Search

Neuromodulation techniques have emerged as promising approaches for treating a wide range of neurological disorders, precisely delivering electrical stimulation to modulate abnormal neuronal activity. While leveraging the unique capabilities of AI holds immense potential for responsive neurostimulation, it appears as an extremely challenging proposition where real-time (low-latency) processing, low-power consumption, and heat constraints are limiting factors. The use of sophisticated AI-driven models for personalized neurostimulation depends on the back-telemetry of data to external systems (e.g. cloud-based medical mesosystems and ecosystems). While this can be a solution, integrating continuous learning within implantable neuromodulation devices for several applications, such as seizure prediction in epilepsy, is an open question. We believe neuromorphic architectures hold an outstanding potential to open new avenues for sophisticated on-chip analysis of neural signals and AI-driven personalized treatments. With more than three orders of magnitude reduction in the total data required for data processing and feature extraction, the high power- and memory-efficiency of neuromorphic computing to hardware-firmware co-design can be considered as the solution-in-the-making to resource-constraint implantable neuromodulation systems. This perspective introduces the concept of Neuromorphic Neuromodulation , a new breed of closed-loop responsive feedback system. It highlights its potential to revolutionize implantable brain-machine microsystems for patient-specific treatment., (© The Author(s) 2024. Published by Oxford University Press on behalf of National Academy of Sciences.)

Abstract: Chimeric antigen receptor (CAR) T-cell therapies have demonstrated transformative efficacy in treating B-cell malignancies. However, high costs and manufacturing complexities hinder their widespread use. To overcome these hurdles, we have developed the VivoVec platform, a lentiviral vector capable of generating CAR T cells in vivo. Here, we describe the incorporation of T-cell activation and costimulatory signals onto the surface of VivoVec particles (VVPs) in the form of a multidomain fusion protein and show enhanced in vivo transduction and improved CAR T-cell antitumor functionality. Furthermore, in the absence of lymphodepleting chemotherapy, administration of VVPs into nonhuman primates resulted in the robust generation of anti-CD20 CAR T cells and the complete depletion of B cells for >10 weeks. These data validate the VivoVec platform in a translationally relevant model and support its transition into human clinical testing, offering a paradigm shift in the field of CAR T-cell therapies., (© 2024 American Society of Hematology. Published by Elsevier Inc. Licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0), permitting only noncommercial, nonderivative use with attribution. All other rights reserved.)

Introduction: Structural and functional abnormalities in the cortical-striatal network (CSN) are hypothesised to play a key role in the pathogenesis of neurological disease-associated fatigue. Some small-scale functional MRI (fMRI) studies have suggested that poststroke fatigue (PSF) is related to focal functional connectivity (FC) changes. To date, there has been no published large-scale fMRI study on PSF. This planned study will examine the role of the CSN FC on PSF., Methods and Analysis: The planned study will be a prospective cohort study conducted at the Neurology Unit of the Prince of Wales Hospital. We will recruit 738 participants. The project duration will be 36 months. A psychiatrist will administer the Fatigue Severity Scale (FSS) at 3 months (P1) following the index stroke. PSF is defined as an FSS Score≥4.0. PSF severity will be defined by the FSS total score at P1. Participants with PSF at P1 will undergo two follow-up assessments at 9 (P2) and 15 (P3) months post stroke. PSF remission at P2 or P3 will be defined as a 50% reduction in FSS. Participants will undergo MRI examinations within 2 weeks of the 3-month poststroke assessment. Structural MRI, resting-state fMRI and diffusion tensor imaging will be performed. FC, structural connectivity, infarcts, cerebral microbleeds and white matter hyperintensities will be analysed. For the primary analysis, the effect of PSF on the FC, structural connectivity and diffusion metrics of CSN of stroke survivors, voxel-wise two-sample t-tests will be performed with FDR correction for multiple comparison and significance level set at p<0.05., Ethics and Dissemination: Ethical approval was obtained from the Joint Chinese University of Hong Kong-New Territories East Cluster clinical research ethics committee. The study findings will be shared through peer-reviewed journal publications, national and international conferences and social media platforms., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

This study introduces two models, ConvLSTM2D-liquid time-constant network (CLTC) and ConvLSTM2D-closed-form continuous-time neural network (CCfC), designed for abnormality identification using electrocardiogram (ECG) data. Trained on the Telehealth Network of Minas Gerais (TNMG) subset dataset, both models were evaluated for their performance, generalizability capacity, and resilience. They demonstrated comparable results in terms of F1 scores and AUROC values. The CCfC model achieved slightly higher accuracy, while the CLTC model showed better handling of empty channels. Remarkably, the models were successfully deployed on a resource-constrained microcontroller, proving their suitability for edge device applications. Generalization capabilities were confirmed through the evaluation on the China Physiological Signal Challenge 2018 (CPSC) dataset. The models' efficient resource utilization, occupying 70.6% of memory and 9.4% of flash memory, makes them promising candidates for real-world healthcare applications. Overall, this research advances abnormality identification in ECG data, contributing to the progress of AI in healthcare., (© 2024. The Author(s).)

Background: Vaccine-related acute myocarditis is recognized as a rare and specific vaccine complication following mRNA-based COVID-19 vaccinations. The precise mechanisms remain unclear. We hypothesized that natural killer (NK) cells play a central role in its pathogenesis., Methods: Samples from 60 adolescents with vaccine-related myocarditis were analyzed, including pro-inflammatory cytokines, cardiac troponin T, genotyping, and immunophenotyping of the corresponding activation subsets of NK cells, monocytes, and T cells. Results were compared with samples from 10 vaccinated individuals without myocarditis and 10 healthy controls., Findings: Phenotypically, high levels of serum cytokines pivotal for NK cells, including interleukin-1β (IL-1β), interferon α2 (IFN-α2), IL-12, and IFN-γ, were observed in post-vaccination patients with myocarditis, who also had high percentage of CD57 + NK cells in blood, which in turn correlated positively with elevated levels of cardiac troponin T. Abundance of the CD57 + NK subset was particularly prominent in males and in those after the second dose of vaccination. Genotypically, killer cell immunoglobulin-like receptor (KIR) KIR2DL5B(-)/KIR2DS3(+)/KIR2DS5(-)/KIR2DS4del(+) was a risk haplotype, in addition to single-nucleotide polymorphisms related to the NK cell-specific expression quantitative trait loci DNAM-1 and FuT11, which also correlated with cardiac troponin T levels in post-vaccination patients with myocarditis., Conclusion: Collectively, these data suggest that NK cell activation by mRNA COVID-19 vaccine contributed to the pathogenesis of acute myocarditis in genetically and epidemiologically vulnerable subjects., Funding: This work was funded by the Hong Kong Collaborative Research Fund (CRF) 2020/21 and the CRF Coronavirus and Novel Infectious Diseases Research Exercises (reference no. C7149-20G)., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Chimeric antigen receptor (CAR) designs that incorporate pharmacologic control are desirable; however, designs suitable for clinical translation are needed. We designed a fully human, rapamycin-regulated drug product for targeting CD33+ tumors called dimerizaing agent-regulated immunoreceptor complex (DARIC33). T cell products demonstrated target-specific and rapamycin-dependent cytokine release, transcriptional responses, cytotoxicity, and in vivo antileukemic activity in the presence of as little as 1 nM rapamycin. Rapamycin withdrawal paused DARIC33-stimulated T cell effector functions, which were restored following reexposure to rapamycin, demonstrating reversible effector function control. While rapamycin-regulated DARIC33 T cells were highly sensitive to target antigen, CD34+ stem cell colony-forming capacity was not impacted. We benchmarked DARIC33 potency relative to CD19 CAR T cells to estimate a T cell dose for clinical testing. In addition, we integrated in vitro and preclinical in vivo drug concentration thresholds for off-on state transitions, as well as murine and human rapamycin pharmacokinetics, to estimate a clinically applicable rapamycin dosing schedule. A phase I DARIC33 trial has been initiated (PLAT-08, NCT05105152), with initial evidence of rapamycin-regulated T cell activation and antitumor impact. Our findings provide evidence that the DARIC platform exhibits sensitive regulation and potency needed for clinical application to other important immunotherapy targets.