Machine learning with ensemble stacking model for automated sleep staging using dual-channel EEG signal

Abstract Sleep staging is an important part of diagnosing the different types of sleep-related disorders because any discrepancies in the sleep scoring process may cause serious health problems such as misinterpretations of sleep patterns, medication errors, and improper diagnosis. The best way of analyzing sleep staging is visual interpretations of the polysomnography (PSG) signals recordings from the patients, which is a quite tedious task, requires more domain experts, and time-consuming process. This proposed study aims to develop a new automated sleep staging system using the brain EEG signals. Based on a new automated sleep staging system based on an ensemble learning stacking model that integrates Random Forest (RF) and eXtreme Gradient Boosting (XGBoosting). Additionally, this proposed methodology considers the subjects' age, which helps analyze the S1 sleep stage properly. In this study, both linear (time and frequency) and non-linear features are extracted from the pre-processed signals. The most relevant features are selected using the ReliefF weight algorithm. Finally, the selected features are classified through the proposed two-layer stacking model. The proposed methodology performance is evaluated using the two most popular datasets, such as the Sleep-EDF dataset (S-EDF) and Sleep Expanded-EDF database (SE-EDF) under the Rechtschaffen & Kales (R&K) sleep scoring rules. The performance of the proposed method is also compared with the existing published sleep staging methods. The comparison results signify that the proposed sleep staging system has an excellent improvement in classification accuracy for the six-two sleep states classification. In the S-EDF dataset, the overall accuracy and Cohen's kappa coefficient score obtained by the proposed model is (91.10%, 0.87) and (90.68%, 0.86) with inclusion and exclusion of age feature using the Fpz-Cz channel, respectively. Similarly, the Pz-Oz channel's performance is (90.56%, 0.86) with age feature and (90.11%, 0.86) without age feature. The performed results with the SE-EDF dataset using Fpz-Cz channel is (81.32%, 0.77) and (81.06%, 0.76), using Pz-Oz channel with the inclusion and exclusion of the age feature, respectively. Similarly the model achieved an overall accuracy of 96.67% (CT-6), 96.60% (CT-5), 96.28% (CT-4),96.30% (CT-3) and 97.30% (CT-2) for with 16 selected features using S-EDF database. Similarly the model reported an overall accuracy of 85.85%, 84.98%, 85.51%, 85.37% and 87.40% for CT-6 to CT-2 with 18 selected features using SE-EDF database

Population pharmacokinetic model selection assisted by machine learning

A fit-for-purpose structural and statistical model is the first major requirement in population pharmacometric model development. In this manuscript we discuss how this complex and computationally intensive task could benefit from supervised machine learning algorithms. We compared the classical pharmacometric approach with two machine learning methods, genetic algorithm and neural networks, in different scenarios based on simulated pharmacokinetic data. Genetic algorithm performance was assessed using a fitness function based on log-likelihood, whilst neural networks were trained using mean square error or binary cross-entropy loss. Machine learning provided a selection based only on statistical rules and achieved accurate selection. The minimization process of genetic algorithm was successful at allowing the algorithm to select plausible models. Neural network classification tasks achieved the most accurate results. Neural network regression tasks were less precise than neural network classification and genetic algorithm methods. The computational gain obtained by using machine learning was substantial, especially in the case of neural networks. We demonstrated that machine learning methods can greatly increase the efficiency of pharmacokinetic population model selection in case of large datasets or complex models requiring long run-times. Our results suggest that machine learning approaches can achieve a first fast selection of models which can be followed by more conventional pharmacometric approaches

Semimechanistic Clearance Models of Oncology Biotherapeutics and Impact of Study Design: Cetuximab as a Case Study

This study aimed to explore the currently competing and new semimechanistic clearance models for monoclonal antibodies and the impact of clearance model misspecification on exposure metrics under different study designs exemplified for cetuximab. Six clearance models were investigated under four different study designs (sampling density and single/multiple-dose levels) using a rich data set from two cetuximab clinical trials (226 patients with metastatic colorectal cancer) and using the nonlinear mixed-effects modeling approach. A two-compartment model with parallel Michaelis-Menten and time-decreasing linear clearance adequately described the data, the latter being related to post-treatment response. With respect to bias in exposure metrics, the simplified time-varying linear clearance (CL) model was the best alternative. Time-variance of the linear CL component should be considered for biotherapeutics if response impacts pharmacokinetics. Rich sampling at steady-state was crucial for unbiased estimation of Michaelis-Menten elimination in case of the reference (parallel Michaelis-Menten and time-varying linear CL) model

selection of the recommended phase 2 dose for bintrafusp alfa a bifunctional fusion protein targeting tgf β and pd l1

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Translation of liver stage activity of M5717, a Plasmodium elongation factor 2 inhibitor: from bench to bedside

© The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativeco mmons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.Background: Targeting the asymptomatic liver stage of Plasmodium infection through chemoprevention could become a key intervention to reduce malaria-associated incidence and mortality. Methods: M5717, a Plasmodium elongation factor 2 inhibitor, was assessed in vitro and in vivo with readily accessible Plasmodium berghei parasites. In an animal refinement, reduction, replacement approach, the in vitro IC99 value was used to feed a Population Pharmacokinetics modelling and simulation approach to determine meaningful effective doses for a subsequent Plasmodium sporozoite-induced volunteer infection study. Results: Doses of 100 and 200 mg would provide exposures exceeding IC99 in 96 and 100% of the simulated population, respectively. Conclusions: This approach has the potential to accelerate the search for new anti-malarials, to reduce the number of healthy volunteers needed in a clinical study and decrease and refine the animal use in the preclinical phase.This work was funded by the healthcare business of Merck KGaA, Darmstadt, Germany (CrossRef Funder ID: https://doi.org/10.13039/100009945). F.A. is the recipient of an individual Ph.D. fellowship funded by FCT (PD/BD/128371/2017).info:eu-repo/semantics/publishedVersio

Safety, pharmacokinetics, and antimalarial activity of the novel plasmodium eukaryotic translation elongation factor 2 inhibitor M5717: a first-in-human, randomised, placebo-controlled, double-blind, single ascending dose study and volunteer infection study

M5717 is the first plasmodium translation elongation factor 2 inhibitor to reach clinical development as an antimalarial. We aimed to characterise the safety, pharmacokinetics, and antimalarial activity of M5717 in healthy volunteers. This first-in-human study was a two-part, single-centre clinical trial done in Brisbane, QLD, Australia. Part one was a double-blind, randomised, placebo-controlled, single ascending dose study in which participants were enrolled into one of nine dose cohorts (50, 100, 200, 400, 600, 1000, 1250, 1800, or 2100 mg) and randomly assigned (3:1) to M5717 or placebo. A sentinel dosing strategy was used for each dose cohort whereby two participants (one assigned to M5717 and one assigned to placebo) were initially randomised and dosed. Randomisation schedules were generated electronically by independent, unblinded statisticians. Part two was an open-label, non-randomised volunteer infection study using the Plasmodium falciparum induced blood-stage malaria model in which participants were enrolled into three dose cohorts. Healthy men and women of non-childbearing potential aged 18-55 years were eligible for inclusion; individuals in the volunteer infection study were required to be malaria naive. Safety and tolerability (primary outcome of the single ascending dose study and secondary outcome of the volunteer infection study) were assessed by frequency and severity of adverse events. The pharmacokinetic profile of M5717 was also characterised (primary outcome of the volunteer infection study and secondary outcome of the single ascending dose study). Parasite clearance kinetics (primary outcome of the volunteer infection study) were assessed by the parasite reduction ratio and the corresponding parasite clearance half-life; the incidence of recrudescence up to day 28 was determined (secondary outcome of the volunteer infection study). Recrudescent parasites were tested for genetic mutations (exploratory outcome). The trial is registered with ClinicalTrials.gov (NCT03261401). Between Aug 28, 2017, and June 14, 2019, 221 individuals were assessed for eligibility, of whom 66 men were enrolled in the single ascending dose study (eight per cohort for 50-1800 mg cohorts, randomised three M5717 to one placebo, and two in the 2100 mg cohort, randomised one M5717 to one placebo) and 22 men were enrolled in the volunteer infection study (six in the 150 mg cohort and eight each in the 400 mg and 800 mg cohorts). No adverse event was serious; all M5717-related adverse events were mild or moderate in severity and transient, with increased frequency observed at doses above 1250 mg. In the single ascending dose study, treatment-related adverse events occurred in three of 17 individuals in the placebo group; no individual in the 50 mg, 100 mg, or 200 mg groups; one of six individuals in each of the 400 mg, 1000 mg, and 1250 mg groups; two of six individuals in the 600 mg group; and in all individuals in the 1800 mg and 2100 mg groups. In the volunteer infection study, M5717-related adverse events occurred in no participants in the 150 mg or 800 mg groups and in one of eight participants in the 400 mg group. Transient oral hypoesthesia (in three participants) and blurred vision (in four participants) were observed in the 1800 mg or 2100 mg groups and constituted an unknown risk; thus, further dosing was suspended after dosing of the two sentinel individuals in the 2100 mg cohort. Maximum blood concentrations occurred 1-7 h after dosing, and a long half-life was observed (146-193 h at doses ≥200 mg). Parasite clearance occurred in all participants and was biphasic, characterised by initial slow clearance lasting 35-55 h (half-life 231·1 h [95% CI 40·9 to not reached] for 150 mg, 60·4 h [38·6 to 138·6] for 400 mg, and 24·7 h [20·4 to 31·3] for 800 mg), followed by rapid clearance (half-life 3·5 h [3·1 to 4·0] for 150 mg, 3·9 h [3·3 to 4·8] for 400 mg, and 5·5 h [4·8 to 6·4] for 800 mg). Recrudescence occurred in three (50%) of six individuals dosed with 150 mg and two (25%) of eight individuals dosed with 400 mg. Genetic mutations associated with resistance were detected in four cases of parasite recrudescence (two individuals dosed with 150 mg and two dosed with 400 mg). The safety, pharmacokinetics, and antimalarial activity of M5717 support its development as a component of a single-dose antimalarial combination therapy or for malaria prophylaxis. Wellcome Trust and the healthcare business of Merck KGaA, Darmstadt, Germany. [Abstract copyright: Copyright © 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.

Multidimensional Analytical Study of Heart Sounds: A Review

Heart diagnosis by phonocardiography and auscultation is highly dependent on experience and there is a considerable inter-observer variation. The complex structure of the Phonocardiogram (PCG) and the variations due to cardiac contractility can generate additional difficulties for auscultation. This review paper focuses on such critical problem solving issues with a variant of analysis. However, different methods and techniques are also described for detection and analysis of PCG signal and it will certainly aid findings in novel computational tools in biosignal processing