In the absence of a systematic approach to epidemiological modeling in Slovenia, various isolated mathematical epidemiological models emerged shortly after the outbreak of the COVID-19 epidemic. We ...present an epidemiological model adapted to the COVID-19 situation in Slovenia. The standard SEIR model was extended to distinguish between age groups, symptomatic or asymptomatic disease progression, and vaccinated or unvaccinated populations. Evaluation of the model forecasts for 2021 showed the expected behavior of epidemiological modeling: our model adequately predicts the situation up to 4 weeks in advance; the changes in epidemiologic dynamics due to the emergence of a new viral variant in the population or the introduction of new interventions cannot be predicted by the model, but when the new situation is incorporated into the model, the forecasts are again reliable. Comparison with ensemble forecasts for 2022 within the European Covid-19 Forecast Hub showed better performance of our model, which can be explained by a model architecture better adapted to the situation in Slovenia, in particular a refined structure for vaccination, and better parameter tuning enabled by the more comprehensive data for Slovenia. Our model proved to be flexible, agile, and, despite the limitations of its compartmental structure, heterogeneous enough to provide reasonable and prompt short-term forecasts and possible scenarios for various public health strategies. The model has been fully operational on a daily basis since April 2020, served as one of the models for decision-making during the COVID-19 epidemic in Slovenia, and is part of the European Covid-19 Forecast Hub.
Quality control of magnetic resonance imaging includes image validation, which covers also artefact detection. The daily manual review of magnetic resonance images for possible artefacts can be ...time-consuming, so automated methods for computer-assisted quality assessment of magnetic resonance imaging need to be developed.
The aim of this study was to develop automatic detection of Gibbs artefacts in magnetic resonance imaging using a deep learning method called transfer learning, and to demonstrate the potential of this approach for the development of an automatic quality control tool for the detection of such artefacts in magnetic resonance imaging.
The magnetic resonance image dataset of the scanned phantom for quality assurance was created using a turbo spin-echo pulse sequence in the transverse plane. Images were created to include Gibbs artefacts of varying intensities. The images were annotated by two independent reviewers. The annotated dataset was used to develop a method for Gibbs artefact detection using the transfer learning approach. The VGG-16, VGG-19, and ResNet-152 convolutional neural networks were used as pre-trained networks for transfer learning and compared using 5-fold cross-validation.
All accuracies of the classification models were above 97%, while the AUC values were all above 0.99, confirming the high quality of the constructed models.
We show that transfer learning can be successfully used to detect Gibbs artefacts on magnetic resonance images. The main advantages of transfer learning are that it can be applied on small training datasets, the procedures to build the models are not so complicated, and they do not require much computational power. This shows the potential of transfer learning for the more general task of detecting artefacts in magnetic resonance images of patients, which consequently can improve and speed up the process of quality assessment in medical imaging practice.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
In last decade space-density of monitoring stations increased, in to addition also air pollution modeling made big progress. Using diversity of big data can lead to better knowledge about air ...pollution at continental scale. The focus of presented study is the data-driven approach using non-negative matrix factorization to provide new insights and to study the characteristic space-time particulate-matter patterns across Europe. We analyzed the PM10 concentrations obtained from 1097 monitoring stations (AirBase data) and the Monitoring Atmospheric Composition and Climate (MACC) modeled fields for a period of 3 years. We distinguished five characteristic patterns obtained from the AirBase data and five patterns from the MACC data. A comparison between the AirBase and MACC data shows a good spatial overlap for the east Europe, central Europe and the Mediterranean patterns. However, it should be noted that an analysis of the MACC data revealed two additional marine patterns: the Celtic and the North Seas. The Po Valley and Balkan patterns were very clearly identified when analyzing the AirBase data. In order to better understand the influence of the synoptic situation on the particulate-matter concentrations the synoptic meteorological situations were additionally analyzed. The cold season, low wind and very stable conditions, which can last for several days, is the most common situation linked to high concentrations of anthropogenic air pollution with particulate matter. In contrast, for the Mediterranean pattern the most common situation (high factor loadings) is observed during the summer period. This pattern also exhibits a clearer annual cycle. A closer look at the sea-salt patterns (Celtic and North Seas) shows low time-series correlations between these two factors. Nevertheless, the physical mechanism is the same: a steep gradient between the cyclone and the anti-cyclone that causes high winds and, consequently, higher sea-salt production.
•The NNMF of the PM10 AirBase and the MACC data yielded five distinct patterns.•The PM10 MACC data reveals two sea-salt patterns: the Celtic and North Seas.•The east Europe, central Europe and Mediterranean patterns show spatial overlap.•The patterns are influenced by regional and local weather and the PM10 emissions.
Mathematical modelling can be useful for predicting how infectious diseases progress, enabling us to show the likely outcome of an epidemic and help inform public health interventions. Different ...modelling techniques have been used to predict and simulate the spread of COVID-19, but they have not always been useful for epidemiologists and decision-makers. To improve the reliability of the modelling results, it is very important to critically evaluate the data used and to check whether or not due regard has been paid to the different ways in which the disease spreads through the population. As building an epidemiological model that is reliable enough and suits the current epidemiological situation within a country or region, certain criteria must be met in the modelling process. It might be necessary to use a combination of two or more different types of models in order to cover all aspects of epidemic modelling. If we want epidemiological models to be a useful tool in combating the epidemic, we need to engage experts from epidemiology, data science and statistics.
The purpose of this research was to determine how dose area product, effective dose, absorbed doses to specific organs, and image quality changed according to different automatic exposure control ...positions in pelvis imaging. The research was carried out in two parts. The study was conducted on an anthropomorphic phantom and 200 patients referred to pelvic imaging. We measured the dose area product, field size, height, and mass. Then we calculated the effective dose and absorbed dose for individual organs accordingly. Lateral ionizing cells were first positioned in line with the iliac crests (head towards position) and subsequently, with the femoral neck (head away position). All the images were independently evaluated by three radiologists using ViewDEX and objective image analysis was performed measuring contrast-to-noise ratio and signal-to-noise ratio. We found no significant differences in the Siemens Luminos unit in any of the inspected parameters. However, there was a significant difference in dose area product (37.3 %), effective dose (35.7 %) and average absorbed dose to selected individual organs (36.7 %) when the head away position of the patient was used and the image quality increased. Based on these results, we can propose that the optimal position of the patient regarding the ionizing cells is the head away position.
nema
Changes in cardiac autonomic regulation and P-wave characteristics are associated with the occurrence of atrial fibrillation. The purpose of this study was to evaluate whether combined preoperative ...non-invasive determination of cardiac autonomic regulation and PR interval allows for the identification of patients at risk of new-onset atrial fibrillation after cardiac surgery.
RR, PR and QT intervals, and linear and non-linear heart rate variability parameters from 20 min high-resolution electrocardiographic recordings were determined one day before surgery in 150 patients on chronic beta blockers undergoing elective coronary artery bypass grafting, aortic valve replacement, or both, electively.
Thirty-one patients (21%) developed postoperative atrial fibrillation. In the atrial fibrillation group, more arterial hypertension, a greater age, a higher EuroSCORE II, a higher heart rate variability index (pNN50: 9 ± 20 vs. 4 ± 10, p = 0.050), a short PR interval (156 ± 23 vs. 173 ± 31 ms; p = 0.011), and a reduced short-term scaling exponent of the detrended fluctuation analysis (DFA1, 0.96 ± 0.36 vs. 1.11 ± 0.30 ms; p = 0.032) were found compared to the sinus rhythm group. Logistic regression modeling confirmed PR interval, DFA1 and age as the strongest preoperative predictors of postoperative atrial fibrillation (area under the receiver operating characteristic curve = 0.804).
Patients developing atrial fibrillation after cardiac surgery presented with severe cardiac autonomic derangement and a short PR interval preoperatively. The observed state characterizes both altered heart rate regulation and arrhythmic substrate and is strongly related to an increased risk of postoperative atrial fibrillation.
•Cardiac autonomic derangement predisposes to postoperative atrial fibrillation.•Observed autonomic changes are mirrored in non-linear heart rate variability DFA1.•Parasympathetic coactivation not withdrawal closest reflects the mode of derangement.•Shorter PR interval further increases risk of postoperative atrial fibrillation.•Prediction model including age, PR interval and DFA1 yielded AUC of 0.804.
The monitoring of air-pollution constituents like particulate matter (PM10) and black carbon (BC) can provide information about air quality and the dynamics of emissions. Air quality depends on ...natural and anthropogenic sources of emissions as well as the weather conditions. For a one-year period the diurnal concentrations of PM10 and BC in the Port of Koper were analysed by clustering days into similar groups according to the similarity of the BC and PM10 hourly derived day-profiles without any prior assumptions about working and non-working days, weather conditions or hot and cold seasons. The analysis was performed by using k-means clustering with the squared Euclidean distance as the similarity measure. The analysis showed that 10 clusters in the BC case produced 3 clusters with just one member day and 7 clusters that encompasses more than one day with similar BC profiles. Similar results were found in the PM10 case, where one cluster has a single-member day, while 7 clusters contain several member days. The clustering analysis revealed that the clusters with less pronounced bimodal patterns and low hourly and average daily concentrations for both types of measurements include the most days in the one-year analysis. A typical day profile of the BC measurements includes a bimodal pattern with morning and evening peaks, while the PM10 measurements reveal a less pronounced bimodality. There are also clusters with single-peak day-profiles. The BC data in such cases exhibit morning peaks, while the PM10 data consist of noon or afternoon single peaks. Single pronounced peaks can be explained by appropriate cluster wind speed profiles. The analysis also revealed some special day-profiles. The BC cluster with a high midnight peak at 30/04/2010 and the PM10 cluster with the highest observed concentration of PM10 at 01/05/2010 (208.0 μg m−3) coincide with 1 May, which is a national holiday in Slovenia and has very strong tradition of bonfire parties. The clustering of the diurnal concentration showed that various different day-profiles are presented in a cold period, while this is not the case for the hot season. Additional analysis of ship traffic and rain fall data showed that there is no statistically significant difference between the ship gross (bruto) registered tonnage (BRT) values in the case of BC and PM10 clusters, but that there is statistically significant differences between the rain fall in the BC and PM10 clusters. The wind-rose for clusters which included most days in the sampling period indicating that emitted PM10 and BC from Port of Koper were manly transported in the west direction over the sea and in the east direction, where there is in no populated area. Presented analysis showed that both BC and PM10 concentrations were driven by rain intensity and wind speed.
► The BC/PM10 data analysis was performed using k-means clustering. ► Ten-BC and eight-PM10 different typical day-profiles occurred during the one-year. ► The clusters with low daily concentrations include the most days. ► Various different clusters are present in a cold period, but not in the hot season. ► BC pattern reveal a more-pronounced bimodality than PM10 pattern.
In the presented study a comprehensive statistical analysis of the chemical composition of atmospheric particulate matter was carried out. The data were collected from April 2003 to August 2008 with ...a 7-day time resolution in the Northern Adriatic Port of Koper and analyzed by the Proton Induced X-ray method (PIXE). The Positive Matrix Factorization (PMF) analysis of fifteen chemical elements identified six source factors, three natural-regional sources and three local-anthropogenic sources. Heavy machinery, industry and iron ore factor were marked as anthropogenic sources. Heavy machinery source was represented by the elements V, Ni and Cu. The elements Fe and Mn are attributed to the Iron ore source and were explained by the proximity of the bulk-cargo warehouse and the intense handling of iron ore in Port of Koper. The heavy industry source represented by Pb and Zn was the only anthropogenic factor, which shows clear seasonal pattern. In contrast to the local-anthropogenic source factors, natural and regional source factors show significant negative trend. The reduction of the crustal elements Ca, Ti and Sr, joined in a soil source, and sulfur-biomass source, represented by elements K and S, have been attributed to more intense precipitation and to the negative trend of the North Atlantic Oscillation (NAO) index. The negative trend of the Cl and Br elements was in line with the negative trend of the wind speed above the sea surface and the significant sea-wave height.
•Analysis of the elemental concentrations over the period of six years was performed.•PMF analysis identified three natural and three anthropogenic sources.•Only the natural sources show significant negative trends.•Lower NAO index correlates with lower soil and sulfur biomass concentrations.•The observed negative trend of the sea-salt is associated with the decreased wind speeds.
Acute kidney injury (AKI) represents frequent complication after cardiac surgery using cardiopulmonary bypass (CPB). In the hope to enhance earlier more reliable characterization of AKI, we tested ...the utility of neutrophil gelatinase‐associated lipocalin (NGAL) and cystatin C (CysC) in addition to standard creatinine for early determination of AKI after cardiac surgery using CPB. Forty‐one patients met the inclusion criteria. Arterial blood samples collected after induction of general anesthesia were used as baseline, further sampling occurred at CPB termination, 2 h after CPB, on the first and second day after surgery. According to AKIN classification 18 patients (44%) developed AKI (AKI1‐2 groups) and 23 (56%) did not (non‐AKI group). Groups were similar regarding demographics and operative characteristics. CysC levels differed already preoperatively (non‐AKI vs. AKI2; P = 0.045; AKI1 vs. AKI2; P = 0.011), while postoperatively AKI2 group differed on the first day and AKI1 on the second regarding non‐AKI group (P = 0.004; P = 0.021, respectively). NGAL and creatinine showed significant difference already 2 h after CPB between groups AKI2 and non‐AKI and later on the first postoperative day between groups AKI1 and AKI2 (P = 0.028; P = 0.014, respectively). This study shows similar performance of early plasma creatinine and NGAL in patients with preserved preoperative renal function. It demonstrates that creatinine, as well as NGAL, differentiate subsets of patients developing AKI of clinically more advanced grade early after 2 h, also when used single and uncombined.
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