Mechanism of Anticancer Action of Novel Imidazole Platinum(II) Complex Conjugated with G2 PAMAM-OH Dendrimer in Breast Cancer Cells

Transition metal coordination compounds play an important role in the treatment of neoplastic diseases. However, due to their low selectivity and bioavailability, as well as the frequently occurring phenomenon of drug resistance, new chemical compounds that could overcome these phenomena are still being sought. The solution seems to be the synthesis of new metal complexes conjugated with drug carriers, e.g., dendrimers. Numerous literature data have shown that dendrimers improve the bioavailability of the obtained metal complexes, solving the problem of their poor solubility and stability in an aqueous environment and also breaking down inborn and acquired drug resistance. Therefore, the aim of this study was to synthesize a novel imidazole platinum(II) complex conjugated with and without the second-generation PAMAM dendrimer (PtMet2–PAMAM and PtMet2, respectively) and to evaluate its antitumor activity. Cell viability studies indicated that PtMet2–PAMAM exhibited higher cytotoxic activity than PtMet2 in MCF-7 and MDA-MB-231 breast cancer cells at relatively low concentrations. Moreover, our results indicated that PtMet2–PAMAM exerted antiproliferative effects in a zebrafish embryo model. Treatment with PtMet2–PAMAM substantially increased apoptosis in a dose-dependent manner via caspase-9 (intrinsic pathway) and caspase-8 (extrinsic pathway) activation along with pro-apoptotic protein expression modulation. Additionally, we showed that apoptosis can be induced by activating POX, which induces ROS production. Furthermore, our results also clearly showed that the tested compounds trigger autophagy through p38 pathway activation and increase Beclin-1, LC3, AMPK, and mTOR inhibition. The high pro-apoptotic activity and the ability to activate autophagy by the imidazole platinum(II) complex conjugated with a dendrimer may be due to its demonstrated ability to reverse multidrug resistance (MDR) and thereby increase cellular accumulation in breast cancer cells.     

The Co-Culture of Staphylococcal Biofilm and Fibroblast Cell Line: The Correlation of Biological Phenomena with Metabolic NMR1 Footprint

Staphylococcus aureus is one of the most prevalent pathogens associated with several types of biofilm-based infections, including infections of chronic wounds. Mature staphylococcal biofilm is extremely hard to eradicate from a wound and displays a high tendency to induce recurring infections. Therefore, in the present study, we aimed to investigate in vitro the interaction between S. aureus biofilm and fibroblast cells searching for metabolites that could be considered as potential biomarkers of critical colonization and infection. Utilizing advanced microscopy and microbiological methods to examine biofilm formation and the staphylococcal infection process, we were able to distinguish 4 phases of biofilm development. The analysis of staphylococcal biofilm influence on the viability of fibroblasts allowed us to pinpoint the moment of critical colonization—12 h post contamination. Based on the obtained model we performed a metabolomics analysis by ¹H NMR spectroscopy to provide new insights into the pathophysiology of infection. We identified a set of metabolites related to the switch to anaerobic metabolism that was characteristic for staphylococcal biofilm co-cultured with fibroblast cells. The data presented in this study may be thus considered a noteworthy but preliminary step in the direction of developing a new, NMR-based tool for rapid diagnosing of infection in a chronic wound.     

Can Developments in Tissue Optical Clearing Aid Super-Resolution Microscopy Imaging?

The rapid development of super-resolution microscopy (SRM) techniques opens new avenues to examine cell and tissue details at a nanometer scale. Due to compatibility with specific labelling approaches, in vivo imaging and the relative ease of sample preparation, SRM appears to be a valuable alternative to laborious electron microscopy techniques. SRM, however, is not free from drawbacks, with the rapid quenching of the fluorescence signal, sensitivity to spherical aberrations and light scattering that typically limits imaging depth up to few micrometers being the most pronounced ones. Recently presented and robustly optimized sets of tissue optical clearing (TOC) techniques turn biological specimens transparent, which greatly increases the tissue thickness that is available for imaging without loss of resolution. Hence, SRM and TOC are naturally synergistic techniques, and a proper combination of these might promptly reveal the three-dimensional structure of entire organs with nanometer resolution. As such, an effort to introduce large-scale volumetric SRM has already started; in this review, we discuss TOC approaches that might be favorable during the preparation of SRM samples. Thus, special emphasis is put on TOC methods that enhance the preservation of fluorescence intensity, offer the homogenous distribution of molecular probes, and vastly decrease spherical aberrations. Finally, we review examples of studies in which both SRM and TOC were successfully applied to study biological systems.     

Quantification of Dendritic Spines Remodeling under Physiological Stimuli and in Pathological Conditions

Numerous brain diseases are associated with abnormalities in morphology and density of dendritic spines, small membranous protrusions whose structural geometry correlates with the strength of synaptic connections. Thus, the quantitative analysis of dendritic spines remodeling in microscopic images is one of the key elements towards understanding mechanisms of structural neuronal plasticity and bases of brain pathology. In the following article, we review experimental approaches designed to assess quantitative features of dendritic spines under physiological stimuli and in pathological conditions. We compare various methodological pipelines of biological models, sample preparation, data analysis, image acquisition, sample size, and statistical analysis. The methodology and results of relevant experiments are systematically summarized in a tabular form. In particular, we focus on quantitative data regarding the number of animals, cells, dendritic spines, types of studied parameters, size of observed changes, and their statistical significance.     

Lithium bis(fluorosulfonyl)imide-PYR14TFSI ionic liquid electrolyte compatible with graphite

Lithium bis(fluorosulfonyl)imide (LiFSI) in 1-butyl-1-methylpyrrolidinium bis(trifluoromethanesulfonyl)imide (PYR₁₄TFSI) was successfully tested as an electrolyte for graphite composite anodes at elevated temperature of 55 °C. The graphite anode showed a good cyclability during the galvanostatic testing at C/10 rate and 55 °C with the capacity close to theoretical. The formation of SEI in different electrolytes was the subject of study using impedance spectroscopy on symmetrical cells containing two lithium electrodes. The 0.7 m LiFSI in PYR₁₄TFSI exhibits a good ionic conductivity (5.9 mS cm⁻¹ at 55 °C) along with high electrochemical stability and high thermal stability. These properties allow their potential application in large-scale lithium ion batteries with improved safety.     

Structure and properties of 6061 + 26 mass% Si aluminum alloy produced via coupled rapid solidification and KOBO-extrusion of powder

Experiments on mechanical consolidation of rapidly solidified (RS) powder of 6061 + 26 mass% Si alloy were performed using the oscillating-die extrusion method. The RS powder was wrapped in thin-wall 6061-alloy cup 35 mm in diameter and vacuum-compressed by means of 100 ton press. Bars 8 mm in diameter were extruded with cross-section reduction of λ = 19 without any preheating of the charge. Tubes with a diameter/wall thickness of 14 mm/1 mm and cross-section reduction of λ = 33 were also manufactured with success. TEM/STEM observations revealed a very fine structure of as-extruded material and bimodal distribution of quasi-spherical silicon particles. Statistical analysis revealed a silicon fine fraction of 0.1–0.7 μm and a coarse fraction 2.1–2.5 μm in diameter. Examination by means of TEM did not reveal any significant changes in the morphology of the silicon particles, even when a high extrusion ratio and the material annealing after deformation were used. Hot compression tests on as-extruded rods (λ = 19) and preliminary annealed samples were performed at a constant true strain rate of 5 × 10^?3 s^?1 within the temperature range of 293–823 K. High strength of the material and relatively high ductility of samples deformed by compression up to ?_t ? 0.4 were observed. The maximum flow stress value for as-extruded material was reduced with deformation temperature from ～390 to ～3.5 MPa for 293 and 823 K, respectively. Annealing of the samples at 773 K/30 min was found to reduce the maximum flow stress by 30–40%. Tensile strengths of similar as-cast alloys and materials manufactured by means of other powder metallurgy methods were shown for the purpose of comparison.     

Association studies on the porcine RETN, UCP1, UCP3 and ADRB3 genes polymorphism with fatness traits

Searching for effects of candidate gene polymorphisms on fatness traits is an important goal for pig industry. In this study we evaluated polymorphism of four porcine genes involved in energy metabolism (RETN, UCP1, UCP3 and ADRB3). Moreover, their association with fat deposition traits was analyzed in two breeds (Polish Landrace, Polish Large White) and a Polish synthetic line (L990). Altogether, five SNPs were identified, including two novel ones in the 5′-flanking region of the RETN gene and a novel missense substitution in the UCP3. Distribution of these polymorphisms in the studied five breeds and the synthetic line was not uniform. Two of the analyzed SNPs: g.−178G > A in the RETN and g.946C > T in the UCP3 gene revealed a significant association with abdominal fat weight or backfat thickness. Such associations were not observed for the UCP1 or ADRB3 gene polymorphisms. Our study showed that polymorphisms of the UCP3 and RETN genes are potentially associated with porcine fatness traits.     

Forgetting Exceptions is Harmful in Language Learning

We show that in language learning, contrary to received wisdom, keeping exceptional training instances in memory can be beneficial for generalization accuracy. We investigate this phenomenon empirically on a selection of benchmark natural language processing tasks: grapheme-to-phoneme conversion, part-of-speech tagging, prepositional-phrase attachment, and base noun phrase chunking. In a first series of experiments we combine memory-based learning with training set editing techniques, in which instances are edited based on their typicality and class prediction strength. Results show that editing exceptional instances (with low typicality or low class prediction strength) tends to harm generalization accuracy. In a second series of experiments we compare memory-based learning and decision-tree learning methods on the same selection of tasks, and find that decision-tree learning often performs worse than memory-based learning. Moreover, the decrease in performance can be linked to the degree of abstraction from exceptions (i.e., pruning or eagerness). We provide explanations for both results in terms of the properties of the natural language processing tasks and the learning algorithms.     

Analytical solution of stresses and material birefringence inoptical fibers with noncircular cladding

An analytical method for the calculation of thermal stress in polarization-maintaining optical fibers is presented. The method is based on the thermoelastic potential and is appropriate for structures with a noncircular stress-cladding boundary. The analytical solution is compared with the results of the finite element method (FEM) for the case of an elliptical boundary     

Signalling and obfuscation for congestion control

We aim to reduce the social cost of congestion in many smart city applications. In our model of congestion, agents interact over limited resources after receiving signals from a central agent that observes the state of congestion in real time. Under natural models of agent populations, we develop new signalling schemes and show that by introducing a non-trivial amount of uncertainty in the signals, we reduce the social cost of congestion, i.e., improve social welfare. The signalling schemes are efficient in terms of both communication and computation, and are consistent with past observations of the congestion. Moreover, the resulting population dynamics converge under reasonable assumptions.