Influenza is one of the most common human respiratory diseases, and represents a serious public health concern. However, the high mutability of influenza viruses has hampered vaccine development, and ...resistant strains to existing anti-viral drugs have also emerged. Novel anti-influenza therapies are urgently needed, and in this study, we describe the anti-viral properties of a Spirulina (Arthrospira platensis) cold water extract. Anti-viral effects have previously been reported for extracts and specific substances derived from Spirulina, and here we show that this Spirulina cold water extract has low cellular toxicity, and is well-tolerated in animal models at one dose as high as 5,000 mg/kg, or 3,000 mg/kg/day for 14 successive days. Anti-flu efficacy studies revealed that the Spirulina extract inhibited viral plaque formation in a broad range of influenza viruses, including oseltamivir-resistant strains. Spirulina extract was found to act at an early stage of infection to reduce virus yields in cells and improve survival in influenza-infected mice, with inhibition of influenza hemagglutination identified as one of the mechanisms involved. Together, these results suggest that the cold water extract of Spirulina might serve as a safe and effective therapeutic agent to manage influenza outbreaks, and further clinical investigation may be warranted.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Herein, we report the preparation of Pongam seed shells-derived activated carbon and cobalt oxide (∼2–10 nm) nanocomposite (PSAC/Co3O4) by using a general and facile synthesis strategy. The ...as-synthesized PSAC/Co3O4 samples were characterized by a variety of physicochemical techniques. The PSAC/Co3O4-modified electrode is employed in two different applications such as high performance nonenzymatic glucose sensor and supercapacitor. Remarkably, the fabricated glucose sensor is exhibited an ultrahigh sensitivity of 34.2 mA mM–1 cm–2 with a very low detection limit (21 nM) and long-term durability. The PSAC/Co3O4 modified stainless steel electrode possesses an appreciable specific capacitance and remarkable long-term cycling stability. The obtained results suggest the as-synthesized PSAC/Co3O4 is more suitable for the nonenzymatic glucose sensor and supercapacitor applications outperforming the related carbon based modified electrodes, rendering practical industrial applications.
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IJS, KILJ, NUK, PNG, UL, UM
In this paper, two modern adaptive signal processing techniques, empirical intrinsic geometry and synchrosqueezing transform, are applied to quantify different dynamical features of the respiratory ...and electroencephalographic signals. We show that the proposed features are theoretically rigorously supported, as well as capture the sleep information hidden inside the signals. The features are used as input to multiclass support vector machines with the radial basis function to automatically classify sleep stages. The effectiveness of the classification based on the proposed features is shown to be comparable to human expert classification-the proposed classification of awake, REM, N1, N2, and N3 sleeping stages based on the respiratory signal (resp. respiratory and EEG signals) has the overall accuracy 81.7% (resp. 89.3%) in the relatively normal subject group. In addition, by examining the combination of the respiratory signal with the electroencephalographic signal, we conclude that the respiratory signal consists of ample sleep information, which supplements to the information stored in the electroencephalographic signal.
Persistent homology is a recently developed theory in the field of algebraic topology to study shapes of datasets. It is an effective data analysis tool that is robust to noise and has been widely ...applied. We demonstrate a general pipeline to apply persistent homology to study time series, particularly the instantaneous heart rate time series for the heart rate variability (HRV) analysis. The first step is capturing the shapes of time series from two different aspects-the persistent homologies and hence persistence diagrams of its sub-level set and Taken's lag map. Second, we propose a systematic and computationally efficient approach to summarize persistence diagrams, which we coined
. To demonstrate our proposed method, we apply these tools to the HRV analysis and the sleep-wake, REM-NREM (rapid eyeball movement and non rapid eyeball movement) and sleep-REM-NREM classification problems. The proposed algorithm is evaluated on three different datasets via the cross-database validation scheme. The performance of our approach is better than the state-of-the-art algorithms, and the result is consistent throughout different datasets.
Objective: Fluctuations in heart rate are intimately related to changes in the physiological state of the organism. We exploit this relationship by classifying a human participant's wake/sleep status ...using his instantaneous heart rate (IHR) series. Approach: We use a convolutional neural network (CNN) to build features from the IHR series extracted from a whole-night electrocardiogram (ECG) and predict every 30 s whether the participant is awake or asleep. Our training database consists of 56 normal participants, and we consider three different databases for validation; one is private, and two are public with different races and apnea severities. Main results: On our private database of 27 participants, our accuracy, sensitivity, specificity, and values for predicting the wake stage are , 52.4%, 89.4%, and 0.83, respectively. Validation performance is similar on our two public databases. When we use the photoplethysmography instead of the ECG to obtain the IHR series, the performance is also comparable. A robustness check is carried out to confirm the obtained performance statistics. Significance: This result advocates for an effective and scalable method for recognizing changes in physiological state using non-invasive heart rate monitoring. The CNN model adaptively quantifies IHR fluctuation as well as its location in time and is suitable for differentiating between the wake and sleep stages.
Prolonged mechanical ventilation (PMV) is associated with poor outcomes and a high economic cost. The association between protein intake and PMV has rarely been investigated in previous studies. This ...study aimed to investigate the impact of protein intake on weaning from mechanical ventilation. Patients with the PMV (mechanical ventilation ≥6 h/day for ≥21 days) at our hospital between December 2020 and April 2022 were included in this study. Demographic data, nutrition records, laboratory data, weaning conditions, and survival data were retrieved from the patient’s electronic medical records. A total of 172 patients were eligible for analysis. The patients were divided into two groups: weaning success (n = 109) and weaning failure (n = 63). Patients with daily protein intake greater than 1.2 g/kg/day had significant shorter median days of ventilator use than those with less daily protein intake (36.5 vs. 114 days, respectively, p < 0.0001). Daily protein intake ≥1.065 g/kg/day (odds ratio: 4.97, p = 0.033), daily protein intake ≥1.2 g/kg/day (odds ratio: 89.07, p = 0.001), improvement of serum albumin (odds ratio: 3.68, p = 0.027), and BMI (odds ratio: 1.235, p = 0.014) were independent predictor for successful weaning. The serum creatinine level in the 4th week remained similar in patients with daily protein intake either >1.065 g/kg/day or >1.2 g/kg/day (p = 0.5219 and p = 0.7796, respectively). Higher protein intake may have benefits in weaning in patients with PMV and had no negative impact on renal function.
Background
The titration pressure of continuous positive airway pressure (CPAP) is important in patients with obstructive sleep apnea (OSA). This study aimed to understand the difference between ...drug-induced sleep endoscopy (DISE)-guided CPAP titration and conventional sleep center (CSC) CPAP titration in patients with OSA.
Methods
In this randomized, controlled, and single-blind crossover trial, we compared the effects of 1-month CPAP treatment in patients with OSA with either DISE-guided CPAP titration or CSC CPAP titration. Twenty-four patients with OSA were recruited for the study. All patients underwent polysomnography, DISE-guided CPAP titration, and accommodation. Initially, patients were randomly assigned to receive either DISE-guided CPAP titration or CSC CPAP treatment for the first month. They were then switched to other treatments in the second month. The Epworth sleepiness scale (ESS) score was recorded at baseline, 1 and 2 months.
Results
The upper limit of the pressure of DISE-guided titration and CSC CPAP titration was not significantly different (13.9 ± 0.7 vs. 13.5 ± 0.5 cm H
2
O;
P
= 0.92). The residual apnea-hypopnea index and compliance were also not significantly different between the groups. ESS score significantly improved from baseline to 1 month after CPAP treatment in both groups. Both epiglottis (anterior-posterior collapse) and tongue base collapse were significantly associated with 95% CPAP pressure (
P
= 0.031 and 0.038, respectively). After multivariate regression analyses, the epiglottis (anterior-posterior collapse) was an independent factor for 95% CPAP pressure. The incidence rate of bradycardia was 58.3%, which is a safety concern for DISE. Despite the high incidence of bradycardia, all patients with bradycardia recovered with proper management.
Conclusion
Both modalities were comparable in terms of establishing the pressure settings required to treat patients. Further large-scale studies are required to confirm these results.
Trial registration
https://clinicaltrials.gov/
, NCT03523013.
Background
There remains an unmet need in objective tests for diagnosing asthma in children. The objective of this study was to investigate the potential of metabolomic profiles of exhaled breath ...condensate (EBC) to discriminate stable asthma in Asian children in the community.
Methods
One hundred and sixty‐five Asian children (92 stable asthma and 73 non‐asthmatic controls) participating in a population‐based cohort were enrolled and divided into training and validation sets. Nuclear magnetic resonance‐based metabolomic profiles of EBC samples were analyzed by using orthogonal partial least squares discriminant analysis.
Results
EBC metabolomic signature (lactate, formate, butyrate, and isobutyrate) had an area under the receiver operator characteristic curve (AUC) of 0.826 in discriminating children with and without asthma in the training set, which significantly outperformed FeNO (AUC = 0.574; P < .001) and FEV1/FVC % predicted (AUC = 0.569; P < .001). The AUC for EBC metabolomic signature was 0.745 in the validation set, which was slightly but not significantly lower than in the testing set (P = .282). We further extrapolated two potentially involved metabolic pathways, including pyruvate (P = 1.67 × 10−3; impact: 0.14) and methane (P = 1.89 × 10−3; impact: 0.15), as the most likely divergent metabolisms between children with and without asthma.
Conclusion
This study provided evidence supporting the role of EBC metabolomic signature to discriminate stable asthma in Asian children in the community, with a discriminative property outperforming conventional clinical tests such as FeNO or spirometry.
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BFBNIB, DOBA, FZAB, GIS, IJS, IZUM, KILJ, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBMB, UILJ, UKNU, UL, UM, UPUK
Physiologically, the thoracic (THO) and abdominal (ABD) movement signals, captured using wearable piezoelectric bands, provide information about various types of apnea, including central sleep apnea ...(CSA) and obstructive sleep apnea (OSA). However, the use of piezoelectric wearables in detecting sleep apnea events has been seldom explored in the literature. This study explored the possibility of identifying sleep apnea events, including OSA and CSA, by solely analyzing one or both the THO and ABD signals. An adaptive nonharmonic model was introduced to model the THO and ABD signals, which allows us to design features for sleep apnea events. To confirm the suitability of the extracted features, a support vector machine was applied to classify three categories - normal and hypopnea, OSA, and CSA. According to a database of 34 subjects, the overall classification accuracies were on average 75.9% ± 11.7% and 73.8% ± 4.4%, respectively, based on the cross validation. When the features determined from the THO and ABD signals were combined, the overall classification accuracy became 81.8% ± 9.4%. These features were applied for designing a state machine for online apnea event detection. Two event-by-event accuracy indexes, S and I, were proposed for evaluating the performance of the state machine. For the same database, the S index was 84.01% ± 9.06% and the I index was 77.21% ± 19.01%. The results indicate the considerable potential of applying the proposed algorithm to clinical examinations for both screening and homecare purposes.
•A nonlinear multimodal data fusion method is proposed.•The method is designed to suppress the sensor-specific variables.•The method preserves the latent variables measured by two or more ...sensors.•The method relies on multiple applications of alternating diffusion operators.•Good performance of automatic assessment of sleep stage is demonstrated.
The problem of information fusion from multiple data-sets acquired by multimodal sensors has drawn significant research attention over the years. In this paper, we focus on a particular problem setting consisting of a physical phenomenon or a system of interest observed by multiple sensors. We assume that all sensors measure some aspects of the system of interest with additional sensor-specific and irrelevant components. Our goal is to recover the variables relevant to the observed system and to filter out the nuisance effects of the sensor-specific variables. We propose an approach based on manifold learning, which is particularly suitable for problems with multiple modalities, since it aims to capture the intrinsic structure of the data and relies on minimal prior model knowledge. Specifically, we propose a nonlinear filtering scheme, which extracts the hidden sources of variability captured by two or more sensors, that are independent of the sensor-specific components. In addition to presenting a theoretical analysis, we demonstrate our technique on real measured data for the purpose of sleep stage assessment based on multiple, multimodal sensor measurements. We show that without prior knowledge on the different modalities and on the measured system, our method gives rise to a data-driven representation that is well correlated with the underlying sleep process and is robust to noise and sensor-specific effects.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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