• MeSH
• cytomegalovirové infekce * MeSH
• Cytomegalovirus MeSH
• ganciklovir MeSH
• lidé MeSH
• valganciklovir MeSH
• viremie MeSH
• virus BK * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• dopisy MeSH
• komentáře MeSH
• práce podpořená grantem MeSH

• Publikační typ
• abstrakt z konference MeSH

• Publikační typ
• abstrakt z konference 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

STUDY QUESTION: Which actively translated maternal transcripts are differentially regulated between clinically relevant in vitro and in vivo maturation (IVM) conditions in mouse oocytes and zygotes? SUMMARY ANSWER: Our findings uncovered significant differences in the global transcriptome as well as alterations in the translation of specific transcripts encoding components of energy production, cell cycle regulation, and protein synthesis in oocytes and RNA metabolism in zygotes. WHAT IS KNOWN ALREADY: Properly regulated translation of stored maternal transcripts is a crucial factor for successful development of oocytes and early embryos, particularly due to the transcriptionally silent phase of meiosis. STUDY DESIGN, SIZE, DURATION: This is a basic science study utilizing an ICR mouse model, best suited for studying in vivo maturation. In the treatment group, fully grown germinal vesicle oocytes from stimulated ovaries were in vitro matured to the metaphase II (MII) stage either as denuded without gonadotropins (IVM DO), or as cumulus-oocyte complexes (IVM COC) in the presence of 0.075 IU/ml recombinant FSH (rFSH) and 0.075 IU/ml recombinant hCG (rhCG). To account for changes in developmental competence, IVM COC from non-stimulated ovaries (IVM COC-) were included. In vivo matured MII oocytes (IVO) from stimulated ovaries were used as a control after ovulation triggering with rhCG. To simulate standard IVM conditions, we supplemented media with amino acids, vitamins, and bovine serum albumin. Accordingly, in vitro pronuclear zygotes (IMZ) were generated by IVF from IVM DO, and were compared to in vivo pronuclear zygotes (IVZ). All experiments were performed in quadruplicates with samples collected for both polyribosome fractionation and total transcriptome analysis. Samples were collected over three consecutive months. PARTICIPANTS/MATERIALS, SETTING, METHODS: All ICR mice were bred under legal permission for animal experimentation (no. MZE-24154/2021-18134) obtained from the Ministry of Agriculture of the Czech Republic. Actively translated (polyribosome occupied) maternal transcripts were detected in in vitro and in vivo matured mouse oocytes and zygotes by density gradient ultracentrifugation, followed by RNA isolation and high-throughput RNA sequencing. Bioinformatic analysis was performed and subsequent data validation was done by western blotting, radioactive isotope, and mitotracker dye labelling. MAIN RESULTS AND THE ROLE OF CHANCE: Gene expression analysis of acquired polysome-derived high-throughput RNA sequencing data revealed significant changes (RPKM ≥ 0.2; P ≤ 0.005) in translation between in vitro and in vivo matured oocytes and respectively produced pronuclear zygotes. Surprisingly, the comparison between IVM DO and IVM COC RNA-seq data of both fractionated and total transcriptome showed very few transcripts with more than a 2-fold difference. Data validation by radioactive isotope labelling revealed a decrease in global translation bof20% in IVM DO and COC samples in comparison to IVO samples. Moreover, IVM conditions compromised oocyte energy metabolism, which was demonstrated by both changes in polysome recruitment of each of 13 mt-protein-coding transcripts as well as by validation using mitotracker red staining. LARGE SCALE DATA: The data discussed in this publication have been deposited in NCBI's Gene Expression Omnibus and are accessible through GEO Series accession number GSE241633 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE241633). LIMITATIONS, REASONS FOR CAUTION: It is extremely complicated to achieve in vivo consistency in animal model systems such as porcine or bovine. To achieve a high reproducibility of in vivo stimulations, the ICR mouse model was selected. However, careful interpretation of our findings with regard to assisted reproductive techniques has to be made by taking into consideration intra-species differences between the mouse model and humans. Also, the sole effect of the cumulus cells' contribution could not be adequately addressed by comparing IVM COC and IVM DO, because the IVM DO were matured without gonadotropin supplementation. WIDER IMPLICATIONS OF THE FINDINGS: Our findings confirmed the inferiority of standard IVM technology compared with the in vivo approach. It also pointed at compromised biological processes employed in the critical translational regulation of in vitro matured MII oocytes and pronuclear zygotes. By highlighting the importance of proper translational regulation during in vitro oocyte maturation, this study should prompt further clinical investigations in the context of translation. STUDY FUNDING/COMPETING INTEREST(S): This work was supported by the Czech Grant Agency (22-27301S), Charles University Grant Agency (372621), Ministry of Education, Youth and Sports (EXCELLENCE CZ.02.1.01/0.0/0.0/15_003/0000460 OP RDE), and Institutional Research Concept RVO67985904. No competing interest is declared.

• MeSH
• choriogonadotropin farmakologie   MeSH
• embryonální vývoj * fyziologie   MeSH
• IVM techniky * MeSH
• kumulární buňky * metabolismus   MeSH
• myši inbrední ICR * MeSH
• myši MeSH
• oocyty * metabolismus   MeSH
• proteosyntéza MeSH
• transkriptom MeSH
• vývojová regulace genové exprese MeSH
• zvířata MeSH
• zygota metabolismus   MeSH
• Check Tag
• myši MeSH
• ženské pohlaví MeSH
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH