The increasingly frequent improper disposal of lithium-ion batteries (LIB) is leading to concerns about the environmental consequences. When they are poured out, the flammable solvents from the electrolytes in LIBs are the threatening soil and plant contamination. If these liquids spill or leak from batteries, they could enter the soil through various pathways and contaminate crops such as cucumber and tomato plants, which have extensive root systems that may facilitate the absorption of ILs. After absorption, some electrolyte components could accumulate inside the plants and have toxic effects, potentially harming plant growth and crop fields. This study investigated how spilling electrolytes with varying combinations of ILs, organic solvents, and lithium salts in different concentrations affects the growth and development of tomatoes and cucumbers. Special attention was paid to examining the influence of electrolyte components on aerial parts and/or fruits of these plants and the levels of metabolites involved in antioxidant protection under stressful conditions, such as malonyldialdehyde (MDA). In this work, certain ILs with bis(trifluoromethylsulfonyl)imide, NTf2, anion have a phytotoxic effect, which negatively affects cucumber and tomato growth and development.

This study presents the results of volumetric and viscometric measurements of caffeine solutions in an equimolar mixture of ethylene glycol – water, known as antifreeze. Measurements were made in the temperature range T = (283.15 – 313.15) K and up to a caffeine molality of 0.12 mol∙kg-1. Experimental results are supported by molecular dynamics (MD) computer simulations. The obtained results indicate that water molecules have a dominant role in the solvation of caffeine. At the same time, ethylene glycol acts as a dehydrating agent and promotes the self-aggregation of caffeine and the investigated mixture.

The aim of this work was to get a detailed insight into the ion’s interactions along with the structure-making/structure-breaking tendency that has been retrieved through the perusal of calculated parameters from volumetric measurements for aqueous solutions of three newly synthesized ionic liquids: 1-butyl-3-methylimidazolium chlorite, 1-butyl-3-methylimidazolium chlorate and 1-butyl-3-methylimidazolium perchlorate. Further, the antimicrobial activity of synthesized and commercial (1-butyl-3-methylimidazolium chloride) ionic liquids on certain strains of bacteria and fungi was obtained. Antimicrobial tests were performed using the in vitro microdilution method against isolated strains of Escherichia coli, Staphylococcus aureus, Bacillus cereus bacteria, and the fungus Candida guilliermondii. This method is a rapid, quantitative method for the determination of minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) using small amounts of samples (µl) and test compound. Based on the obtained results, the influence of the homologous series of chloride oxyanions on hydration and antimicrobial properties of imidazole-based ionic liquids will be discussed.

In this study, a detailed physicochemical characterization of taurine in water is performed based on density and viscosity measurements in the temperature range from T=(293.15 – 313.15) K. Solubility of taurine increases with the temperature increasing. Data obtained from the volumetric and viscosimetric measurements indicate that taurine does not self-aggregate in water. Molecular dynamic simulations provided insight into how taurine molecules behave in water.

Tetracainium salicylate and tetracainium ibuprofenate were synthesized as active pharmaceutical ingredient ionic liquids (API-ILs). These ILs represent a combination of a drug for local anaesthesia (tetracaine) and nonsteroidal anti-inflammatory drugs (salicylic acid and ibuprofen). After IL synthesis, spectroscopic investigations were performed using infrared and nuclear magnetic resonance spectroscopy to confirm their structures. Differential scanning calorimetry and thermogravimetric analysis determined the obtained thermal behaviour of the ionic liquids. Experimental density, viscosity, and electrical conductivity measurements were performed in a wide temperature range to understand the interactions occurring in the obtained pharmaceutically active ionic liquids. All experimental values were well-fitted by the empirical equations. According to the theoretical calculations, weaker interactions of tetracaine with ibuprofenate than with salicylate are found, ascribed to the decreasing molecular symmetry, weakened hydrogen bonding, and increasing steric hindrance of ibuprofenate's alkyl chain.

The aim of the study is to present and tune a fully automatic deep learning algorithm to segment colorectal cancers (CRC) on MR images, based on a U-Net structure. It is a multicenter study, including 3 different Italian institutions, that used 4 different MRI scanners. Two of them were used for training and tuning the systems, while the other two for the validation. The implemented algorithm consists of a pre-processing step to normalize and to highlight the tumoral area, followed by the CRC segmentation using different U-net structures. Automatic masks were compared with manual segmentations performed by three experienced radiologists, one at each center. The two best performing systems (called mdl2 and mdl3), obtained a median Dice Similarity Coefficient of 0.68(mdl2) - 0.69(mdl3), precision of 0.75(md/2) - 0.71(md/3), and recall of 0.69(mdl2) - 0.73(mdl3) on the validation set. Both systems reached high detection rates, 0.98 and 0.95, respectively, on the validation set. These encouraging results, if confirmed on larger dataset, might improve the management of patients with CRC, since it can be used as a fast and precise tool for further radiomics analyses. Clinical Relevance - To provide a reliable tool able to automatically segment CRC tumors that can be used as first step in future radiomics studies aimed at predicting response to chemotherapy and personalizing treatment.

The aim of this study is to present a fully automatic deep learning algorithm to segment liver Colorectal cancer metastases (lmCRC) on CT images, based on a U-Net structure, comparing nets with and without the transfer learning approach. This is a bi-centric study, enrolling patients who underwent CT exam before (baseline) and after first-line therapy (TP1). Patients were divided into training (using a portion of baseline sequences from both centers) to train the DL model, and two validation sets: one with baseline (valB), and one with TP1 (valTP1) sequences. The reference standard for the automatic segmentations was defined by the manual segmentations performed by an experienced radiologist on the portal phase of the baseline and TP1 CT exam. The best performing model obtained Dice Similarity Coefficient (DSC) of $0.68\pm 0.24$, Precision (Pr) of $0.74\pm 0.27$, Recall (Re) of $0.73\pm 0.26$, Detection Rate (DR) of 93% on the valB, and DSC of $0.61\pm 0.28$, Pr of $0.68\pm 0.31$, Re of $0.65\pm 0.29$ and DR of 88% on the valTP1. These encouraging results, if confirmed on larger dataset, might provide a reliable and robust tool that can be used as first step of future radiomics analyses aimed at predicting response to therapy, improving the management of lmCRC patients.

The use of Deep Learning (DL) algorithms in the medical imaging field is increasing in recent years. However, they require the selection of a set of parameters to properly perform. In this study we evaluated the impact of three factors (the construction of the training set, the number of network layers and the loss function) on the performance of a U-Net system in the segmentation of Locally Advanced Rectal Cancer (LARC) on Magnetic Resonance Imaging (MRI). Images from 3 different institutions and 4 different scanners were used to this scope, for a total of 100 patients. All images underwent a pre-processing step to normalize and to highlight the tumoral area. The sequences of two scanners were used to construct the networks while the remaining sequences were employed for validating the best performing systems. From our results, it emerged that Dice Similarity Coefficient is not affected by any of the evaluated factors. Conversely, the choice of loss function could bias the results towards either precision or recall and, thus, it should be properly performed according to the scope of the network. Moreover, a slightly improvement of the performances was observed using a training set based on clustering, maybe due to a better representation of the heterogeneity characterizing medical images.

Automatic segmentation of the prostate on Magnetic Resonance Imaging (MRI) is one of the topics on which research has focused in recent years as it is a fundamental first step in the building process of a Computer aided diagnosis (CAD) system for cancer detection. Unfortunately, MRI acquired in different centers with different scanners leads to images with different characteristics. In this work, we propose an automatic algorithm for prostate segmentation, based on a U-Net applying transfer learning method in a bi-center setting. First, T2w images with and without endorectal coil from 80 patients acquired at Center A were used as training set and internal validation set. Then, T2w images without endorectal coil from 20 patients acquired at Center B were used as external validation. The reference standard for this study was manual segmentation of the prostate gland performed by an expert operator. The results showed a Dice similarity coefficient >85% in both internal and external validation datasets.Clinical Relevance— This segmentation algorithm could be integrated into a CAD system to optimize computational effort in prostate cancer detection.

Colorectal cancer (CRC) has the second-highest tumor incidence and is a leading cause of death by cancer. Nearly 20% of patients with CRC will have metastases (mts) at the time of diagnosis, and more than 50% of patients with CRC develop metastases during their disease. Unfortunately, only 45% of patients after a chemotherapy will respond to treatment. The aim of this study is to develop and validate a machine learning algorithm to predict response of individual liver mts, using CT scans. Understanding which mts will respond or not will help clinicians in providing a more efficient per-lesion treatment based on patient specific response and not only following a standard treatment. A group of 92 patients was enrolled from two Italian institutions. CT scans were collected, and the portal venous phase was manually segmented by an expert radiologist. Then, 75 radiomics features were extracted both from 7x7 ROIs that moved across the image and from the whole 3D mts. Feature selection was performed using a genetic algorithm. Results are presented as a comparison of the two different approaches of features extraction and different classification algorithms. Accuracy (ACC), sensitivity (SE), specificity (SP), negative and positive predictive values (NPV and PPV) were evaluated for all lesions (per-lesion analysis) and patients (per-patient analysis) in the construction and validation sets. Best results were obtained in the per-lesion analysis from the 3D approach using a Support Vector Machine as classifier. We reached on the training set an ACC of 81%, while on test set, we obtained SE of 76%, SP of 67%, PPV of 69% and NPV of 75%. On the validation set a SE of 61%, SP of 60%, PPV of 57% and NPV of 64% were reached. 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 develop a radiomics signatures predicting single liver mts response to therapy. A personalized mts approach is important to avoid unnecessary toxicity offering more suitable treatments and a better quality of life to oncological patients.

While cross-sectional imaging has seen continuous progress and plays an undiscussed pivotal role in the diagnostic management and treatment planning of patients with rectal cancer, a largely unmet need remains for improved staging accuracy, assessment of treatment response and prediction of individual patient outcome. Moreover, the increasing availability of target therapies has called for developing reliable diagnostic tools for identifying potential responders and optimizing overall treatment strategy on a personalized basis. Radiomics has emerged as a promising, still fully evolving research topic, which could harness the power of modern computer technology to generate quantitative information from imaging datasets based on advanced data-driven biomathematical models, potentially providing an added value to conventional imaging for improved patient management. The present study aimed to illustrate the contribution that current radiomics methods applied to magnetic resonance imaging can offer to managing patients with rectal cancer.