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Targeting the Regulating Subunit R2Alpha regarding Health proteins Kinase Any inside Human Glioblastoma by way of shRNA-Expressing Lentiviral Vectors.
Mechanically ventilated patients typically exhibit abnormal respiratory sounds. Squawks are short inspiratory adventitious sounds that may occur in patients with pneumonia, such as COVID-19 patients. In this work we devised a method for squawk detection in mechanically ventilated patients by developing algorithms for respiratory cycle estimation, squawk candidate identification, feature extraction, and clustering. The best classifier reached an F1 of 0.48 at the sound file level and an F1 of 0.66 at the recording session level. These preliminary results are promising, as they were obtained in noisy environments. This method will give health professionals a new feature to assess the potential deterioration of critically ill patients.Repetitive atrial conduction patterns are often observed during human atrial fibrillation (AF). Repetitive patterns may be associated with AF drivers such as focal and micro-reentrant mechanisms. Therefore, tools for repetitive activity detection are of great interest as they may allow to identify the leading electrophysiological AF mechanism in an individual patient. Recurrence plots (RP) are efficient tools for repetitive activity visualization. Construction of an RP requires embedding of epicardial atrial electrograms into a phase space. In this study, we compared the conventional Takens' embedding approach and three novel approaches based on a priori AF cycle length (AFCL) information. Approaches were bench-marked based on the similarity of the RPs they produce with a previously proposed technique, the sensitivity and specificity to detect the repetitive patterns, as well as the capability to estimate overall repetitive pattern prevalence. All techniques were applied to high-density epicardial direct contact mapping recordings in AF patients with paroxysmal AF (n=12) and persistent AF (n=9). Compared to a reference method the proposed novel techniques achieved significantly higher similarity and sensitivity values (p less then 0.01) than conventional embedding, in both paroxysmal and persistent patients. Moreover, estimated prevalences were robust against the various degrees of AF complexity present in the recordings.Clinical relevance- This study presents three novel approaches for detection of repetitive patterns of conduction during AF in atrial direct contact recordings, which may aid in the identification of the leading AF mechanism in an individual patient.The paper proposes accurate Blood Pressure Monitoring (BPM) based on a single-site Photoplethysmographic (PPG) sensor and provides an energy-efficient solution on edge cuffless wearable devices. Continuous PPG signal preprocessed and used as input of the Artificial Neural Network (ANN), and outputs systolic BP (SBP), diastolic BP (DBP), and mean arterial BP (MAP) values for each heartbeat. The improvement of the BPM accuracy is obtained by removing outliers in the preprocessing step and the whole-based inputs compared to parameter-based inputs extracted from the PPG signal. Performance obtained is 3.42 ± 5.42 mmHg (MAE ± RMSD) for SBP, 1.92 ± 3.29 mmHg for DBP, and 2.21 ± 3.50 mmHg for MAP which is competitive compared to other studies. This is the first BPM solution with edge computing artificial intelligence as we have learned so far. Evaluation experiments on real hardware show that the solution takes 42.2 ms, 18.2 KB RAM, and 2.1 mJ average energy per reading.EEG can be used to characterize the electrical activity of the cerebral cortex, but it is also susceptible to interference. Compared with the other artifacts, Electrooculogram (EOG) artifacts have a greater impact on EEG processing and are more difficult to remove. Here, we mainly compared the regression and ICA algorithms both based on the EOG channels for the effect of removing EOG artifacts in the Stroop experiment of methamphetamine addicts. From the perspective of time domain and power spectral density, the ICA algorithm based on the EOG channels is more effective. However, the regression algorithm based on the EOG channels is less complex, more time-saving, and more suitable for real-time tasks.Clinical Relevance- For clinical purposes, this research has a certain reference value for selecting appropriate methods of removing EOG artifacts when processing the EEG of methamphetamine addicts.Recent studies have attempted to recognize emotions by extracting features from electroencephalographic (EEG) signals using either linear and stationary, or linear and non-stationary transformations. However, as EEG signals are non-linear and non-stationary, it seems that a non-linear and non-stationary transformation may be more suitable. Despite the attractiveness of this hypothesis, until now, little studies have used such transformation. The current work presents a comparison between an approach to recognize positive and negative emotions using a non-linear and non-stationary transformation (Hilbert-Huang Transformation) with an approach using linear and non-stationary transformation (Discrete Wavelet Transform). The two approaches were compared using 200 EEG signals recorded from 10 subjects. The comparison indicated that an approach using the Hilbert-Huang Transformation statistically significantly classified emotions more accurately than a Wavelet-based approach (P less then 0.02). This result implies that Hilbert-Huang Transformation is a promising tool to increase the prediction of emotional states, thereby helping to designing and developing more robust emotion recognition approaches.Clinical relevance- This remarks the potential of the Hilbert-Huang transform to enhance EEG-based emotion recognition systems, which can potentially help to diagnose and treat mental diseases, such as autism and depression.Photoplethysmography (PPG) is a completely noninvasive, optical method of assessing blood flow dynamics in peripheral vasculature. Wearable devices for PPG recording are becoming increasingly popular, due to their cost-effectiveness and ease of use. For these reasons, many recent scientific studies have proposed the use of pulse rate variability (PRV) extracted from PPG as a surrogate for heart rate variability (HRV), in monitoring autonomic activity and cardiovascular health.In this work, we used a cross-mapping approach, a methodology based on chaos theory, to compare PRV and HRV dynamics, and investigate their agreement according to age and gender of healthy subjects. We used ECG and PPG data acquired from 57 subjects (41 young and 16 elderly) during resting state in the supine position. Signals were gathered from the publicly available VORTAL dataset. Our results showed a statistically significant decrease of PRV reliability as an HRV surrogate in old participants, which was confirmed as significant when only men subjects were analyzed (p-value less then 0.01).Our findings, although preliminary, suggest greater caution in the use of PPG devices for monitoring cardiovascular health, especially in elderly men.Circadian rhythms of blood pressure (BP) have key diagnostic significance in the assessment of hypertension. The night-time dip or rise in BP (10-20% decrease or increase compared to daytime BP), for example, has been shown to be a strong indicator for cardiovascular disease. NVP-DKY709 However, current methods for assessing the circadian rhythms of BP can be disruptive to sleep, work, and daily activities. Pulse arrival time (PAT) has been suggested as a surrogate measure of BP. This work investigates the presence of a circadian rhythm in PAT and evaluates its application to classify nocturnal BP dip or rise. 769 patients who were discharged home from the ICU were selected from the MIMIC database. Our results show a clear and observable circadian rhythm of PAT that is strongly inversely correlated with BP (r = -0.89). The ratios between nocturnal and diurnal changes in PAT accurately classifies an individual as a nocturnal BP dipper (AUC = 0.72) or a riser (AUC = 0.71).Clinical Relevance-This work shows that you can accurately assess an individuals's circadian rhythm of BP using PAT.The mismatch response (MMR) is thought to be a neurophysiological measure of novel auditory detection that could serve as a translational biomarker of various neurological diseases. When recorded with electroencephalography (EEG) or magnetoencephalography (MEG), the MMR is traditionally extracted by subtracting the event-related potential/field (ERP/ERF) elicited in response to "deviant" sounds that occur randomly within a train of repetitive "standard" sounds. To overcome the limitations of this subtraction procedure, we propose a novel method which we call weighted-BSST/k, which uses only the deviant response to derive the MMR. We hypothesized that this novel weighted-BSST/k method highlights responses related to the detection of the deviant stimulus and is more sensitive than independent component analysis (ICA). To test this hypothesis and the validity and efficacy of the weighted-BSST/k in comparison with ICA (infomax), we evaluated the methods in 12 healthy adults. Auditory stimuli were presented at a constant rate of 2 Hz. Frequency MMRs at a sensor level were obtained from the bilateral temporal lobes with the subtraction approach at 96-276 ms (the MMR time range), defined on the basis of spatio-temporal cluster permutation analysis. In the application of the weighted-BSST/k, the deviant responses were given a constant weight on the MMR time range. The ERF elicited by the weighted deviant responses demonstrated one or a few dominant components representing the MMR with a high signal-to-noise ratio and similar topography to that of the sensor space analysis using the subtraction approach. In contrast, infomax or weighted-infomax revealed many minor or pseudo components as constituents of the MMR. Our new approach may assist in using the MMR in basic and clinical research.Clinical Relevance-Our proposed method opens a new and potentially useful way to analyze event-related MEG/EEG data.During minimally invasive and image-guided procedures, vital parameters have to be recorded for patient safety. In the Magnetic Resonance Tomograph (MRT) environment the Magneto Hydrodynamic (MHD) effect emerges, under the high magnetic field strength in the signal recording of an Electrocardiography (ECG) system. To allow the investigation of this effect, newly developed wireless ECG and Impedance cardiography (ICG) devices using a network time protocol for accurate synchronization of the collected data will be presented. The developed ICG and tetrapolar electrodes were designed to comply with the IEC 60601-1 standard. One subject was instructed to alternate phases of end-inspiration breath hold with the regular breathing cycle and concurrent synchronized ECG and ICG were collected.Over the past twenty years, functional connectivity of the human brain has been studied in detail using tools from complex network theory. These methods include graph theoretic metrics ranging from the micro-scale such as the degree of a node to the macro-scale such as the small worldness of the brain network. However, most of these network models focus on average activity within a time window of interest and given frequency band. Therefore, they cannot capture the changes in network connectivity across time and different frequency bands. Recently, multilayer brain networks have attracted a lot of attention as they can capture the full view of neuronal connectivity. In this paper, we introduce a multilayer view of the functional connectivity network of the brain, where each layer corresponds to a different frequency band. We construct multi-frequency connectivity networks from electroencephalogram data where the intra-layer edges are quantified by phase synchrony while the inter-layer edges are quantified by phase-amplitude coupling.
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