Evaluating the Window Size's Role in Automatic EEG Epilepsy Detection.

Electroencephalography is one of the most commonly used methods for extracting information about the brain's condition and can be used for diagnosing epilepsy. The EEG signal's wave shape contains vital information about the brain's state, which can be challenging to analyse and interpret by a human observer. Moreover, the characteristic waveforms of epilepsy (sharp waves, spikes) can occur randomly through time. Considering all the above reasons, automatic EEG signal extraction and analysis using computers can significantly impact the successful diagnosis of epilepsy. This research explores the impact of different window sizes on EEG signals' classification accuracy using four machine learning classifiers. The machine learning methods included a neural network with ten hidden nodes trained using three different training algorithms and the k-nearest neighbours classifier. The neural network training methods included the Broyden-Fletcher-Goldfarb-Shanno algorithm, the multistart method for global optimization problems, and a genetic algorithm. The current research utilized the University of Bonn dataset containing EEG data, divided into epochs having 50% overlap and window lengths ranging from 1 to 24 s. Then, statistical and spectral features were extracted and used to train the above four classifiers. The outcome from the above experiments showed that large window sizes with a length of about 21 s could positively impact the classification accuracy between the compared methods.

COVID-19 Predictive Models Based on Grammatical Evolution.

A feature construction method that incorporates a grammatical guided procedure is presented here to predict the monthly mortality rate of the COVID-19 pandemic. Three distinct use cases were obtained from publicly available data and three corresponding datasets were created for that purpose. The proposed method is based on constructing artificial features from the original ones. After the artificial features are generated, the original data set is modified based on these features and a machine learning model, such as an artificial neural network, is applied to the modified data. From the comparative experiments done, it was clear that feature construction has an advantage over other machine learning methods for predicting pandemic elements.

Grammatical Evolution for Features of Epileptic Oscillations in Clinical Intracranial Electroencephalograms.

This paper presents grammatical evolution (GE) as an approach to select and combine features for detecting epileptic oscillations within clinical intracranial electroencephalogram (iEEG) recordings of patients with epilepsy. Clinical iEEG is used in preoperative evaluations of a patient who may have surgery to treat epileptic seizures. Literature suggests that pathological oscillations may indicate the region(s) of brain that cause epileptic seizures, which could be surgically removed for therapy. If this presumption is true, then the effectiveness of surgical treatment could depend on the effectiveness in pinpointing critically diseased brain, which in turn depends on the most accurate detection of pathological oscillations. Moreover, the accuracy of detecting pathological oscillations depends greatly on the selected feature(s) that must objectively distinguish epileptic events from average activity, a task that visual review is inevitably too subjective and insufficient to resolve. Consequently, this work suggests an automated algorithm that incorporates grammatical evolution (GE) to construct the most sufficient feature(s) to detect epileptic oscillations within the iEEG of a patient. We estimate the performance of GE relative to three alternative methods of selecting or combining features that distinguish an epileptic gamma (~65-95 Hz) oscillation from normal activity: forward sequential feature-selection, backward sequential feature-selection, and genetic programming. We demonstrate that a detector with a grammatically evolved feature exhibits a sensitivity and selectivity that is comparable to a previous detector with a genetically programmed feature, making GE a useful alternative to designing detectors.

Modelling Hydrocortisone Pharmacokinetics on a Subcutaneous Pulsatile Infusion Replacement Strategy in Patients with Adrenocortical Insufficiency.

In the context of glucocorticoid (GC) therapeutics, recent studies have utilised a subcutaneous hydrocortisone (HC) infusion pump programmed to deliver multiple HC pulses throughout the day, with the purpose of restoring normal circadian and ultradian GC rhythmicity. A key challenge for the advancement of novel HC replacement therapies is the calibration of infusion pumps against cortisol levels measured in blood. However, repeated blood sampling sessions are enormously labour-intensive for both examiners and examinees. These sessions also have a cost, are time consuming and are occasionally unfeasible. To address this, we developed a pharmacokinetic model approximating the values of plasma cortisol levels at any point of the day from a limited number of plasma cortisol measurements. The model was validated using the plasma cortisol profiles of 9 subjects with disrupted endogenous GC synthetic capacity. The model accurately predicted plasma cortisol levels (mean absolute percentage error of 14%) when only four plasma cortisol measurements were provided. Although our model did not predict GC dynamics when HC was administered in a way other than subcutaneously or in individuals whose endogenous capacity to produce GCs is intact, it was found to successfully be used to support clinical trials (or practice) involving subcutaneous HC delivery in patients with reduced endogenous capacity to synthesize GCs.

A methodology for automated CPA extraction using liver biopsy image analysis and machine learning techniques.

BACKGROUND AND OBJECTIVE: Collagen proportional area (CPA) extraction in liver biopsy images provides the degree of fibrosis expansion in liver tissue, which is the most characteristic histological alteration in hepatitis C virus (HCV). Assessment of the fibrotic tissue is currently based on semiquantitative staging scores such as Ishak and Metavir. Since its introduction as a fibrotic tissue assessment technique, CPA calculation based on image analysis techniques has proven to be more accurate than semiquantitative scores. However, CPA has yet to reach everyday clinical practice, since the lack of standardized and robust methods for computerized image analysis for CPA assessment have proven to be a major limitation. METHODS: The current work introduces a three-stage fully automated methodology for CPA extraction based on machine learning techniques. Specifically, clustering algorithms have been employed for background-tissue separation, as well as for fibrosis detection in liver tissue regions, in the first and the third stage of the methodology, respectively. Due to the existence of several types of tissue regions in the image (such as blood clots, muscle tissue, structural collagen, etc.), classification algorithms have been employed to identify liver tissue regions and exclude all other non-liver tissue regions from CPA computation. RESULTS: For the evaluation of the methodology, 79 liver biopsy images have been employed, obtaining 1.31% mean absolute CPA error, with 0.923 concordance correlation coefficient. CONCLUSIONS: The proposed methodology is designed to (i) avoid manual threshold-based and region selection processes, widely used in similar approaches presented in the literature, and (ii) minimize CPA calculation time.

Changes in the cytokine profile in first-episode, drug-naïve patients with psychosis after short-term antipsychotic treatment.

An increasing body of evidence suggests that antipsychotic medication can cause immunological changes that could be attributed to the amelioration of psychotic symptoms or the metabolic side effects of the drugs. So far, the results of the studies remain controversial. Our aim was to compare the levels of interleukin (IL) IL-2, IL-6 and transforming growth factor-ß2 (TGF-ß2) in drug-naïve, first-episode patients with psychosis before and after six weeks of antipsychotic medication. Thirty-nine first-episode patients with psychosis were enrolled in the study. Serum levels of IL-2, IL-6 and TGF-ß2 were measured by enzyme linked immunosorbent assay (ELISA) before and six weeks after the initiation of antipsychotics. In addition, clinical psychopathology was assessed using Positive and Negative Syndrome Scale (PANSS) before and after treatment. Serum levels of IL-2 were significantly increased six weeks after the initiation of antipsychotic treatment (p <0.001) while TGF-ß2 levels were decreased (p <0.001). IL-6 levels were overall increased (p <0.004), but this occurred in a non-linear way. These findings, although preliminary, provide further evidence that antipsychotic treatment in patients with psychosis may be correlated with immunological changes but further research is needed.

Biometric Digital Health Technology for Measuring Motor Function in Parkinson's Disease: Results from a Feasibility and Patient Satisfaction Study.

OBJECTIVES: To assess the feasibility, predictive value, and user satisfaction of objectively quantifying motor function in Parkinson's disease (PD) through a tablet-based application (iMotor) using self-administered tests. METHODS: PD and healthy controls (HCs) performed finger tapping, hand pronation-supination and reaction time tasks using the iMotor application. RESULTS: Thirty-eight participants (19 with PD and 17 HCs) were recruited in the study. PD subjects were 53% male, with a mean age of 67.8 years (±8.8), mean disease duration of 6.5 years (±4.6), Movement Disorders Society version of the Unified Parkinson Disease Rating Scale III score 26.3 (±6.7), and Hoehn & Yahr stage 2. In the univariate analysis, most tapping variables were significantly different in PD compared to HC. Tap interval provided the highest predictive ability (90%). In the multivariable logistic regression model reaction time (reaction time test) (p = 0.021) and total taps (two-target test) (p = 0.026) were associated with PD. A combined model with two-target (total taps and accuracy) and reaction time produced maximum discriminatory performance between HC and PD. The overall accuracy of the combined model was 0.98 (95% confidence interval: 0.93-1). iMotor use achieved high rates of patients' satisfaction as evaluated by a patient satisfaction survey. CONCLUSION: iMotor differentiated PD subjects from HCs using simple alternating tasks of motor function. Results of this feasibility study should be replicated in larger, longitudinal, appropriately designed, controlled studies. The impact on patient care of at-home iMotor-assisted remote monitoring also deserves further evaluation.

Tablet-Based Application for Objective Measurement of Motor Fluctuations in Parkinson Disease.

BACKGROUND: The motor subscale of the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS-III) has limited applicability for the assessment of motor fluctuations in the home setting. METHODS: To assess whether a self-administered, tablet-based application can reliably quantify differences in motor performance using two-target finger tapping and forearm pronation-supination tasks in the ON (maximal dopaminergic medication efficacy) and OFF (reemergence of parkinsonian deficits) medication states, we recruited 11 Parkinson disease (PD) patients (age, 60.6 ± 9.0 years; disease duration, 12.8 ± 4.1 years) and 11 healthy age-matched controls (age, 62.5 ± 10.5 years). The total number of taps, tap interval, tap duration, and tap accuracy were algorithmically calculated by the application, using the more affected side in patients and the dominant hand in healthy controls. RESULTS: Compared to the OFF state, PD patients showed a higher number of taps (84.2 ± 20.3 vs. 54.9 ± 26.9 taps; p = 0.0036) and a shorter tap interval (375.3 ± 97.2 vs. 708.2 ± 412.8 ms; p = 0.0146) but poorer tap accuracy (2,008.4 ± 995.7 vs. 1,111.8 ± 901.3 pixels; p = 0.0055) for the two-target task in the ON state, unaffected by the magnitude of coexistent dyskinesia. Overall, test-retest reliability was high (r >0.75) and the discriminatory ability between OFF and ON states was good (0.60 ≤ AUC ≤ 0.82). The correlations between tapping data and MDS-UPDRS-III scores were only moderate (-0.55 to 0.55). CONCLUSIONS: A self-administered, tablet-based application can reliably distinguish between OFF and ON states in fluctuating PD patients and may be sensitive to additional motor phenomena, such as accuracy, not captured by the MDS-UPDRS-III.

Configurational Entropy Reallocation and Complex Loop Dynamics of the Mosquito-Stage Pvs25 Protein Complexed with the Fab Fragment of the Malaria Transmission Blocking Antibody 2A8.

Pvs25 is a protein of unique 3D structure, and it is characterized by the presence of repeated EGF-like domains and 11 disulfide bonds. It is a very important candidate for the transmission-blocking malaria vaccine, as it plays an important role in mosquito infection by Plasmodium parasites. Recently, the X-ray structure of the protein complexed with the transmission blocking antibody 2A8 has been reported. In this study, we report the loop reorganization of the Pvs25 protein based on configurational entropy calculations and dihedral principal component analysis as revealed from the protein complex and free molecular dynamics simulations. While the total entropy of the protein was estimated to be almost the same in the free and complex trajectories, the partition of the entropy contribution in the loop fragments of the protein revealed interesting entropy reallocation after the 2A8 antibody binding. Interestingly, the 51-71 protein loop experienced a significant reduction in its configurational entropy, while other parts of the protein did not show any difference in it, or even showed an entropy increase. This trend in entropy redistribution was found to be in direct relationship with specific interactions with the antibody's binding site. Results from root-mean-square fluctuations/deviations and dihedral angle principal component analysis further support this finding.

Homology modeling and molecular dynamics simulations of MUC1-9/H-2K(b) complex suggest novel binding interactions.

Human MUC1 is over-expressed in human adenocarcinomas and has been used as a target for immunotherapy studies. The 9-mer MUC1-9 peptide has been identified as one of the peptides which binds to murine MHC class I H-2K(b). The structure of MUC1-9 in complex with H-2K(b) has been modeled and simulated with classical molecular dynamics, based on the x-ray structure of the SEV9 peptide/H-2K(b) complex. Two independent trajectories with the solvated complex (10 ns in length) were produced. Approximately 12 hydrogen bonds were identified during both trajectories to contribute to peptide/MHC complex, as well as 1-2 water mediated hydrogen bonds. Stability of the complex was also confirmed by buried surface area analysis, although the corresponding values were about 20% lower than those of the original x-ray structure. Interestingly, a bulged conformation of the peptide's central region, partially characterized as a ß-turn, was found exposed form the binding groove. In addition, P1 and P9 residues remained bound in the A and F binding pockets, even though there was a suggestion that P9 was more flexible. The complex lacked numerous water mediated hydrogen bonds that were present in the reference peptide x-ray structure. Moreover, local displacements of residues Asp4, Thr5 and Pro9 resulted in loss of some key interactions with the MHC molecule. This might explain the reduced affinity of the MUC1-9 peptide, relatively to SEV9, for the MHC class I H-2K(b).

Molecular dynamics simulation of antimicrobial peptide arenicin-2: beta-hairpin stabilization by noncovalent interactions.

Arenicin-2 is a 21 residue antimicrobial cyclic peptide, possessing one disulphide bond between residues Cys(3) and Cys(20). NMR and CD studies suggested that the structure of arenicin-2 in water represented a well formed, but highly twisted beta-hairpin. To investigate the spatial arrangement of the peptide side chains and to get a clear view of its possible amphipathic properties we performed molecular dynamics in explicit water. Four independent trajectories, 50 ns in length, were produced, starting from various initial conformations or by applying different simulation conditions. Arenicin-2 retained its beta-hairpin structure during simulations, although the residues close to strand ends were found to escape from the ideal hairpin conformation. The type I' beta-turn connecting the two strands fluctuated between type IV and II' beta-turn. Conversely, the right-handed twist of the beta-hairpin was well conserved with average twist value 203 degrees +/- 19 degrees per eight residues. Several nonbonded interactions, like hydrophobic interactions between aliphatic side chains, cation/pi-aromatic interactions, CH...pi aromatic bond and water bridges, contributed to the hairpin stabilization.

Insights into the structure of the PmrD protein with molecular dynamics simulations.

Resistance to cationic antimicrobial peptide polymyxin B from Gram-negative bacteria is accomplished by two-component systems (TCSs), protein complexes PmrA/PmrB and PhoP/PhoQ. PmrD is the first protein identified to mediate the connectivity between two TCSs. The 3D structure of PmrD has been recently solved by NMR and its unique fold was revealed. Here, a molecular dynamics study is presented started from the NMR structure. Numerous hydrophobic and electrostatic interactions were identified to contribute to PmrD's 3D stability. Moreover, the mobility of the five loops that connect the protein's six beta-strands has been explored. Solvent-accessible surface area calculation revealed that a Leucine-rich hydrophobic cluster of the protein stabilized the protein's structure.