Ocrelizumab (OCR) je humanizovaná monoklonální protilátka, selektivně zaměřená proti CD20+ B-lymfocytů, která je schválena k léčbě relabující remitentní a primárně progresivní roztroušené sklerózy. OCR je podáván intravenózně po dobu 3,5 hodiny. S následnou observací a premedikací stráví pacient v infuzním centru asi 5,5 hodiny. Primárním cílem studie ENSEMBLE PLUS bylo porovnání frekvence a závažnosti reakcí souvisejících s infuzí (IRRs) mezi skupinami pacientů s konvenční a kratší 2hodinovou infuzí, které se vyskytly během nebo do 24 hodin od první randomizované dávky. V souboru 580 pacientů se IRR vyskytla v konvenční infuzní skupině u 23,1 % pacientů ve srovnání s 24,6 % ve skupině s kratší infuzí. Většina IRR byla mírná nebo středně těžká, pouze jeden pacient v každé infuzní skupině prodělal těžkou IRR. Nevyskytly se žádné závažné, život ohrožující ani smrtelné IRR. Výsledky prokazují, že frekvence a závažnost IRR byla při porovnání konvenční a kratší infuze OCR podobná.

Ocrelizumab (OCR) is humanized monoclonal antibody selectively directed against CD20 + B cells that is approved for the treatment of relapsing remitting and primary progressive multiple sclerosis. OCR is administered intravenously for 3.5 hours. With observation and premedication will the patient spend about 5.5 hours in the infusion centre. The primary objective of the ENSEMBLE PLUS study was to compare the frequency and severity of infusion-related reactions (IRRs) between groups of patients with conventional and shorter 2-hours infusions occurring within or within 24 hours after the first randomized dose. In the population of 580 patients, IRR occurred in the conventional group in 23.1% of patients compared to 24.6% in the shorter infusion group. Most IRRs were mild or moderate, only one patient in each infusion group underwent a severe IRR. There were no serious, life-threatening or fatal IRRs. The results demonstrate that the frequency and severity of IRRs were similar when compared conventional and shorter OCR infusions. Conclusion: The intervention study provides evidence that shorter infusion administration of OCR is safe, reduces the time spent in the infusion centre, and reduces the overall patient and site staff burden, especially taking into account the current COVID-19 pandemic.

• Klíčová slova
• studie ENSEMBLE PLUS, ocrelizumab,
• MeSH
• humanizované monoklonální protilátky * aplikace a dávkování   terapeutické užití   MeSH
• intravenózní infuze MeSH
• lidé MeSH
• nežádoucí účinky léčiv MeSH
• randomizované kontrolované studie jako téma MeSH
• roztroušená skleróza * farmakoterapie   MeSH
• vztah mezi dávkou a účinkem léčiva MeSH
• Check Tag
• lidé MeSH

• MeSH
• dechová práce MeSH
• hemodynamika MeSH
• lidé MeSH
• studenti MeSH
• tělesná námaha MeSH
• tělesná výkonnost MeSH
• Check Tag
• lidé MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH

• Konspekt
• Veřejné zdraví a hygiena
• NLK Obory
• veřejné zdravotnictví

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

Repair of damaged DNA is a dynamic process that requires careful orchestration of a multitude of enzymes, adaptor proteins, and chromatin constituents. In this issue of Cell, Lisby et al. (2004) provide a visual glimpse into how the diverse signaling and repair machines are organized in space and time around the deadliest genetic lesions--the DNA double-strand breaks.

• MeSH
• buněčné jádro * genetika   metabolismus   ultrastruktura   MeSH
• diagnostické zobrazování metody   přístrojové vybavení   MeSH
• DNA * genetika   metabolismus   MeSH
• fosfotransferasy genetika   metabolismus   MeSH
• jaderné proteiny genetika   metabolismus   MeSH
• lidé MeSH
• oprava DNA * genetika   MeSH
• poškození DNA * genetika   MeSH
• proteiny buněčného cyklu genetika   metabolismus   MeSH
• Saccharomyces cerevisiae genetika   metabolismus   ultrastruktura   MeSH
• zvířata MeSH
• Check Tag
• lidé MeSH
• zvířata 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

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

• MeSH
• elektrická stimulace MeSH
• experimenty na zvířatech MeSH
• kočky MeSH
• mícha MeSH
• neurony MeSH
• retikulární formace MeSH
• Check Tag
• kočky MeSH