A blend of creatine nitrate and creatinine has demonstrated promising bioavailability; however, prior studies have not thoroughly examined its pharmacokinetics and safety profiles, particularly its impact on kidney stress indicators, such as serum cystatin C. This study aimed to assess the effects of varying doses of creatine nitrate-creatinine intervention on pharmacokinetics and safety in healthy humans. Ten young adults (mean age 26.1 ± 5.0 years; 5 females) volunteered for this double-blind, crossover, randomized controlled trial. The participants were randomly assigned to receive either a low-dose creatine nitrate-creatinine mixture (CN-CRN-Low; 1.5 g of creatine nitrate and 1.5 g of creatinine), a high-dose creatine nitrate-creatinine mixture (CN-CRN-High; 3 g of creatine nitrate and 3 g of creatinine), or 1.5 g of creatine nitrate (CONTROL) in both a single-dose pharmacokinetics experiment, and a 14-day safety trial. Both CN-CRN-Low and CN-CRN-High interventions displayed increased volume of distribution and total clearance compared to the CONTROL intervention (P < 0.05) in a single-dose pharmacokinetics experiment. Additionally, the CN-CRN-High intervention showed significantly higher creatine maximum serum concentrations compared to the other interventions (P < 0.05). Serum cystatin C levels remained unchanged across all interventions (P = 0.65), with no participants experiencing abnormal cystatin C concentrations or major changes in other safety biomarkers. The present study demonstrates dose-specific utilization of creatine nitrate-creatinine intervention, with the mixture induced no kidney damage. Further studies are needed to explore the potential functional and performance benefits of creatine nitrate-creatinine supplementation in diverse clinical and athletic cohorts.

In this work, the solubility of creatine, creatinine, guanidinoacetic acid, and their hydrochlorides in water at atmospheric pressure and in the temperature range T = (293.15 - 313.15) K was determined by the gravimetric method. The thermodynamic parameters of dissolution in water for the mentioned compounds were calculated. The solubility increases significantly by converting the zwitterionic structures of creatine and guanidinoacetic acid into a cationic form, i.e. hydrochloride salt. The effect of increasing solubility is more pronounced for guanidinoacetic acid and decreases with temperature for both compounds. A simple process of transforming electrically neutral zwitterionic structures into cations represents a good way to increase the solubility in water and bioavailability of biologically active compounds.

In the last years, Deep learning (DL) has become an active research topic in the field of medical image analysis, in particular for the automatic segmentation of pathological volumes. In order to develop a robust and generalizable system, it is of crucial importance to define the most suitable training set, according to both the model and the aim. Nevertheless, there are still no common guidelines specifying the most appropriate sampling and dimensionality of the set. The aim of the study is to assess how different sampling methods, e.g., stratified and random, and different sizes of the training set affect the performances of DL models for automatic segmentation. All DL algorithms were based on a U-Net structure and were trained using a real-world multi-center and multi-scanner pelvic MRI database. The performances were evaluated and compared using the Dice Similarity Coefficient between manual and automatic masks and the number of false negatives obtained by the different algorithms. Our results suggest that if the size of the training set is sufficiently large, using a stratified approach based on dendrograms does not strongly affect the performances of the nets, otherwise leads to higher results. Further analysis is needed using different stratification methods and sample sizes.

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.

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 the z-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_score and ComBat that 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.

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.

Abstract 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.