Solubility and Solvation Properties of Pharmaceutically Active Ionic Liquid Benzocainium Ibuprofenate in Natural Deep Eutectic Solvent Menthol-Lauric Acid.

Due to their appealing physiochemical properties, particularly in the pharmaceutical industry, deep eutectic solvents (DESs) and ionic liquids (ILs) are utilized in various research fields and industries. The presented research analyzes the thermodynamic properties of a deep eutectic solvent created from natural molecules, menthol and lauric acid in a 2:1 molar ratio, and an ionic liquid based on two active pharmaceutical ingredients, benzocainium ibuprofenate. Initially, the low solubility of benzocainium ibuprofenate in water was observed, and a hydrophobic natural deep eutectic mixture of menthol:lauric acid in a 2:1 ratio was prepared to improve benzocainium ibuprofenate solubility. In order to determine the solvent properties of DESs and ILs mixtures at different temperatures and their molecular interactions to enhance the solvent performance, the apparent molar volume, limiting apparent molar expansibility, and viscosity B coefficient were estimated in temperature range from 293.15 K to 313.15 K and varying concentration of benzocainium ibuprofenate.

Could normalization improve robustness of abdominal MRI radiomic features?

Radiomics-based systems could improve the management of oncological patients by supporting cancer diagnosis, treatment planning, and response assessment. However, one of the main limitations of these systems is the generalizability and reproducibility of results when they are applied to images acquired in different hospitals by different scanners. Normalization has been introduced to mitigate this issue, and two main approaches have been proposed: one rescales the image intensities (image normalization), the other the feature distributions for each center (feature normalization). The aim of this study is to evaluate how different image and feature normalization methods impact the robustness of 93 radiomics features acquired using a multicenter and multi-scanner abdominal Magnetic Resonance Imaging (MRI) dataset. To this scope, 88 rectal MRIs were retrospectively collected from 3 different institutions (4 scanners), and for each patient, six 3D regions of interest on the obturator muscle were considered. The methods applied were min-max, 1st-99th percentiles and 3-Sigma normalization, z-score standardization, mean centering, histogram normalization, Nyul-Udupa and ComBat harmonization. The Mann-Whitney U-test was applied to assess features repeatability between scanners, by comparing the feature values obtained for each normalization method, including the case in which no normalization was applied. Most image normalization methods allowed to reduce the overall variability in terms of intensity distributions, while worsening or showing unpredictable results in terms of feature robustness, except for thez-score, which provided a slight improvement by increasing the number of statistically similar features from 9/93 to 10/93. Conversely, feature normalization methods positively reduced the overall variability across the scanners, in particular, 3sigma,z_scoreandComBatthat increased the number of similar features (79/93). According to our results, it emerged that none of the image normalization methods was able to strongly increase the number of statistically similar features.

Normalization Strategies in Multi-Center Radiomics Abdominal MRI: Systematic Review and Meta-Analyses.

Goal: Artificial intelligence applied to medical image analysis has been extensively used to develop non-invasive diagnostic and prognostic signatures. However, these imaging biomarkers should be largely validated on multi-center datasets to prove their robustness before they can be introduced into clinical practice. The main challenge is represented by the great and unavoidable image variability which is usually addressed using different pre-processing techniques including spatial, intensity and feature normalization. The purpose of this study is to systematically summarize normalization methods and to evaluate their correlation with the radiomics model performances through meta-analyses. This review is carried out according to the PRISMA statement: 4777 papers were collected, but only 74 were included. Two meta-analyses were carried out according to two clinical aims: characterization and prediction of response. Findings of this review demonstrated that there are some commonly used normalization approaches, but not a commonly agreed pipeline that can allow to improve performance and to bridge the gap between bench and bedside.

Virtual biopsy in abdominal pathology: where do we stand?

In recent years, researchers have explored new ways to obtain information from pathological tissues, also exploring non-invasive techniques, such as virtual biopsy (VB). VB can be defined as a test that provides promising outcomes compared to traditional biopsy by extracting quantitative information from radiological images not accessible through traditional visual inspection. Data are processed in such a way that they can be correlated with the patient's phenotypic expression, or with molecular patterns and mutations, creating a bridge between traditional radiology, pathology, genomics, and artificial intelligence (AI). Radiomics is the backbone of VB, since it allows the extraction and selection of features from radiological images, feeding them into AI models in order to derive lesions' pathological characteristics and molecular status. Presently, the output of VB provides only a gross approximation of the findings of tissue biopsy. However, in the future, with the improvement of imaging resolution and processing techniques, VB could partially substitute the classical surgical or percutaneous biopsy, with the advantage of being non-invasive, comprehensive, accounting for lesion heterogeneity, and low cost. In this review, we investigate the concept of VB in abdominal pathology, focusing on its pipeline development and potential benefits.

Synthesis and evolution of physicochemical properties of new pharmaceutically active ionic liquids - tetracainium salicylate and tetracainium ibuprofenate.

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.

A fully automatic deep learning algorithm to segment rectal Cancer on MR images: a multi-center study.

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.

MRI-based radiomics to predict response in locally advanced rectal cancer: comparison of manual and automatic segmentation on external validation in a multicentre study.

BACKGROUND: Pathological complete response after neoadjuvant chemoradiotherapy in locally advanced rectal cancer (LARC) is achieved in 15-30% of cases. Our aim was to implement and externally validate a magnetic resonance imaging (MRI)-based radiomics pipeline to predict response to treatment and to investigate the impact of manual and automatic segmentations on the radiomics models. METHODS: Ninety-five patients with stage II/III LARC who underwent multiparametric MRI before chemoradiotherapy and surgical treatment were enrolled from three institutions. Patients were classified as responders if tumour regression grade was 1 or 2 and nonresponders otherwise. Sixty-seven patients composed the construction dataset, while 28 the external validation. Tumour volumes were manually and automatically segmented using a U-net algorithm. Three approaches for feature selection were tested and combined with four machine learning classifiers. RESULTS: Using manual segmentation, the best result reached an accuracy of 68% on the validation set, with sensitivity 60%, specificity 77%, negative predictive value (NPV) 63%, and positive predictive value (PPV) 75%. The automatic segmentation achieved an accuracy of 75% on the validation set, with sensitivity 80%, specificity 69%, and both NPV and PPV 75%. Sensitivity and NPV on the validation set were significantly higher (p = 0.047) for the automatic versus manual segmentation. CONCLUSION: Our study showed that radiomics models can pave the way to help clinicians in the prediction of tumour response to chemoradiotherapy of LARC and to personalise per-patient treatment. The results from the external validation dataset are promising for further research into radiomics approaches using both manual and automatic segmentations.

Preparation and characterization of innovative electrospun nanofibers loaded with pharmaceutically applicable ionic liquids.

Keeping up with cutting edge research in the field of drug delivery, the overall goal of this study was to develop innovative electrospun nanofibers loaded with ionic liquids (ILs) as active pharmaceutical ingredients (APIs). For the first time, a novel approach was examined by combining biocompatible polymer, poly (ethylene oxide) (PEO), and pharmaceutical ILs in an electrospinning process to develop nanofibers with high drug loading (up to 47%). Firstly, two well-known local anaesthetic drugs, lidocaine and procaine, were modified into ILs with the salicylate, forming lidocaine salicylate and procaine salicylate. Its dual-functional nature and increased water solubility for 4- to 10-fold depending on the drug used contribute to overcoming current hurdles encountered by APIs such as poor solubility, low bioavailability, and polymorphism of the solid-state. Nanofibers were formulated using solutions tested for density, viscosity, electrical conductivity, and small-angle X-ray scattering by varying PEO molecular weight and the PEO to IL mass ratio. Scanning electron microscopy showed the surface morphology of the obtained nanofibers, while Fourier transform infrared spectroscopy and differential scanning calorimetry confirmed IL in the nanofibers in an amorphous state. Thus, nanofibers with incorporated IL represent well-known drugs in the new form and a novel dermal application delivery system.

Comparison of radiomics approaches to predict resistance to 1st line chemotherapy in liver metastatic colorectal cancer.

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.

Deep learning model for automatic prostate segmentation on bicentric T2w images with and without endorectal coil.

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.

Design and analysis of interactions in ionic liquids based on procaine and pharmaceutically active anions.

The present work focuses on modifying a local anaesthetic drug procaine into an ionic liquid and evaluating the resulting thermal behaviour and structural changes. Counter ions, salicylate, ibuprofenate, and docusate, were chosen due to different hydrogen-bonding abilities, molecular size, charge distribution, and functional groups. After synthesis of procaine salicylate, procaine ibuprofenate, and procaine docusate, spectroscopic investigations were performed using infrared (IR) and nuclear magnetic resonance (NMR) spectroscopy to confirm proton transfer. Differential scanning calorimetry (DSC) and thermogravimetric (TG) analysis were used to determine the obtained ionic liquids' thermal behaviour. Experimental measurements of density, viscosity, and electrical conductivity were performed to get insight into the interactions occurring in the obtained ionic liquids. The viscosity and electrical conductivity data were analysed using the Vogel-Fulcher-Tammann (VFT) equation, while thermal expansion coefficients were calculated from measured density data. The obtained results found that the synthesised procaine salicylate and procaine docusate an ionic liquid's behaviours, including weak intermolecular forces, while procaine ibuprofenate showed more liquid co-crystal behaviour due to the absence of proton transfer for ibuprofen. In a theoretical phase of the investigation, the density functional theory (DFT) and molecular dynamics (MD) calculations were conducted. The obtained descriptors and radial distribution functions were used to analyse the interactions between ions of synthesised ionic liquids. In addition, solubility determination results proved that procaine transformation into procaine salicylate and procaine ibuprofenate ionic liquids enhanced its solubility in water, while procaine docusate reduces procaine solubility.

Radiomics and Magnetic Resonance Imaging of Rectal Cancer: From Engineering to Clinical Practice.

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.

An innovative radiomics approach to predict response to chemotherapy of liver metastases based on CT images.

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.

A Convolutional Neural Network based system for Colorectal cancer segmentation on MRI images.

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.

Physicochemical and structural properties of lidocaine-based ionic liquids with anti-inflammatory anions.

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.

Correlation between lipophilicity of newly synthesized ionic liquids and selected Fusarium genus growth rate.

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.

The solvation properties and effect of d-fructose on the taste behavior of Citrus aurantium active components in aqueous solutions.

The densities and viscosities of synephrine hydrochloride and octopamine hydrochloride aqueous solutions were determined. Apparent molar volumes, apparent molar volume at infinite dilution, viscosity B-coefficients and hydration number were calculated, and it was found that synephrine hydrochloride acts as a better structure maker than octopamine hydrochloride in aqueous solutions. The densities of the investigated salts were measured in aqueous d-fructose solutions, and the corresponding apparent molar volumes of transfer at infinite dilution were determined. Its taste behavior was discussed based on the calculated values for apparent specific volume and intrinsic viscosity. Molecular dynamics simulations and radial distribution functions were applied in order to understand the nature of the interactions and water structuring in the studied systems. The change in taste behavior was observed with increasing concentration of the cosolute, and it was found that the addition of sugar increases the bitterness of the solution. From a molecular docking study on the bitter receptor TAS2R38, the strongest interactions for synephrine-HCl were noted causing the most bitter taste.

Hydrophilic interaction chromatography coupled to tandem mass spectrometry as a method for simultaneous determination of guanidinoacetate and creatine.

The biosynthesis of creatine (Cr) is closely related to the bioavailability of guanidinoacetate (GAA). The lack of one or the other may compromise their role in the energy transport and cell signaling. A reliable estimate of their levels in biological samples is imperative since they are important markers of many metabolic disorders. Therefore, a new LC-MS/MS method for simultaneous determination and quantification of GAA and Cr by multiple reaction monitoring (MRM) mode was developed based on the hydrophilic interaction chromatography (HILIC) and response surface methodology (RSM) for the optimization of chromatographic parameters. The optimized parameters ensured good separation of these similar, very polar molecules (chromatographic resolution > 1.5) without prior derivatization step in a short analysis run (6 min). The developed method was validated to ensure accurate (R, 75.1-101.6%), precise (RSD < 20%) and low quantification (LOQ of 0.025 µg mL-1 for GAA and 0.006 µg mL-1 for Cr) of the tested analytes and the use of matrix-matched calibration eliminated variable effects of complex matrices such as human plasma and urine. Therefore, this method can be implemented in medical laboratories as a tool for the diagnostics of creatine deficiencies and monitoring of guanidinoacetate and creatine supplementation regimes in biological samples.