Multiple myeloma incidence and mortality trends in the United States, 1999-2020.

Multiple myeloma (MM) is a plasma cell disorder accounting for approximately 10% of hematologic malignancies. There is limited epidemiological evidence regarding the long-term trends and disparities in MM in the US. We conducted a multiple time point cross-sectional study using MM incidence rate data from the Surveillance, Epidemiology, and End Results (SEER) database and mortality data from the CDC Wide-Ranging Online Data for Epidemiologic Research (CDC WONDER) Underlying Cause of Death database between 1999 and 2020. During this period, MM incidence has steadily increased, while MM mortality has steadily decreased, with substantial racial and ethnic disparities. Non-Hispanic Black individuals exhibited the highest incidence rates, which consistently rose from 12.02 (95% CI 10.54, 13.64) in 1999 to 14.20 (95% CI 12.93, 15.55) per 100,000 population by 2020. Non-Hispanic American Indian/Native Alaskans and Asian/Pacific Islanders demonstrated the lowest incidence rates of 5.59 (95% CI 2.69, 10.04) and 3.56 (95% CI 2.94, 4.27) per 100,000 population in 1999 to 5.76 (95% CI 3.49, 8.90) and 3.92 (95% CI 3.46, 4.42) per 100,000 population, respectively, by 2020. Disparities by gender, age, US census region, and rurality were observed, underscoring the importance of targeted, equity-centered interventions and MM screening initiatives for at-risk populations.

Disparities in Cancer Mortality among Disaggregated Asian American Subpopulations, 2018-2021.

Federal, state, and institutional data collection practices and analyses involving Asian Americans as a single, aggregated group obscure critical health disparities among the vast diversity of Asian American subpopulations. Using from the Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research (CDC WONDER) Underlying Causes of Death database, we conducted a cross-sectional study using data on disaggregated Asian American subgroups (Asian Indian, Chinese, Filipino, Japanese, Korean, Vietnamese, other Asians) between 2018 and 2021. We examine deaths from 22 cancer types and in situ, benign neoplasms, identified using ICD-10 codes C00-C97 and D00-D48. Overall, our study comprised 327,311 Asian American decedents, with a mean age of death at 70.57 years (SD=2.79), wherein females accounted for approximately half of the sample (n=36,596/73,207; 49.99%). Notably, compared to the aggregated Asian American reference group, we found higher proportions of deaths from total cancers among Chinese (25.99% vs. 22.37% [ref]), Korean (25.29% vs. 22.37% [ref]), and Vietnamese (24.98% vs. 22.37% [ref]) subgroups. In contrast, total cancer deaths were less prevalent among Asian Indians (17.49% vs. 22.37% [ref]), Japanese (18.90% vs. 22.37% [ref]), and other Asians (20.37% vs. 22.37% [ref]). We identified further disparities by cancer type, sex, and age. Disaggregated data collection and analyses are imperative to understanding differences in cancer mortality among Asian American subgroups, illustrating at-risk populations with greater granularity. Future studies should aim to describe the association between these trends and social, demographic, and environmental risk factors.

Staphylococcus aureus exacerbates dermal IL-33/ILC2 axis activation through evoking RIPK3/MLKL-mediated necroptosis of dry skin.

Atopic dermatitis (AD) is a persistent skin disease typified by symptoms of dry skin and recurrent eczema. Patients with AD are at heightened risk for Staphylococcus aureus infection. Group 2 innate lymphoid cells (ILC2s) are mainly activated by epithelial cell-derived cytokines IL-33 and involved in the pathogenesis of AD. However, little is known about the effect of skin delipidization on the epithelial cell-derived cytokines and dermal ILC2s in AD. In our study, we investigated the mechanism by which S. aureus infection modulates and exacerbates the pathogenesis of dry skin, leading to type 2 inflammation in the context of innate immunity. In vivo, we found that S. aureus infection aggravated delipidization-induced dermal IL-33 release and dermal ILC2 accumulation, which exacerbated skin inflammation. We also noticed that Il33fl/fl K14cre mice and Tlr2-/- mice exhibited attenuated skin inflammation. In vitro, treatment with necroptosis inhibitors reduced IL-33 release from S. aureus-infected keratinocytes. Mechanistically, we observed an increase in the necroptosis-associated kinases, MLKL and RIPK3, in S. aureus-infected mice, indicating that IL-33 release was associated with necroptotic cell death responses. Our results reveal that S. aureus infection-elicited keratinocyte necroptosis contributes to IL-33-mediated type 2 inflammation, which exacerbates the pathogenesis of dry skin.

Deep learning-based solution for smart contract vulnerabilities detection.

This paper aims to explore the application of deep learning in smart contract vulnerabilities detection. Smart contracts are an essential part of blockchain technology and are crucial for developing decentralized applications. However, smart contract vulnerabilities can cause financial losses and system crashes. Static analysis tools are frequently used to detect vulnerabilities in smart contracts, but they often result in false positives and false negatives because of their high reliance on predefined rules and lack of semantic analysis capabilities. Furthermore, these predefined rules quickly become obsolete and fail to adapt or generalize to new data. In contrast, deep learning methods do not require predefined detection rules and can learn the features of vulnerabilities during the training process. In this paper, we introduce a solution called Lightning Cat which is based on deep learning techniques. We train three deep learning models for detecting vulnerabilities in smart contract: Optimized-CodeBERT, Optimized-LSTM, and Optimized-CNN. Experimental results show that, in the Lightning Cat we propose, Optimized-CodeBERT model surpasses other methods, achieving an f1-score of 93.53%. To precisely extract vulnerability features, we acquire segments of vulnerable code functions to retain critical vulnerability features. Using the CodeBERT pre-training model for data preprocessing, we could capture the syntax and semantics of the code more accurately. To demonstrate the feasibility of our proposed solution, we evaluate its performance using the SolidiFI-benchmark dataset, which consists of 9369 vulnerable contracts injected with vulnerabilities from seven different types.

Neutrophil extracellular trap production and CCL4L2 expression influence corticosteroid response in asthma.

The association between neutrophil extracellular traps (NETs) and response to inhaled corticosteroids (ICS) in asthma is unclear. To better understand this relationship, we analyzed the blood transcriptomes from children with controlled and uncontrolled asthma in the Taiwanese Consortium of Childhood Asthma Study using weighted gene coexpression network analysis and pathway enrichment methods. We identified 298 uncontrolled asthma-specific differentially expressed genes and one gene module associated with neutrophil-mediated immunity, highlighting a potential role for neutrophils in uncontrolled asthma. We also found that NET abundance was associated with nonresponse to ICS in patients. In a neutrophilic airway inflammation murine model, steroid treatment could not suppress neutrophilic inflammation and airway hyperreactivity. However, NET disruption with deoxyribonuclease I (DNase I) efficiently inhibited airway hyperreactivity and inflammation. Using neutrophil-specific transcriptomic profiles, we found that CCL4L2 was associated with ICS nonresponse in asthma, which was validated in human and murine lung tissue. CCL4L2 expression was also negatively correlated with pulmonary function change after ICS treatment. In summary, steroids fail to suppress neutrophilic airway inflammation, highlighting the potential need to use alternative therapies such as leukotriene receptor antagonists or DNase I that target the neutrophil-associated phenotype. Furthermore, these results highlight CCL4L2 as a potential therapeutic target for individuals with asthma refractory to ICS.

Butyrate inhibits Staphylococcus aureus-aggravated dermal IL-33 expression and skin inflammation through histone deacetylase inhibition.

Atopic dermatitis (AD) is an inflammatory skin disease caused by the disruption of skin barrier, and is dominated by the type 2 immune responses. Patients with AD have a high risk of developing Staphylococcus aureus infection. Interleukin-33 (IL-33), an alarmin, has been implicated in the pathophysiology of AD development. Butyrate, a short chain fatty acid known to be produced from the fermentation of glycerol by the commensal skin bacterium, Staphylococcus epidermidis, has been reported to possess antimicrobial and anti-inflammatory properties that suppress inflammatory dermatoses. However, little is known about the effects of butyrate on dermal IL-33 expression and associated immune response in S. aureus-aggravated skin inflammation in the context of AD. To decipher the underlying mechanism, we established an AD-like mouse model with epidermal barrier disruption by delipidizing the dorsal skin to induce AD-like pathophysiology, followed by the epicutaneous application of S. aureus and butyrate. We discovered that S. aureus infection exacerbated IL-33 release from keratinocytes and aggravated dermal leukocyte infiltration and IL-13 expression. Moreover, we showed that butyrate could attenuate S. aureus-aggravated skin inflammation with decreased IL-33, IL-13, and leukocyte infiltration in the skin. Mechanistically, we demonstrated that butyrate suppressed IL-33 expression and ameliorated skin inflammation through histone deacetylase 3 (HDAC3) inhibition. Overall, our findings revealed the potential positive effect of butyrate in controlling inflammatory skin conditions in AD aggravated by S. aureus infection.

Serine-/Cysteine-Based sp2-Iminoglycolipids as Novel TLR4 Agonists: Evaluation of Their Adjuvancy and Immunotherapeutic Properties in a Murine Model of Asthma.

Glycolipids with TLR4 agonistic properties can serve either as therapeutic agents or as vaccine adjuvants by stimulating the development of proinflammatory responses. Translating them to the clinical setting is hampered by synthetic difficulties, the lack of stability in biological media, and/or a suboptimal profile of balanced immune mediator secretion. Here, we show that replacement of the sugar fragment by an sp2-iminosugar moiety in a prototypic TLR4 agonist, CCL-34, yields iminoglycolipid analogues that retain or improve their biological activity in vitro and in vivo and can be accessed through scalable protocols with total stereoselectivity. Their adjuvant potential is manifested in their ability to induce the secretion of proinflammatory cytokines, prime the maturation of dendritic cells, and promote the proliferation of CD8+ T cells, pertaining to a Th1-biased profile. Additionally, their therapeutic potential for the treatment of asthma, a Th2-dominated inflammatory pathology, has been confirmed in an ovalbumin-induced airway hyperreactivity mouse model.

Prognostic interictal electroencephalographic biomarkers and models to assess antiseizure medication efficacy for clinical practice: A scoping review.

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.

A cross-sectional investigation of cognition and epileptiform discharges in juvenile absence epilepsy.

OBJECTIVES: Despite the prevalence of cognitive symptoms in the idiopathic generalized epilepsies (IGEs), cognitive dysfunction in juvenile absence epilepsy (JAE), a common yet understudied IGE subtype, remains poorly understood. This descriptive study provides a novel, comprehensive characterization of cognitive functioning in a JAE sample and examines the relationship between cognition and 24-h epileptiform discharge load. METHOD: Forty-four individuals diagnosed with JAE underwent cognitive assessment using Woodcock Johnson III Test of Cognitive Abilities with concurrent 24-h ambulatory EEG monitoring. Generalized epileptiform discharges of any length, and prolonged generalized discharges ≥3 s were quantified across wakefulness and sleep. The relationship between standardized cognitive scores and epileptiform discharges was assessed through regression models. RESULTS: Cognitive performances in overall intellectual ability, acquired comprehension-knowledge, processing speed, long-term memory storage and retrieval, and executive processes were 0.63-1.07 standard deviation (SD) units lower in the JAE group compared to the population reference mean, adjusted for educational attainment. Prolonged discharges (≥3 s) were recorded in 20 patients (47.6%) from 42 available electroencephalography (EEG) studies and were largely unreported. Duration and number of prolonged discharges were associated with reduced processing speed and long-term memory storage and retrieval. SIGNIFICANCE: Cognitive dysfunction is seen in patients with JAE across various cognitive abilities, including those representing more stable processes like general intellect. During 24-h EEG, prolonged epileptiform discharges are common yet underreported in JAE despite treatment, and they show moderate effects on cognitive abilities. If epileptiform burden is a modifiable predictor of cognitive dysfunction, therapeutic interventions should consider quantitative 24-h EEG with routine neuropsychological screening. The growing recognition of the spectrum of neuropsychological comorbidities of IGE highlights the value of multidisciplinary approaches to explore the causes and consequences of cognitive deficits in epilepsy.

Hyperactivation of p53 using CRISPRa kills human papillomavirus-driven cervical cancer cells.

Clinical and pre-clinical work for a number of cancer types has demonstrated relatively positive outcomes and effective tumour regression when the level and function of p53, a well-established tumour suppressor, is restored. Human papillomavirus (HPV)-driven cancers encode the E6 oncoprotein, which leads to p53 degradation, to allow the carcinogenic process to proceed. Indeed, there have been several attempts to revive p53 function in HPV-driven cancers by both pharmacological and genetic means to increase p53 bioavailability. Here, we employed a CRISPR activation (CRISPRa) approach to overcome HPV-mediated silencing of p53 by hyperexpressing the p53 gene promoter. Our data show that CRISPRa-mediated hyperexpression of p53 leads to HPV+ cervical cancer cell killing and the reduction of cell proliferation. This proof-of-concept data suggest that increasing p53 bioavailability may potentially be a promising therapeutic approach for the treatment of HPV-driven cancers.

Response to photic stimulation as a measure of cortical excitability in epilepsy patients.

Studying states and state transitions in the brain is challenging due to nonlinear, complex dynamics. In this research, we analyze the brain's response to non-invasive perturbations. Perturbation techniques offer a powerful method for studying complex dynamics, though their translation to human brain data is under-explored. This method involves applying small inputs, in this case via photic stimulation, to a system and measuring its response. Sensitivity to perturbations can forewarn a state transition. Therefore, biomarkers of the brain's perturbation response or "cortical excitability" could be used to indicate seizure transitions. However, perturbing the brain often involves invasive intracranial surgeries or expensive equipment such as transcranial magnetic stimulation (TMS) which is only accessible to a minority of patient groups, or animal model studies. Photic stimulation is a widely used diagnostic technique in epilepsy that can be used as a non-invasive perturbation paradigm to probe brain dynamics during routine electroencephalography (EEG) studies in humans. This involves changing the frequency of strobing light, sometimes triggering a photo-paroxysmal response (PPR), which is an electrographic event that can be studied as a state transition to a seizure state. We investigate alterations in the response to these perturbations in patients with genetic generalized epilepsy (GGE), with (n = 10) and without (n = 10) PPR, and patients with psychogenic non-epileptic seizures (PNES; n = 10), compared to resting controls (n = 10). Metrics of EEG time-series data were evaluated as biomarkers of the perturbation response including variance, autocorrelation, and phase-based synchrony measures. We observed considerable differences in all group biomarker distributions during stimulation compared to controls. In particular, variance and autocorrelation demonstrated greater changes in epochs close to PPR transitions compared to earlier stimulation epochs. Comparison of PPR and spontaneous seizure morphology found them indistinguishable, suggesting PPR is a valid proxy for seizure dynamics. Also, as expected, posterior channels demonstrated the greatest change in synchrony measures, possibly reflecting underlying PPR pathophysiologic mechanisms. We clearly demonstrate observable changes at a group level in cortical excitability in epilepsy patients as a response to perturbation in EEG data. Our work re-frames photic stimulation as a non-invasive perturbation paradigm capable of inducing measurable changes to brain dynamics.

Biopterin metabolism and nitric oxide recoupling in cancer.

Tetrahydrobiopterin is a cofactor necessary for the activity of several enzymes, the most studied of which is nitric oxide synthase. The role of this cofactor-enzyme relationship in vascular biology is well established. Recently, tetrahydrobiopterin metabolism has received increasing attention in the field of cancer immunology and immunotherapy due to its involvement in the cytotoxic T cell response. Past research has demonstrated that when the availability of BH4 is low, as it is in chronic inflammatory conditions and tumors, electron transfer in the active site of nitric oxide synthase becomes uncoupled from the oxidation of arginine. This results in the production of radical species that are capable of a direct attack on tetrahydrobiopterin, further depleting its local availability. This feedforward loop may act like a molecular switch, reinforcing low tetrahydrobiopterin levels leading to altered NO signaling, restrained immune effector activity, and perpetual vascular inflammation within the tumor microenvironment. In this review, we discuss the evidence for this underappreciated mechanism in different aspects of tumor progression and therapeutic responses. Furthermore, we discuss the preclinical evidence supporting a clinical role for tetrahydrobiopterin supplementation to enhance immunotherapy and radiotherapy for solid tumors and the potential safety concerns.

Identification of a PD-L1+Tim-1+ iNKT subset that protects against fine particulate matter-induced airway inflammation.

Although air pollutants such as fine particulate matter (PM2.5) are associated with acute and chronic lung inflammation, the etiology of PM2.5-induced airway inflammation remains poorly understood. Here we report that PM2.5 triggered airway hyperreactivity (AHR) and neutrophilic inflammation with concomitant increases in Th1 and Th17 responses and epithelial cell apoptosis. We found that γδ T cells promoted neutrophilic inflammation and AHR through IL-17A. Unexpectedly, we found that invariant natural killer T (iNKT) cells played a protective role in PM2.5-induced pulmonary inflammation. Specifically, PM2.5 activated a suppressive CD4- iNKT cell subset that coexpressed Tim-1 and programmed cell death ligand 1 (PD-L1). Activation of this suppressive subset was mediated by Tim-1 recognition of phosphatidylserine on apoptotic cells. The suppressive iNKT subset inhibited γδ T cell expansion and intrinsic IL-17A production, and the inhibitory effects of iNKT cells on the cytokine-producing capacity of γδ T cells were mediated in part by PD-1/PD-L1 signaling. Taken together, our findings underscore a pathogenic role for IL-17A-producing γδ T cells in PM2.5-elicited inflammation and identify PD-L1+Tim-1+CD4- iNKT cells as a protective subset that prevents PM2.5-induced AHR and neutrophilia by inhibiting γδ T cell function.

The ketone body ß-hydroxybutyrate mitigates ILC2-driven airway inflammation by regulating mast cell function.

Ketone bodies are increasingly understood to have regulatory effects on immune cell function, with ß-hydroxybutyrate (BHB) exerting a predominantly anti-inflammatory response. Dietary strategies to increase endogenous ketone body availability such as the ketogenic diet (KD) have recently been shown to alleviate inflammation of the respiratory tract. However, the role of BHB has not been addressed. Here, we observe that BHB suppresses group 2 innate lymphoid cell (ILC2)-mediated airway inflammation. Central to this are mast cells, which support ILC2 proliferation through interleukin-2 (IL-2). Suppression of the mast cell/IL-2 axis by BHB attenuates ILC2 proliferation and the ensuing type 2 cytokine response and immunopathology. Mechanistically, BHB directly inhibits mast cell function in part through GPR109A activation. Similar effects are achieved with either the KD or 1,3-butanediol. Our data reveal the protective role of BHB in ILC2-driven airway inflammation, which underscores the potential therapeutic value of ketone body supplementation for the management of asthma.

Preclinical safety study of a fully implantable, sub-scalp ring electrode array for long-term EEG recordings.

OBJECTIVE: Long-term electroencephalogram (EEG) recordings can aid diagnosis and management of various neurological conditions such as epilepsy. In this study we characterize the safety and stability of a clinical grade ring electrode arrays by analyzing EEG recordings, fluoroscopy, and computed tomography (CT) imaging with long-term implantation and histopathological tissue response. APPROACH: Seven animals were chronically implanted with EEG recording array consisting of four electrode contacts. Recordings were made bilaterally using a bipolar longitudinal montage. The array was connected to a fully implantable micro-processor controlled electronic device with two low-noise differential amplifiers and a transmitter-receiver coil. An external wearable was used to power, communicate with the implant via an inductive coil, and store the data. The sub-scalp electrode arrays were made using medical grade silicone and platinum. The electrode arrays were tunneled in the subgaleal cleavage plane between the periosteum and the overlying dermis. These were implanted for 3-7 months before euthanasia and histopathological assessment. EEG and impedance were recorded throughout the study. MAIN RESULTS: Impedance measurements remained low throughout the study for 11 of 12 channels over the recording period ranged from 3 to 5 months. There was also a steady amplitude of slow-wave EEG and chewing artifact (noise). The post-mortem CT and histopathology showed the electrodes remained in the subgaleal plane in 6 of 7 sheep. There was minimal inflammation with a thin fibrotic capsule that ranged from 4 to 101µm. There was a variable fibrosis in the subgaleal plane extending from 210 to 3617µm (S3-S7) due to surgical cleavage. One sheep had an inflammatory reaction due to electrode extrusion. The passive electrode array extraction force was around 1N. SIGNIFICANCE: Results show sub-scalp electrode placement was safe and stable for long term implantation. This is advantageous for diagnosis and management of neurological conditions where long-term, EEG monitoring is required.

SLM2 Is A Novel Cardiac Splicing Factor Involved in Heart Failure due to Dilated Cardiomyopathy.

Alternative mRNA splicing is a fundamental process to increase the versatility of the genome. In humans, cardiac mRNA splicing is involved in the pathophysiology of heart failure. Mutations in the splicing factor RNA binding motif protein 20 (RBM20) cause severe forms of cardiomyopathy. To identify novel cardiomyopathy-associated splicing factors, RNA-seq and tissue-enrichment analyses were performed, which identified up-regulated expression of Sam68-Like mammalian protein 2 (SLM2) in the left ventricle of dilated cardiomyopathy (DCM) patients. In the human heart, SLM2 binds to important transcripts of sarcomere constituents, such as those encoding myosin light chain 2 (MYL2), troponin I3 (TNNI3), troponin T2 (TNNT2), tropomyosin 1/2 (TPM1/2), and titin (TTN). Mechanistically, SLM2 mediates intron retention, prevents exon exclusion, and thereby mediates alternative splicing of the mRNA regions encoding the variable proline-, glutamate-, valine-, and lysine-rich (PEVK) domain and another part of the I-band region of titin. In summary, SLM2 is a novel cardiac splicing regulator with essential functions for maintaining cardiomyocyte integrity by binding to and processing the mRNAs of essential cardiac constituents such as titin.

Source data from a systematic review and meta-analysis of EEG and MEG studies investigating functional connectivity in idiopathic generalized epilepsy.

This article describes source data from a systematic review and meta-analysis of electroencephalography (EEG) and magnetoencephalography (MEG) studies investigating functional connectivity in idiopathic generalized epilepsy. Data selection, analysis and reporting was performed according to PRISMA guidelines. Eligible studies for review were identified from human case-control, and cohort studies. Twenty-two studies were included in the review. Extracted descriptive data included sample characteristics, acquisition of EEG or MEG recordings and network construction. Reported differences between IGE and control groups in functional connectivity or network metrics were extracted as the main outcome measure. Qualitative group differences in functional connectivity were synthesized through narrative review. Meta-analysis was performed for group-level, quantitative estimates of common network metrics clustering coefficient, path length, mean degree and nodal strength. Six studies were included in the meta-analysis. Risk of bias was assessed across all studies. Raw and synthesized data for included studies are reported, alongside effect size and heterogeneity statistics from meta-analyses. Network neurosciences is a rapidly expanding area of research, with significant potential for clinical applications in epilepsy. This data article provides novel, statistical estimates of brain network differences from patients with IGE relative to healthy controls, across the existing literature. Increasing data accessibility supports study replication and improves study comparability for future reviews, enabling a better understanding of network characteristics in IGE.

Resting-state functional connectivity in the idiopathic generalized epilepsies: A systematic review and meta-analysis of EEG and MEG studies.

For idiopathic generalized epilepsies (IGE), brain network analysis is emerging as a biomarker for potential use in clinical care. To determine whether people with IGE show alterations in resting-state brain connectivity compared to healthy controls, and to quantify these differences, we conducted a systematic review and meta-analysis of EEG and magnetoencephalography (MEG) functional connectivity and network studies. The review was conducted according to PRISMA guidelines. Twenty-two studies were eligible for inclusion. Outcomes from individual studies supported hypotheses for interictal, resting-state brain connectivity alterations in IGE patients compared to healthy controls. In contrast, meta-analysis from six studies of common network metrics clustering coefficient, path length, mean degree and nodal strength showed no significant differences between IGE and control groups (effect sizes ranged from -0.151 -1.78). The null findings of the meta-analysis and the heterogeneity of the included studies highlights the importance of developing standardized, validated methodologies for future research. Network neuroscience has significant potential as both a diagnostic and prognostic biomarker in epilepsy, though individual variability in network dynamics needs to be better understood and accounted for.

Machine Learning Analysis of Time-Dependent Features for Predicting Adverse Events During Hemodialysis Therapy: Model Development and Validation Study.

BACKGROUND: Hemodialysis (HD) therapy is an indispensable tool used in critical care management. Patients undergoing HD are at risk for intradialytic adverse events, ranging from muscle cramps to cardiac arrest. So far, there is no effective HD device-integrated algorithm to assist medical staff in response to these adverse events a step earlier during HD. OBJECTIVE: We aimed to develop machine learning algorithms to predict intradialytic adverse events in an unbiased manner. METHODS: Three-month dialysis and physiological time-series data were collected from all patients who underwent maintenance HD therapy at a tertiary care referral center. Dialysis data were collected automatically by HD devices, and physiological data were recorded by medical staff. Intradialytic adverse events were documented by medical staff according to patient complaints. Features extracted from the time series data sets by linear and differential analyses were used for machine learning to predict adverse events during HD. RESULTS: Time series dialysis data were collected during the 4-hour HD session in 108 patients who underwent maintenance HD therapy. There were a total of 4221 HD sessions, 406 of which involved at least one intradialytic adverse event. Models were built by classification algorithms and evaluated by four-fold cross-validation. The developed algorithm predicted overall intradialytic adverse events, with an area under the curve (AUC) of 0.83, sensitivity of 0.53, and specificity of 0.96. The algorithm also predicted muscle cramps, with an AUC of 0.85, and blood pressure elevation, with an AUC of 0.93. In addition, the model built based on ultrafiltration-unrelated features predicted all types of adverse events, with an AUC of 0.81, indicating that ultrafiltration-unrelated factors also contribute to the onset of adverse events. CONCLUSIONS: Our results demonstrated that algorithms combining linear and differential analyses with two-class classification machine learning can predict intradialytic adverse events in quasi-real time with high AUCs. Such a methodology implemented with local cloud computation and real-time optimization by personalized HD data could warn clinicians to take timely actions in advance.

Seizure Forecasting Using a Novel Sub-Scalp Ultra-Long Term EEG Monitoring System.

Accurate identification of seizure activity, both clinical and subclinical, has important implications in the management of epilepsy. Accurate recognition of seizure activity is essential for diagnostic, management and forecasting purposes, but patient-reported seizures have been shown to be unreliable. Earlier work has revealed accurate capture of electrographic seizures and forecasting is possible with an implantable intracranial device, but less invasive electroencephalography (EEG) recording systems would be optimal. Here, we present preliminary results of seizure detection and forecasting with a minimally invasive sub-scalp device that continuously records EEG. Five participants with refractory epilepsy who experience at least two clinically identifiable seizures monthly have been implanted with sub-scalp devices (Minder®), providing two channels of data from both hemispheres of the brain. Data is continuously captured via a behind-the-ear system, which also powers the device, and transferred wirelessly to a mobile phone, from where it is accessible remotely via cloud storage. EEG recordings from the sub-scalp device were compared to data recorded from a conventional system during a 1-week ambulatory video-EEG monitoring session. Suspect epileptiform activity (EA) was detected using machine learning algorithms and reviewed by trained neurophysiologists. Seizure forecasting was demonstrated retrospectively by utilizing cycles in EA and previous seizure times. The procedures and devices were well-tolerated and no significant complications have been reported. Seizures were accurately identified on the sub-scalp system, as visually confirmed by periods of concurrent conventional scalp EEG recordings. The data acquired also allowed seizure forecasting to be successfully undertaken. The area under the receiver operating characteristic curve (AUC score) achieved (0.88), which is comparable to the best score in recent, state-of-the-art forecasting work using intracranial EEG.