Various tools have been developed to predict B-cell epitopes. We proposed a multistrategy approach by integrating two ensemble learning techniques, namely bagging and meta-decision tree, with a threshold-based cost-sensitive method. By exploiting the synergy among multiple retrainable inductive learners, it directly learns a tree-like classification architecture from the data, and is not limited by a prespecified structure. In addition, we introduced a new three-dimensional sphere-based structural feature to improve the window-based linear features for increased residue description. We performed independent and cross-validation tests, and compared with previous ensemble meta-learners and state-of-the-art B-cell epitope prediction tools using bound-state and unboundstate antigens. The results demonstrated the superior performance of the bagging meta-decision tree approach compared with single epitope predictors, and showed performance comparable to previous meta-learners. The new approach—requiring no predictions from other B-cell epitope tools—is more flexible and applicable than are previous meta-learners relying on specific pretrained B-cell epitope prediction tools.

• Klíčová slova
• ensemble learning, meta-decision tree,
• MeSH
• epitopy B-lymfocytární imunologie   klasifikace   MeSH
• peptidy imunologie   klasifikace   MeSH
• rozhodovací stromy MeSH
• strojové učení MeSH
• výpočetní biologie MeSH

INTRODUCTION: Ocrelizumab is an approved intravenously administered anti-CD20 antibody for multiple sclerosis (MS). The safety profile and patient preference for conventional versus shorter ocrelizumab infusions were investigated in the ENSEMBLE PLUS study. METHODS: ENSEMBLE PLUS was a randomized, double-blind substudy to the single-arm ENSEMBLE study (NCT03085810), comparing outcomes in patients with early-stage relapsing-remitting MS receiving ocrelizumab 600 mg over the approved 3.5-h (conventional) versus 2-h (shorter) infusion. The primary endpoint was the proportion of patients with infusion-related reactions (IRRs) following the first randomized dose (RD); the secondary endpoint included IRR frequency at subsequent RDs. RESULTS: At first RD, the number of patients with an IRR in the conventional (101/373; 27.1%) versus shorter (107/372; 28.8%) infusion group was similar (difference, stratified estimates [95% CI]: 1.9% [- 4.4, 8.2]). Most IRRs (conventional: 99.4%; shorter: 97.7%) were mild/moderate. IRR frequency decreased over the course of RDs; three patients discontinued from the shorter infusion arm but continued with conventional infusion. Overall, > 98% of IRRs resolved without sequelae in both groups. Pre-randomization throat irritation was predictive of future throat irritation as an IRR symptom. Adverse events (AEs) and serious AEs were consistent with the known ocrelizumab safety profile. On completion of ENSEMBLE PLUS, most patients chose to remain on (95%) or switch to (80%) shorter infusion. CONCLUSION: ENSEMBLE PLUS demonstrates the safety and tolerability of shorter ocrelizumab infusions. Most patients remained on/switched to shorter infusion after unblinding; IRRs did not strongly influence patient decisions. CLINICAL TRIALS REGISTRATION: Substudy of ENSEMBLE (NCT03085810). REGISTRATION: March 21, 2017.

• MeSH
• dospělí MeSH
• dvojitá slepá metoda MeSH
• humanizované monoklonální protilátky * aplikace a dávkování   škodlivé účinky   MeSH
• imunologické faktory * aplikace a dávkování   škodlivé účinky   MeSH
• intravenózní infuze MeSH
• lidé středního věku MeSH
• lidé MeSH
• relabující-remitující roztroušená skleróza * farmakoterapie   MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• multicentrická studie MeSH
• randomizované kontrolované studie MeSH

Backgroung: Type 1 diabetes is a disease that adversely affects the daily life of a large percentage of people worldwide. Daily glucose levels regulation and useful advices provided to patients regarding their diet are essential for diabetes treatment. For this reason, the interest of the academic community has focused on developing innovative systems, such as decision support systems, based on glucose prediction algorithms. The present work presents the predictive capabilities of ensemble methods compared to individual algorithms while combining each method with compartment models for fast acting insulin absorption simulation. Methods: An approach of combining widely used glycemia prediction algorithms is proposed and three different ensemble methods (Linear, Bagging and Boosting metaregressor) are applied and evaluated on their ability to provide accurate predictions for 30, 45 and 60 minutes ahead prediction horizon. Moreover, glycemia levels, long and short acting insulin dosages and consumed carbohydrates from six type one people with diabetes are used as input data and the results are evaluated in terms of root-mean square error and Clarke error grid analysis. Results: According to results, ensemble methods can provide more accurate glucose concentration in comparison to individual algorithms. Bagging metaregressor, specifically, performed better than individual algorithms in all prediction horizons for small datasets. Bagging ensemble method improved the percentage in zone A according to Clarkes error grid analysis by 4% and in some cases by 9%. Moreover, compartment models are proved to improve results in combination with any method at any prediction horizon. This strengthen the potential practical usefulness of the ensemble methods and the importance of building accurate compartment models.

• MeSH
• algoritmy MeSH
• diabetes mellitus 1. typu * farmakoterapie   MeSH
• inzulin MeSH
• krevní glukóza MeSH
• lidé MeSH
• selfmonitoring glykemie MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH

Glioma is the most pernicious cancer of the nervous system, with histological grade influencing the survival of patients. Despite many studies on the multimodal treatment approach, survival time remains brief. In this study, a novel two-stage ensemble of an ensemble-type machine learning-based predictive framework for glioma detection and its histograde classification is proposed. In the proposed framework, five characteristics belonging to 135 subjects were considered: human telomerase reverse transcriptase (hTERT), chitinase-like protein (YKL-40), interleukin 6 (IL-6), tissue inhibitor of metalloproteinase-1 (TIMP-1) and neutrophil/lymphocyte ratio (NLR). These characteristics were examined using distinctive ensemble-based machine learning classifiers and combination strategies to develop a computer-aided diagnostic system for the non-invasive prediction of glioma cases and their grade. In the first stage, the analysis was conducted to classify glioma cases and control subjects. Machine learning approaches were applied in the second stage to classify the recognised glioma cases into three grades, from grade II, which has a good prognosis, to grade IV, which is also known as glioblastoma. All experiments were evaluated with a five-fold cross-validation method, and the classification results were analysed using different statistical parameters. The proposed approach obtained a high value of accuracy and other statistical parameters compared with other state-of-the-art machine learning classifiers. Therefore, the proposed framework can be utilised for designing other intervention strategies for the prediction of glioma cases and their grades.

• MeSH
• gliom * diagnóza   MeSH
• lidé MeSH
• magnetická rezonanční tomografie MeSH
• nádory mozku * diagnóza   MeSH
• strojové učení * MeSH
• stupeň nádoru MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH

BACKGROUND: Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise the predictive performance of such forecasts if multiple models are combined into an ensemble. Here, we report on the performance of ensembles in predicting COVID-19 cases and deaths across Europe between 08 March 2021 and 07 March 2022. METHODS: We used open-source tools to develop a public European COVID-19 Forecast Hub. We invited groups globally to contribute weekly forecasts for COVID-19 cases and deaths reported by a standardised source for 32 countries over the next 1-4 weeks. Teams submitted forecasts from March 2021 using standardised quantiles of the predictive distribution. Each week we created an ensemble forecast, where each predictive quantile was calculated as the equally-weighted average (initially the mean and then from 26th July the median) of all individual models' predictive quantiles. We measured the performance of each model using the relative Weighted Interval Score (WIS), comparing models' forecast accuracy relative to all other models. We retrospectively explored alternative methods for ensemble forecasts, including weighted averages based on models' past predictive performance. RESULTS: Over 52 weeks, we collected forecasts from 48 unique models. We evaluated 29 models' forecast scores in comparison to the ensemble model. We found a weekly ensemble had a consistently strong performance across countries over time. Across all horizons and locations, the ensemble performed better on relative WIS than 83% of participating models' forecasts of incident cases (with a total N=886 predictions from 23 unique models), and 91% of participating models' forecasts of deaths (N=763 predictions from 20 models). Across a 1-4 week time horizon, ensemble performance declined with longer forecast periods when forecasting cases, but remained stable over 4 weeks for incident death forecasts. In every forecast across 32 countries, the ensemble outperformed most contributing models when forecasting either cases or deaths, frequently outperforming all of its individual component models. Among several choices of ensemble methods we found that the most influential and best choice was to use a median average of models instead of using the mean, regardless of methods of weighting component forecast models. CONCLUSIONS: Our results support the use of combining forecasts from individual models into an ensemble in order to improve predictive performance across epidemiological targets and populations during infectious disease epidemics. Our findings further suggest that median ensemble methods yield better predictive performance more than ones based on means. Our findings also highlight that forecast consumers should place more weight on incident death forecasts than incident case forecasts at forecast horizons greater than 2 weeks. FUNDING: AA, BH, BL, LWa, MMa, PP, SV funded by National Institutes of Health (NIH) Grant 1R01GM109718, NSF BIG DATA Grant IIS-1633028, NSF Grant No.: OAC-1916805, NSF Expeditions in Computing Grant CCF-1918656, CCF-1917819, NSF RAPID CNS-2028004, NSF RAPID OAC-2027541, US Centers for Disease Control and Prevention 75D30119C05935, a grant from Google, University of Virginia Strategic Investment Fund award number SIF160, Defense Threat Reduction Agency (DTRA) under Contract No. HDTRA1-19-D-0007, and respectively Virginia Dept of Health Grant VDH-21-501-0141, VDH-21-501-0143, VDH-21-501-0147, VDH-21-501-0145, VDH-21-501-0146, VDH-21-501-0142, VDH-21-501-0148. AF, AMa, GL funded by SMIGE - Modelli statistici inferenziali per governare l'epidemia, FISR 2020-Covid-19 I Fase, FISR2020IP-00156, Codice Progetto: PRJ-0695. AM, BK, FD, FR, JK, JN, JZ, KN, MG, MR, MS, RB funded by Ministry of Science and Higher Education of Poland with grant 28/WFSN/2021 to the University of Warsaw. BRe, CPe, JLAz funded by Ministerio de Sanidad/ISCIII. BT, PG funded by PERISCOPE European H2020 project, contract number 101016233. CP, DL, EA, MC, SA funded by European Commission - Directorate-General for Communications Networks, Content and Technology through the contract LC-01485746, and Ministerio de Ciencia, Innovacion y Universidades and FEDER, with the project PGC2018-095456-B-I00. DE., MGu funded by Spanish Ministry of Health / REACT-UE (FEDER). DO, GF, IMi, LC funded by Laboratory Directed Research and Development program of Los Alamos National Laboratory (LANL) under project number 20200700ER. DS, ELR, GG, NGR, NW, YW funded by National Institutes of General Medical Sciences (R35GM119582; the content is solely the responsibility of the authors and does not necessarily represent the official views of NIGMS or the National Institutes of Health). FB, FP funded by InPresa, Lombardy Region, Italy. HG, KS funded by European Centre for Disease Prevention and Control. IV funded by Agencia de Qualitat i Avaluacio Sanitaries de Catalunya (AQuAS) through contract 2021-021OE. JDe, SMo, VP funded by Netzwerk Universitatsmedizin (NUM) project egePan (01KX2021). JPB, SH, TH funded by Federal Ministry of Education and Research (BMBF; grant 05M18SIA). KH, MSc, YKh funded by Project SaxoCOV, funded by the German Free State of Saxony. Presentation of data, model results and simulations also funded by the NFDI4Health Task Force COVID-19 (https://www.nfdi4health.de/task-force-covid-19-2) within the framework of a DFG-project (LO-342/17-1). LP, VE funded by Mathematical and Statistical modelling project (MUNI/A/1615/2020), Online platform for real-time monitoring, analysis and management of epidemic situations (MUNI/11/02202001/2020); VE also supported by RECETOX research infrastructure (Ministry of Education, Youth and Sports of the Czech Republic: LM2018121), the CETOCOEN EXCELLENCE (CZ.02.1.01/0.0/0.0/17-043/0009632), RECETOX RI project (CZ.02.1.01/0.0/0.0/16-013/0001761). NIB funded by Health Protection Research Unit (grant code NIHR200908). SAb, SF funded by Wellcome Trust (210758/Z/18/Z).

• MeSH
• COVID-19 * diagnóza   epidemiologie   MeSH
• epidemie * MeSH
• infekční nemoci * MeSH
• lidé MeSH
• předpověď MeSH
• retrospektivní studie MeSH
• statistické modely MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Research Support, N.I.H., Extramural MeSH
• Research Support, U.S. Gov't, Non-P.H.S. MeSH
• Research Support, U.S. Gov't, P.H.S. MeSH

Machine learning (ML) is a growing field that provides tools for automatic pattern recognition. The neuroimaging community currently tries to take advantage of ML in order to develop an auxiliary diagnostic tool for schizophrenia diagnostics. In this letter, we present a classification framework based on features extracted from magnetic resonance imaging (MRI) data using two automatic whole-brain morphometry methods: voxel-based (VBM) and deformation-based morphometry (DBM). The framework employs a random subspace ensemble-based artificial neural network classifier-in particular, a multilayer perceptron (MLP). The framework was tested on data from first-episode schizophrenia patients and healthy controls. The experiments differed in terms of feature extraction methods, using VBM, DBM, and a combination of both morphometry methods. Thus, features of different types were available for model adaptation. As we expected, the combination of features increased the MLP classification accuracy up to 73.12%-an improvement of 5% versus MLP-based only on VBM or DBM features. To further verify the findings, other comparisons using support vector machines in place of MLPs were made within the framework. However, it cannot be concluded that any classifier was better than another.

• MeSH
• diagnóza počítačová metody   MeSH
• lidé MeSH
• magnetická rezonanční tomografie * metody   MeSH
• mozek diagnostické zobrazování   MeSH
• neuronové sítě * MeSH
• rozpoznávání automatizované metody   MeSH
• schizofrenie diagnostické zobrazování   MeSH
• Check Tag
• lidé MeSH
• mužské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

Non-invasive fetal electrocardiography appears to be one of the most promising fetal monitoring techniques during pregnancy and delivery nowadays. This method is based on recording electrical potentials produced by the fetal heart from the surface of the maternal abdomen. Unfortunately, in addition to the useful fetal electrocardiographic signal, there are other interference signals in the abdominal recording that need to be filtered. The biggest challenge in designing filtration methods is the suppression of the maternal electrocardiographic signal. This study focuses on the extraction of fetal electrocardiographic signal from abdominal recordings using a combination of independent component analysis, recursive least squares, and ensemble empirical mode decomposition. The method was tested on two databases, the Fetal Electrocardiograms, Direct and Abdominal with Reference Heartbeats Annotations and the PhysioNet Challenge 2013 database. The evaluation was performed by the assessment of the accuracy of fetal QRS complexes detection and the quality of fetal heart rate determination. The effectiveness of the method was measured by means of the statistical parameters as accuracy, sensitivity, positive predictive value, and F1-score. Using the proposed method, when testing on the Fetal Electrocardiograms, Direct and Abdominal with Reference Heartbeats Annotations database, accuracy higher than 80% was achieved for 11 out of 12 recordings with an average value of accuracy 92.75% [95% confidence interval: 91.19-93.88%], sensitivity 95.09% [95% confidence interval: 93.68-96.03%], positive predictive value 96.36% [95% confidence interval: 95.05-97.17%] and F1-score 95.69% [95% confidence interval: 94.83-96.35%]. When testing on the Physionet Challenge 2013 database, accuracy higher than 80% was achieved for 17 out of 25 recordings with an average value of accuracy 78.24% [95% confidence interval: 73.44-81.85%], sensitivity 81.79% [95% confidence interval: 76.59-85.43%], positive predictive value 87.16% [95% confidence interval: 81.95-90.35%] and F1-score 84.08% [95% confidence interval: 80.75-86.64%]. Moreover, the non-invasive ST segment analysis was carried out on the records from the Fetal Electrocardiograms, Direct and Abdominal with Reference Heartbeats Annotations database and achieved high accuracy in 7 from in total of 12 records (mean values μ < 0.1 and values of ±1.96σ < 0.1).

• MeSH
• algoritmy * MeSH
• břicho fyziologie   MeSH
• databáze faktografické MeSH
• elektrokardiografie metody   MeSH
• lidé MeSH
• matky statistika a číselné údaje   MeSH
• monitorování plodu metody   MeSH
• plod fyziologie   MeSH
• počítačové zpracování signálu přístrojové vybavení   MeSH
• srdeční frekvence plodu fyziologie   MeSH
• těhotenství MeSH
• Check Tag
• lidé MeSH
• těhotenství MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

The automated detection of arrhythmia in a Holter ECG signal is a challenging task due to its complex clinical content and data quantity. It is also challenging due to the fact that Holter ECG is usually affected by noise. Such noise may be the result of the regular activity of patients using the Holter ECG-partially unplugged electrodes, short-time disconnections due to movement, or disturbances caused by electric devices or infrastructure. Furthermore, regular patient activities such as movement also affect the ECG signals and, in connection with artificial noise, may render the ECG non-readable or may lead to misinterpretation of the ECG. OBJECTIVE: In accordance with the PhysioNet/CinC Challenge 2017, we propose a method for automated classification of 1-lead Holter ECG recordings. APPROACH: The proposed method classifies a tested record into one of four classes-'normal', 'atrial fibrillation', 'other arrhythmia' or 'too noisy to classify'. It uses two machine learning methods in parallel. The first-a bagged tree ensemble (BTE)-processes a set of 43 features based on QRS detection and PQRS morphology. The second-a convolutional neural network connected to a shallow neural network (CNN/NN)-uses ECG filtered by nine different filters (8×  envelograms, 1×  band-pass). If the output of CNN/NN reaches a specific level of certainty, its output is used. Otherwise, the BTE output is preferred. MAIN RESULTS: The proposed method was trained using a reduced version of the public PhysioNet/CinC Challenge 2017 dataset (8183 records) and remotely tested on the hidden dataset on PhysioNet servers (3658 records). The method achieved F1 test scores of 0.92, 0.82 and 0.74 for normal recordings, atrial fibrillation and recordings containing other arrhythmias, respectively. The overall F1 score measured on the hidden test-set was 0.83. SIGNIFICANCE: This F1 score led to shared rank #2 in the follow-up PhysioNet/CinC Challenge 2017 ranking.

• MeSH
• diagnóza počítačová metody   MeSH
• elektrokardiografie metody   MeSH
• fibrilace síní diagnóza   MeSH
• lidé MeSH
• počítačové zpracování signálu * MeSH
• rozpoznávání automatizované metody   MeSH
• strojové učení * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

The nucleocapsid protein of the SARS-CoV-2 virus comprises two RNA-binding domains and three regions that are intrinsically disordered. While the structures of the RNA-binding domains have been solved using protein crystallography and NMR, current knowledge of the conformations of the full-length nucleocapsid protein is rather limited. To fill in this knowledge gap, we combined coarse-grained molecular simulations with data from small-angle X-ray scattering (SAXS) experiments using the ensemble refinement of SAXS (EROS) method. Our results show that the dimer of the full-length nucleocapsid protein exhibits large conformational fluctuations with its radius of gyration ranging from about 4 to 8 nm. The RNA-binding domains do not make direct contacts. The disordered region that links these two domains comprises a hydrophobic α-helix which makes frequent and nonspecific contacts with the RNA-binding domains. Each of the intrinsically disordered regions adopts conformations that are locally compact, yet on average, much more extended than Gaussian chains of equivalent lengths. We offer a detailed picture of the conformational ensemble of the nucleocapsid protein dimer under near-physiological conditions, which will be important for understanding the nucleocapsid assembly process.

• MeSH
• COVID-19 * MeSH
• difrakce rentgenového záření MeSH
• konformace proteinů MeSH
• lidé MeSH
• maloúhlový rozptyl MeSH
• nukleokapsida - proteiny chemie   MeSH
• nukleokapsida MeSH
• SARS-CoV-2 * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

BACKGROUND AND OBJECTIVES: Early treatment of multiple sclerosis (MS) reduces disease activity and the risk of long-term disease progression. Effectiveness of ocrelizumab is established in relapsing MS (RMS); however, data in early RMS are lacking. We evaluated the 4-year effectiveness and safety of ocrelizumab as a first-line therapy in treatment-naive patients with recently diagnosed relapsing-remitting MS (RRMS). METHODS: ENSEMBLE was a prospective, 4-year, international, multicenter, single-arm, open-label, phase IIIb study. Patients were treatment naive, aged 18-55 years, had early-stage RRMS with a disease duration ≤3 years, Expanded Disability Status Scale (EDSS) score ≤3.5, and ≥1 clinically reported relapse(s) or ≥1 signs of brain inflammatory activity on MRI in the prior 12 months. Patients received IV ocrelizumab 600 mg every 24 weeks. Effectiveness endpoints over 192 weeks were proportion of patients with no evidence of disease activity (NEDA-3; defined as absence of relapses, 24-week confirmed disability progression [CDP], and MRI measures, with prespecified MRI rebaselining at week 8), 24-week/48-week CDP and 24-week confirmed disability improvement, annualized relapse rate (ARR), mean change in EDSS score from baseline, and safety. Cognitive status, patient-reported outcomes, and serum neurofilament light chain (NfL) were assessed. Descriptive analysis was performed on the intention-to-treat population. RESULTS: Baseline characteristics (N = 678) were consistent with early-stage RRMS (n = 539 patients, 64.6% female, age 40 years and younger; median age: 31.0 years; duration since: MS symptom onset 0.78 years, RRMS diagnosis 0.24 years; mean baseline EDSS score [SD] 1.71 [0.95]). At week 192, most of the patients had NEDA-3 (n = 394/593, 66.4%), 85.0% had no MRI activity, 90.9% had no relapses, and 81.8% had no 24-week CDP over the study duration. Adjusted ARR at week 192 was low (0.020, 95% CI 0.015-0.027). NfL levels were reduced to and remained within the healthy donor range, by week 48 and week 192, respectively. No new or unexpected safety signals were observed. DISCUSSION: Disease activity based on clinical and MRI measures was absent in most of the patients treated with ocrelizumab over 4 years in the ENSEMBLE study. Safety was consistent with the known profile of ocrelizumab. Although this single-arm study was limited by lack of a parallel group for comparison of outcome measures, the positive benefit-risk profile observed may provide confidence to adopt ocrelizumab as a first-line treatment in newly diagnosed patients with early RMS. CLASSIFICATION OF EVIDENCE: This study provides Class IV evidence that adult patients with early-stage MS who were treatment naive maintained low disease activity (NEDA-3) over 4 years with ocrelizumab treatment; no new safety signals were detected. TRIAL REGISTRATION INFORMATION: ClinicalTrials.gov Identifier NCT03085810; first submitted March 16, 2017; first patient enrolled: March 27, 2017; available at clinicaltrials.gov/ct2/show/NCT03085810.

• MeSH
• dospělí MeSH
• humanizované monoklonální protilátky * terapeutické užití   škodlivé účinky   aplikace a dávkování   MeSH
• imunologické faktory * terapeutické užití   škodlivé účinky   aplikace a dávkování   MeSH
• lidé středního věku MeSH
• lidé MeSH
• magnetická rezonanční tomografie MeSH
• mladiství MeSH
• mladý dospělý MeSH
• posuzování pracovní neschopnosti MeSH
• progrese nemoci MeSH
• prospektivní studie MeSH
• relabující-remitující roztroušená skleróza * farmakoterapie   diagnostické zobrazování   MeSH
• výsledek terapie MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mladiství MeSH
• mladý dospělý MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• klinické zkoušky, fáze III MeSH
• multicentrická studie MeSH