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These results indicated the tandem Y/Lu-DOTATATE therapy for patients with disseminated/inoperable NET as highly effective and safe, considering long-term side effects. In the majority of patients, clinical improvement was observed.

Glycated hemoglobin A1c (HbA1c) concentration in blood is an index of the glycemic control widely used in diabetology. The aim of the work was to validate two mathematical models of HbA1c formation (assuming irreversible or reversible glycation, respectively) and select a model, which was able to predict changes of HbA1c concentration in response to varying glycemia courses with higher accuracy. The experimental procedure applied consisted of an original combination of: in vivo continuous glucose concentration monitoring, long-term in vitro culturing of the human erythrocytes and mathematical modeling of HbA1c formation in vivo and in vitro with HbA1c values scaled according to the most specific analytical methods. Sixteen experiments were conducted in vitro using blood samples collected from healthy volunteer and stable type 1 diabetic patients whose glycemia was estimated beforehand based on long-term monitoring. The mean absolute difference of the measured and predicted HbA1c concentrations for the in vitro experiments were equal to 0.64 +/- 0.29% and 1.42 +/- 0.16% (p = 0.0007) for irreversible and for reversible model, respectively, meaning that the irreversible model was able to predict the glycation kinetics with a higher accuracy. This model was also more sensitive to a deviation of the erythrocytes life span.

The objectives were as follows: (1) estimating mean value of the overall hemoglobin glycation rate constant (k); (2) analyzing inter-individual variability of k; (3) verifying ability of the hemoglobin A1c (HbA1c) formation model to predict changes of HbA1c during red blood cells cultivation in vitro and to reproduce the clinical data. The mean k estimated in a group of 10 non-diabetic subjects was equal to 1.257 ± 0.114 × 10(-9) L mmol(-1) s(-1). The mean k was not affected by a way of estimation of glycemia. The mean k differed less than 20% from values reported earlier and it was almost identical to the mean values calculated on basis of the selected published data. Analysis of variability of k suggests that inter-individual heterogeneity of HbA1c formation is limited or rare. The HbA1c mathematical model was able to predict changes of HbA1c in vitro resulting from different glucose levels and to reproduce a linear relationship of HbA1c and average glucose obtained in the A1C-Derived Average Glucose Study. This study demonstrates that the glycation model with the same k value might be used in majority of individuals as a tool supporting interpretation of HbA1c in different clinical situations.

The composition of the diatom assemblages was analysed in four rivers of Upper Silesia, Poland in 2017. The diatom assemblages studied were found to reflect anthropogenic salinization caused by mining activities. The assemblages in those rivers characterised by the highest salinity (Bolina and Mleczna) showed a relatively low taxonomic richness. The diatom assemblages were dominated by species typical of brackish or marine waters. The rivers with a minimal or weak anthropogenic impact (Centuria and Mitręga) supported taxonomically richer diatom assemblages typical of mid-altitude siliceous or calcareous streams (respectively), that have a fine particulate substratum. The presence of a new species, Planothidium nanum sp. nov., was revealed. The new species shows a unique set of morphological characters, including small size; its elliptical outline as well as very widely-spaced central striae on the sternum valve (sinus) and widely-spaced central striae on the raphe valve allow to separate it from other similar Planothidium.

The Baltic Sea (~393 000 km2) is the largest brackish sea in the world and its hydrographic and environmental conditions are strongly dependent on the frequency of saline water inflows from the North Sea. To improve our understanding of the natural variability of the Baltic Sea ecosystem detailed reconstructions of past saline water inflow changes based on palaeoecological archives are needed. Here we present a high‐resolution study of benthic foraminiferal assemblages accompanied by sediment geochemistry (loss on ignition, total organic carbon) and other microfossil data (ostracods and cladocerans) from a well‐dated 8‐m‐long gravity core taken in the Bornholm Basin. The foraminiferal diversity in the core is low and dominated by species of Elphidium. The benthic foraminiferal faunas in the central Baltic require oxic bottom water conditions and salinities >11–12 PSU. Consequently, shell abundance peaks in the record reflect frequent saline water inflow phases. The first appearance of foraminiferal tests and ostracods in the investigated sediment core is dated to c. 6.9 cal. ka BP and attributed to the first inflows of saline and oxygenated bottom waters into the Bornholm Basin during the Littorina Sea transgression. The transgression terminated the Ancylus Lake phase, reflected in the studied record by abundant cladocerans. High absolute foraminiferal abundances are found within two time intervals: (i) c. 5.5–4.0 cal. ka BP (Holocene Thermal Maximum) and (ii) c. 1.3–0.75 cal. ka BP (Medieval Climate Anomaly). Our data also show three intervals of absent or low saline water inflows: (i) c. 6.5–6.0 cal. ka BP, (ii) c. 3.0–2.3 cal. ka BP and (iii) c. 0.5–0.1 cal. ka BP (Little Ice Age). Our study demonstrates a strong effect of saline and well‐oxygenated water inflows from the Atlantic Ocean on the Baltic Sea ecosystem over millennial time scales, which is linked to the major climate transitions over the last 7 ka.

Recent advances in metagenomics provided a valuable alternative to culture-based approaches for better sampling viral diversity. However, some of newly identified viruses lack sequence similarity to any of previously sequenced ones, and cannot be easily assigned to their hosts. Here we present a bioinformatic approach to this problem. We developed classifiers capable of distinguishing eukaryotic viruses from the phages achieving almost 95% prediction accuracy. The classifiers are wrapped in Host Taxon Predictor (HTP) software written in Python which is freely available at https://github.com/wojciech-galan/viruses_classifier . HTP’s performance was later demonstrated on a collection of newly identified viral genomes and genome fragments. In summary, HTP is a culture- and alignment-free approach for distinction between phages and eukaryotic viruses. We have also shown that it is possible to further extend our method to go up the evolutionary tree and predict whether a virus can infect narrower taxa.