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Macrophages often pervade solid tumors, but their nearest neighbor organization is understudied and potentially enables key functions such as phagocytosis. Here, we observe dynamic macrophage clusters in tumors under conditions that maximize cancer cell phagocytosis and use reductionist approaches to uncover pathways to cluster formation and roles for tumor-intrusive pseudopodia, which we term 'intrudopodia'. Macrophage clusters form over hours on low- adhesion substrates after M1 polarization with interferons, including T cell-derived cytokines, and yet clusters prove fluid on timescales of minutes. Clusters also sort from M2 macrophages that disperse on the same substrates. M1 macrophages upregulate specific cell-cell adhesion receptors but suppress actomyosin contractility, and while both pathways contribute to cluster formation, decreased cortical tension was predicted to unleash pseudopodia. Macrophage neighbors in tumor spheroids indeed extend intrudopodia between adjacent cancer cell junctions - at least when phagocytosis conditions are maximized, and coordinated intrudopodia help detach and individualize cancer cells for rapid engulfment. Macrophage clusters thereby provide a cooperative advantage for phagocytosis to overcome solid tumor cohesion.

Solid tumors generally exhibit chromosome copy number variation, which is typically caused by chromosomal instability (CIN) in mitosis. The resulting aneuploidy can drive evolution and associates with poor prognosis in various cancer types as well as poor response to T-cell checkpoint blockade in melanoma. Macrophages and the SIRPα-CD47 checkpoint are understudied in such contexts. Here, CIN is induced in poorly immunogenic B16F10 mouse melanoma cells using spindle assembly checkpoint MPS1 inhibitors that generate persistent micronuclei and diverse aneuploidy while skewing macrophages toward a tumoricidal 'M1-like' phenotype based on markers and short-term anti-tumor studies. Mice bearing CIN-afflicted tumors with wild-type CD47 levels succumb similar to controls, but long-term survival is maximized by SIRPα blockade on adoptively transferred myeloid cells plus anti-tumor monoclonal IgG. Such cells are the initiating effector cells, and survivors make de novo anti-cancer IgG that not only promote phagocytosis of CD47-null cells but also suppress tumor growth. CIN does not affect the IgG response, but pairing CIN with maximal macrophage anti-cancer activity increases durable cures that possess a vaccination-like response against recurrence., Competing Interests: BH, MW, HZ, SP, LD, JA, AC, MT, NO, TM, DD No competing interests declared, (© 2023, Hayes et al.)

Chromosomal instability (CIN), a state in which cells undergo mitotic aberrations that generate chromosome copy number variations, generates aneuploidy and is thought to drive cancer evolution. Although associated with poor prognosis and reduced immune response, CIN generates aneuploidy-induced stresses that could be exploited for immunotherapies. In such contexts, macrophages and the CD47-SIRPα checkpoint are understudied. Here, CIN is induced pharmacologically induced in poorly immunogenic B16F10 mouse melanoma cells, generating persistent micronuclei and diverse aneuploidy while skewing macrophages towards an anti-cancer M1-like phenotype, based on RNA-sequencing profiling, surface marker expression and short-term antitumor studies. These results further translate to in vivo efficacy: Mice bearing CIN-afflicted tumors with wild-type CD47 levels survive only slightly longer relative to chromosomally stable controls, but long-term survival is maximized when combining macrophage-stimulating anti-tumor IgG opsonization and some form of disruption of the CD47-SIRPα checkpoint. Survivors make multi-epitope, de novo anti-cancer IgG that promote macrophage-mediated phagocytosis of CD47 knockout B16F10 cells and suppress tumoroids in vitro and growth of tumors in vivo . CIN does not greatly affect the level of the IgG response compared to previous studies but does significantly increase survival. These results highlight an unexpected therapeutic benefit from CIN when paired with maximal macrophage anti-cancer activity: an anti-cancer vaccination-like antibody response that can lead to more durable cures and further potentiate cell-mediated acquired immunity.

Phagocytic elimination of solid tumors by innate immune cells seems attractive for immunotherapy, particularly because of the possibilities for acquired immunity. However, the approach remains challenging, with blockade of the macrophage checkpoint CD47 working in immunodeficient mice and against highly immunogenic tumors but not in the clinic where tumors are poorly immunogenic. Even when mouse tumors of poorly immunogenic B16F10 melanoma are opsonized to drive engulfment with a suitable monoclonal antibody (mAb), anti-CD47 blockade remains insufficient. Using both in vitro immuno-tumoroids and in vivo mouse models, we show with CRISPR interference (CRISPRi) that a relatively uniform minimum repression of CD47 by 80% is needed for phagocytosis to dominate net growth when combined with an otherwise ineffective mAb (anti-Tyrp1). Heterogeneity enriches for CD47-high cells, but mice that eliminate tumors generate prophagocytic IgGs that increase in titer with CD47 repression and with tumor accumulation of macrophages, although deeper repression does not improve survival. Given well-known limitations of antibody permeation into solid tumors, our studies clarify benchmarks for CD47 disruption that should be more clinically feasible and safer but just as effective as complete ablation. Additionally, safe but ineffective opsonization in human melanoma trials suggests that combinations with deep repression of CD47 could prove effective and initiate durable immunity., (© The Author(s) 2023. Published by Oxford University Press on behalf of National Academy of Sciences.)

The mechanical environment of a cell can have many effects, but whether it impacts the DNA sequence of a cell has remained unexamined. To investigate this, we developed a live-cell method to measure changes in chromosome numbers. We edited constitutive genes with GFP or RFP tags on single alleles and discovered that cells that lose Chromosome reporters (ChReporters) become non-fluorescent. We applied our new tools to confined mitosis and to inhibition of the putative tumor suppressor myosin-II. We quantified compression of mitotic chromatin in vivo and demonstrated that similar compression in vitro resulted in cell death, but also rare and heritable ChReptorter loss. Myosin-II suppression rescued lethal multipolar divisions and maximized ChReporter loss during three-dimensional (3D) compression and two-dimensional (2D) lateral confinement, but not in standard 2D culture. ChReporter loss was associated with chromosome mis-segregation, rather than just the number of divisions, and loss in vitro and in mice was selected against in subsequent 2D cultures. Inhibition of the spindle assembly checkpoint (SAC) caused ChReporter loss in 2D culture, as expected, but not during 3D compression, suggesting a SAC perturbation. Thus, ChReporters enable diverse studies of viable genetic changes, and show that confinement and myosin-II affect DNA sequence and mechano-evolution., Competing Interests: Competing interests The authors declare no competing or financial interests., (© 2023. Published by The Company of Biologists Ltd.)

The macrophage checkpoint interaction CD47-SIRPα is an emerging target for cancer therapy, but clinical trials of monoclonal anti-CD47 show efficacy only in liquid tumors when combined with tumor-opsonizing IgG. Here, in challenging metastatic solid tumors, CD47 deletion shows no effect on tumor growth unless combined with otherwise ineffective tumor-opsonization, and we likewise show wild-type metastases are suppressed by SIRPα-blocked macrophages plus tumor-opsonization. Lung tumor nodules of syngeneic B16F10 melanoma cells with CD47 deletion show opsonization drives macrophage phagocytosis of B16F10s, consistent with growth versus phagocytosis calculus for exponential suppression of cancer. Wild-type CD47 levels on metastases in lungs of immunocompetent mice and on human metastases in livers of immunodeficient mice show that systemic injection of antibody-engineered macrophages also suppresses growth. Such in vivo functionality can be modulated by particle pre-loading of the macrophages. Thus, even though CD47-SIRPα disruption and tumor-opsonizing IgG are separately ineffective against established metastatic solid tumors, their combination in molecular and cellular therapies prolongs survival.

The macrophage checkpoint is an anti-phagocytic interaction between signal regulatory protein alpha (SIRPα) on a macrophage and CD47 on all types of cells - ranging from blood cells to cancer cells. This interaction has emerged over the last decade as a potential co-target in cancer when combined with other anti-cancer agents, with antibodies against CD47 and SIRPα currently in preclinical and clinical development for a variety of hematological and solid malignancies. Monotherapy with CD47 blockade is ineffective in human clinical trials against many tumor types tested to date, except for rare cutaneous and peripheral lymphomas. In contrast, pre-clinical results show efficacy in multiple syngeneic mouse models of cancer, suggesting that many of these tumor models are more immunogenic and likely artificial compared to human tumors. However, combination therapies in humans of anti-CD47 with agents such as the anti-tumor antibody rituximab do show efficacy against liquid tumors (lymphoma) and are promising. Here, we review such trials as well as key interaction and structural features of CD47-SIRPα., (© The Author(s) 2020. Published by Oxford University Press on behalf of Antibody Therapeutics.)

Immunotherapies against some solid tumour types have recently shown unprecedented, durable cures in the clinic, and the most successful thus far involves blocking inhibitory receptor 'checkpoints' on T cells. A similar approach with macrophages is emerging by blocking the ubiquitously expressed 'marker of self' CD47 from binding the inhibitory receptor SIRPα on macrophages. Here, we first summarize available information on the safety and efficacy of CD47 blockade, which raises some safety concerns with the clearance of 'self' cells but also suggests some success against haematological (liquid) and solid cancers. Checkpoint blockade generally benefits from parallel activation of the immune cell, which can occur for macrophages in multiple ways, such as by combination with a second, tumour-opsonizing antibody and perhaps also via rigidity sensing. Cytoskeletal forces in phagocytosis and inhibitory 'self'-signalling are thus reviewed together with macrophage mechanosensing, which extends to regulating levels of SIRPα and the nuclear protein lamin A, which affects phenotype and cell trafficking. Considerations of such physical factors in cancer and the immune system can inform the design of new immunotherapies and help to refine existing therapies to improve safety and efficacy. This article is part of a discussion meeting issue 'Forces in cancer: interdisciplinary approaches in tumour mechanobiology'.

Epilepsy is defined by the seemingly random occurrence of spontaneous seizures. The ability to anticipate seizures would enable preventative treatment strategies. A central but unresolved question concerns the relationship of seizure timing to fluctuating rates of interictal epileptiform discharges (here termed interictal epileptiform activity, IEA), a marker of brain irritability observed between seizures by electroencephalography (EEG). Here, in 37 subjects with an implanted brain stimulation device that detects IEA and seizures over years, we find that IEA oscillates with circadian and subject-specific multidien (multi-day) periods. Multidien periodicities, most commonly 20-30 days in duration, are robust and relatively stable for up to 10 years in men and women. We show that seizures occur preferentially during the rising phase of multidien IEA rhythms. Combining phase information from circadian and multidien IEA rhythms provides a novel biomarker for determining relative seizure risk with a large effect size in most subjects.

Background: Interictal epileptiform discharges (IEDs) are an electrographic biomarker of epilepsy. Despite their crucial role in diagnosing epilepsy, heterogeneous findings exist on the mechanisms underlying their occurrence and the effects of anti-seizure medications (ASMs) on IEDs. Methods: We conducted a study to investigate the association between IED frequency and the serum concentration of two commonly used ASMs, levetiracetam (LEV) and lamotrigine (LTG). We included 56 patients undergoing a continuous video EEG monitoring in our center with tapering of ASM. IED frequency was analyzed using automated and semiautomated methods and serum samples were collected sequentially throughout the stay. Results: The cohort consisted of 41 patients (23 female, 18 male), between 19 and 64 years (mean 37.42 years), most of which were diagnosed with focal epilepsy (93%). IED frequency increased after ASM reduction revealing a negative correlation similarly with LEV and LTG serum concentrations (p = 0.0057 and p = 0.0426, respectively). Discussion: Notably, we observed a significant increase in IED frequency following dose reduction or discontinuation of both medications. This effect was reversed after ASM were re-dosed. This may indicate the suppressive properties of LEV and LTG against epileptic seizures. Furthermore, our study highlights the importance of ASM discontinuation, which may be required for capturing IEDs during diagnostic continuous EEG monitoring, and not be fully explained by circadian or ultradian rhythms alone. Conclusion: Our findings contribute to the understanding of ASM effects on IED frequency dynamics and suggest seizure suppressive properties of LEV and LTG. [ABSTRACT FROM AUTHOR]

Epilepsy, characterized by recurrent seizures, is influenced by biological rhythms, such as circadian, seasonal, and menstrual cycles. These rhythms affect the frequency, severity, and timing of seizures, although the precise mechanisms underlying these associations remain unclear. This review examines the role of biological clocks, particularly the core circadian genes Bmal1, Clock, Per, and Cry, in regulating neuronal excitability and epilepsy susceptibility. We explore how the sleep-wake cycle, particularly non-rapid eye movement sleep, increases the risk of seizures, and discuss the circadian modulation of neurotransmitters like gamma-aminobutyric acid and glutamate. We explore clinical implications, including chronotherapy which refers to the practice of timing medical treatments to align with the body's natural biological rhythms, such as the circadian rhythm. Chronotherapy aligns anti-seizure medication administration with biological rhythms. We also discuss rhythm-based neuromodulation strategies, such as adaptive deep brain stimulation, which may dynamically change stimulation in response to predicted seizures in patients, provide additional therapeutic options. This review emphasizes the potential of integrating biological rhythm analysis into personalized epilepsy management, offering novel approaches to optimize treatment and improve patient outcomes. Future research should focus on understanding individual variability in seizure rhythms and harnessing technological innovations to enhance seizure prediction, precision treatment, and long-term management. [ABSTRACT FROM AUTHOR]

Genomic instability is a driver and accelerator of tumorigenesis and influences disease outcomes across cancer types. Although genomic instability has been associated with immune evasion and worsened disease prognosis, emerging evidence shows that genomic instability instigates pro-inflammatory signaling and enhances the immunogenicity of tumor cells, making them more susceptible to immune recognition. While this paradoxical role of genomic instability in cancer is complex and likely context-dependent, understanding it is essential for improving the success rates of cancer immunotherapy. In this review, we provide an overview of the underlying mechanisms that link genomic instability to proinflammatory signaling and increased immune surveillance in the context of cancer, as well as discuss how genomically unstable tumors evade the immune system. A better understanding of the molecular crosstalk between genomic instability, inflammatory signaling, and immune surveillance could guide the exploitation of immunotherapeutic vulnerabilities in cancer. [ABSTRACT FROM AUTHOR]

Intrinsic biological clocks drive the circadian rhythm, which coordinates the physiological and pathophysiological processes in the body. Recently, a bidirectional relationship between circadian rhythms and several neurological diseases has been reported. Neurological diseases can lead to the disruption of circadian homeostasis, thereby increasing disease severity. Therefore, optimizing the current treatments through circadian-based approaches, including adjusted dosing, changing lifestyle, and targeted interventions, offer a promising opportunity for better clinical outcomes and precision medicine. In this review, we provide detailed implications of the circadian rhythm in neurological diseases through bench-to-bedside approaches. Furthermore, based on the unsatisfactory clinical outcomes, we critically discuss the potential of circadian-based interventions, which may encourage more studies in this discipline, with the hope of improving treatment efficacy in neurological diseases. [ABSTRACT FROM AUTHOR]

Objective: To analyze the local field potentials (LFPs) in patients with focal drugresistant epilepsy (DRE) from the anterior nucleus of the thalamus (ANT) during inter-ictal state and seizure state. Method: ANT stereotactic EEG (SEEG) recordings were studied in four patients with focal temporal lobe epilepsy. SEEG data was classified as inter-ictal and ictal state and sub-categorized into electrographic (ESz), focal aware seizure (FAS), focal with impaired awareness (FIA), or focal to bilateral tonic-clonic seizure (FBTC). LFP was analyzed at 4 Hz, 8 Hz, 16 Hz, 32 Hz, high gamma (100 Hz), and ripples (200 Hz) using spectrogram analysis and a statistical comparison of normalized power spectral density (PSD) averaged during seizures versus preictal baseline segments. Result: The LFP recordings were analyzed for 162 seizures (127 ESz, 23 FAS, 6 FIA, and 6 FBTC). Based on time-frequency data (spectrogram), a broad band of activity, occurring between 2 and 6 Hz and centered at 4 Hz, and thin-band activity occurring specifically at 8 Hz on the frequency spectrogram were observed during the inter-ictal state. Statistically significant changes in LFP-PSD were seen for FAS, FIA, and FBTC. We observed a significant gain in LFP at the lower frequency band during FAS at 4 Hz, FIA, and FBTC at 4, 8, and 16 Hz while also observing increases at higher frequencies during FBTC at 100 and 200 Hz and a decrease during FAS seizures at 32 Hz. In contrast, no significant change in LFP power was seen for electrographic seizures. Interpretation: Our observations from a limited dataset indicate that all clinical seizure types, but not electrographic seizures, caused a change in ANT-LFP based on the magnitude of the associated power spectral density (PSD). Future work will be needed to validate the use of ANT-LFP at these frequencies as accurate measurements of seizure occurrence and severity. This work represents a first step toward understanding ANT thalamic LFP patterns during focal seizures and developing adaptive DBS strategies. [ABSTRACT FROM AUTHOR]

Introduction: Neurologic circadian influences, including sleep/wake transitions, processes (e.g., hormonal variation), and behavioral patterns (e.g., consumption of food and oral medications), may affect seizure patterns. Specific circadian patterns of seizures have been reported depending on type, onset location, and severity; however, data on patterns for patients with seizure clusters and effectiveness of rescue therapy by time of day are limited. Methods: We conducted post hoc analyses using patient diary data from the phase 3 safety study of diazepam nasal spray, which is indicated for acute treatment of seizure clusters in patients with epilepsy aged =6 years. Patients were administered age- and weight-based doses; second doses could be administered if needed to control a seizure cluster. We assessed clock timing of seizure-cluster onset along with second-dose use as a proxy for effectiveness. Treatment-emergent adverse events were recorded. Results: Seizure-cluster onset was observed to be generally highest during mornings and late evenings and lowest in the early evening and middle of the night. Second-dose use was not consistently associated with a specific time of day. The safety profile was consistent with that expected from previous studies of diazepam nasal spray. Conclusion: These results suggest that diazepam nasal spray can be effectively administered at any time of day. [ABSTRACT FROM AUTHOR]

Purpose: The research endeavors to explore the implications of CD47 in cancer immunotherapy effectiveness. Specifically, there is a gap in comprehending the influence of CD47 on the tumor immune microenvironment, particularly in relation to CD8 + T cells. Our study aims to elucidate the prognostic and immunological relevance of CD47 to enhance insights into its prospective utilities in immunotherapeutic interventions. Methods: Differential gene expression analysis, prognosis assessment, immunological infiltration evaluation, pathway enrichment analysis, and correlation investigation were performed utilizing a combination of R packages, computational algorithms, diverse datasets, and patient cohorts. Validation of the concept was achieved through the utilization of single-cell sequencing technology. Results: CD47 demonstrated ubiquitous expression across various cancer types and was notably associated with unfavorable prognostic outcomes in pan-cancer assessments. Immunological investigations unveiled a robust correlation between CD47 expression and T-cell infiltration rather than T-cell exclusion across multiple cancer types. Specifically, the CD47-high group exhibited a poorer prognosis for the cytotoxic CD8 + T cell Top group compared to the CD47-low group, suggesting a potential impairment of CD8 + T cell functionality by CD47. The exploration of mechanism identified enrichment of CD47-associated differentially expressed genes in the CD8 + T cell exhausted pathway in multiple cancer contexts. Further analyses focusing on the CD8 TCR Downstream Pathway and gene correlation patterns underscored the significant involvement of TNFRSF9 in mediating these effects. Conclusion: A robust association exists between CD47 and the exhaustion of CD8 + T cells, potentially enabling immune evasion by cancer cells and thereby contributing to adverse prognostic outcomes. Consequently, genes such as CD47 and those linked to T-cell exhaustion, notably TNFRSF9, present as promising dual antigenic targets, providing critical insights into the field of immunotherapy. [ABSTRACT FROM AUTHOR]

Numerous physiological processes are cyclical, but sampling these processes densely enough to perform frequency decomposition and subsequent analyses can be challenging. Mathematical approaches for decomposition and reconstruction of sparsely and irregularly sampled signals are well established but have been under-utilized in physiological applications. We developed a basis pursuit denoising with polynomial detrending (BPWP) model that recovers oscillations and trends from sparse and irregularly sampled timeseries. We validated this model on a unique dataset of long-term inter-ictal epileptiform discharge (IED) rates from human hippocampus recorded with a novel investigational device with continuous local field potential sensing. IED rates have well established circadian and multiday cycles related to sleep, wakefulness, and seizure clusters. Given sparse and irregular samples of IED rates from multi-month intracranial EEG recordings from ambulatory humans, we used BPWP to compute narrowband spectral power and polynomial trend coefficients and identify IED rate cycles in three subjects. In select cases, we propose that random and irregular sampling may be leveraged for frequency decomposition of physiological signals. Trial Registration:NCT03946618. Author summary: Circadian and multiday cycles are an important part of many long-term neuro-behavioral phenomena such as pathological inter-ictal epileptiform discharges (IEDs) and seizures in epilepsy. Long-term, ambulatory, neuro-behavioral monitoring in human patients involves complex recording systems that can be subject to intermittent, irregular data loss and storage limitations, resulting in sparse, irregularly sampled data. Cycle identification in sparse data or irregular data using traditional frequency decomposition techniques typically requires interpolation to create a regular timeseries. Using unique, long-term recordings of pathological brain activity in people with epilepsy implanted with an investigational device, we developed a method to identify cycles in sparse, irregular neuro-behavioral data without interpolation. We anticipate this approach will enable retrospective cycle identification in sparse neuro-behavioral timeseries and support prospective sparse sampling in monitoring systems to enable long-term monitoring of patients and to extend storage capacity in a variety of ambulatory monitoring applications. [ABSTRACT FROM AUTHOR]

The Epileptor is a phenomenological model for seizure activity that is used in a personalized large-scale brain modeling framework, the Virtual Epileptic Patient, with the aim of improving surgery outcomes for drug-resistant epileptic patients. Transitions between interictal and ictal states are modeled as bifurcations, enabling the definition of seizure classes in terms of onset/offset bifurcations. This establishes a taxonomy of seizures grounded in their essential underlying dynamics and the Epileptor replicates the activity of the most common class, as observed in patients with focal epilepsy, which is characterized by square-wave bursting properties. The Epileptor also encodes an additional mechanism to account for interictal spikes and spike and wave discharges. Here we use insights from a more generic model for square-wave bursting, based on the Unfolding Theory approach, to guide the bifurcation analysis of the Epileptor and gain a deeper understanding of the model and the role of its parameters. We show how the Epileptor's parameters can be modified to produce activities for other seizures classes of the taxonomy, as observed in patients, so that the large-scale brain models could be further personalized. Some of these classes have already been described in the literature in the Epileptor, others, predicted by the generic model, are new. Finally, we unveil how the interaction with the additional mechanism for spike and wave discharges alters the bifurcation structure of the main burster. Author summary: This work focuses on a model, the Epileptor, which mimics seizure activity observed in many patients with focal epilepsy. The Epileptor forms the core of a large-scale brain modeling framework, known as the Virtual Epileptic Patient. This framework is developed to provide clinicians with a platform for testing outcomes of virtual surgeries and stimulations, and for finding optimal strategies to halt seizures in drug-resistant epileptic patients. However, models of brain activity, such as the Epileptor, can contain a large number of parameters, making it a daunting task to appreciate the full range of behaviors they can produce. Here, we approach this problem using a simpler model as a guide. The simpler model provides a map to orient ourselves, allowing us to identify key parameters for investigation and suggesting how to tweak them to obtain a broader range of behaviors. This is important because this repertoire of behaviors includes different classes of seizures that can be used to make the Virtual Epileptic Patient framework more patient-specific and potentially increase its predictive power. [ABSTRACT FROM AUTHOR]

Cluster of differentiation 47 (CD47) occupies the outer membrane of human cells, where it binds to soluble and cell surface receptors on the same and other cells, sculpting their topography and resulting in a pleiotropic receptor‐multiligand interaction network. It is a focus of drug development to temper and accentuate CD47‐driven immune cell liaisons, although consideration of on‐target CD47 effects remain neglected. And yet, a late clinical trial of a CD47‐blocking antibody was discontinued, existent trials were restrained, and development of CD47‐targeting agents halted by some pharmaceutical companies. At this point, if CD47 can be exploited for clinical advantage remains to be determined. Herein an airing is made of the seemingly conflicting actions of CD47 that reflect its position as a junction connecting receptors and signalling pathways that impact numerous human cell types. Prospects of CD47 boosting and blocking are considered along with potential therapeutic implications for autoimmune diseases and cancer. [ABSTRACT FROM AUTHOR]

Background: The dynamic interaction between cancer cells and tumour-associated macrophages (TAMs) in the hypoxic tumour microenvironment (TME) is an active barrier to the effector arm of the antitumour immune response. Cancer-secreted exosomes are emerging mediators of this cancer-stromal cross-talk in the TME; however, the mechanisms underlying this interaction remain unclear. Methods: Exosomes were isolated with ExoQuick exosome precipitation solution. The polarizing effect of TAMs was evaluated by flow cytometry, western blot analysis, immunofluorescence staining and in vitro phagocytosis assays. Clinical cervical cancer specimens and an in vivo xenograft model were also employed. Results: Our previous study showed that hypoxia increased the expression of ZEB1 in cervical squamous cell carcinoma (CSCC) cells, which resulted in increased infiltration of TAMs. Here, we found that hypoxia-induced ZEB1 expression is closely correlated with CD47-SIRPα axis activity in CSCC, which enables cancer cells to evade phagocytosis by macrophages and promotes tumour progression. ZEB1 was found to directly activate the transcription of the CD47 gene in hypoxic CSCC cells. We further showed that endogenous ZEB1 was characteristically enriched in hypoxic CSCC cell-derived exosomes and transferred into macrophages via these exosomes to promote SIRPα+ TAM polarization. Intriguingly, exosomal ZEB1 retained transcriptional activity and reprogrammed SIRPα+ TAMs via activation of the STAT3 signalling pathway in vitro and in vivo. STAT3 inhibition reduced the polarizing effect induced by exosomal ZEB1. Knockdown of ZEB1 increased the phagocytosis of CSCC cells by macrophages via decreasing CD47 and SIRPα expression. Conclusions: Our results suggest that hypoxia-induced ZEB1 promotes immune evasion in CSCC by strengthening the CD47-SIRPα axis. ZEB1-targeted therapy in combination with CD47-SIRPα checkpoint immunotherapy may improve the outcomes of CSCC patients in part by disinhibiting innate immunity. [ABSTRACT FROM AUTHOR]

In recent years, the therapeutic (re)activation of innate anticancer immunity has gained prominence, with therapeutic blocking of the interaction of Signal Regulatory Protein (SIRP)-a with its ligand CD47 yielding complete responses in refractory and relapsed B cell lymphoma patients. SIRP-a has as crucial inhibitory role on phagocytes, with e.g., its aberrant activation enabling the escape of cancer cells from immune surveillance. SIRP-a belongs to a family of paired receptors comprised of not only immune-inhibitory, but also putative immune-stimulatory receptors. Here, we report that an as yet uninvestigated SIRP family member, SIRP-beta 2 (SIRP-ß2), is strongly expressed under normal physiological conditions in macrophages and granulocytes at protein level. Endogenous expression of SIRP-ß2 on granulocytes correlated with trogocytosis of cancer cells. Further, ectopic expression of SIRP-ß2 stimulated macrophage adhesion, differentiation and cancer cell phagocytosis as well as potentiated macrophage-mediated activation of T cell Receptor-specific T cell activation. SIRP-ß2 recruited the immune activating adaptor protein DAP12 to positively regulate innate immunity, with the charged lysine 202 of SIRP-ß2 being responsible for interaction with DAP12. Mutation of lysine 202 to leucine lead to a complete loss of the increased adhesion and phagocytosis. In conclusion, SIRPß2 is a novel positive regulator of innate anticancer immunity and a potential costimulatory target for innate immunotherapy. [ABSTRACT FROM AUTHOR]

Objective: Epilepsy is characterized by spontaneous seizures that recur at unexpected times. Nonetheless, using years‐long electroencephalographic (EEG) recordings, we previously found that patient‐reported seizures consistently occur when interictal epileptiform activity (IEA) cyclically builds up over days. This multidien (multiday) interictal–ictal relationship, which is shared across patients, may bear phasic information for forecasting seizures, even if individual patterns of seizure timing are unknown. To test this rigorously in a large retrospective dataset, we pretrained algorithms on data recorded from a group of patients, and forecasted seizures in other, previously unseen patients. Methods: We used retrospective long‐term data from participants (N = 159) in the RNS System clinical trials, including intracranial EEG recordings (icEEG), and from two participants in the UNEEG Medical clinical trial of a subscalp EEG system (sqEEG). Based on IEA detections, we extracted instantaneous multidien phases and trained generalized linear models (GLMs) and recurrent neural networks (RNNs) to forecast the probability of seizure occurrence at a 24‐h horizon. Results: With GLMs and RNNs, seizures could be forecasted above chance in 79% and 81% of previously unseen subjects with a median discrimination of area under the curve (AUC) =.70 and.69 and median Brier skill score (BSS) =.07 and.08. In direct comparison, individualized models had similar median performance (AUC =.67, BSS =.08), but for fewer subjects (60%). Moreover, calibration of pretrained models could be maintained to accommodate different seizure rates across subjects. Significance: Our findings suggest that seizure forecasting based on multidien cycles of IEA can generalize across patients, and may drastically reduce the amount of data needed to issue forecasts for individuals who recently started collecting chronic EEG data. In addition, we show that this generalization is independent of the method used to record seizures (patient‐reported vs. electrographic) or IEA (icEEG vs. sqEEG). [ABSTRACT FROM AUTHOR]

To date, the unpredictability of seizures remains a source of suffering for people with epilepsy, motivating decades of research into methods to forecast seizures. Originally, only few scientists and neurologists ventured into this niche endeavor, which, given the difficulty of the task, soon turned into a long and winding road. Over the past decade, however, our narrow field has seen a major acceleration, with trials of chronic electroencephalographic devices and the subsequent discovery of cyclical patterns in the occurrence of seizures. Now, a burgeoning science of seizure timing is emerging, which in turn informs best forecasting strategies for upcoming clinical trials. Although the finish line might be in view, many challenges remain to make seizure forecasting a reality. This review covers the most recent scientific, technical, and medical developments, discusses methodology in detail, and sets a number of goals for future studies. [ABSTRACT FROM AUTHOR]

Objective: One of the most disabling aspects of living with chronic epilepsy is the unpredictability of seizures. Cumulative research in the past decades has advanced our understanding of the dynamics of seizure risk. Technological advances have recently made it possible to record pertinent biological signals, including electroencephalogram (EEG), continuously. We aimed to assess whether patient‐specific seizure forecasting is possible using remote, minimally invasive ultra‐long‐term subcutaneous EEG. Methods: We analyzed a two‐center cohort of ultra‐long‐term subcutaneous EEG recordings, including six patients with drug‐resistant focal epilepsy monitored for 46–230 days with median 18 h/day of recorded data, totaling >11 000 h of EEG. Total electrographic seizures identified by visual review ranged from 12 to 36 per patient. Three candidate subject‐specific long short‐term memory network deep learning classifiers were trained offline and pseudoprospectively on preictal (1 h before) and interictal (>1 day from seizures) EEG segments. Performance was assessed relative to a random predictor. Periodicity of the final forecasts was also investigated with autocorrelation. Results: Depending on each architecture, significant forecasting performance was achieved in three to five of six patients, with overall mean area under the receiver operating characteristic curve of.65–.74. Significant forecasts showed sensitivity ranging from 64% to 80% and time in warning from 10.9% to 44.4%. Overall, the output of the forecasts closely followed patient‐specific circadian patterns of seizure occurrence. Significance: This study demonstrates proof‐of‐principle for the possibility of subject‐specific seizure forecasting using a minimally invasive subcutaneous EEG device capable of ultra‐long‐term at‐home recordings. These results are encouraging for the development of a prospective seizure forecasting trial with minimally invasive EEG. [ABSTRACT FROM AUTHOR]

Seizures beget seizures is a longstanding theory that proposed that seizure activity can impact the structural and functional properties of the brain circuits in ways that contribute to epilepsy progression and the future occurrence of seizures. Originally proposed by Gowers, this theory continues to be quoted in the pathophysiology of epilepsy. We critically review the existing data and observations on the consequences of recurrent seizures on brain networks and highlight a range of factors that speak for and against the theory. The existing literature demonstrates clearly that ictal activity, especially if recurrent, induces molecular, structural, and functional changes including cell loss, connectivity reorganization, changes in neuronal behavior, and metabolic alterations. These changes have the potential to modify the seizure threshold, contribute to disease progression, and recruit wider areas of the epileptic network into epileptic activity. Repeated seizure activity may, thus, act as a pathological positive‐feedback mechanism that increases seizure likelihood. On the other hand, the time course of self‐limited epilepsies and the presence of seizure remission in two thirds of epilepsy cases and various chronic epilepsy models oppose the theory. Experimental work showed that seizures could induce neural changes that increase the seizure threshold and decrease the risk of a subsequent seizure. Due to the complex nature of epilepsies, it is wrong to consider only seizures as the key factor responsible for disease progression. Epilepsy worsening can be attributed to the various forms of interictal epileptiform activity or underlying disease mechanisms. Although seizure activity can negatively impact brain structure and function, the "seizures beget seizures" theory should not be used dogmatically but with extreme caution. [ABSTRACT FROM AUTHOR]

Sleep and wake are defined through physiological and behavioral criteria and can be typically separated into non‐rapid eye movement (NREM) sleep stages N1, N2, and N3, rapid eye movement (REM) sleep, and wake. Sleep and wake states are not homogenous in time. Their properties vary during the night and day cycle. Given that brain activity changes as a function of NREM, REM, and wake during the night and day cycle, are seizures more likely to occur during NREM, REM, or wake at a specific time? More generally, what is the relationship between sleep–wake cycles and epilepsy? We will review specific examples from clinical data and results from experimental models, focusing on the diversity and heterogeneity of these relationships. We will use a top‐down approach, starting with the general architecture of sleep, followed by oscillatory activities, and ending with ionic correlates selected for illustrative purposes, with respect to seizures and interictal spikes. The picture that emerges is that of complexity; sleep disruption and pathological epileptic activities emerge from reorganized circuits. That different circuit alterations can occur across patients and models may explain why sleep alterations and the timing of seizures during the sleep–wake cycle are patient‐specific. [ABSTRACT FROM AUTHOR]

A critical question regarding how focal seizures start is whether we can identify particular cell classes that drive the pathological process. This was the topic for debate at the recent International Conference for Technology and Analysis of Seizures (ICTALS) meeting (July 2022, Bern, CH) that we summarize here. The debate has been fueled in recent times by the introduction of powerful new ways to manipulate subpopulations of cells in relative isolation, mostly using optogenetics. The motivation for resolving the debate is to identify novel targets for therapeutic interventions through a deeper understanding of the etiology of seizures. [ABSTRACT FROM AUTHOR]

Collaborative efforts between basic scientists, engineers, and clinicians are enabling translational epileptology. In this article, we summarize the recent advances presented at the International Conference for Technology and Analysis of Seizures (ICTALS 2022): (1) novel developments of structural magnetic resonance imaging; (2) latest electroencephalography signal‐processing applications; (3) big data for the development of clinical tools; (4) the emerging field of hyperdimensional computing; (5) the new generation of artificial intelligence (AI)–enabled neuroprostheses; and (6) the use of collaborative platforms to facilitate epilepsy research translation. We highlight the promise of AI reported in recent investigations and the need for multicenter data‐sharing initiatives. [ABSTRACT FROM AUTHOR]

A lot of mileage has been made recently on the long and winding road toward seizure forecasting. Here we briefly review some selected milestones passed along the way, which were discussed at the International Conference for Technology and Analysis of Seizures—ICTALS 2022—convened at the University of Bern, Switzerland. Major impetus was gained recently from wearable and implantable devices that record not only electroencephalography, but also data on motor behavior, acoustic signals, and various signals of the autonomic nervous system. This multimodal monitoring can be performed for ultralong timescales covering months or years. Accordingly, features and metrics extracted from these data now assess seizure dynamics with a greater degree of completeness. Most prominently, this has allowed the confirmation of the long‐suspected cyclical nature of interictal epileptiform activity, seizure risk, and seizures. The timescales cover daily, multi‐day, and yearly cycles. Progress has also been fueled by approaches originating from the interdisciplinary field of network science. Considering epilepsy as a large‐scale network disorder yielded novel perspectives on the pre‐ictal dynamics of the evolving epileptic brain. In addition to discrete predictions that a seizure will take place in a specified prediction horizon, the community broadened the scope to probabilistic forecasts of a seizure risk evolving continuously in time. This shift of gears triggered the incorporation of additional metrics to quantify the performance of forecasting algorithms, which should be compared to the chance performance of constrained stochastic null models. An imminent task of utmost importance is to find optimal ways to communicate the output of seizure‐forecasting algorithms to patients, caretakers, and clinicians, so that they can have socioeconomic impact and improve patients' well‐being. [ABSTRACT FROM AUTHOR]

For >30 years, the Eilat Conference on New Antiepileptic Drugs and Devices has provided a forum for the discussion of advances in the development of new therapies for seizures and epilepsy. The EILAT XVII conference took place in Madrid, Spain, on May 5-8, 2024. Participants included basic scientists and clinical investigators from industry and academia, other health care professionals, and representatives from lay organizations. We summarize in this article information on treatments in preclinical and in early clinical development discussed at the conference. These include AMT-260, a gene therapy designed to downregulate the expression of Glu2K subunits of kainate receptors, in development for the treatment of drug-resistant seizures associated with mesial temporal sclerosis; BHV-7000, a selective activator of heteromeric Kv7.2/7.3 potassium channels, in development for the treatment of focal epilepsy; ETX101, a recombinant adeno-associated virus serotype 9 designed to increase Na V 1.1 channel density in inhibitory γ-aminobutyric acidergic (GABAergic) neurons, in development for the treatment of SCN1A-positive Dravet syndrome; GAO-3-02, a compound structurally related to synaptamide, which exerts antiseizure activity at least in part through an action on cannabinoid type 2 receptors; LRP-661, a structural analogue of cannabidiol, in development for the treatment of seizures associated with Lennox-Gastaut syndrome, Dravet syndrome, and tuberous sclerosis complex; OV329, a selective inactivator of GABA aminotransferase, in development for the treatment of drug-resistant seizures; PRAX-628, a functionally selective potent sodium channel modulator with preference for the hyperexcitable state of sodium channels, in development for the treatment of focal seizures; RAP-219, a selective negative allosteric modulator of transmembrane α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor regulatory protein γ-8, in development for the treatment of focal seizures; and rozanolixizumab, a humanized anti-neonatal Fc receptor monoclonal antibody, in development for the treatment of LGI1 autoimmune encephalitis. Treatments in more advanced development are summarized in Part II of this report., (© 2024 International League Against Epilepsy.)

A significant proportion of temporal lobe epilepsy (TLE) patients experience drug-resistant seizures associated with mesial temporal sclerosis, in which there is extensive cell loss in the hippocampal CA1 and CA3 subfields, with a relative sparing of dentate gyrus granule cells and CA2 pyramidal neurons (PNs). A role for CA2 in seizure generation was suggested based on findings of a reduction in CA2 synaptic inhibition (Williamson and Spencer, 1994) and the presence of interictal-like spike activity in CA2 in resected hippocampal tissue from TLE patients (Wittner et al., 2009). We recently found that in the pilocarpine-induced status epilepticus (PILO-SE) mouse model of TLE there was an increase in CA2 intrinsic excitability associated with a loss of CA2 synaptic inhibition. Furthermore, chemogenetic silencing of CA2 significantly reduced seizure frequency, consistent with a role of CA2 in promoting seizure generation and/or propagation (Whitebirch et al., 2022). In the present study, we explored the cellular basis of this inhibitory deficit using immunohistochemical and electrophysiological approaches in PILO-SE male and female mice. We report a widespread decrease in the density of pro-cholecystokinin-immunopositive (CCK1) interneurons and a functional impairment of CCK1 interneuron-mediated inhibition of CA2 PNs. We also found a disruption in the perisomatic perineuronal net in the CA2 stratum pyramidale. Such pathologic alterations may contribute to an enhanced excitation of CA2 PNs and CA2-dependent seizure activity in the PILO-SE mouse model. [ABSTRACT FROM AUTHOR]

Epilepsy is a serious neurological disorder characterised by a tendency to have recurrent, spontaneous, seizures. Classically, seizures are assumed to occur at random. However, recent research has uncovered underlying rhythms both in seizures and in key signatures of epilepsy—so-called interictal epileptiform activity—with timescales that vary from hours and days through to months. Understanding the physiological mechanisms that determine these rhythmic patterns of epileptiform discharges remains an open question. Many people with epilepsy identify precipitants of their seizures, the most common of which include stress, sleep deprivation and fatigue. To quantify the impact of these physiological factors, we analysed 24-hour EEG recordings from a cohort of 107 people with idiopathic generalized epilepsy. We found two subgroups with distinct distributions of epileptiform discharges: one with highest incidence during sleep and the other during day-time. We interrogated these data using a mathematical model that describes the transitions between background and epileptiform activity in large-scale brain networks. This model was extended to include a time-dependent forcing term, where the excitability of nodes within the network could be modulated by other factors. We calibrated this forcing term using independently-collected human cortisol (the primary stress-responsive hormone characterised by circadian and ultradian patterns of secretion) data and sleep-staged EEG from healthy human participants. We found that either the dynamics of cortisol or sleep stage transition, or a combination of both, could explain most of the observed distributions of epileptiform discharges. Our findings provide conceptual evidence for the existence of underlying physiological drivers of rhythms of epileptiform discharges. These findings should motivate future research to explore these mechanisms in carefully designed experiments using animal models or people with epilepsy. Author summary: 65 million people have epilepsy worldwide. Many of these people report specific triggers that make their seizures (the primary symptom of epilepsy) more likely. Here, we use a mathematical model to understand the relationship between possible triggers and rhythms in epileptiform activity observed across the day. The mathematical model describes the activity of connected brain regions, and how the excitability of these regions can change in response to different stimuli. Based on data collected from people with idiopathic generalized epilepsy, we identify transitions between sleep stages and variation in concentration of the stress-hormone cortisol as candidate factors that influence how likely it is for epileptiform activity to occur. By including those factors into the model, we show they can explain most of the daily variability. More broadly, our approach provides a framework for better understanding what factors drive the occurrence of epileptiform activity and offers the potential to suggest experiments that can validate model predictions. [ABSTRACT FROM AUTHOR]

Objective: Randomized controlled trials (RCTs) in epilepsy for drug treatments are plagued by high costs. One potential remedy is to reduce placebo response via better control over regression to the mean (RTM). Here, RTM represents an initial observed seizure rate higher than the long‐term average, which gradually settles closer to the average, resulting in apparent response to treatment. This study used simulation to clarify the relationship between eligibility criteria and RTM. Methods: Using a statistically realistic seizure diary simulator, the impact of RTM on placebo response and trial efficacy was explored by varying eligibility criteria for a traditional treatment phase II/III RCT for drug‐resistant epilepsy. Results: When the baseline period was included in the eligibility criteria, increasingly larger fractions of RTM were observed (25%–47% vs. 23%–25%). Higher fractions of RTM corresponded with higher expected placebo responses (50% responder rate [RR50]: 2%–9% vs. 0%–8%) and lower statistical efficacy (RR50: 47%–67% vs. 47%–81%). The exclusion of baseline from eligibility criteria was shown to decrease the number of patients needed by roughly 30%. Significance: The manipulation of eligibility criteria for RCTs has a predictable and important impact on RTM, and therefore on placebo response; the difference between drug and placebo was more easily detected. This in turn impacts trial efficacy and therefore cost. This study found dramatic improvements in efficacy and cost when baseline was not included in eligibility. [ABSTRACT FROM AUTHOR]

The impedance is a fundamental electrical property of brain tissue, playing a crucial role in shaping the characteristics of local field potentials, the extent of ephaptic coupling, and the volume of tissue activated by externally applied electrical brain stimulation. We tracked brain impedance, sleep–wake behavioral state, and epileptiform activity in five people with epilepsy living in their natural environment using an investigational device. The study identified impedance oscillations that span hours to weeks in the amygdala, hippocampus, and anterior nucleus thalamus. The impedance in these limbic brain regions exhibit multiscale cycles with ultradian (;1.5–1.7 h), circadian (;21.6–26.4 h), and infradian (;20–33 d) periods. The ultradian and circadian period cycles are driven by sleep–wake state transitions between wakefulness, nonrapid eye movement (NREM) sleep, and rapid eye movement (REM) sleep. Limbic brain tissue impedance reaches a minimum value in NREM sleep, intermediate values in REM sleep, and rises through the day during wakefulness, reaching a maximum in the early evening before sleep onset. Infradian (;20–33 d) impedance cycles were not associated with a distinct behavioral correlate. Brain tissue impedance is known to strongly depend on the extracellular space (ECS) volume, and the findings reported here are consistent with sleep–wake–dependent ECS volume changes recently observed in the rodent cortex related to the brain glymphatic system. We hypothesize that human limbic brain ECS changes during sleep–wake state transitions underlie the observed multiscale impedance cycles. Impedance is a simple electrophysiological biomarker that could prove useful for tracking ECS dynamics in human health, disease, and therapy. [ABSTRACT FROM AUTHOR]

Epilepsy is a common neurological disorder, with one third of patients not responding to currently available antiepileptic drugs. The proportion of pharmacoresistant epilepsies has remained unchanged for many decades. To cure epilepsy and control seizures requires a paradigm shift in the development of new approaches to epilepsy diagnosis and treatment. Contemporary medicine has benefited from the exponential growth of computational modeling, and the application of network dynamics theory to understanding and treating human brain disorders. In epilepsy, the introduction of these approaches has led to personalized epileptic network modeling that can explore the patient's seizure genesis and predict the functional impact of resection on its individual network's propensity to seize. The application of the dynamic systems approach to neurostimulation therapy of epilepsy allows designing stimulation strategies that consider the patient's seizure dynamics and long‐term fluctuations in the stability of their epileptic networks. In this article, we review, in a nontechnical fashion suitable for a broad neuroscientific audience, recent progress in personalized dynamic brain network modeling that is shaping the future approach to the diagnosis and treatment of epilepsy. [ABSTRACT FROM AUTHOR]

Objective: Previous studies suggested that patients with epilepsy might be able to forecast their own seizures. This study aimed to assess the relationships between premonitory symptoms, perceived seizure risk, and future and recent self‐reported and electroencephalographically (EEG)‐confirmed seizures in ambulatory patients with epilepsy in their natural home environments. Methods: Long‐term e‐surveys were collected from patients with and without concurrent EEG recordings. Information obtained from the e‐surveys included medication adherence, sleep quality, mood, stress, perceived seizure risk, and seizure occurrences preceding the survey. EEG seizures were identified. Univariate and multivariate generalized linear mixed‐effect regression models were used to estimate odds ratios (ORs) for the assessment of the relationships. Results were compared with the seizure forecasting classifiers and device forecasting literature using a mathematical formula converting OR to equivalent area under the curve (AUC). Results: Fifty‐four subjects returned 10 269 e‐survey entries, with four subjects acquiring concurrent EEG recordings. Univariate analysis revealed that increased stress (OR = 2.01, 95% confidence interval [CI] = 1.12–3.61, AUC =.61, p =.02) was associated with increased relative odds of future self‐reported seizures. Multivariate analysis showed that previous self‐reported seizures (OR = 5.37, 95% CI = 3.53–8.16, AUC =.76, p <.001) were most strongly associated with future self‐reported seizures, and high perceived seizure risk (OR = 3.34, 95% CI = 1.87–5.95, AUC =.69, p <.001) remained significant when prior self‐reported seizures were added to the model. No correlation with medication adherence was found. No significant association was found between e‐survey responses and subsequent EEG seizures. Significance: Our results suggest that patients may tend to self‐forecast seizures that occur in sequential groupings and that low mood and increased stress may be the result of previous seizures rather than independent premonitory symptoms. Patients in the small cohort with concurrent EEG showed no ability to self‐predict EEG seizures. The conversion from OR to AUC values facilitates direct comparison of performance between survey and device studies involving survey premonition and forecasting. [ABSTRACT FROM AUTHOR]

Mycobacterium abscessus (Mabs) drives life-shortening mortality in cystic fibrosis (CF) patients, primarily because of its resistance to chemotherapeutic agents. To date, our knowledge on the host and bacterial determinants driving Mabs pathology in CF patient lung remains rudimentary. Here, we used human airway organoids (AOs) microinjected with smooth (S) or rough (R-)Mabs to evaluate bacteria fitness, host responses to infection, and new treatment efficacy. We show that S Mabs formed biofilm, and R Mabs formed cord serpentines and displayed a higher virulence. While Mabs infection triggers enhanced oxidative stress, pharmacological activation of antioxidant pathways resulted in better control of Mabs growth and reduced virulence. Genetic and pharmacological inhibition of the CFTR is associated with better growth and higher virulence of S and R Mabs. Finally, pharmacological activation of antioxidant pathways inhibited Mabs growth, at least in part through the quinone oxidoreductase NQO1, and improved efficacy in combination with cefoxitin, a first line antibiotic. In conclusion, we have established AOs as a suitable human system to decipher mechanisms of CF-driven respiratory infection by Mabs and propose boosting of the NRF2-NQO1 axis as a potential host-directed strategy to improve Mabs infection control. Author summary: Pulmonary infection by Non Tuberculosis Mycobacteria is a rising concern for patients with cystic fibrosis (CF), especially Mycobacterium abscessus (Mabs). Mabs exists as two morphotypes. CF patients are generally infected by the S morphotype present in the environment, which can switch to the R morphotype displaying higher virulence. Due to its resistance to antibiotics, treatments againt Mabs often fail, calling for complementary therapeutical strategies. Here we adapted the human airway organoid technology to model Mabs infection in the context of CF, decipher mechanisms of host-pathogen interaction that can be pharmacologically targeted to improve infection control. We found that Mabs R induces higher host oxidative stress and cell death, hallmarks of its virulence, which are enhanced in the CF context. Boosting the host oxidative pathway using antioxidants improves infection control by a frontline antibiotic. Our study provides CF patient-derived airway organoids as a relevant human-based, animal-free system for CF-driven Mabs infection and evaluation of innovative therapeutic strategies. [ABSTRACT FROM AUTHOR]

Objective: We studied the rate dynamics of interictal events occurring over fast‐ultradian time scales, as commonly examined in clinics to guide surgical planning in epilepsy. Methods: Stereo‐electroencephalography (SEEG) traces of 35 patients with good surgical outcome (Engel I) were analyzed. For this we developed a general data mining method aimed at clustering the plethora of transient waveform shapes including interictal epileptiform discharges (IEDs) and assessed the temporal fluctuations in the capability of mapping the epileptogenic zone (EZ) of each type of event. Results: We found that the fast‐ultradian dynamics of the IED rate may effectively impair the precision of EZ identification, and appear to occur spontaneously, that is, not triggered by or exclusively associated with a particular cognitive task, wakefulness, sleep, seizure occurrence, post‐ictal state, or antiepileptic drug withdrawal. Propagation of IEDs from the EZ to the propagation zone (PZ) could explain the observed fast‐ultradian fluctuations in a reduced fraction of the analyzed patients, suggesting that other factors like the excitability of the epileptogenic tissue could play a more relevant role. A novel link was found between the fast‐ultradian dynamics of the overall rate of polymorphic events and the rate of specific IEDs subtypes. We exploited this feature to estimate in each patient the 5 min interictal epoch for near‐optimal EZ and resected‐zone (RZ) localization. This approach produces at the population level a better EZ/RZ classification when compared to both (1) the whole time series available in each patient (p =.084 for EZ, p <.001 for RZ, Wilcoxon signed‐rank test) and (2) 5 min epochs sampled randomly from the interictal recordings of each patient (p <.05 for EZ, p <.001 for RZ, 105 random samplings). Significance: Our results highlight the relevance of the fast‐ultradian IED dynamics in mapping the EZ, and show how this dynamics can be estimated prospectively to inform surgical planning in epilepsy. [ABSTRACT FROM AUTHOR]

Purpose: Undifferentiated pleomorphic sarcoma (UPS) is associated with poor prognosis. Recently, signal regulatory protein alpha (SIRPα), which is the immune checkpoint of macrophages, and T cell immunoreceptor with immunoglobulin and immunoreceptor tyrosine-based inhibition motif domains (TIGIT), which is the immune checkpoint of T cells and natural killer cells, have been considered as potential targets for cancer immunotherapy. This study aimed to assess the value of SIRPα and TIGIT as prognostic factors of UPS. Materials and methods: The cBio Cancer Genomics Portal was used to analyze mRNA expression data of 50 UPS cases in the Cancer Genome Atlas. We retrieved 49 UPS cases and performed immunohistochemistry (IHC) to detect programmed death ligand 1 (PD-L1), SIRPα, CD68, CD163, TIGIT, CD155, and CD8. Results: SIRPα was positively associated with CD163 (Pearson's r = 0.51, p = 0.0002) as per open access data and IHC of the cohort (p = 0.002), which revealed that SIRPα-positive macrophage infiltration was higher in UPS cells with ≥ 1% PD-L1 expression than that in UPS cells with < 1% PD-L1 expression (p = 0.047). TIGIT was positively correlated with PD-L1 (r = 0.54, p < 0.0001) and CD8A (r = 0.98, p < 0.0001). In 35 of 49 cases, IHC revealed high levels of TIGIT expression on tumor cells. Furthermore, TIGIT expression on tumor cells was negatively correlated with CD155-positive (p = 0.0144) and CD8-positive (p = 0.0487) cell infiltration. Survival analysis showed that the high degree of SIRPα-positive macrophage infiltration was associated with poor overall survival and metastasis (p < 0.0001, p = 0.0006, respectively). Conclusion: SIRPα-positive macrophages infiltrated UPS cells, which predicted poor prognosis. High TIGIT expression on tumor cells was associated with decreased levels of tumor-infiltrating macrophages in UPS. [ABSTRACT FROM AUTHOR]

Objective: The factors that influence seizure timing are poorly understood, and seizure unpredictability remains a major cause of disability. Work in chronobiology has shown that cyclical physiological phenomena are ubiquitous, with daily and multiday cycles evident in immune, endocrine, metabolic, neurological, and cardiovascular function. Additionally, work with chronic brain recordings has identified that seizure risk is linked to daily and multiday cycles in brain activity. Here, we provide the first characterization of the relationships between the cyclical modulation of a diverse set of physiological signals, brain activity, and seizure timing. Methods: In this cohort study, 14 subjects underwent chronic ambulatory monitoring with a multimodal wrist‐worn sensor (recording heart rate, accelerometry, electrodermal activity, and temperature) and an implanted responsive neurostimulation system (recording interictal epileptiform abnormalities and electrographic seizures). Wavelet and filter–Hilbert spectral analyses characterized circadian and multiday cycles in brain and wearable recordings. Circular statistics assessed electrographic seizure timing and cycles in physiology. Results: Ten subjects met inclusion criteria. The mean recording duration was 232 days. Seven subjects had reliable electroencephalographic seizure detections (mean = 76 seizures). Multiday cycles were present in all wearable device signals across all subjects. Seizure timing was phase locked to multiday cycles in five (temperature), four (heart rate, phasic electrodermal activity), and three (accelerometry, heart rate variability, tonic electrodermal activity) subjects. Notably, after regression of behavioral covariates from heart rate, six of seven subjects had seizure phase locking to the residual heart rate signal. Significance: Seizure timing is associated with daily and multiday cycles in multiple physiological processes. Chronic multimodal wearable device recordings can situate rare paroxysmal events, like seizures, within a broader chronobiology context of the individual. Wearable devices may advance the understanding of factors that influence seizure risk and enable personalized time‐varying approaches to epilepsy care. [ABSTRACT FROM AUTHOR]

Wearable devices have attracted significant attention in epilepsy research in recent years for their potential to enhance patient care through improved seizure monitoring and forecasting. This narrative review presents a detailed overview of the current clinical state of the art while addressing how devices that assess autonomic nervous system (ANS) function reflect seizures and central nervous system (CNS) state changes. This includes a description of the interactions between the CNS and the ANS, including physiological and epilepsy-related changes affecting their dynamics. We first discuss technical aspects of measuring autonomic biosignals and considerations for using ANS sensors in clinical practice. We then review recent seizure detection and seizure forecasting studies, highlighting their performance and capability for seizure detection and forecasting using devices measuring ANS biomarkers. Finally, we address the field's challenges and provide an outlook for future developments., (© 2024 The Author(s). Epilepsia published by Wiley Periodicals LLC on behalf of International League Against Epilepsy.)

Antiseizure medication (ASM) is the primary treatment for epilepsy. In clinical practice, methods to assess ASM efficacy (predict seizure freedom or seizure reduction), during any phase of the drug treatment lifecycle, are limited. This scoping review identifies and appraises prognostic electroencephalographic (EEG) biomarkers and prognostic models that use EEG features, which are associated with seizure outcomes following ASM initiation, dose adjustment, or withdrawal. We also aim to summarize the population and context in which these biomarkers and models were identified and described, to understand how they could be used in clinical practice. Between January 2021 and October 2022, four databases, references, and citations were systematically searched for ASM studies investigating changes to interictal EEG or prognostic models using EEG features and seizure outcomes. Study bias was appraised using modified Quality in Prognosis Studies criteria. Results were synthesized into a qualitative review. Of 875 studies identified, 93 were included. Biomarkers identified were classed as qualitative (visually identified by wave morphology) or quantitative. Qualitative biomarkers include identifying hypsarrhythmia, centrotemporal spikes, interictal epileptiform discharges (IED), classifying the EEG as normal/abnormal/epileptiform, and photoparoxysmal response. Quantitative biomarkers were statistics applied to IED, high‐frequency activity, frequency band power, current source density estimates, pairwise statistical interdependence between EEG channels, and measures of complexity. Prognostic models using EEG features were Cox proportional hazards models and machine learning models. There is promise that some quantitative EEG biomarkers could be used to assess ASM efficacy, but further research is required. There is insufficient evidence to conclude any specific biomarker can be used for a particular population or context to prognosticate ASM efficacy. We identified a potential battery of prognostic EEG biomarkers, which could be combined with prognostic models to assess ASM efficacy. However, many confounders need to be addressed for translation into clinical practice. [ABSTRACT FROM AUTHOR]

Macrophages are ancient, phagocytic immune cells thought to have their origins 500 million years ago in metazoan phylogeny. The understanding of macrophages has evolved to encompass their foundational roles in development, homeostasis, tissue repair, inflammation, and immunity. Notably, macrophages display high plasticity in response to environmental cues, capable of a strikingly wide variety of dynamic gene signatures and phenotypes. Macrophages are also involved in many pathological states including neural disease, asthma, liver disease, heart disease, cancer, and others. In cancer, most tumor-associated immune cells are macrophages, coined tumor-associated macrophages (TAMs). While some TAMs can display anti-tumor properties such as phagocytizing tumor cells and orchestrating an immune response, most macrophages in the tumor microenvironment are immunosuppressive and pro-tumorigenic. Macrophages have been implicated in all stages of cancer. Therefore, interest in manipulating macrophages as a therapeutic strategy against cancer developed as early as the 1970s. Companion dogs are a strong comparative immuno-oncology model for people due to documented similarities in the immune system and spontaneous cancers between the species. Data from clinical trials in humans and dogs can be leveraged to further scientific advancements that benefit both species. This review aims to provide a summary of the current state of knowledge on macrophages in general, and an in-depth review of macrophages as a therapeutic strategy against cancer in humans and companion dogs. [ABSTRACT FROM AUTHOR]

Objective: A realistic seizure diary simulator is currently unavailable for many research needs, including clinical trial analysis and evaluation of seizure detection and seizure‐forecasting tools. In recent years, important statistical features of seizure diaries have been characterized. These include (1) heterogeneity of individual seizure frequencies, (2) the relation between average seizure rate and standard deviation, (3) multiple risk cycles, (4) seizure clusters, and (5) limitations on inter‐seizure intervals. The present study unifies these features into a single model. Methods: Our approach, Cyclic Heterogeneous Overdispersed Clustered Open‐source L‐relationship Adjustable Temporally limited E‐diary Simulator (CHOCOLATES) is based on a hierarchical model centered on a gamma Poisson generator with several modifiers. This model accounts for the aforementioned statistical properties. The model was validated by simulating 10 000 randomized clinical trials (RCTs) of medication to compare with 23 historical RCTs. Metrics of 50% responder rate (RR50) and median percent change (MPC) were evaluated. We also used CHOCOLATES as input to a seizure‐forecasting tool to test the flexibility of the model. We examined the area under the receiver‐operating characteristic (ROC) curve (AUC) for test data with and without cycles and clusters. Results: The model recapitulated typical findings in 23 historical RCTs without the necessity of introducing an additional "placebo effect." The model produced the following RR50 values: placebo: 17 ± 4%; drug 38 ± 5%; and the following MPC values: placebo: 13 ± 6%; drug 40 ± 4%. These values are similar to historical data: for RR50: placebo, 21 ± 10%, drug: 43 ± 13%; and for MPC: placebo: 17 ± 10%, drug: 41 ± 11%. The seizure forecasts achieved an AUC of 0.68 with cycles and clusters, whereas without them the AUC was 0.51. Significance: CHOCOLATES represents the most realistic seizure occurrence simulator to date, based on observations from thousands of patients in different contexts. This tool is open source and flexible, and can be used for many applications, including clinical trial simulation and testing of seizure‐forecasting tools. [ABSTRACT FROM AUTHOR]

Introduction: Receptor/ligand pair immune checkpoints are inhibitors that regulate immunity as vital "Don't Find-Me" signals to the adaptive immune system, additionally, the essential goals of anti-cancer therapy. Moreover, the immune checkpoints are involved in treatment resistance in cancer therapy. The immune checkpoints as a signal from "self" and their expression on healthy cells prevent phagocytosis. Cells (e.g., senescent and/or apoptotic cells) with low immune checkpoints, such as low CD47 and/or PD-L1, are phagocytosed, which is necessary for tissue integrity and homeostasis maintenance. In other words, cancer cells induce increased CD47 expression in the tumor microenvironment (TME), avoiding their clearance by immune cells. PD-L1 and/or CD47 expression tumors have also been employed as biomarkers to guide cure prospects. Thus, targeting innate and adaptive immune checkpoints might improve the influence of the treatments on tumor cells. However, the CD47 regulation in the TME stands intricate, so much of this process has stayed a riddle. In this line, less attention has been paid to cytokines in TME. Cytokines are significant regulators of tumor immune surveillance, and they do this by controlling the actions of the immune cell. Recently, it has been suggested that different types of cytokines at TME might cooperate with others that contribute to the regulation of CD47 and/or PD-L1. Materials and methods: The data were searched in available databases and a Web Search engine (PubMed, Scopus, and Google Scholar) using related keywords in the title, abstract, and keywords. Conclusion: Given the significant role of pro/anti-inflammatory signaling in the TME, we discuss the present understanding of pro/anti-inflammatory signaling implications in "Don't Eat-Me" regulation signals, particularly CD47, in the pathophysiology of cancers and come up with innovative opinions for the clinical transformation and personalized medicine. [ABSTRACT FROM AUTHOR]