms have been proposed for identifying such communities, each identifying different communities within the same network. Here, (using test-retest data from the Human Connectome Project), the repeatability of thirty-three community detection algorithms, each paired with seven different graph construction schemes were assessed. Repeatability of community partition depended heavily on both the community detection algorithm and graph construction scheme. Hard community detection algorithms (in which each node is assigned to only one community) outperformed soft ones (in which each node can belong to more than one community). The highest repeatability was observed for the fast multi-scale community detection algorithm paired with a graph construction scheme that combines nine white matter metrics. This pair also gave the highest similarity between representative group community affiliation and individual community affiliation. Connector hubs had higher repeatability than provincial hubs. Our results provide a workflow for repeatable identification of structural brain networks communities, based on the optimal pairing of community detection algorithm and graph construction scheme.

Optimization of graph construction can significantly increase the power of structural brain network studies.

Structural brain networks derived from diffusion magnetic resonance imaging data have been used extensively to describe the human brain, and graph theory has allowed quantification of their network properties. Schemes used to construct the graphs that represent the structural brain networks differ in the metrics they use as edge weights and the algorithms they use to define the network topologies. In this work, twenty graph construction schemes were considered. The schemes use the number of streamlines, the fractional anisotropy, the mean diffusivity or other attributes of the tracts to define the edge weights, and either an absolute threshold or a data-driven algorithm to define the graph topology. The test-retest data of the Human Connectome Project were used to compare the reproducibility of the graphs and their various attributes (edges, topologies, graph theoretical metrics) derived through those schemes, for diffusion images acquired with three different diffusion weightings. The impact of the scheme on the statistical power of the study and on the number of participants required to detect a difference between populations or an effect of an intervention was also calculated. The reproducibility of the graphs and their attributes depended heavily on the graph construction scheme. Graph reproducibility was higher for schemes that used thresholding to define the graph topology, while data-driven schemes performed better at topology reproducibility (mean similarities of 0.962 and 0.984 respectively, for graphs derived from diffusion images with b=2000 s/mm2). Additionally, schemes that used thresholding resulted in better reproducibility for local graph theoretical metrics (intra-class correlation coefficients (ICC) of the order of 0.8), compared to data-driven schemes. Thresholded and data-driven schemes resulted in high (0.86 or higher) ICCs only for schemes that use exclusively the number of streamlines to construct the graphs. Crucially, the number of participants required to detect a difference between populations or an effect of an intervention could change by a factor of two or more depending on the scheme used, affecting the power of studies to reveal the effects of interest.

Fronto-Parietal Subnetworks Flexibility Compensates For Cognitive Decline Due To Mental Fatigue.

Fronto-parietal subnetworks were revealed to compensate for cognitive decline due to mental fatigue by community structure analysis. Here, we investigate changes in topology of subnetworks of resting-state fMRI networks due to mental fatigue induced by prolonged performance of a cognitively demanding task, and their associations with cognitive decline. As it is well established that brain networks have modular organization, community structure analyses can provide valuable information about mesoscale network organization and serve as a bridge between standard fMRI approaches and brain connectomics that quantify the topology of whole brain networks. We developed inter- and intramodule network metrics to quantify topological characteristics of subnetworks, based on our hypothesis that mental fatigue would impact on functional relationships of subnetworks. Functional networks were constructed with wavelet correlation and a data-driven thresholding scheme based on orthogonal minimum spanning trees, which allowed detection of communities with weak connections. A change from pre- to posttask runs was found for the intermodule density between the frontal and the temporal subnetworks. Seven inter- or intramodule network metrics, mostly at the frontal or the parietal subnetworks, showed significant predictive power of individual cognitive decline, while the network metrics for the whole network were less effective in the predictions. Our results suggest that the control-type fronto-parietal networks have a flexible topological architecture to compensate for declining cognitive ability due to mental fatigue. This community structure analysis provides valuable insight into connectivity dynamics under different cognitive states including mental fatigue.

Complexity of brain activity and connectivity in functional neuroimaging.

Understanding the complexity of human brain dynamics and brain connectivity across the repertoire of functional neuroimaging and various conditions, is of paramount importance. Novel measures should be designed tailored to the input focusing on multichannel activity and dynamic functional brain connectivity (DFBC). Here, we defined a novel complexity index (CI) from the field of symbolic dynamics that quantifies patterns of different words up to a length from a symbolic sequence. The CI characterizes the complexity of the brain activity. We analysed DFBC by adopting the sliding window approach using imaginary part of phase locking value (iPLV) for EEG/ECoG/MEG and wavelet coherence (WC) for fMRI. Both intra and cross-frequency couplings (CFC) namely phase-to-amplitude were estimated using iPLV/WC at every snapshot of the DFBC. Using proper surrogate analysis, we defined the dominant intrinsic coupling mode (DICM) per pair of regions-of-interest (ROI). The spatiotemporal probability distribution of DICM were reported to reveal the most prominent coupling modes per condition and modality. Finally, a novel flexibility index is defined that quantifies the transition of DICM per pair of ROIs between consecutive time windows. The whole methodology was demonstrated using four neuroimaging datasets (EEG/ECoG/MEG/fMRI). Finally, we succeeded to totally discriminate healthy controls from schizophrenic using FI and dynamic reconfiguration of DICM. Anaesthesia independently of the drug caused a global decreased of complexity in all frequency bands with the exception in δ and alters the dynamic reconfiguration of DICM. CI and DICM of MEG/fMRI resting-state recordings in two spatial scales were high reliable.

The use of domperidone increases the completion rate of small bowel capsule endoscopy: does this come at the expense of diagnostic yield?

BACKGROUND: The completion rate (CR) of small bowel capsule endoscopy (SBCE) has been reported at 81.3% to 84.8%. Prokinetic agents are used to increase CR and (theoretically) diagnostic yield (DY). Domperidone has not been widely used in SBCE; unlike metoclopramide, it lacks extrapyramidal adverse effects. OBJECTIVES: This was a retrospective study. This study aimed to assess gastric transit time (GTT), small bowel transit time (SBTT), and the CR of SBCE when using domperidone. Furthermore, we aimed to compare the CR of 2 different SBCE systems (MiroCam, PillCam). Consecutive SBCE examinations (January 2008 to October 2012) from a tertiary referral center were analyzed. RESULTS: In the aforementioned period, a total of 635 SBCE examinations were performed: 379/635 (59.7%) with PillCamSB and 256 (40.3%) with MiroCam. In 437/635 (68.8%) examinations, liquid domperidone (5 mg) was administered for capsule ingestion, whereas 198 (31.2%) ingested the capsule without any domperidone. Although the 2 groups were comparable, the median age of patients who received domperidone was higher compared with patients who did not receive (58 vs. 48 y, P=0.027). In our cohort, the overall CR of SBCE was 88.9%. The 2 SBCE systems showed equivalent CR (PillCamSB 88.9%, MiroCam 89.1%; P =0.96). The use of liquid domperidone increased CR (91.1% vs. 84.3%; P =0.042). Interestingly, the use of domperidone with PillCamSB was associated with reduced DY for vascular, inflammatory, and polyps/mass-type lesions. This effect was not seen in the MiroCam group. Furthermore, the median GTT and the median SBTT did not differ between the 2 groups (GTT/SBTT with domperidone 26.0'/221.0' and without 29.0'/228.0', respectively; P=0.461/P=0.477). A higher CR was noted when domperidone was used with PillCamSB (93.0% vs. 89.5%, P=0.012) than with MiroCam (84.4% vs. 83.3%, P=0.08). LIMITATIONS: The major limitations of this study were the retrospective design of the study and limited numbers on MiroCam with no domperidone. CONCLUSIONS: In conclusion, the use of domperidone increases the CR of SBCE with PillCamSB. However, this increase does not translate into higher DY. A smart, tailored approach, which may include domperidone, purgatives, and real-time viewers, may be used in the clinical practice to improve DY until technology delivering capsules with much longer battery time becomes available.

Our experience with informative and communication technologies (ICT) in dementia.

Our research is implementing high quality next generation services for the Prediction, Early Diagnosis, Monitoring, and Support of patients with Cognitive Impairment (Subjective Cognitive Impairment -SCI-, Mild Cognitive Impairment -MCI-, Mild Dementia) and Education and Training for all stakeholders. Prediction, Early Diagnosis and Monitoring: The first idea was to Research and Develop a novel System using motion detection devices, depth cameras, and intelligent objects of everyday use (ranging from cooking implements such as kitchen to furniture (e.g. sofa, bed, etc.) which are appropriately adapted in order to capture changes of subject's Activities of Daily Living -ADL- and behavioural patterns (including mobility, nutrition, exercising and medication schedule). We also demonstrated the potential of a virtual supermarket (VSM) cognitive training game as a screening tool for patients with MCI in a sample of older adults. We have indicated that this VSM application displayed a correct classification rate (CCR) of 87.30%, achieving a level of diagnostic accuracy similar to standardized neuropsychological tests, which are the gold standard for MCI screening http://www.en-noisis.gr/ Support of patients: Cognitive tasks and cognitive exercises for patients suffering from Alzheimer's Disease (AD) through web-based applications. These exercises have been developed in such a way in order to exploit rich interactive multimedia interfaces (including music) based on human computer interaction principles. To this direction we are implementing a web based portal with supportive services such as (a) on-line monitoring of patient's progress by health care professionals, (b) statistical representation of patients' progress. Multimedia enriched cognitive exercises in virtual reality form (i.e. 3D Serious Games) use suitable modalities for such activities through the creation probable of new brain cells and by assisting the brain to find out alternative methods to execute functions, which are controlled by damaged brain regions. Another program the "robot-programming-as-cognitive-training" approach aims to explore the impact that the activity of programming a friendly robot might have on AD and MCI patients' condition. http://aspad.csd.auth.gr. Another study aimed at investigating the benefits of combined physical and cognitive training on global cognition while assessing the effect of training dosage and exploring the role of several potential effect modifiers. The results indicate that combined physical and cognitive training improves global cognition in a dose-responsive manner but these benefits may be less pronounced in older adults with mild dementia. The long-lasting impact of combined training on the incidence and trajectory of cognitive disorders in relation to its severity should be assessed in future long-term trials. www.longlastingmemories.eu. Finally, Symbiosis is a revolutionary system aiming at providing integrated solutions to a series of problems related with MCI and AD. It is the first integrated AD support system that takes into account patient's response in an adaptive way that fulfills each patient's special needs and provides to caregivers and doctors considerable facilitations, unlocking the potential of innovative supporting role. www.youtube.com/watch?v=BDkLz-T-jYE. Education and training for all stakeholders (i.e. health professionals and informal and formal caregivers) through distance education platforms and e-collaboration services. To augment this effort, the research team integrates biofeedback modules for stress measurement in teleconferences in order to support the emotional awareness of the participants. The depression, anxiety and burden of caregivers were reduced significantly in the same way as in a face to face intervention. http://aspad.csd.auth.gr. In conclusion ICT can help health professionals and caregivers to support in a better way the patients with cognitive, functional and behavioral problems.

ℛSCZ: A Riemannian schizophrenia diagnosis framework based on the multiplexity of EEG-based dynamic functional connectivity patterns.

Abnormal electrophysiological (EEG) activity has been largely reported in schizophrenia (SCZ). In the last decade, research has focused to the automatic diagnosis of SCZ via the investigation of an EEG aberrant activity and connectivity linked to this mental disorder. These studies followed various preprocessing steps of EEG activity focusing on frequency-dependent functional connectivity brain network (FCBN) construction disregarding the topological dependency among edges. FCBN belongs to a family of symmetric positive definite (SPD) matrices forming the Riemannian manifold. Due to its unique geometric properties, the whole analysis of FCBN can be performed on the Riemannian geometry of the SPD space. The advantage of the analysis of FCBN on the SPD space is that it takes into account all the pairwise interdependencies as a whole. However, only a few studies have adopted a FCBN analysis on the SPD manifold, while no study exists on the analysis of dynamic FCBN (dFCBN) tailored to SCZ. In the present study, I analyzed two open EEG-SCZ datasets under a Riemannian geometry of SPD matrices for the dFCBN analysis proposing also a multiplexity index that quantifies the associations of multi-frequency brainwave patterns. I adopted a machine learning procedure employing a leave-one-subject-out cross-validation (LOSO-CV) using snapshots of dFCBN from (N-1) subjects to train a battery of classifiers. Each classifier operated in the inter-subject dFCBN distances of sample covariance matrices (SCMs) following a rhythm-dependent decision and a multiplex-dependent one. The proposed ℛSCZ decoder supported both the Riemannian geometry of SPD and the multiplexity index DC reaching an absolute accuracy (100 %) in both datasets in the virtual default mode network (DMN) source space.

Evaluation of a Virtual Reality-Based Open Educational Resource Software.

OBJECTIVES: Virtual reality (VR) teaching methods have potential to support medical students acquire increasing amounts of knowledge. EVENT (Easy VR EducatioN Tool) is an open educational resource software for immersive VR environments, which is designed for use without programming skills. In this work, EVENT was used in a medical student VR course on pancreatic cancer. METHODS: Medical students were invited to participate in the course. Before and after VR simulation, participants completed a multiple-choice knowledge assessment, with a maximum score of 10, and a VR experience questionnaire. The primary endpoint compared pre- and post-VR simulation test scores. Secondary endpoints included usability and factors that could affect learning growth and test results. RESULTS: Data from 117 of the 135 participating students was available for analysis. Student test scores improved by an average of 3.4 points (95% CI 3.1-3.7, P < 0.001) after VR course. The secondary endpoints of gender, age, prior knowledge regarding the medical subject, professional training completed in the medical field, video game play, three-dimensional imagination skills, or cyber-sickness had no major impact on test scores or final ranking (top or bottom 25%). The 27 students whose post-VR simulation test scores ranked in the top 25% had no prior experience with VR. The average System Usability Scale score was 86.1, which corresponds to an excellent outcome for user-friendliness. Questionnaire responses post-VR simulation show students (81.2% [95/117]) interest in more VR options in medical school. CONCLUSIONS: We present a freely available software that allows for the development of VR teaching lessons without programming skills.

Validation of the ViGaTu Immersive Virtual Reality Endoscopy Training System for Physicians and Nurses.

BACKGROUND AND AIMS: Endoscopy simulators are primarily designed to provide training in interventions performed during procedures. Peri-interventional tasks such as checking patient data, filling out forms for team time-out, patient monitoring, and performing sedation are often not covered. This study assesses the face, content, and construct validity of the ViGaTu (Virtual Gastro Tutor) immersive virtual reality (VR) simulator in teaching these skills. METHODS: 71 nurses and physicians were invited to take part in VR training. The participants experienced an immersive VR simulation of an endoscopy procedure, including setting up the endoscopic devices, checking sign-in and team time-out forms, placing monitoring devices, and performing sedation. The actions performed by the participants and their timing were continuously recorded. Face and content validity, as well as the System Usability Scale (SUS), were then assessed. RESULTS: 43 physicians and 28 nurses from 43 centers took a mean of 27.8 min (standard deviation ± 14.42 min) to complete the simulation. Seventy-five percent of the items for assessing face validity were rated as realistic, and 60% of items assessing content validity and usefulness of the simulation for different learning goals were rated as useful by the participants (four out of five on a Likert scale). The SUS score was 70, demonstrating a high degree of usability. With regard to construct validity, experienced endoscopy staff were significantly faster in setting up the endoscope tower and instruments than beginners. CONCLUSIONS: This multicenter study presents a new type of interdisciplinary endoscopy training system featuring peri-interventional tasks and sedation in an immersive VR environment.

Intrinsic functional brain connectivity changes following aerobic exercise, computerized cognitive training, and their combination in physically inactive healthy late-middle-aged adults: the Projecte Moviment.

Lifestyle interventions have positive neuroprotective effects in aging. However, there are still open questions about how changes in resting-state functional connectivity (rsFC) contribute to cognitive improvements. The Projecte Moviment is a 12-week randomized controlled trial of a multimodal data acquisition protocol that investigated the effects of aerobic exercise (AE), computerized cognitive training (CCT), and their combination (COMB). An initial list of 109 participants was recruited from which a total of 82 participants (62% female; age = 58.38 ± 5.47) finished the intervention with a level of adherence > 80%. Only in the COMB group, we revealed an extended network of 33 connections that involved an increased and decreased rsFC within and between the aDMN/pDMN and a reduced rsFC between the bilateral supplementary motor areas and the right thalamus. No global and especially local rsFC changes due to any intervention mediated the cognitive benefits detected in the AE and COMB groups. Projecte Moviment provides evidence of the clinical relevance of lifestyle interventions and the potential benefits when combining them.

Systematic evaluation of fMRI data-processing pipelines for consistent functional connectomics.

Functional interactions between brain regions can be viewed as a network, enabling neuroscientists to investigate brain function through network science. Here, we systematically evaluate 768 data-processing pipelines for network reconstruction from resting-state functional MRI, evaluating the effect of brain parcellation, connectivity definition, and global signal regression. Our criteria seek pipelines that minimise motion confounds and spurious test-retest discrepancies of network topology, while being sensitive to both inter-subject differences and experimental effects of interest. We reveal vast and systematic variability across pipelines' suitability for functional connectomics. Inappropriate choice of data-processing pipeline can produce results that are not only misleading, but systematically so, with the majority of pipelines failing at least one criterion. However, a set of optimal pipelines consistently satisfy all criteria across different datasets, spanning minutes, weeks, and months. We provide a full breakdown of each pipeline's performance across criteria and datasets, to inform future best practices in functional connectomics.

Immunogenicity of third dose COVID-19 vaccine strategies in patients who are immunocompromised with suboptimal immunity following two doses (OCTAVE-DUO): an open-label, multicentre, randomised, controlled, phase 3 trial.

BACKGROUND: The humoral and T-cell responses to booster COVID-19 vaccine types in multidisease immunocompromised individuals who do not generate adequate antibody responses to two COVID-19 vaccine doses, is not fully understood. The OCTAVE DUO trial aimed to determine the value of third vaccinations in a wide range of patients with primary and secondary immunodeficiencies. METHODS: OCTAVE-DUO was a prospective, open-label, multicentre, randomised, controlled, phase 3 trial investigating humoral and T-cell responses in patients who are immunocompromised following a third vaccine dose with BNT162b2 or mRNA-1273, and of NVX-CoV2373 for those with lymphoid malignancies. We recruited patients who were immunocompromised from 11 UK hospitals, aged at least 18 years, with previous sub-optimal responses to two doses of SARS-CoV-2 vaccine. Participants were randomly assigned 1:1 (1:1:1 for those with lymphoid malignancies), stratified by disease, previous vaccination type, and anti-spike antibody response following two doses. Individuals with lived experience of immune susceptibility were involved in the study design and implementation. The primary outcome was vaccine-specific immunity defined by anti-SARS-CoV-2 spike antibodies (Roche Diagnostics UK and Ireland, Burgess Hill, UK) and T-cell responses (Oxford Immunotec, Abingdon, UK) before and 21 days after the third vaccine dose analysed by a modified intention-to-treat analysis. The trial is registered with the ISRCTN registry, ISRCTN 15354495, and the EU Clinical Trials Register, EudraCT 2021-003632-87, and is complete. FINDINGS: Between Aug 4, 2021 and Mar 31, 2022, 804 participants across nine disease cohorts were randomly assigned to receive BNT162b2 (n=377), mRNA-1273 (n=374), or NVX-CoV2373 (n=53). 356 (45%) of 789 participants were women, 433 (55%) were men, and 659 (85%) of 775 were White. Anti-SARS-CoV-2 spike antibodies measured 21 days after the third vaccine dose were significantly higher than baseline pre-third dose titres in the modified intention-to-treat analysis (median 1384 arbitrary units [AU]/mL [IQR 4·3-7990·0] compared with median 11·5 AU/mL [0·4-63·1]; p<0·001). Of participants who were baseline low responders, 380 (90%) of 423 increased their antibody concentrations to more than 400 AU/mL. Conversely, 166 (54%) of 308 baseline non-responders had no response after the third dose. Detectable T-cell responses following the third vaccine dose were seen in 494 (80%) of 616 participants. There were 24 serious adverse events (BNT612b2 eight [33%] of 24, mRNA-1273 12 [50%], NVX-CoV2373 four [17%]), two (8%) of which were categorised as vaccine-related. There were seven deaths (1%) during the trial, none of which were vaccine-related. INTERPRETATION: A third vaccine dose improved the serological and T-cell response in the majority of patients who are immunocompromised. Individuals with chronic renal disease, lymphoid malignancy, on B-cell targeted therapies, or with no serological response after two vaccine doses are at higher risk of poor response to a third vaccine dose. FUNDING: Medical Research Council, Blood Cancer UK.

Assessing the Repeatability of Multi-Frequency Multi-Layer Brain Network Topologies Across Alternative Researcher's Choice Paths.

There is a growing interest in the neuroscience community on the advantages of multilayer functional brain networks. Researchers usually treated different frequencies separately at distinct functional brain networks. However, there is strong evidence that these networks share complementary information while their interdependencies could reveal novel findings. For this purpose, neuroscientists adopt multilayer networks, which can be described mathematically as an extension of trivial single-layer networks. Multilayer networks have become popular in neuroscience due to their advantage to integrate different sources of information. Here, Ι will focus on the multi-frequency multilayer functional connectivity analysis on resting-state fMRI (rs-fMRI) recordings. However, constructing a multilayer network depends on selecting multiple pre-processing steps that can affect the final network topology. Here, I analyzed the rs-fMRI dataset from a single human performing scanning over a period of 18 months (84 scans in total), and the rs-fMRI dataset containing 25 subjects with 3 repeat scans. I focused on assessing the reproducibility of multi-frequency multilayer topologies exploring the effect of two filtering methods for extracting frequencies from BOLD activity, three connectivity estimators, with or without a topological filtering scheme, and two spatial scales. Finally, I untangled specific combinations of researchers' choices that yield consistently brain networks with repeatable topologies, giving me the chance to recommend best practices over consistent topologies.

SARS-CoV-2-specific immune responses and clinical outcomes after COVID-19 vaccination in patients with immune-suppressive disease.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) immune responses and infection outcomes were evaluated in 2,686 patients with varying immune-suppressive disease states after administration of two Coronavirus Disease 2019 (COVID-19) vaccines. Overall, 255 of 2,204 (12%) patients failed to develop anti-spike antibodies, with an additional 600 of 2,204 (27%) patients generating low levels (<380 AU ml-1). Vaccine failure rates were highest in ANCA-associated vasculitis on rituximab (21/29, 72%), hemodialysis on immunosuppressive therapy (6/30, 20%) and solid organ transplant recipients (20/81, 25% and 141/458, 31%). SARS-CoV-2-specific T cell responses were detected in 513 of 580 (88%) patients, with lower T cell magnitude or proportion in hemodialysis, allogeneic hematopoietic stem cell transplantation and liver transplant recipients (versus healthy controls). Humoral responses against Omicron (BA.1) were reduced, although cross-reactive T cell responses were sustained in all participants for whom these data were available. BNT162b2 was associated with higher antibody but lower cellular responses compared to ChAdOx1 nCoV-19 vaccination. We report 474 SARS-CoV-2 infection episodes, including 48 individuals with hospitalization or death from COVID-19. Decreased magnitude of both the serological and the T cell response was associated with severe COVID-19. Overall, we identified clinical phenotypes that may benefit from targeted COVID-19 therapeutic strategies.