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Rationale: Exacerbation of chronic obstructive pulmonary disease commonly causes hospitalization. The change in lung mechanics during exacerbation and its relationship to symptoms in spontaneously breathing individuals has not been described.Objective: We hypothesized that changes in both airflow and lung volumes would occur during an exacerbation, but that only volume change would relate to symptomatic improvement.Methods: Lung mechanics and resting dyspnea were recorded in 22 hospitalized patients during recovery from exacerbation.Measurements: Spirometry, inspiratory capacity, respiratory system resistance and reactance, tidal breathing patterns, and expiratory flow limitation were recorded after nebulized bronchodilator therapy on the first 3 d after admission, at discharge, and 6 wk postadmission (Day 42). Prebronchodilator measurements were taken on Day 2, at discharge, and on Day 42.Main Results: Postbronchodilator inspiratory capacity increased 0.23 +/- 0.07 L by discharge and 0.42 +/- 0.1 L by Day 42, FEV[1] rose 0.09 +/- 0.04 and 0.2 +/- 0.05 L at discharge and Day 42, respectively, and FVC increased 0.21 +/- 0.08 and 0.47 +/- 0.09 L at discharge and Day 42 (all p < 0.05). Consistent reduction in dyspnea was seen as the exacerbation resolved. Respiratory system resistance, FEV[1]/FVC, and expiratory flow limitation were unchanged throughout, indicating that changes in lung volume rather than airflow resistance predominated.Conclusions: Improvement in operating lung volumes is the principal change seen as a chronic obstructive pulmonary disease exacerbation resolves and increase in inspiratory capacity is a useful guide to a reduction in dyspnea. [ABSTRACT FROM AUTHOR]

Middle East Respiratory Syndrome Coronavirus (MERS-CoV) is an enveloped, positive-sense RNA virus that emerged in 2012, causing sporadic cases and localized outbreaks of severe respiratory illness with high fatality rates. A characteristic feature of the immune response to MERS-CoV infection is low type I IFN induction, despite its importance in viral clearance. The non-structural proteins (nsps) of other coronaviruses have been shown to block IFN production. However, the role of nsp5 from MERS-CoV in IFN induction of human respiratory cells is unclear. In this study, we elucidated the role of MERS-CoV-nsp5, the viral main protease, in modulating the host's antiviral responses in human bronchial epithelial BEAS 2b cells. We found that overexpression of MERS-CoV-nsp5 had a dose-dependent inhibitory effect on IFN-β promoter activation and cytokine production induced by HMW-poly(I:C). It also suppressed IFN-β promoter activation triggered by overexpression of key components in the RIG-I-like receptor (RLR) pathway, including RIG-I, MAVS, IKK-ε and IRF3. Moreover, the overexpression of MERS-CoV-nsp5 did not impair expression or phosphorylation of IRF3, but suppressed the nuclear translocation of IRF3. Further investigation revealed that MERS-CoV-nsp5 specifically interacted with IRF3. Using docking and molecular dynamic (MD) simulations, we also found that amino acids on MERS-CoV-nsp5, IRF3, and KPNA4 may participate in protein-protein interactions. Additionally, we uncovered protein conformations that mask the nuclear localization signal (NLS) regions of IRF3 and KPNA4 when interacting with MERS-CoV-nsp5, suggesting a mechanism by which this viral protein blocks IRF3 nuclear translocation. Of note, the IFN-β expression was restored after administration of protease inhibitors targeting nsp5, indicating this suppression of IFN-β production was dependent on the enzyme activity of nsp5. Collectively, our findings elucidate a mechanism by which MERS-CoV-nsp5 disrupts the host's innate antiviral immunity and thus provides insights into viral pathogenesis., (© 2024. The Author(s).)

The Pfizer-BioNTech vaccine was one of the essential tools in curtailing the COVID-19 pandemic. Unlike conventional vaccines, this newly approved mRNA vaccine is taken up by cells, which leads to the synthesis of the specific viral Spike antigen. The vaccine was initially introduced for adults, and the immunological profile of adult recipients is well-characterized. The vaccine was approved for paediatric use much later after its efficacy and safety had been confirmed in children. However, the complete picture of how the paediatric immune system in children reacts to the vaccine is not well documented. Therefore, in order to better understand the immune response in children, we analysed the humoral response, immune cell count, and interferon signalling in paediatric vaccine recipients ranging between 5 and 17 years of age. Our findings suggest that the paediatric recipients elicit a robust humoral response that is sustained for at least three months. We also found that the vaccine triggered a transient lymphocytopenia similar to that observed during viral infection. Interestingly, we also found that the vaccine may sensitise the interferon signalling pathway, priming the cells to mount a potent response when exposed to interferons during a subsequent infection. The study offers new insights into the workings of the paediatric immune system and innate immunity, thereby opening the doors for further research in this field.

Human respiratory viruses are the most prevalent cause of disease in humans, with the highly infectious RSV being the leading cause of infant bronchiolitis and viral pneumonia. Responses to type I IFNs are the primary defense against viral infection. However, RSV proteins have been shown to antagonize type I IFN-mediated antiviral innate immunity, specifically dampening intracellular IFN signaling. Respiratory epithelial cells are the main target for RSV infection. In this study, we found RSV-NS1 interfered with the IFN-α JAK/STAT signaling pathway of epithelial cells. RSV-NS1 expression significantly enhanced IFN-α-mediated phosphorylation of STAT1, but not pSTAT2; and neither STAT1 nor STAT2 total protein levels were affected by RSV-NS1. However, expression of RSV-NS1 significantly reduced ISRE and GAS promoter activity and anti-viral IRG expression. Further mechanistic studies demonstrated RSV-NS1 bound STAT1, with protein modeling indicating a possible interaction site between STAT1 and RSV-NS1. Nuclear translocation of STAT1 was reduced in the presence of RSV-NS1. Additionally, STAT1's interaction with the nuclear transport adapter protein, KPNA1, was also reduced, suggesting a mechanism by which RSV blocks STAT1 nuclear translocation. Indeed, reducing STAT1's access to the nucleus may explain RSV's suppression of IFN JAK/STAT promoter activation and antiviral gene induction. Taken together these results describe a novel mechanism by which RSV controls antiviral IFN-α JAK/STAT responses, which enhances our understanding of RSV's respiratory disease progression., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Efstathiou, Zhang, Kandwal, Fayne, Molloy and Stevenson.)

Objective: To evaluate the utility of a fully automated deep learning -based quantitative measure of EEG background, Brain State of the Newborn (BSN), for early prediction of clinical outcome at four years of age., Methods: The EEG monitoring data from eighty consecutive newborns was analyzed using the automatically computed BSN trend. BSN levels during the first days of life (a of total 5427 hours) were compared to four clinical outcome categories: favorable, cerebral palsy (CP), CP with epilepsy, and death. The time dependent changes in BSN-based prediction for different outcomes were assessed by positive/negative predictive value (PPV/NPV) and by estimating the area under the receiver operating characteristic curve (AUC)., Results: The BSN values were closely aligned with four visually determined EEG categories (p < 0·001), as well as with respect to clinical milestones of EEG recovery in perinatal Hypoxic Ischemic Encephalopathy (HIE; p < 0·003). Favorable outcome was related to a rapid recovery of the BSN trend, while worse outcomes related to a slow BSN recovery. Outcome predictions with BSN were accurate from 6 to 48 hours of age: For the favorable outcome, the AUC ranged from 95 to 99% (peak at 12 hours), and for the poor outcome the AUC ranged from 96 to 99% (peak at 12 hours). The optimal BSN levels for each PPV/NPV estimate changed substantially during the first 48 hours, ranging from 20 to 80., Conclusions: We show that the BSN provides an automated, objective, and continuous measure of brain activity in newborns., Significance: The BSN trend discloses the dynamic nature that exists in both cerebral recovery and outcome prediction, supports individualized patient care, rapid stratification and early prognosis., (Copyright © 2024 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.)

Background: In children, objective, quantitative tools that determine functional neurodevelopment are scarce and rarely scalable for clinical use. Direct recordings of cortical activity using routinely acquired electroencephalography (EEG) offer reliable measures of brain function., Methods: We developed and validated a measure of functional brain age (FBA) using a residual neural network-based interpretation of the paediatric EEG. In this cross-sectional study, we included 1056 children with typical development ranging in age from 1 month to 18 years. We analysed a 10- to 15-min segment of 18-channel EEG recorded during light sleep (N1 and N2 states)., Findings: The FBA had a weighted mean absolute error (wMAE) of 0.85 years (95% CI: 0.69-1.02; n = 1056). A two-channel version of the FBA had a wMAE of 1.51 years (95% CI: 1.30-1.73; n = 1056) and was validated on an independent set of EEG recordings (wMAE = 2.27 years, 95% CI: 1.90-2.65; n = 723). Group-level maturational delays were also detected in a small cohort of children with Trisomy 21 (Cohen's d = 0.36, p = 0.028)., Interpretation: A FBA, based on EEG, is an accurate, practical and scalable automated tool to track brain function maturation throughout childhood with accuracy comparable to widely used physical growth charts., Funding: This research was supported by the National Health and Medical Research Council, Australia, Helsinki University Diagnostic Center Research Funds, Finnish Academy, Finnish Paediatric Foundation, and Sigrid Juselius Foundation., Competing Interests: Declaration of interests J.A.R and S.V. hold a licensed patent on the burst metrics used in this paper. J.A.R. declares grants received from The Margaret Pemberton Foundation and the receipt of EEG equipment from Cadwell Industries. J.C. declares grants received from the following funding bodies: Medical Research Fund (MRFF), National Health and Medical Research Council (NHMRC), Children's Hospital Foundation Fellowship. J.C. acknowledges paid lectures for the Sleep Health Foundation. K.K.I., A.K., M.W., S.J.V., L.L, L.M.H. and N.J.S declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Objective . To overcome the effects of site differences in EEG-based brain age prediction in preterm infants. Approach . We used a 'bag of features' with a combination function estimated using support vector regression (SVR) and feature selection (filter then wrapper) to predict post-menstrual age (PMA). The SVR was trained on a dataset containing 138 EEG recordings from 37 preterm infants (site 1). A separate set of 36 EEG recordings from 36 preterm infants was used to validate the age predictor (site 2). The feature distributions were compared between sites and a restricted feature set was constructed using only features that were not significantly different between sites. The mean absolute error between predicted age and PMA was used to define the accuracy of prediction and successful validation was defined as no significant differences in error between site 1 (cross-validation) and site 2. Main results . The age predictor based on all features and trained on site 1 was not validated on site 2 ( p < 0.001; MAE site 1 = 1.0 weeks, n = 59 versus MAE site 2 = 2.1 weeks, n = 36). The MAE was improved by training on a restricted features set (MAE site 1 = 1.0 weeks, n = 59 versus MAE site 2 = 1.1 weeks, n = 36), resulting in a validated age predictor when applied to site 2 ( p = 0.68). The features selected from the restricted feature set when training on site 1 closely aligned with features selected when trained on a combination of data from site 1 and site 2. Significance . The ability of EEG classifiers, such as brain age prediction, to maintain accuracy on data collected at other sites may be challenged by unexpected, site-dependent differences in EEG signals. Permitting a small amount of data leakage between sites improves generalization, leading towards universal methods of EEG interpretation in preterm infants., (© 2023 Institute of Physics and Engineering in Medicine.)

Functional brain age measures in children, derived from the electroencephalogram (EEG), offer direct and objective measures in assessing neurodevelopmental status. Here we explored the effectiveness of 32 preselected 'handcrafted' EEG features in predicting brain age in children. These features were benchmarked against a large library of highly comparative multivariate time series features (>7000 features). Results showed that age predictors based on handcrafted EEG features consistently outperformed a generic set of time series features. These findings suggest that optimization of brain age estimation in children benefits from careful preselection of EEG features that are related to age and neurodevelopmental trajectory. This approach shows potential for clinical translation in the future.Clinical Relevance-Handcrafted EEG features provide an accurate functional neurodevelopmental biomarker that tracks brain function maturity in children.

Background: Preterm birth predisposes infants to adverse outcomes that, without early intervention, impacts their long-term health. To assist bedside monitoring, we developed a tool to track the autonomic maturation of the preterm by assessing heart rate variability (HRV) changes during intensive care., Methods: Electrocardiogram (ECG) recordings were longitudinally recorded in 67 infants (26-38 weeks postmenstrual age (PMA)). Supervised machine learning was used to generate a functional autonomic age (FAA), by combining 50 computed HRV features from successive 5-minute ECG epochs (median of 23 epochs per infant). Performance of the FAA was assessed by correlation to PMA, clinical outcomes and the infant's functional brain age (FBA), an index of maturation derived from the electroencephalogram., Results: The FAA was strongly correlated to PMA (r = 0.86, 95% CI: 0.83-0.93) with a mean absolute error (MAE) of 1.66 weeks and also accurately estimated FBA (MAE = 1.58 weeks, n = 54 infants). The relationship between PMA and FAA was not confounded by neurodevelopmental outcome (p = 0.18, n = 45), sex (p = 0.88, n = 56), patent ductus arteriosus (p = 0.08, n = 56), IVH (p = 0.63, n = 56) or body weight at birth (p = 0.95, n = 56)., Conclusions: The FAA, an index derived from the ubiquitous ECG signal, offers direct avenues towards estimating autonomic maturation at the bedside during intensive care monitoring., Impact: The development of a tool to track functional autonomic age in preterm infants based on heart rate variability features in the electrocardiogram provides a rapid and specialized view of autonomic maturation at the bedside. Functional autonomic age is linked closely to postmenstrual age and central nervous system function response, as determined by its relationship to functional brain age from the electroencephalogram. Tracking functional autonomic age during neonatal intensive care unit monitoring offers a unique insight into cardiovascular health in infants born extremely preterm and their maturational trajectories to term age., (© 2022. The Author(s), under exclusive licence to the International Pediatric Research Foundation, Inc.)

Background: Electroencephalogram (EEG) monitoring is recommended as routine in newborn neurocritical care to facilitate early therapeutic decisions and outcome predictions. EEG's larger-scale implementation is, however, hindered by the shortage of expertise needed for the interpretation of spontaneous cortical activity, the EEG background. We developed an automated algorithm that transforms EEG recordings to quantified interpretations of EEG background and provides simple intuitive visualisations in patient monitors., Methods: In this method-development and proof-of-concept study, we collected visually classified EEGs from infants recovering from birth asphyxia or stroke. We used unsupervised learning methods to explore latent EEG characteristics, which guided the supervised training of a deep learning-based classifier. We assessed the classifier performance using cross-validation and an external validation dataset. We constructed a novel measure of cortical function, brain state of the newborn (BSN), from the novel EEG background classifier and a previously published sleep-state classifier. We estimated clinical utility of the BSN by identification of two key items in newborn brain monitoring, the onset of continuous cortical activity and sleep-wake cycling, compared with the visual interpretation of the raw EEG signal and the amplitude-integrated (aEEG) trend., Findings: We collected 2561 h of EEG from 39 infants (gestational age 35·0-42·1 weeks; postnatal age 0-7 days). The external validation dataset included 105 h of EEG from 31 full-term infants. The overall accuracy of the EEG background classifier was 92% in the whole cohort (95% CI 91-96; range 85-100 for individual infants). BSN trend values were closely related to the onset of continuous EEG activity or sleep-wake cycling, and BSN levels showed robust difference between aEEG categories. The temporal evolution of the BSN trends showed early diverging trajectories in infants with severely abnormal outcomes., Interpretation: The BSN trend can be implemented in bedside patient monitors as an EEG interpretation that is intuitive, transparent, and clinically explainable. A quantitative trend measure of brain function might harmonise practices across medical centres, enable wider use of brain monitoring in neurocritical care, and might facilitate clinical intervention trials., Funding: European Training Networks Funding Scheme, the Academy of Finland, Finnish Pediatric Foundation (Lastentautiensäätiö), Aivosäätiö, Sigrid Juselius Foundation, HUS Children's Hospital, HUS Diagnostic Center, National Health and Medical Research Council of Australia., Competing Interests: Declaration of interests We declare no competing interests., (Copyright © 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Foetal growth restriction (FGR) and being born small for gestational age (SGA) are associated with neurodevelopmental delay. Early diagnosis of neurological damage is difficult in FGR and SGA neonates. Electroencephalography (EEG) has the potential as a tool for the assessment of brain development in FGR/SGA neonates. In this review, we analyse the evidence base on the use of EEG for the assessment of neonates with FGR or SGA. We found consistent findings that FGR/SGA is associated with measurable changes in the EEG that present immediately after birth and persist into childhood. Early manifestations of FGR/SGA in the EEG include changes in spectral power, symmetry/synchrony, sleep-wake cycling, and the continuity of EEG amplitude. Later manifestations of FGR/SGA into infancy and early childhood include changes in spectral power, sleep architecture, and EEG amplitude. FGR/SGA infants had poorer neurodevelopmental outcomes than appropriate for gestational age controls. The EEG has the potential to identify FGR/SGA infants and assess the functional correlates of neurological damage. IMPACT: FGR/SGA neonates have significantly different EEG activity compared to AGA neonates. EEG differences persist into childhood and are associated with adverse neurodevelopmental outcomes. EEG has the potential for early identification of brain impairment in FGR/SGA neonates., (© 2022. The Author(s).)

Objective: To develop and validate an automated method for bedside monitoring of sleep state fluctuations in neonatal intensive care units., Methods: A deep learning-based algorithm was designed and trained using 53 EEG recordings from a long-term (a)EEG monitoring in 30 near-term neonates. The results were validated using an independent dataset from 30 polysomnography recordings. In addition, we constructed Sleep State Trend (SST), a bedside-ready means for visualizing classifier outputs., Results: The accuracy of quiet sleep detection in the training data was 90%, and the accuracy was comparable (85-86 %) in all bipolar derivations available from the 4-electrode recordings. The algorithm generalized well to a polysomnography dataset, showing 81% overall accuracy despite different signal derivations. SST allowed an intuitive, clear visualization of the classifier output., Conclusions: Fluctuations in sleep states can be detected at high fidelity from a single EEG channel, and the results can be visualized as a transparent and intuitive trend in the bedside monitors., Significance: The Sleep State Trend (SST) may provide caregivers and clinical studies a real-time view of sleep state fluctuations and its cyclicity., (Copyright © 2022 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.)

Long-term control of SARS-CoV-2 outbreaks depends on the widespread coverage of effective vaccines. In Australia, two-dose vaccination coverage of above 90% of the adult population was achieved. However, between August 2020 and August 2021, hesitancy fluctuated dramatically. This raised the question of whether settings with low naturally derived immunity, such as Queensland where less than [Formula: see text] of the population is known to have been infected in 2020, could have achieved herd immunity against 2021's variants of concern. To address this question, we used the agent-based model Covasim. We simulated outbreak scenarios (with the Alpha, Delta and Omicron variants) and assumed ongoing interventions (testing, tracing, isolation and quarantine). We modelled vaccination using two approaches with different levels of realism. Hesitancy was modelled using Australian survey data. We found that with a vaccine effectiveness against infection of 80%, it was possible to control outbreaks of Alpha, but not Delta or Omicron. With 90% effectiveness, Delta outbreaks may have been preventable, but not Omicron outbreaks. We also estimated that a decrease in hesitancy from 20% to 14% reduced the number of infections, hospitalizations and deaths by over 30%. Overall, we demonstrate that while herd immunity may not be attainable, modest reductions in hesitancy and increases in vaccine uptake may greatly improve health outcomes. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.

Exposure to environmental adversities during early brain development, such as preterm birth, can affect early brain organization. Here, we studied whether development of cortical activity networks in preterm infants may be improved by a multimodal environmental enrichment via bedside facilitation of mother-infant emotional connection. We examined functional cortico-cortical connectivity at term age using high-density electroencephalography recordings in infants participating in a randomized controlled trial of Family Nurture Intervention (FNI). Our results identify several large-scale, frequency-specific network effects of FNI, most extensively in the alpha frequency in fronto-central cortical regions. The connectivity strength in this network was correlated to later neurocognitive performance, and it was comparable to healthy term-born infants rather than the infants receiving standard care. These findings suggest that preterm neurodevelopmental care can be improved by a biologically driven environmental enrichment, such as early facilitation of direct human connection.

Neonatal seizure detection algorithms (SDA) are approaching the benchmark of human expert annotation. Measures of algorithm generalizability and non-inferiority as well as measures of clinical efficacy are needed to assess the full scope of neonatal SDA performance. We validated our neonatal SDA on an independent data set of 28 neonates. Generalizability was tested by comparing the performance of the original training set (cross-validation) to its performance on the validation set. Non-inferiority was tested by assessing inter-observer agreement between combinations of SDA and two human expert annotations. Clinical efficacy was tested by comparing how the SDA and human experts quantified seizure burden and identified clinically significant periods of seizure activity in the EEG. Algorithm performance was consistent between training and validation sets with no significant worsening in AUC (p > 0.05, n = 28). SDA output was inferior to the annotation of the human expert, however, re-training with an increased diversity of data resulted in non-inferior performance (Δκ = 0.077, 95% CI: -0.002-0.232, n = 18). The SDA assessment of seizure burden had an accuracy ranging from 89 to 93%, and 87% for identifying periods of clinical interest. The proposed SDA is approaching human equivalence and provides a clinically relevant interpretation of the EEG., (Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Unusually for a viral infection, the immunological phenotype of severe COVID-19 is characterised by a depleted lymphocyte and elevated neutrophil count, with the neutrophil-to-lymphocyte ratio correlating with disease severity. Neutrophils are the most abundant immune cell in the bloodstream and comprise different subpopulations with pleiotropic actions that are vital for host immunity. Unique neutrophil subpopulations vary in their capacity to mount antimicrobial responses, including NETosis (the generation of neutrophil extracellular traps), degranulation and de novo production of cytokines and chemokines. These processes play a role in antiviral immunity, but may also contribute to the local and systemic tissue damage seen in acute SARS-CoV-2 infection. Neutrophils also contribute to complications of COVID-19 such as thrombosis, acute respiratory distress syndrome and multisystem inflammatory disease in children. In this Progress review, we discuss the anti-viral and pathological roles of neutrophils in SARS-CoV-2 infection, and potential therapeutic strategies for COVID-19 that target neutrophil-mediated inflammatory responses., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 McKenna, Wubben, Isaza-Correa, Melo, Mhaonaigh, Conlon, O’Donnell, Ní Cheallaigh, Hurley, Stevenson, Little and Molloy.)

Severe acute respiratory syndrome coronavirus (SARS-CoV)-2 emerged in China in 2019 and has since travelled the world infecting millions. SARS-CoV-2 causes Corona Virus Disease (COVID-19), that has to date taken over 4 million lives. The Kingdom of Bahrain's vaccine roll-out has consisted of Sinopharm's BBIBP-CorV (Sinopharm) and Pfizer/BioNtech's BNT162b2 (Pfizer/BioNtech). Testing for SARS-CoV-2 anti-Spike (S) antibodies is a useful technique in estimating an individual's immune protection against the infection. In this study we evaluated S antibody levels by electro-chemiluminescence immunoassay in 379 individuals double vaccinated with Sinopharm and 15 of whom were given a booster with the Pfizer/BioNtech vaccine. Among our double vaccinated cohort, we found a spectrum of S antibody levels. Indeed, we found that a significant proportion of individuals with low S antibody levels had clinical conditions, which were mainly immune-related disorders. Furthermore, a significant proportion of individuals with low S antibody levels were above 50 years of age. Finally, we observed a significant increase in S antibody levels after the Pfizer/BioNtech booster was administered. These findings reveal that while a large proportion of Sinopharm vaccinated individuals did not develop high levels of antibodies against the S protein, a booster dose of the Pfizer/BioNtech vaccine significantly enhances S antibody levels, revealing this "triple dose" vaccination strategy as a useful method of ensuring protective immunity against SARS-CoV-2., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Farid, Herrera-Uribe and Stevenson.)

We used an agent-based model Covasim to assess the risk of sustained community transmission of SARSCoV-2/COVID-19 in Queensland (Australia) in the presence of high-transmission variants of the virus. The model was calibrated using the demographics, policies, and interventions implemented in the state. Then, using the calibrated model, we simulated possible epidemic trajectories that could eventuate due to leakage of infected cases with high-transmission variants, during a period without recorded cases of locally acquired infections, known in Australian settings as "zero community transmission". We also examined how the threat of new variants reduces given a range of vaccination levels. Specifically, the model calibration covered the first-wave period from early March 2020 to May 2020. Predicted epidemic trajectories were simulated from early February 2021 to late March 2021. Our simulations showed that one infected agent with the ancestral (A.2.2) variant has a 14% chance of crossing a threshold of sustained community transmission (SCT) (i.e., > 5 infections per day, more than 3 days in a row), assuming no change in the prevailing preventative and counteracting policies. However, one agent carrying the alpha (B.1.1.7) variant has a 43% chance of crossing the same threshold; a threefold increase with respect to the ancestral strain; while, one agent carrying the delta (B.1.617.2) variant has a 60% chance of the same threshold, a fourfold increase with respect to the ancestral strain. The delta variant is 50% more likely to trigger SCT than the alpha variant. Doubling the average number of daily tests from ∼ 6,000 to 12,000 results in a decrease of this SCT probability from 43 to 33% for the alpha variant. However, if the delta variant is circulating we would need an average of 100,000 daily tests to achieve a similar decrease in SCT risk. Further, achieving a full-vaccination coverage of 70% of the adult population, with a vaccine with 70% effectiveness against infection, would decrease the probability of SCT from a single seed of alpha from 43 to 20%, on par with the ancestral strain in a naive population. In contrast, for the same vaccine coverage and same effectiveness, the probability of SCT from a single seed of delta would decrease from 62 to 48%, a risk slightly above the alpha variant in a naive population. Our results demonstrate that the introduction of even a small number of people infected with high-transmission variants dramatically increases the probability of sustained community transmission in Queensland. Until very high vaccine coverage is achieved, a swift implementation of policies and interventions, together with high quarantine adherence rates, will be required to minimise the probability of sustained community transmission., (© 2022. The Author(s).)

Coronaviruses (CoVs) have caused several global outbreaks with relatively high mortality rates, including Middle East Respiratory Syndrome coronavirus (MERS)-CoV, which emerged in 2012, and Severe Acute Respiratory Syndrome (SARS)-CoV-1, which appeared in 2002. The recent emergence of SARS-CoV-2 highlights the need for immediate and greater understanding of the immune evasion mechanisms used by CoVs. Interferon (IFN)-α is the body's natural antiviral agent, but its Janus kinase/signal transducer and activators of transcription (JAK/STAT) signalling pathway is often antagonized by viruses, thereby preventing the upregulation of essential IFN stimulated genes (ISGs). Therapeutic IFN-α has disappointingly weak clinical responses in MERS-CoV and SARS-CoV-1 infected patients, indicating that these CoVs inhibit the IFN-α JAK/STAT pathway. Here we show that in lung alveolar A549 epithelial cells expression of MERS-CoV-nsp2 and SARS-CoV-1-nsp14, but not MERS-CoV-nsp5, increased basal levels of total and phosphorylated STAT1 & STAT2 protein, but reduced IFN-α-mediated phosphorylation of STAT1-3 and induction of MxA. While MERS-CoV-nsp2 and SARS-CoV-1-nsp14 similarly increased basal levels of STAT1 and STAT2 in bronchial BEAS-2B epithelial cells, unlike in A549 cells, they did not enhance basal pSTAT1 nor pSTAT2. However, both viral proteins reduced IFN-α-mediated induction of pSTAT1-3 and ISGs (MxA, ISG15 and PKR) in BEAS-2B cells. Furthermore, even though IFN-α-mediated induction of pSTAT1-3 was not affected by MERS-CoV-nsp5 expression in BEAS-2B cells, downstream ISG induction was reduced, revealing that MERS-CoV-nsp5 may use an alternative mechanism to reduce antiviral ISG induction in this cell line. Indeed, we subsequently discovered that all three viral proteins inhibited STAT1 nuclear translocation in BEAS-2B cells, unveiling another layer of inhibition by which these viral proteins suppress responses to Type 1 IFNs. While these observations highlight cell line-specific differences in the immune evasion effects of MERS-CoV and SARS-CoV-1 proteins, they also demonstrate the broad spectrum of immune evasion strategies these deadly coronaviruses use to stunt antiviral responses to Type IFN.

Flaviviruses are known to cause a variety of diseases in humans in different parts of the world. There are very limited numbers of antivirals to combat flavivirus infection, and therefore new drug targets must be explored. The flavivirus NS2B-NS3 proteases are responsible for the cleavage of the flavivirus polyprotein, which is necessary for productive viral infection and for causing clinical infections; therefore, they are a promising drug target for devising novel drugs against different flaviviruses. This review highlights the structural details of the NS2B-NS3 proteases of different flaviviruses, and also describes potential antiviral drugs that can interfere with the viral protease activity, as determined by various studies. Moreover, optimized in vitro reaction conditions for studying the NS2B-NS3 proteases of different flaviviruses may vary and have been incorporated in this review. The increasing availability of the in silico and crystallographic/structural details of flavivirus NS2B-NS3 proteases in free and drug-bound states can pave the path for the development of promising antiflavivirus drugs to be used in clinics. However, there is a paucity of information available on using animal cells and models for studying flavivirus NS2B-NS3 proteases, as well as on the testing of the antiviral drug efficacy against NS2B-NS3 proteases. Therefore, on the basis of recent studies, an effort has also been made to propose potential cellular and animal models for the study of flavivirus NS2B-NS3 proteases for the purposes of exploring flavivirus pathogenesis and for testing the efficacy of possible drugs targets, in vitro and in vivo.

The current COVID-19 pandemic has highlighted the need for the research community to develop a better understanding of viruses, in particular their modes of infection and replicative lifecycles, to aid in the development of novel vaccines and much needed anti-viral therapeutics. Several viruses express proteins capable of forming pores in host cellular membranes, termed "Viroporins". They are a family of small hydrophobic proteins, with at least one amphipathic domain, which characteristically form oligomeric structures with central hydrophilic domains. Consequently, they can facilitate the transport of ions through the hydrophilic core. Viroporins localise to host membranes such as the endoplasmic reticulum and regulate ion homeostasis creating a favourable environment for viral infection. Viroporins also contribute to viral immune evasion via several mechanisms. Given that viroporins are often essential for virion assembly and egress, and as their structural features tend to be evolutionarily conserved, they are attractive targets for anti-viral therapeutics. This review discusses the current knowledge of several viroporins, namely Influenza A virus (IAV) M2, Human Immunodeficiency Virus (HIV)-1 Viral protein U (Vpu), Hepatitis C Virus (HCV) p7, Human Papillomavirus (HPV)-16 E5, Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV) Open Reading Frame (ORF)3a and Polyomavirus agnoprotein. We highlight the intricate but broad immunomodulatory effects of these viroporins and discuss the current antiviral therapies that target them; continually highlighting the need for future investigations to focus on novel therapeutics in the treatment of existing and future emergent viruses.

Background and Objective: To develop a computational algorithm that detects and identifies different artefact types in neonatal electroencephalography (EEG) signals., Methods: As part of a larger algorithm, we trained a Residual Deep Neural Network on expert human annotations of EEG recordings from 79 term infants recorded in a neonatal intensive care unit (112 h of 18-channel recording). The network was trained using 10 fold cross validation in Matlab. Artefact types included: device interference, EMG, movement, electrode pop, and non-cortical biological rhythms. Performance was assessed by prediction statistics and further validated on a separate independent dataset of 13 term infants (143 h of 3-channel recording). EEG pre-processing steps, and other post-processing steps such as averaging probability over a temporal window, were also included in the algorithm., Results: The Residual Deep Neural Network showed high accuracy (95%) when distinguishing periods of clean, artefact-free EEG from any kind of artefact, with a median accuracy for individual patient of 91% (IQR: 81%-96%). The accuracy in identifying the five different types of artefacts ranged from 57%-92%, with electrode pop being the hardest to detect and EMG being the easiest. This reflected the proportion of artefact available in the training dataset. Misclassification as clean was low for each artefact type, ranging from 1%-11%. The detection accuracy was lower on the validation set (87%). We used the algorithm to show that EEG channels located near the vertex were the least susceptible to artefact., Conclusion: Artefacts can be accurately and reliably identified in the neonatal EEG using a deep learning algorithm. Artefact detection algorithms can provide continuous bedside quality assessment and support EEG review by clinicians or analysis algorithms., Competing Interests: Declaration of Competing Interest None of the authors have potential conflicts of interest to be disclosed, (Copyright © 2021 Elsevier B.V. All rights reserved.)

Neonatal brain monitoring in the neonatal intensive care units (NICU) requires a continuous review of the spontaneous cortical activity, i.e., the electroencephalograph (EEG) background activity. This needs development of bedside methods for an automated assessment of the EEG background activity. In this paper, we present development of the key components of a neonatal EEG background classifier, starting from the visual background scoring to classifier design, and finally to possible bedside visualization of the classifier results. A dataset with 13,200 5-minute EEG epochs (8-16 channels) from 27 infants with birth asphyxia was used for classifier training after scoring by two independent experts. We tested three classifier designs based on 98 computational features, and their performance was assessed with respect to scoring system, pre- and post-processing of labels and outputs, choice of channels, and visualization in monitor displays. The optimal solution achieved an overall classification accuracy of 97% with a range across subjects of 81-100%. We identified a set of 23 features that make the classifier highly robust to the choice of channels and missing data due to artefact rejection. Our results showed that an automated bedside classifier of EEG background is achievable, and we publish the full classifier algorithm to allow further clinical replication and validation studies., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Montazeri, Pinchefsky, Tse, Marchi, Kohonen, Kauppila, Airaksinen, Tapani, Nevalainen, Hahn, Tam, Stevenson and Vanhatalo.)

Neutrophils are the most abundant innate immune cell with critical anti-microbial functions. Since the discovery of granulocytes at the end of the nineteenth century, the cells have been given many names including phagocytes, polymorphonuclear neutrophils (PMN), granulocytic myeloid derived suppressor cells (G-MDSC), low density neutrophils (LDN) and tumor associated neutrophils (TANS). This lack of standardized nomenclature for neutrophils suggest that biologically distinct populations of neutrophils exist, particularly in disease, when in fact these may simply be a manifestation of the plasticity of the neutrophil as opposed to unique populations. In this review, we profile the surface markers and granule expression of each stage of granulopoiesis to offer insight into how each stage of maturity may be identified. We also highlight the remarkable surface marker expression profiles between the supposed neutrophil populations., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 McKenna, Mhaonaigh, Wubben, Dwivedi, Hurley, Kelly, Stevenson, Little and Molloy.)

Purpose: The COVID-19 pandemic has led to over 92 million cases and 1.9 million deaths worldwide since its outbreak. Public health responses have focused on identifying symptomatic individuals to halt spread. However, evidence is accruing that asymptomatic individuals are infectious and contributing to this global pandemic., Methods: Observational data of 320 index cases and their 1289 positive contacts from the National COVID-19 Database in Bahrain were used to analyze symptoms, infectivity rate and PCR Cycle threshold (Ct) values., Results: No significant difference (p = 1.0) in proportions of symptomatic (n = 160; 50.0%) and asymptomatic index cases (n = 160; 50.0%) were seen; however, SARS-CoV-2 positive contact cases were predominantly asymptomatic (n = 1127, 87.4%). Individuals aged 0-19 years constituted a larger proportion of positive contact cases (20.8%) than index cases (4.7%; p < 0.001). A total of 22% of the positive contacts were infected by symptomatic male index cases aged between 30-39 years. The total numbers of exposed contacts (p = 0.33), infected contacts (p = 0.81) and hence infectivity rate (p = 0.72) were not different between symptomatic and asymptomatic index cases. PCR Ct values were higher in asymptomatic compared to symptomatic index cases (p < 0.001), and higher in asymptomatic compared to symptomatic positive contacts (p < 0.001). No differences between the infectivity rates of index cases with Ct values <30 and values ≥30 were observed (p = 0.13)., Conclusion: These data reveal that the high asymptomatic incidence of SARS-CoV-2 infection in Bahrain and subsequent positive contacts from an index case were more likely to be asymptomatic, showing the high "silent" risk of transmission and need for comprehensive screening for each positive infection to help halt the ongoing pandemic., (Copyright © 2021 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Coronaviruses are a large family of zoonotic RNA viruses, whose infection can lead to mild or lethal respiratory tract disease. Severe Acute Respiratory Syndrome-Coronavirus-1 (SARS-CoV-1) first emerged in Guangdong, China in 2002 and spread to 29 countries, infecting 8089 individuals and causing 774 deaths. In 2012, Middle East Respiratory Syndrome-Coronavirus (MERS-CoV) emerged in Saudi Arabia and has spread to 27 countries, with a mortality rate of ~34%. In 2019, SARS-CoV-2 emerged and has spread to 220 countries, infecting over 100,000,000 people and causing more than 2,000,000 deaths to date. These three human coronaviruses cause diseases of varying severity. Most people develop mild, common cold-like symptoms, while some develop acute respiratory distress syndrome (ARDS). The success of all viruses, including coronaviruses, relies on their evolved abilities to evade and modulate the host anti-viral and pro-inflammatory immune responses. However, we still do not fully understand the transmission, phylogeny, epidemiology, and pathogenesis of MERS-CoV and SARS-CoV-1 and -2. Despite the rapid application of a range of therapies for SARS-CoV-2, such as convalescent plasma, remdesivir, hydroxychloroquine and type I interferon, no fully effective treatment has been determined. Remarkably, COVID-19 vaccine research and development have produced several offerings that are now been administered worldwide. Here, we summarise an up-to-date understanding of epidemiology, immunomodulation and ongoing anti-viral and immunosuppressive treatment strategies. Indeed, understanding the interplay between coronaviruses and the anti-viral immune response is crucial to identifying novel targets for therapeutic intervention, which may even prove invaluable for the control of future emerging coronavirus.

Objective: To develop a non-invasive and clinically practical method for a long-term monitoring of infant sleep cycling in the intensive care unit., Methods: Forty three infant polysomnography recordings were performed at 1-18 weeks of age, including a piezo element bed mattress sensor to record respiratory and gross-body movements. The hypnogram scored from polysomnography signals was used as the ground truth in training sleep classifiers based on 20,022 epochs of movement and/or electrocardiography signals. Three classifier designs were evaluated in the detection of deep sleep (N3 state): support vector machine (SVM), Long Short-Term Memory neural network, and convolutional neural network (CNN)., Results: Deep sleep was accurately identified from other states with all classifier variants. The SVM classifier based on a combination of movement and electrocardiography features had the highest performance (AUC 97.6%). A SVM classifier based on only movement features had comparable accuracy (AUC 95.0%). The feature-independent CNN resulted in roughly comparable accuracy (AUC 93.3%)., Conclusion: Automated non-invasive tracking of sleep state cycling is technically feasible using measurements from a piezo element situated under a bed mattress., Significance: An open source infant deep sleep detector of this kind allows quantitative, continuous bedside assessment of infant's sleep cycling., Competing Interests: JR was a shareholder and a part time employee in sensor manufacturer Emfit Ltd. Emfit did not have any role in study design, data analysis or publication process. This work was mostly done before JR was employed by Emfit. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Ranta, Airaksinen, Kirjavainen, Vanhatalo and Stevenson.)

The human immune response can be divided into two arms: innate and adaptive immunity. The innate immune system consists of "hard-wired" responses encoded by host germline genes. In contrast, the adaptive response consists of gene elements that are somatically rearranged to assemble antigen-binding molecules with specificity for individual foreign structures. In contrast to the adaptive immune system, which depends upon T and B lymphocytes, innate immune protection is a task performed by cells of both hematopoietic and non-hematopoietic origin. Hematopoietic cells involved in innate immune responses include macrophages, dendritic cells, mast cell, neutrophils, eosinophils, natural killer (NK) cells and natural killer T cells. The induction of an adaptive immune response begins when a pathogen is ingested by an Antigen Presenting Cell (APC), such as the Dendritic cell (DC), in the infected tissue. DCs bridge the gap between first line innate responses and powerful adaptive immune responses, by internalizing, processing and presenting antigens on Major Histocompatibility Complex (MHC) and MHC-like molecules to the adaptive immune cells In addition to DCs, macrophages and B cells are deemed antigen presenting cells (Llewelyn & Cohen, 2002)., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Every year there are > 33 million cases of Respiratory Syncytial Virus (RSV)-related respiratory infection in children under the age of five, making RSV the leading cause of lower respiratory tract infection (LRTI) in infants. RSV is a global infection, but 99% of related mortality is in low/middle-income countries. Unbelievably, 62 years after its identification, there remains no effective treatment nor vaccine for this deadly virus, leaving infants, elderly and immunocompromised patients at high risk. The success of all pathogens depends on their ability to evade and modulate the host immune response. RSV has a complex and intricate relationship with our immune systems, but a clearer understanding of these interactions is essential in the development of effective medicines. Therefore, in a bid to update and focus our research community's understanding of RSV's interaction with immune defences, this review aims to discuss how our current knowledgebase could be used to combat this global viral threat.

Objectives: To determine the accuracy of, and agreement among, EEG and aEEG readers' estimation of maturity and a novel computational measure of functional brain age (FBA) in preterm infants., Methods: Seven experts estimated the postmenstrual ages (PMA) in a cohort of recordings from preterm infants using cloud-based review software. The FBA was calculated using a machine learning-based algorithm. Error analysis was used to determine the accuracy of PMA assessments and intraclass correlation (ICC) was used to assess agreement between experts., Results: EEG recordings from a PMA range 25 to 38 weeks were successfully interpreted. In 179 recordings from 62 infants interpreted by all human readers, there was moderate agreement between experts (aEEG ICC = 0.724; 95%CI:0.658-0.781 and EEG ICC = 0.517; 95%CI:0.311-0.664). In 149 recordings from 61 infants interpreted by all human readers and the FBA algorithm, random and systematic errors in visual interpretation of PMA were significantly higher than the computational FBA estimate. Tracking of maturation in individual infants showed stable FBA trajectories, but the trajectories of the experts' PMA estimate were more likely to be obscured by random errors. The accuracy of visual interpretation of PMA estimation was compromised by neurodevelopmental outcome for both aEEG and EEG review., Interpretation: Visual assessment of infant maturity is possible from the EEG or aEEG, with an average of human experts providing the highest accuracy. Tracking PMA of individual infants was hampered by errors in experts' estimates. FBA provided the most accurate maturity assessment and has potential as a biomarker of early outcome., (© 2020 The Authors. Annals of Clinical and Translational Neurology published by Wiley Periodicals LLC on behalf of American Neurological Association.)

Objective: A major challenge in the care of preterm infants is the early identification of compromised neurological development. While several measures are routinely used to track anatomical growth, there is a striking lack of reliable and objective tools for tracking maturation of early brain function; a cornerstone of lifelong neurological health. We present a cot-side method for measuring the functional maturity of the newborn brain based on routinely available neurological monitoring with electroencephalography (EEG)., Methods: We used a dataset of 177 EEG recordings from 65 preterm infants to train a multivariable prediction of functional brain age (FBA) from EEG. The FBA was validated on an independent set of 99 EEG recordings from 42 preterm infants. The difference between FBA and postmenstrual age (PMA) was evaluated as a predictor for neurodevelopmental outcome., Results: The FBA correlated strongly with the PMA of an infant, with a median prediction error of less than 1 week. Moreover, individual babies follow well-defined individual trajectories. The accuracy of the FBA applied to the validation set was statistically equivalent to the training set accuracy. In a subgroup of infants with repeated EEG recordings, a persistently negative predicted age difference was associated with poor neurodevelopmental outcome., Interpretation: The FBA enables the tracking of functional neurodevelopment in preterm infants. This establishes proof of principle for growth charts for brain function, a new tool to assist clinical management and identify infants who will benefit most from early intervention., (© 2020 The Authors. Annals of Clinical and Translational Neurology published by Wiley Periodicals LLC on behalf of American Neurological Association.)

Viruses use a spectrum of immune evasion strategies that enable infection and replication. The acute phase of hepatitis C virus (HCV) infection is characterized by nonspecific and often mild clinical symptoms, suggesting an immunosuppressive mechanism that, unless symptomatic liver disease presents, allows the virus to remain largely undetected. We previously reported that HCV induced the regulatory protein suppressor of cytokine signaling (SOCS)3, which inhibited TNF-α-mediated inflammatory responses. However, the mechanism by which HCV up-regulates SOCS3 remains unknown. Here we show that the HCV protein, p7, enhances both SOCS3 mRNA and protein expression. A p7 inhibitor reduced SOCS3 induction, indicating that p7's ion channel activity was required for optimal up-regulation of SOCS3. Short hairpin RNA and chemical inhibition revealed that both the Janus kinase-signal transducer and activator of transcription (JAK-STAT) and MAPK pathways were required for p7-mediated induction of SOCS3. HCV-p7 expression suppressed TNF-α-mediated IκB-α degradation and subsequent NF-κB promoter activity, revealing a new and functional, anti-inflammatory effect of p7. Together, these findings identify a molecular mechanism by which HCV-p7 induces SOCS3 through STAT3 and ERK activation and demonstrate that p7 suppresses proinflammatory responses to TNF-α, possibly explaining the lack of inflammatory symptoms observed during early HCV infection.-Convery, O., Gargan, S., Kickham, M., Schroder, M., O'Farrelly, C., Stevenson, N. J. The hepatitis C virus (HCV) protein, p7, suppresses inflammatory responses to tumor necrosis factor (TNF)-α via signal transducer and activator of transcription (STAT)3 and extracellular signal-regulated kinase (ERK)-mediated induction of suppressor of cytokine signaling (SOCS)3.

The aim of this study was to develop methods for detecting the nonstationary periodic characteristics of neonatal electroencephalographic (EEG) seizures by adapting estimates of the correlation both in the time (spike correlation; SC) and time-frequency domain (time-frequency correlation; TFC). These measures were incorporated into a seizure detection algorithm (SDA) based on a support vector machine to detect periods of seizure and nonseizure. The performance of these nonstationary correlation measures was evaluated using EEG recordings from 79 term neonates annotated by three human experts. The proposed measures were highly discriminative for seizure detection (median AUC SC : 0.933 IQR: 0.821-0.975, median AUC TFC : 0.883 IQR: 0.707-0.931). The resultant SDA applied to multi-channel recordings had a median AUC of 0.988 (IQR: 0.931-0.998) when compared to consensus annotations, outperformed two state-of-the-art SDAs ( p < 0 . 0 0 1 ) and was noninferior to the human expert for 73/79 of neonates.

For the premature newborn, little is known about changes in brain activity during transition to extra-uterine life. We aim to quantify these changes in relation to the longer-term maturation of the developing brain. We analysed EEG for up to 72 hours after birth from 28 infants born <32 weeks of gestation. These infants had favourable neurodevelopment at 2 years of age and were without significant neurological compromise at time of EEG monitoring. Quantitative EEG was generated using features representing EEG power, discontinuity, spectral distribution, and inter-hemispheric connectivity. We found rapid changes in cortical activity over the 3 days distinct from slower changes associated with gestational age: for many features, evolution over 1 day after birth is equivalent to approximately 1 to 2.5 weeks of maturation. Considerable changes in the EEG immediately after birth implies that postnatal adaption significantly influences cerebral activity for early preterm infants. Postnatal age, in addition to gestational age, should be considered when analysing preterm EEG within the first few days after birth.

Neonatal seizures are widely considered a neurological emergency with a need for prompt treatment, yet they are known to present a highly elusive target for bedside clinicians. Recent studies have suggested that the design of a neonatal seizure treatment trial will profoundly influence the sample size, which may readily increase to hundreds or even thousands as the achieved effect size diminishes to clinical irrelevance. The self-limiting and rapidly resolving nature of neonatal seizures diminishes the measurable treatment effect every hour after seizure onset and any effect may potentially be confused with spontaneous resolution, precluding the value of many observational studies. The large individual variability in seizure occurrence over time and between etiologies challenges group comparisons, while the absence of clinical signs mandates quantification of seizure occurrence with continuous multi-channel EEG monitoring. A biologically sound approach that views neonatal seizures as a functional cot-side biomarker rather than an object to treat can overcome these challenges., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Anti-retroviral therapy successfully suppresses HIV-1 infection, but fails to provide a cure. During infection Type 1 IFNs normally play an essential role in viral clearance, but in vivo IFN-α only has a modest impact on HIV-1 infection, suggesting its possible targeting by HIV. Here, we report that the HIV protein, Vif, inhibits effective IFN-α signalling via degradation of essential JAK/STAT pathway components. We found that STAT1 and STAT3 are specifically reduced in HEK293T cells expressing Vif and that full length, infectious HIV-1 IIIB strain promotes their degradation in a Vif-dependent manner. HIV-1 IIIB infection of myeloid ThP-1 cells also reduced the IFN-α-mediated induction of the anti-viral gene, ISG15, but not MxA, revealing a functional consequence of this HIV-1-mediated immune evasion strategy. Interestingly, while total STAT levels were not reduced upon in vitro IIIB infection of primary human PBMCs, IFN-α-mediated phosphorylation of STAT1 and STAT3 and ISG induction were starkly reduced, with removal of Vif (IIIBΔVif), partially restoring pSTATs, ISG15 and MxB induction. Similarly, pSTAT1 and pSTAT3 expression and IFN-α-induced ISG15 were reduced in PBMCs from HIV-infected patients, compared to healthy controls. Furthermore, IFN-α pre-treatment of a CEM T lymphoblast cells significantly inhibited HIV infection/replication (measured by cellular p24), only in the absence of Vif (IIIBΔVif), but was unable to suppress full length IIIB infection. When analysing the mechanism by which Vif might target the JAK/STAT pathway, we found Vif interacts with both STAT1 and STAT3, (but not STAT2), and its expression promotes ubiquitination and MG132-sensitive, proteosomal degradation of both proteins. Vif's Elongin-Cullin-SOCS-box binding motif enables the formation of an active E3 ligase complex, which we found to be required for Vif's degradation of STAT1 and STAT3. In fact, the E3 ligase scaffold proteins, Cul5 and Rbx2, were also found to be essential for Vif-mediated proteasomal degradation of STAT1 and STAT3. These results reveal a target for HIV-1-Vif and demonstrate how HIV-1 impairs the anti-viral activity of Type 1 IFNs, possibly explaining why both endogenous and therapeutic IFN-α fail to activate more effective control over HIV infection., (Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.)

Viral infections, including HIV, trigger the production of type I interferons (IFNs), which in turn, activate a signalling cascade that ultimately culminates with the expression of anti-viral proteins. Mounting evidence suggests that type I IFNs, in particular IFN-α, play a pivotal role in limiting acute HIV infection. Highly active anti-retroviral treatment reduces viral load and increases life expectancy in HIV positive patients; however, it fails to fully eliminate latent HIV reservoirs. To revisit HIV as a curable disease, this article reviews a body of literature that highlights type I IFNs as mediators in the control of HIV infection, with particular focus on the anti-HIV restriction factors induced and/or activated by IFN-α. In addition, we discuss the relevance of type I IFN treatment in the context of HIV latency reversal, novel therapeutic intervention strategies and the potential for full HIV clearance.

Objectives: To measure changes in the visual interpretation of the EEG by the human expert for neonatal seizure detection when reducing the number of recording electrodes., Methods: EEGs were recorded from 45 infants admitted to the neonatal intensive care unit (NICU). Three experts annotated seizures in EEG montages derived from 19, 8 and 4 electrodes. Differences between annotations were assessed by comparing intra-montage with inter-montage agreement (K)., Results: Three experts annotated 4464 seizures across all infants and montages. The inter-expert agreement was not significantly altered by the number of electrodes in the montage (p = 0.685, n = 43). Reducing the number of EEG electrodes altered the seizure annotation for all experts. Agreement between the 19-electrode montage (K 19,19  = 0.832) was significantly higher than the agreement between 19 and 8-electrode montages (dK = 0.114; p < 0.001, n = 42) or 19 and 4-electrode montages (dK = 0.113, p < 0.001, n = 43). Seizure burden and number were significantly underestimated by the 4 and 8-electrode montage (p < 0.001). No significant difference in agreement was found between 8 and 4-electrode montages (dK = 0.002; p = 0.07, n = 42)., Conclusions: Reducing the number of EEG electrodes from 19 electrodes resulted in slight but significant changes in seizure detection., Significance: Four-electrode montages for routine EEG monitoring are comparable to eight electrodes for seizure detection in the NICU., (Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.)

Minimally invasive, automated cot-side tools for monitoring early neurological development can be used to guide individual treatment and benchmark novel interventional studies. We develop an automated estimate of the EEG maturational age (EMA) for application to serial recordings in preterm infants. The EMA estimate was based on a combination of 23 computational features estimated from both the full EEG recording and a period of low EEG activity (46 features in total). The combination function (support vector regression) was trained using 101 serial EEG recordings from 39 preterm infants with a gestational age less than 28 weeks and normal neurodevelopmental outcome at 12 months of age. EEG recordings were performed from 24 to 38 weeks post-menstrual age (PMA). The correlation between the EMA and the clinically determined PMA at the time of EEG recording was 0.936 (95%CI: 0.932-0.976; n = 39). All infants had an increase in EMA between the first and last EEG recording and 57/62 (92%) of repeated measures within an infant had an increasing EMA with PMA of EEG recording. The EMA is a surrogate measure of age that can accurately determine brain maturation in preterm infants.

Aim: To develop a method that segments preterm EEG into bursts and inter-bursts by extracting and combining multiple EEG features., Methods: Two EEG experts annotated bursts in individual EEG channels for 36 preterm infants with gestational age < 30 weeks. The feature set included spectral, amplitude, and frequency-weighted energy features. Using a consensus annotation, feature selection removed redundant features and a support vector machine combined features. Area under the receiver operator characteristic (AUC) and Cohen's kappa (κ) evaluated performance within a cross-validation procedure., Results: The proposed channel-independent method improves AUC by 4-5% over existing methods (p < 0.001, n=36), with median (95% confidence interval) AUC of 0.989 (0.973-0.997) and sensitivity-specificity of 95.8-94.4%. Agreement rates between the detector and experts' annotations, κ=0.72 (0.36-0.83) and κ=0.65 (0.32-0.81), are comparable to inter-rater agreement, κ=0.60 (0.21-0.74)., Conclusions: Automating the visual identification of bursts in preterm EEG is achievable with a high level of accuracy. Multiple features, combined using a data-driven approach, improves on existing single-feature methods., (Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Interferon-alpha (IFN-α) is a potent anti-viral cytokine, critical to the host immune response against viruses. IFN-α is first produced upon viral detection by pathogen recognition receptors. Following its expression, IFN-α embarks upon a complex downstream signalling cascade called the JAK/STAT pathway. This signalling pathway results in the expression of hundreds of effector genes known as interferon stimulated genes (ISGs). These genes are the basis for an elaborate effector mechanism and ultimately, the clearance of viral infection. ISGs mark an elegant mechanism of anti-viral host defence that warrants renewed research focus in our global efforts to treat existing and emerging viruses. By understanding the mechanistic role of individual ISGs we anticipate the discovery of a new "treasure trove" of anti-viral mediators that may pave the way for more effective, targeted and less toxic anti-viral therapies. Therefore, with the aim of highlighting the value of the innate type 1 IFN response in our battle against viral infection, this review outlines both historic and recent advances in understanding the IFN-α JAK/STAT pathway, with a focus on new research discoveries relating to specific ISGs and their potential role in curing existing and future emergent viral infections.

The cytokine, Interferon (IFN)-α, induces a wide spectrum of anti-viral mediators, via the Janus kinase/signal transducer and activator of transcription (JAK/STAT) pathway. STAT1 and STAT2 are well characterised to upregulate IFN-stimulated gene (ISG) expression; but even though STAT3 is also activated by IFN-α, its role in anti-viral ISG induction is unclear. Several viruses, including Hepatitis C and Mumps, reduce cellular STAT3 protein levels, via the promotion of ubiquitin-mediated proteasomal degradation. This viral immune evasion mechanism suggests an undiscovered anti-viral role for STAT3 in IFN-α signalling. To investigate STAT3's functional involvement in this Type I IFN pathway, we first analysed its effect upon the replication of two viruses, Influenza and Vaccinia. Viral plaque assays, using Wild Type (WT) and STAT3-/- Murine Embryonic Fibroblasts (MEFs), revealed that STAT3 is required for the inhibition of Influenza and Vaccinia replication. Furthermore, STAT3 shRNA knockdown also enhanced Influenza replication and hindered induction of several, well characterised, anti-viral ISGs: PKR, OAS2, MxB and ISG15; while STAT3 expression had no effect upon induction of a separate ISG group: Viperin, IFI27, CXCL10 and CCL5. These discoveries reveal, for the first time, an anti-viral role for STAT3 in the IFN-α pathway and characterise a requirement for STAT3 in the expression of specific ISGs. These findings also identify STAT3 as a therapeutic target against viral infection and highlight it as an essential pathway component for endogenous and therapeutic IFN-α responsiveness.