In this work, the absorption spectra of cobalt(II) nitrate and bromide complexes in the composition 0.3Ca(NO3)2 – 0.7NH4NO3 – H2O have been investigated in the 400-800 nm range of wavelength at T = 328.15 K and atmospheric pressure P = 101.3 kPa. Spectra were recorded in solutions with variable water content (R = H2O/salt mole ratio; R = 1.0, 1.2 and 1.6). The blue shift of the absorption maximum with the water content increase (R) suggest simultaneous coordination by water molecules and nitrate ions. From an analysis of the spectra, it can be concluded that the following: [Co(NO3)4(H2O)2]2−, [Co(NO3)2Br2]2− and [CoBr4]2− complexes were formed. The overall stability constants of these complexes species spectra were calculated at T = 328.15 K.

Liver metastases (mts) from colorectal cancer (CRC) can have different responses to chemotherapy in the same patient. The aim of this study is to develop and validate a machine learning algorithm to predict response of individual liver mts. 22 radiomic features (RF) were computed on pretreatment portal CT scans following a manual segmentation of mts. RFs were extracted from 7x7 Region of Interests (ROIs) that moved across the image by step of 2 pixels. Liver mts were classified as non-responder (R-) if their largest diameter increased more than 3 mm after 3 months of treatment and responder (R+), otherwise. Features selection (FS) was performed by a genetic algorithm and classification by a Support Vector Machine (SVM) classifier. Sensitivity, specificity, negative (NPV) and positive (PPV) predictive values were evaluated for all lesions in the training and validation sets, separately. On the training set, we obtained sensitivity of 86%, specificity of 67%, PPV of 89% and NPV of 61%, while, on the validation set, we reached a sensitivity of 73%, specificity of 47%, PPV of 64% and NPV of 57%. Specificity was biased by the low number of R- lesions on the validation set. The promising results obtained in the validation dataset should be extended to a larger cohort of patient to further validate our method.Clinical Relevance— to personalize treatment of patients with metastastic colorectal cancer, based on the likelihood of response to chemotherapy of each liver metastasis.

The aim of the study is to present a new Convolutional Neural Network (CNN) based system for the automatic segmentation of the colorectal cancer. The algorithm implemented consists of several steps: a pre-processing to normalize and highlights the tumoral area, the classification based on CNNs, and a post-processing aimed at reducing false positive elements. The classification is performed using three CNNs: each of them classifies the same regions of interest acquired from three different MR sequences. The final segmentation mask is obtained by a majority voting. Performances were evaluated using a semi-automatic segmentation revised by an experienced radiologist as reference standard. The system obtained Dice Similarity Coefficient (DSC) of 0.60, Precision (Pr) of 0.76 and Recall (Re) of 0.55 on the testing set. After applying the leave-one-out validation, we obtained a median DSC=0.58, Pr=0.74, Re=0.54. The promising results obtained by this system, if validated on a larger dataset, could strongly improve personalized medicine.

Predicting response to neo-adjuvant chemotherapy of liver metastases (mts) using CT images is of key importance to provide personalized treatments. However, manual segmentation of mts should be avoid to develop methods that could be integrated into the clinical practice. The aim of this study is to evaluate if and how much automatic segmentation can affect a radiomics-based method to predict response to neoadjuvant chemotherapy of individual liver mts. To this scope, we developed an automatic deep learning method to segment liver mts, based on the U-net architecture, and we compared the classification results of a classifier fed with manual and automatic masks. In the validation set composed of 39 liver mts, the automatic deeplearning algorithm was able to detect 82% of mts, with a median precision of 67%. Using manual and automatic masks, we obtained the same classification in 19/32 mts. In case of mts with largest diameter > 20 mm, the precision of the segmentation does not impact the classification results and we obtained the same classification with both masks. Conversely, with smaller mts, we showed that a Dice coefficient of at least 0.5 should be obtained to extract the same information from the two segmentations. This are very important results in the perspective of using radiomics-based approach to predict response to therapy into clinical practice. Indeed, either precisely manually segment all lesions or refine them after automatic segmentation is a time-consuming task that cannot be performed on a daily basis.

One of the main problems during in the treatment of anal cancer with chemotherapy and radiation is the occurrence of Hematologic Toxicity (HT). In particular, during radiotherapy it is crucial to spare Bone Marrow (BM), since the radiation dose received by BM in pelvic bones predicts the onset of HT. In this direction, the most popular strategies are based on the identification of the hematopoietically active BM (actBM), that is the part of BM in charge of blood cells generation, using MRI, SPECT or PET, but no approached have been proposed based on CT. In this study we compare four different classifiers in recognizing actBM from CT images using 36 radiomic features. We used Genetic Algorithms (GAs) to simultaneously optimize the feature subsets and the classifier parameters, separately for three pelvic subregions: iliac bone marrow (IBM), lower pelvis bone marrow (LPBM), and lumbosacral bone marrow (LSBM). The obtained classifiers were applied to CT sequences of a cohort of 25 patients affected by carcinoma of the anal canal. Classifiers results were compared with the actBM identified from 18FDG-PET (reference standard, RS). It emerged that the performances of the 4 classifiers are similar and they are satisfactory for IBM and LSBM subregions (Dice > 0.7) whereas they are poor for LPBM (Dice < 0.5).

BACKGROUND: Measles is a highly contagious infectious disease caused by morbillivirus which usually affects young children. Once thought to have been eradicated, measles continues to be the leading cause of morbidity and mortality in the world. AIM: The purpose of this research is to analyze the risk factors and clinical characteristics of children hospitalized at the Pediatric Clinic under the diagnosis of measles during the epidemic in Sarajevo Canton 2019. METHOD: We applied a retrospective analysis of medical histories of 23 patients who were hospitalized under the diagnosis of measles at the Pediatric Clinic of the Clinical Center in Sarajevo from January to June 2019. We divided patients into two groups: Infants and children over one year of age. We diagnosed measles clinically, or through the serum IgM ELISA test for measles virus. RESULTS: A total of 23 patients, aged 1 month to 14 years, were hospitalized at the Pediatric Clinic, accounting for 3.5% of the total number of the diseased children. The largest numbers of hospitalized patients were infants 9 (39.1%). Comorbidities were present in 9 (39.1%) subjects, and the most common complication was bronchopneumonia, present in as many as half of the infants. There were four patients who needed mechanical ventilation (17.39%); three of whom were infants; and two lethal outcomes (8.69%), both in infancy. CONCLUSION: Responsible behavior of parents, health professionals, and society as a whole can prevent the far-reaching consequences of non-vaccination. Infants are critically endangered, as the most sensitive part of population, especially if the collective immunity is impaired.

The purpose of this paper was to examine the density, viscosity and electrical conductivity at different temperatures, as well as the thermal stability and structural properties of previously reported ionic liquids based on active pharmaceutical ingredients. Lidocaine-based ionic liquids, with ibuprofen and salicylate as counterion, were prepared first. Their structures were confirmed by infrared, mass and 1H and 13C nuclear magnetic resonance spectroscopy. The Newtonian behaviour of lidocaine ibuprofenate was confirmed from viscosity measurement results, while it was impossible to determine for lidocaine salicylate. The interactions and structures of the studied ionic liquids were analyzed based on the measured density values, viscosity, electrical conductivity, and calculated values of thermal expansion coefficients and activation energy of viscous flow. From a theoretical aspect, DFT and MD calculations were performed. The obtained descriptors and radial distribution, as well as structural functions, were used to understand the structural organization of the synthesized ionic liquids.

In this work, the possibilities and benefits of using an ionic liquid as a potential dietary supplement are presented and discussed for the first time. Ionic liquids prevent the development of microorganisms due to high ion concentration and thus, prevent perishability of the food products. Thermal stability, structure, as well as the experimental density and viscosity in the temperature range from 20 to 50?C and at the atmospheric pressure 1x105 Pa of newly synthesized cholinium taurate ionic liquid, [Chol][Tau], are determined. According to the performed physicochemical characterization, it can be concluded that the synthesized ionic liquid is suitable for application in the food industry. The temperature variation of viscosity and density is discussed in terms of processes, packaging, and storage of [Chol][Tau]. Also, the antiproliferative activity of [Chol][Tau] is determined and compared with those obtained for ascorbic acid and Aspirin? as the standards.

The purpose of the present study was to examine the effectiveness of 23 different synthesized ionic liquids (ILs) on Fusarium culmorum and Fusarium oxysporum growth rate. The strategy of IL synthesis was a structural modification of ionic liquids through changing the polarity of imidazolium and pycolinium cations and replacing halide anions with well known antifungal anions (cinnamate, caffeate and mandelate). The findings clearly suggest that the type of alkyl chain on the cation is the most determining factor for IL toxicity. In order to examine how IL structure affects their toxicity towards Fusarium genus, lipophilic descriptor A log P is calculated from density functional theory and correlated with Fusarium growth rate. All these results demonstrate the high level of the interdependency of lipophilicity and toxicity for investigated ILs towards the Fusarium genus. The data collected in this research suggest that the inhibitory influence of ILs is more pronounced in the case of F. oxysporum.

For the first time, the dicationic biological molecule agmatine in the synthesis of three novel ionic liquids, agmatine ibuprofenate, agmatine salicylate, and agmatine nicotinate, as well as of six...