Long term detection and quantification of SARS-CoV-2 RNA in wastewater in Bahrain.

Wastewater-based epidemiology is a corroborated environmental surveillance tool in the global fight against SARS-CoV-2. The analysis of wastewater for detection of SARS-CoV-2 RNA may assist policymakers to survey a specific infectious community. Herein, we report on a long-term quantification study in Bahrain to investigate the incidence of the SARS-CoV-2 RNA in wastewater during the COVID-19 pandemic. The ∼260,000 population of Muharraq Island in Bahrain is served by a discrete sewerage catchment, and all wastewater flows to a single large Sewage Treatment Plant (STP) with a capacity of 100,000 m3/day. The catchment is predominately domestic, but also serves several hospitals and Bahrain's international airport. Flow-weighted 24-h composite wastewater samples for the period February 2020 to October 2021 were analyzed for the presence of SARS-CoV-2 N1, N2 and E genes. A Spearman rank correlation demonstrated a moderate correlation between the concentration of SARS-CoV-2 N1, N2 and E genes in the wastewater samples and the number of COVID-19 cases reported on the same day of the sampling. SARS-CoV-2 viral genes were detected in wastewater samples shortly after the first cases of COVID-19 were reported by the health authorities in Bahrain by reverse transcription-polymerase chain reaction (RT-qPCR). The viral genes were detected in 55 of 65 samples (84.62%) during the whole study period and the concentration range was found to be between 0 and 11,508 RNA copies/mL across the viral genes tested (in average N1: 518.4, N2: 366.8 and E: 649.3 copies/mL). Furthermore, wastewater samples from two COVID-19-dedicated quarantine facilities were analysed and detected higher SARS-CoV-2 gene concentrations (range 27-19,105 copies/mL; in average N1: 5044, N2: 4833 and E: 8663 copies/mL). Our results highlight the potential use of RT-qPCR for SARS-CoV-2 detection and quantification in wastewater and present the moderate correlation between concentration of SARS-CoV-2 genes with reported COVID-19 cases for a specified population. Indeed, this study identifies this technique as a mechanism for long term monitoring of SARS-CoV-2 infection levels and hence provides public health and policymakers with a useful environmental surveillance tool during and after the current pandemic.

The interplay between the immune system and viruses.

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).

Revisiting respiratory syncytial virus's interaction with host immunity, towards novel therapeutics.

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.

A dataset of neonatal EEG recordings with seizure annotations.

Neonatal seizures are a common emergency in the neonatal intensive care unit (NICU). There are many questions yet to be answered regarding the temporal/spatial characteristics of seizures from different pathologies, response to medication, effects on neurodevelopment and optimal detection. The dataset presented in this descriptor contains EEG recordings from human neonates, the visual interpretation of the EEG by the human experts, supporting clinical data and codes to assist access. Multi-channel EEG was recorded from 79 term neonates admitted to the NICU at the Helsinki University Hospital. The median recording duration was 74 min (IQR: 64 to 96 min). The presence of seizures in the EEGs was annotated independently by three experts. An average of 460 seizures were annotated per expert in the dataset; 39 neonates had seizures and 22 were seizure free, by consensus. The dataset can be used as a reference set of neonatal seizures, in studies of inter-observer agreement and for the development of automated methods of seizure detection and other EEG analyses.

Functional maturation in preterm infants measured by serial recording of cortical activity.

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.

SOCS3 revisited: a broad regulator of disease, now ready for therapeutic use?

Since their discovery, SOCS have been characterised as regulatory cornerstones of intracellular signalling. While classically controlling the JAK/STAT pathway, their inhibitory effects are documented across several cascades, underpinning their essential role in homeostatic maintenance and disease. After 20 years of extensive research, SOCS3 has emerged as arguably the most important family member, through its regulation of both cytokine- and pathogen-induced cascades. In fact, low expression of SOCS3 is associated with autoimmunity and oncogenesis, while high expression is linked to diabetes and pathogenic immune evasion. The induction of SOCS3 by both viruses and bacteria and its impact upon inflammatory disorders, underscores this protein's increasing clinical potential. Therefore, with the aim of highlighting SOCS3 as a therapeutic target for future development, this review revisits its multi-faceted immune regulatory functions and summarises its role in a broad ranges of diseases.

Estimating functional brain maturity in very and extremely preterm neonates using automated analysis of the electroencephalogram.

OBJECTIVE: To develop an automated estimate of EEG maturational age (EMA) for preterm neonates. METHODS: The EMA estimator was based on the analysis of hourly epochs of EEG from 49 neonates with gestational age (GA) ranging from 23 to 32weeks. Neonates had appropriate EEG for GA based on visual interpretation of the EEG. The EMA estimator used a linear combination (support vector regression) of a subset of 41 features based on amplitude, temporal and spatial characteristics of EEG segments. Estimator performance was measured with the mean square error (MSE), standard deviation of the estimate (SD) and the percentage error (SE) between the known GA and estimated EMA. RESULTS: The EMA estimator provided an unbiased estimate of EMA with a MSE of 82days (SD=9.1days; SE=4.8%) which was significantly lower than a nominal reading (the mean GA in the dataset; MSE of 267days, SD of 16.3days, SE=8.4%: p<0.001). The EMA estimator with the lowest MSE used amplitude, spatial and temporal EEG characteristics. CONCLUSIONS: The proposed automated EMA estimator provides an accurate estimate of EMA in early preterm neonates. SIGNIFICANCE: Automated analysis of the EEG provides a widely accessible, noninvasive and continuous assessment of functional brain maturity.

Artefact detection in neonatal EEG.

Artefact detection is an important component of any automated EEG analysis. It is of particular importance in analyses such as sleep state detection and EEG grading where there is no null state. We propose a general artefact detection system (GADS) based on the analysis of the neonatal EEG. This system aims to detect both major and minor artefacts (a distinction based primarily on amplitude). As a result, a two-stage system was constructed based on 14 features extracted from EEG epochs at multiple time scales: [2, 4, 16, 32]s. These features were combined in a support vector machine (SVM) in order to determine the presence of absence of artefact. The performance of the GADS was estimated using a leave-one-out cross-validation applied to a database of hour long recordings from 51 neonates. The median AUC was 1.00 (IQR: 0.95-1.00) for the detection of major artefacts and 0.89 (IQR: 0.83-0.95) for the detection of minor artefacts.

Robustness of time frequency distribution based features for automated neonatal EEG seizure detection.

In this paper we examined the robustness of a feature-set based on time-frequency distributions (TFDs) for neonatal EEG seizure detection. This feature-set was originally proposed in literature for neonatal seizure detection using a support vector machine (SVM). We tested the performance of this feature-set with a smoothed Wigner-Ville distribution and modified B distribution as the underlying TFDs. The seizure detection system using time-frequency signal and image processing features from the TFD of the EEG signal using modified B distribution was able to achieve a median receiver operator characteristic area of 0.96 (IQR 0.91-0.98) tested on a large clinical dataset of 826 h of EEG data from 18 full-term newborns with 1389 seizures. The mean AUC was 0.93.

An automated system for grading EEG abnormality in term neonates with hypoxic-ischaemic encephalopathy.

Automated analysis of the neonatal EEG has the potential to assist clinical decision making for neonates with hypoxic-ischaemic encephalopathy. This paper proposes a method of automatically grading the degree of abnormality in an hour long epoch of neonatal EEG. The automated grading system (AGS) was based on a multi-class linear classifier grading of short-term epochs of EEG which were converted into a long-term grading of EEG using a majority vote operation. The features used in the AGS were summary measurements of two sub-signals extracted from a quadratic time-frequency distribution: the amplitude modulation and instantaneous frequency. These sub-signals were based on a model of EEG as a multiplication of a coloured random process with a slowly varying pseudo-periodic waveform and may be related to macroscopic neurophysiological function. The 4 grade AGS had a classification accuracy of 83% compared to human annotation of the EEG (level of agreement, κ = 0.76). Features estimated on the developed sub-signals proved more effective at grading the EEG than measures based solely on the EEG and the incorporation of additional sub-grades based on EEG states into the AGS also improved performance.

A nonparametric feature for neonatal EEG seizure detection based on a representation of pseudo-periodicity.

Automated methods of neonatal EEG seizure detection attempt to highlight the evolving, stereotypical, pseudo-periodic, nature of EEG seizure while rejecting the nonstationary, modulated, coloured stochastic background in the presence of various EEG artefacts. An important aspect of neonatal seizure detection is, therefore, the accurate representation and detection of pseudo-periodicity in the neonatal EEG. This paper describes a method of detecting pseudo-periodic components associated with neonatal EEG seizure based on a novel signal representation; the nonstationary frequency marginal (NFM). The NFM can be considered as an alternative time-frequency distribution (TFD) frequency marginal. This method integrates the TFD along data-dependent, time-frequency paths that are automatically extracted from the TFD using an edge linking procedure and has the advantage of reducing the dimension of a TFD. The reduction in dimension simplifies the process of estimating a decision statistic designed for the detection of the pseudo-periodicity associated with neonatal EEG seizure. The use of the NFM resulted in a significant detection improvement compared to existing stationary and nonstationary methods. The decision statistic estimated using the NFM was then combined with a measurement of EEG amplitude and nominal pre- and post-processing stages to form a seizure detection algorithm. This algorithm was tested on a neonatal EEG database of 18 neonates, 826 h in length with 1389 seizures, and achieved comparable performance to existing second generation algorithms (a median receiver operating characteristic area of 0.902; IQR 0.835-0.943 across 18 neonates).

Chronic hepatitis C infection blocks the ability of dendritic cells to secrete IFN-α and stimulate T-cell proliferation.

Dendritic cells (DCs) are likely to play a key role in the compromised T-cell function associated with hepatitis C Virus (HCV) infection. However, studies of DC function in HCV-infected patients to date have yielded conflicting findings possibly because of patient and virus heterogeneity. Here, we report the characterization of monocyte-derived DCs obtained from a homogenous cohort of women who were infected with HCV genotype 1b following exposure to contaminated anti-D immunoglobulin from a single donor source. Patients included in the study had not received anti-viral therapy and all had mild liver disease. We show that phenotypically normal monocyte-derived dendritic cells (MDDCs) (CD11c(+) HLA(-) DR(+) CD1a(+) CD14(lo) ) can be obtained from these patients. These cells respond to both Poly(I:C) and LPS, by up-regulating expression of CD86. They secrete high levels of IL-8 and CCL5 in response to LPS, an indication that the MyD88-dependent and MyD88-independent signalling pathways downstream of TLR4 ligation are functioning normally. However, these cells are poor stimulators of T-cell proliferation in allogeneic mixed lymphocyte reactions. Furthermore, patient MDDCs fail to secrete IFN-α in response to poly(I:C) or IFN-ß stimulation. Altered DC function may contribute to impaired cellular immune responses and chronicity of disease following HCV infection in this cohort. An effective therapeutic vaccine for chronic HCV infection will most likely need to target DCs to elicit an appropriate cellular response; therefore, it is important to resolve how the DCs of different patient cohorts respond to stimulation via TLRs.

Quantitative EEG analysis in neonatal hypoxic ischaemic encephalopathy.

OBJECTIVE: To test the hypothesis that quantitative EEG (qEEG) measures are associated with a grading of HIE based on the visual interpretation of neonatal EEG (EEG/HIE). METHODS: Continuous multichannel video-EEG data were recorded for up to 72 h. One-hour EEG segments from each recording were visually analysed and graded by two electroencephalographers (EEGers) blinded to clinical data. Several qEEG measures were calculated for each EEG segment. Kruskal-Wallis testing with post hoc analysis and multiple linear regression were used to assess the hypothesis. RESULTS: Fifty-four full-term infants with HIE were studied. The relative delta power, skewness, kurtosis, amplitude, and discontinuity were significantly different across four EEG/HIE grades (p<0.05). A linear combination of these qEEG measures could predict the EEG/HIE grade assigned by the EEGers with an accuracy of 89%. CONCLUSION: Quantitative analysis of background EEG activity has shown that measures based on the amplitude, frequency content and continuity of the EEG are associated with a visual interpretation of the EEG performed by experienced EEGers. SIGNIFICANCE: Identifying qEEG measures that can separate between EEG/HIE grades is an important first step towards creating a classifier for automated detection of EEG/HIE grades.

A nonlinear model of newborn EEG with nonstationary inputs.

Newborn EEG is a complex multiple channel signal that displays nonstationary and nonlinear characteristics. Recent studies have focussed on characterizing the manifestation of seizure on the EEG for the purpose of automated seizure detection. This paper describes a novel model of newborn EEG that can be used to improve seizure detection algorithms. The new model is based on a nonlinear dynamic system; the Duffing oscillator. The Duffing oscillator is driven by a nonstationary impulse train to simulate newborn EEG seizure and white Gaussian noise to simulate newborn EEG background. The use of a nonlinear dynamic system reduces the number of parameters required in the model and produces more realistic, life-like EEG compared with existing models. This model was shown to account for 54% of the linear variation in the time domain, for seizure, and 85% of the linear variation in the frequency domain, for background. This constitutes an improvement in combined performance of 6%, with a reduction from 48 to 4 model parameters, compared to an optimized implementation of the best performing existing model.

Effect of oxygen on recovery from maximal exercise in patients with chronic obstructive pulmonary disease.

BACKGROUND: The effects of oxygen on recovery from exercise in patients with chronic obstructive pulmonary disease (COPD) are not clearly known. A study was undertaken to determine whether oxygen given after maximal exercise reduced the degree of dynamic hyperinflation and so reduced the perception of breathlessness. METHODS: Eighteen patients with moderate to severe COPD performed maximal symptom limited exercise on a cycle ergometer. During recovery they received either air or oxygen at identical flow rates in a randomised, single blind, crossover design. Inspiratory capacity, breathing pattern data, dyspnoea intensity, and leg fatigue scores were collected at regular intervals during recovery. At a subsequent visit patients underwent a similar protocol but with a face mask in situ to eliminate the effects of instrumentation. RESULTS: When oxygen was given the time taken for resolution of dynamic hyperinflation was significantly shorter (mean difference between air and oxygen 6.61(1.65) minutes (95% CI 3.13 to 10.09), p = 0.001). Oxygen did not, however, reduce the perception of breathlessness during recovery nor did it affect the time taken to return to baseline dyspnoea scores in either the instrumented or non-instrumented state (mean difference 2.11 (1.41) minutes (95% CI -0.88 to 5.10), p = 0.15). CONCLUSIONS: Oxygen reduces the degree of dynamic hyperinflation during recovery from exercise but does not make patients feel less breathless than breathing air. This suggests that factors other than lung mechanics may be important during recovery from exercise, or it may reflect the cooling effect of both air and oxygen.