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.