Východisko. Zvýšení glykémie nad normální mez je u kriticky nemocných pacientů častým jevem. Řada studií dokazuje, že u některých skupin nemocných vede normalizace glykémie intenzifikovanou inzulínovou terapií k výraznému snížení mortality, délky hospitalizace i počtu komplikací. Cílem této pilotní studie bylo porovnat kompenzaci glykémie s použitím počítačového plně automatického prediktivního kontrolního algoritmu s variabilním intervalem zadávání glykémie (eMPC) oproti rutinnímu protokolu pro kontrolu glykémie u kardiochirurgických pacientů (RP) v peri- a pooperačním období. Metody a výsledky. Do studie bylo zařazeno celkem 20 pacientů (14 mužů a 6 žen, průměrný věk 68±10 let, BMI 28,3±5,0 kg/m2). Deset pacientů bylo randomizováno pro léčbu s použitím eMPC protokolu a 10 pacientů za použití RP. Všichni pacienti podstoupili plánovanou kardiochirurgickou operaci a byli léčeni kontinuální infuzí s inzulínem se snahou udržení glykémie v rozmezí 4,4–6,1 mmol/l po dobu 24 hodin. Průměrná hladina glukózy byla signifikantně nižší v eMPC skupině než v RP skupině (5,80±0,45 vs. 7,23±0,84 mmol/l, p<0,05), celková průměrná doba v cílovém rozmezí glykémie byla delší v eMPC než RP skupině (67,6±8,7 % vs. 27,6±15,8 %, p<0,05), zatímco průměrná doba nad cílovým rozmezím byla v eMPC skupině významně kratší. Průměrná rychlost infůze inzulínu byla vyšší u eMPC než u RP skupiny (4,18±1,19 vs. 3,24±1,43 IU/hod., p<0,05). Průměrný interval odběrů glykémie byl signifikantně kratší u eMPC než u RP skupiny (1,51±0,24 vs. 2,03±0,16 hod., p<0,05). V žádné ze skupin se nevyskytla těžší hypoglykémie. Závěry. Výsledky naší pilotní studie dokazují, že eMPC algoritmus je efektivnější při kompenzaci glykémie v peri- a pooperačním období u pacientů po kardiochirurgické operaci a srovnatelně bezpečný oproti rutinnímu protokolu v udržení glykémie.

Background. Increased blood glucose levels are frequently observed in critically ill patients. Recent studies have shown that the normalization of glycemia by intensive insulin therapy decreases mortality, length of the hospitalization and number of complications. Methods and Results. The aim of this pilot study was to compare blood glucose control by an automated model predictive control algorithm with variable sampling rate (eMPC) with routine glucose management protocol (RP) in peri- and postoperative period in cardiac surgery patients. 20 patients were included into this study (14 men and 6 women, mean age 68±10 let, BMI 28.3±5.0 kg/m2). 10 patients were randomized for treatment using eMPC algorithm and 10 patients for routine protocol. All patients underwent elective cardiac surgery and were treated with continuous insulin infusion to maintain glycemia in target range 4.4–6.1 mmol/l. The study duration was 24 hours. Mean blood glucose was significantly lower in eMPC vs. RP group (5.80±0.45 vs. 7.23±0.84 mmol/l, p<0.05). Percentage of time in target range was significantly higher in eMPC vs. RP group (67.6±8.7 % vs. 27.6±15.8 %, p<0.05). Percentage of time above the target range was higher in RP vs. eMPC group. Average insulin infusion rate was higher in eMPC vs. RP group (4.18±1.19 vs. 3.24±1.43 IU/hour, p<0.05). Average sampling interval was significantly shorter in eMPC vs. RP group (1.51±0.24 vs. 2.03±0.16 hour, p<0.05). No severe hypoglycaemia in either group occurred during the study. Conclusions. The results of our pilot study suggest that eMPC algorithm is more effective in maintaining euglycemia in peri- and post-operative period in patients after cardiac surgery and comparably safe as compared to RP.

• MeSH
• Algorithms MeSH
• Adult MeSH
• Financing, Organized MeSH
• Body Mass Index MeSH
• Data Interpretation, Statistical MeSH
• Insulin administration & dosage   pharmacology   therapeutic use   MeSH
• Insulin Resistance physiology   MeSH
• Cardiac Surgical Procedures methods   nursing   MeSH
• Clinical Protocols MeSH
• Blood Glucose analysis   metabolism   MeSH
• Humans MeSH
• Mortality MeSH
• Perioperative Care methods   MeSH
• Pilot Projects MeSH
• Computers statistics & numerical data   trends   utilization   MeSH
• Postoperative Complications prevention & control   therapy   MeSH
• Postoperative Care methods   MeSH
• Primary Prevention MeSH
• Randomized Controlled Trials as Topic statistics & numerical data   MeSH
• Aged MeSH
• Treatment Outcome MeSH
• Check Tag
• Adult MeSH
• Humans MeSH
• Male MeSH
• Aged MeSH
• Female MeSH

Hyperglykémie je stav, který je u pacientů přijímaných na jednotky intenzívní péče diagnostikován velmi často a je spojen se značně zvýšenou morbiditou a mortalitou, zejména u pacientů, kteří se před hospitalizací neléčili s diabetem. Podle zjištění řady klinických studií korekce hyperglykémie zlepšuje prognózu u kriticky nemocných pacientů. Nejnovější doporučení uvádějí limity glykémie u kriticky nemocných pacientů mezi 7 a 10 mmol/l, s výjimkou kardiologických pacientů, u kterých je vhodná pevnější kontrola hladiny cukru v krvi. Pro kontrolu podávání inzulínu zdravotními sestrami za účelem regulace hladiny cukru v krvi u kriticky nemocných pacientů se využívá řada protokolů. Kromě klasických papírových protokolů jsou dobré zkušenosti s protokoly založenými na matematickém či prediktivním modelu. Praktická aplikace prediktivního modelu vede k lepší kontrole hladiny cukru v krvi ve stanovených limitech, a dále také ke sníženému výskytu hypoglykemických epizod a nižší variabilitě naměřených hodnot cukru v krvi. V blízké budoucnosti je možno očekávat vývoj plně automatizovaného systému, s jehož pomocí bude glykémie neustále monitorována pomocí zabudovaného senzoru, na základě čehož budou dávky inzulínu pravidelně upravovány.

Hyperglycaemia is often present in patients admitted to intensive care units. It is associated with significantly increased mortality and morbidity, mainly in patients who did not have diabetes mellitus before hospitalisation. According to many clinical studies, correcting hyperglycaemia leads to an improved prognosis for critically ill patients. Going by the latest recommendations, the glycaemia limits for critically ill patients should be between 7 and 10 mmol/l, with the exception of cardiologic patients, who may benefit from a tighter control of glycaemia levels. There are various protocols in use for application of insulin doses by a nurse, for the purpose of blood sugar control in critically ill patients. Apart from the classical paper protocols there is already good experience with protocols based on mathematical or predictive models. The application of the predictive model leads to better control of blood sugar in the selected limits, with lower incidence of hypoglycaemic episodes and lower variability of the measured glycaemia. In near future, development of a fully automated system can be expected, with the help of which glycaemia will be continuously monitored by a sensor in the device and the dose of insulin will be changed accordingly.

• Keywords
• kontrola glykémie,
• MeSH
• Hyperglycemia * diagnosis   etiology   drug therapy   MeSH
• Insulin administration & dosage   MeSH
• Blood Glucose analysis   MeSH
• Humans MeSH
• Monitoring, Physiologic methods   instrumentation   MeSH
• Critical Care * methods   MeSH
• Predictive Value of Tests MeSH
• Point-of-Care Systems MeSH
• Check Tag
• Humans MeSH

N-linked glycosylation is known to be a crucial factor for the therapeutic efficacy and safety of monoclonal antibodies (mAbs) and many other glycoproteins. The nontemplate process of glycosylation is influenced by external factors which have to be tightly controlled during the manufacturing process. In order to describe and predict mAb N-linked glycosylation patterns in a CHO-S cell fed-batch process, an existing dynamic mathematical model has been refined and coupled to an unstructured metabolic model. High-throughput cell culture experiments carried out in miniaturized bioreactors in combination with intracellular measurements of nucleotide sugars were used to tune the parameter configuration of the coupled models as a function of extracellular pH, manganese and galactose addition. The proposed modeling framework is able to predict the time evolution of N-linked glycosylation patterns during a fed-batch process as a function of time as well as the manipulated variables. A constant and varying mAb N-linked glycosylation pattern throughout the culture were chosen to demonstrate the predictive capability of the modeling framework, which is able to quantify the interconnected influence of media components and cell culture conditions. Such a model-based evaluation of feeding regimes using high-throughput tools and mathematical models gives rise to a more rational way to control and design cell culture processes with defined glycosylation patterns. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:1135-1148, 2016.

• MeSH
• Models, Biological * MeSH
• Bioreactors MeSH
• Time Factors MeSH
• CHO Cells MeSH
• Cricetulus MeSH
• Glycosylation MeSH
• Hydrogen-Ion Concentration MeSH
• Cells, Cultured MeSH
• Antibodies, Monoclonal chemistry   metabolism   MeSH
• Animals MeSH
• Check Tag
• Animals MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

Motivace: Infarkt myokardu a mozková mrtvice před- stavují závažný zdravotní problém ve většině rozvinutých zemí. Tato studie zkoumá genetickou dispozici pro akutní infarkt myokardu v české populaci. Metody a výsledky: Celogenomová studie genových ex- presí je spárovanou studií případů a kontrol. Vzorky peri- ferní krve kontrolních osob byly spárovány se vzorky paci- entů na základě pohlaví, věku, příznaku diabetes mellitus a kouření. Pacienti s infarktem byli rozděleni do dvou sku- pin podle toho, zda přežili období 6 měsíců od infarktu. Použili jsme metodu limma (Linear Models for Micro- array Data) pro identifikaci diferenciálních genových ex- presí. Metoda smrštěných centroidů pomohla identifikovat množiny diferenciálně exprimovaných genů s prediktivními vlastnostmi na nezávislých vzorcích. Prediktivní vlastnosti byly ověřeny pomocí bootstrapu. Ukazuje se, že 60 tran- skriptů je diferenciálně exprimováno z klinického i statis- tického hlediska mezi pacienty, kteří nepřežili šestiměsíční období, vzhledem ke kontrolním osobám. Přitom žádné ta- kové transkripty nebyly pozorovány mezi pacienty, kteří přežili. Mezi dvěma skupinami pacientů s infarktem vychází 14 di- ferenciálně exprimovaných transkriptů. Prediktivní mode- lování umožnilo vytipovat 16 ze 60 transkriptů, které nej- lépe diskriminují mezi kontrolami a pacienty, kteří zemřeli během šestiměsíčního období na kardiovaskulární onemoc- nění. Obdobný výběr nelze provést pro přeživší pacienty, protože pro ně vyšly všechny geny nesignifikantní. Za po- moci smrštěných centroidů bylo vytipováno 11 ze 14 tran- skriptů, které nejlépe diskriminují mezi oběma skupinami pacientů s infarktem. Závěry: Studie identifikovala geny asociované se zvýše- ným genetickým rizikem akutního infarktu myokardu, a to včetně genů asociovaných s úmrtím během šestiměsíčního období po výskytu infarktu.

Background: Myocardial infarction and stroke represent a major public health problem in most developing coun- tries. This study explores genetic predisposition of acute myocardial infarction in the Czech population. Methods and Results: Genome-wide expression study used matched case-control design. Peripheral blood sam- ples of the controls were matched to those of cases based on gender, age, status of diabetes mellitus and smoking status. Six months cardiovascular survival status of the cases was used to identify two distinct subgroups among the cases. Linear models for microarray data were em- ployed to identify differential gene expression. Shrunken centroids technique helped in identifying the subsets of differentially expressed genes with predictive properties in independent samples. Predictive properties were evaluated using bootstrap sampling. Sixty transcripts were found to be both clinically and statistically differentially expressed among the cases not surviving the six months follow-up period relative to controls, while no such transcripts were observed among other surviving cases. The two subgroups of cases exhibited fourteen differen- tially expressed transcripts. Predictive modeling indicated sixteen out of sixty transcripts to best discriminate be- tween the controls and cases that died during the follow-up period from cardiovascular causes, while for the surviving cases the already non-significant set of transcripts could not be further reduced. Eleven out of fourteen transcripts were found to best discriminate between the two groups of cases using shrunken centroids. Conclusions: The study identified genes associated with excess genetic risk of acute myocardial infarction, including those associated with the six months fatality of the cases.

• Keywords
• prediktivní modelování,
• MeSH
• Acute Disease MeSH
• Genome-Wide Association Study * methods   statistics & numerical data   MeSH
• Gene Expression MeSH
• Genetic Predisposition to Disease * genetics   MeSH
• Genetic Markers MeSH
• Genetic Testing methods   statistics & numerical data   MeSH
• Myocardial Infarction * genetics   mortality   MeSH
• Humans MeSH
• Linear Models MeSH
• Microarray Analysis MeSH
• Sensitivity and Specificity MeSH
• Case-Control Studies MeSH
• Check Tag
• Humans MeSH

Baroreflex regulation of blood pressure primarily moderates its fluctuations and also affects mean blood pressure. Heart rate baroreflex sensitivity is described as changes of the inter-beat interval induced by a change of blood pres- sure of 1 mmHg (BRS). BRS is decreased in many cardiovascular diseases (hypertension, diabetes mellitus, obesity, cardiac failure, etc.). Decreased BRS in disposed individuals, especially after myocardial infarction, increases the risk of sudden cardiac death. Therefore, early diagnosis of BRS decrease gains in importance. This article describes dif- ferent methods of determination of baroreflex sensitivity. The methods are based on evaluation of the spontaneous fluctuation of heart rate and blood pressure (spectral, sequential or nonlinear methods), or of primary changes of blood pressure induced by a vasoactive substance or a physiological manoeuvre and corresponding changes of cardiac intervals (Valsalva manoeuvre, phenylephrine administration). Each method has its advantages and disadvantages resulting from a different difficulty of calculation or from inclusion of different deviations in the results, which are not directly linked with baroreflex. Baroreflex regulating total peripheral resistance is less described. A mathematical model of baroreflex blood pressure regulation by fluctuation of heart rate and peripheral resistance is presented in this paper.

• Keywords
• autonomní regulace, citlivost baroreflexu, metody stanovení citlivosti baroreflexu, model baroreflexu,
• MeSH
• Baroreflex * physiology   drug effects   MeSH
• Diagnostic Techniques, Cardiovascular MeSH
• Hypertension diagnosis   blood   physiopathology   MeSH
• Clinical Trials as Topic MeSH
• Humans MeSH
• Blood Pressure Determination * methods   utilization   MeSH
• Models, Cardiovascular MeSH
• Monitoring, Physiologic history   methods   trends   utilization   MeSH
• Signal Processing, Computer-Assisted MeSH
• Predictive Value of Tests MeSH
• Blood Flow Velocity MeSH
• Heart Rate MeSH
• Check Tag
• Humans MeSH
• Publication type
• Research Support, Non-U.S. Gov't MeSH

• MeSH
• Cardiovascular Diseases epidemiology   prevention & control   MeSH
• Risk Factors MeSH
• Aged MeSH
• Patient Education as Topic MeSH
• Check Tag
• Aged MeSH

• Conspectus
• Lékařské vědy. Lékařství
• NML Fields
• kardiologie
• geriatrie
• angiologie
• NML Publication type
• publikace WHO

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 * diagnosis   epidemiology   MeSH
• Epidemics * MeSH
• Communicable Diseases * MeSH
• Humans MeSH
• Forecasting MeSH
• Retrospective Studies MeSH
• Models, Statistical MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't 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

Understanding of the bone remodelling process has considerably increased during the last 20 years. Since the ability to simulate (and predict) the effects of bone remodelling offers substantial insights, several models have been proposed to describe this phenomenon. The strength of the presented model is that it includes biochemical control factors (e.g., the necessity of cell-to-cell contact, which is mediated by the RANKL-RANK-OPG chain during osteoclastogenesis) and mechanical stimulation, the governing equations are derived from interaction kinetics (e.g., mass is preserved in running reactions), and the parameters are measurable. Behaviour of the model is in accordance with experimental and clinical observations, such as the role of dynamic loading, the inhibitory effect of dynamic loading on osteoclastogenesis, the observation that polykaryon osteoclasts are activated and formed by a direct cell-to-cell contact, and the correct concentrations of osteoblasts, osteoclasts, and osteocytes. The model does not yet describe the bone remodelling process in complete detail, but the implemented simplifications describe the key features and further details of control mechanisms may be added.

• MeSH
• Models, Biological MeSH
• Bone Density physiology   MeSH
• Humans MeSH
• RANK Ligand physiology   MeSH
• Stress, Mechanical MeSH
• Osteogenesis physiology   MeSH
• Osteoclasts physiology   MeSH
• Osteoprotegerin physiology   MeSH
• Receptor Activator of Nuclear Factor-kappa B physiology   MeSH
• Bone Remodeling physiology   MeSH
• Signal Transduction physiology   MeSH
• Thermodynamics MeSH
• Weight-Bearing physiology   MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

Our goal was to identify highly accurate empirical models for the prediction of the risk of febrile seizure (FS) and FS recurrence. In a prospective, three-arm, case-control study, we enrolled 162 children (age 25.8 ± 17.1 months old, 71 females). Participants formed one case group (patients with FS) and two control groups (febrile patients without seizures and healthy controls). The impact of blood iron status, peak body temperature, and participants' demographics on FS risk and recurrence was investigated with univariate and multivariate statistics. Serum iron concentration, iron saturation, and unsaturated iron-binding capacity differed between the three investigated groups (pFWE < 0.05). These serum analytes were key variables in the design of novel multivariate linear mixture models. The models classified FS risk with higher accuracy than univariate approaches. The designed bi-linear classifier achieved a sensitivity/specificity of 82%/89% and was closest to the gold-standard classifier. A multivariate model assessing FS recurrence provided a difference (pFWE < 0.05) with a separating sensitivity/specificity of 72%/69%. Iron deficiency, height percentile, and age were significant FS risk factors. In addition, height percentile and hemoglobin concentration were linked to FS recurrence. Novel multivariate models utilizing blood iron status and demographic variables predicted FS risk and recurrence among infants and young children with fever.

• MeSH
• Iron Deficiencies * MeSH
• Seizures, Febrile * diagnosis   etiology   MeSH
• Fever complications   MeSH
• Infant MeSH
• Humans MeSH
• Child, Preschool MeSH
• Case-Control Studies MeSH
• Iron MeSH
• Check Tag
• Infant MeSH
• Humans MeSH
• Male MeSH
• Child, Preschool MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

• MeSH
• Eye Movements MeSH
• Psychomotor Disorders MeSH
• Saccades MeSH

• Conspectus
• Psychologie
• NML Fields
• oftalmologie
• psychologie, klinická psychologie
• NML Publication type
• kolektivní monografie