Impact of biopolymer matrices on relaxometric properties of MRI contrast agents and their application to Nanotechnology

Magnetic Resonance Imaging (MRI) represents the first-line diagnostic imaging modality for numerous indications. It is a clinically well-established, non-invasive technique providing three dimensional whole body anatomical and functional imaging. It takes advantage of the magnetic properties of water protons present in the body and their tissue-dependent behaviour. High magnetic fields (1.5 T and above) are clinically favoured because of their higher signal-to-noise ratio, capability for MR spectroscopy, and other forms of functional MRI, high speed imaging, and high resolution imaging. Signal intensity in MRI is related to the relaxation rate of in vivo water protons and can be enhanced by the administration of a contrast agent (CA) prior to scanning. These CAs utilize paramagnetic metal ions and enhance the contrast in an MR image by positively influencing the relaxation rates of water protons in the immediate surroundings of the tissue in which they localize. Among different CAs, Gadolinium contrast medium is used in up to 30% of MRI scans and the most clinically-used MRI. However, Gadolinium (Gd), like most of the clinically-used CAs, is characterized by a relaxivity well below its theoretical limit, lacks in tissue specificity and, in addition, it causes heavy allergic effects and serious nephrotoxicity. In this framework, Port et al. have reported that the rigidification of MRI CAs, obtained through covalent or non-covalent binding to macromolecules, could be favourable to an increase in relaxivity of the metal-chelate. Later, Decuzzi et al. have proved that it is possible to modify through the geometrical confinement the magnetic properties of MRI CAs by controlling their characteristic correlation times without the chemical modification of the chelate structure. Furthermore, Courant et al. have highlighted the capability of combined hydrogels to boost the relaxivity of Gd-based CAs. Despite several experimental studies addressed in this field, a comprehensive knowledge of the mechanisms involved in the relaxation enhancement due to the entrapment of CAs in polymer-based architectures is still missing. In particular, the role played by the water at the interface between polymer chains and MRI CAs has not been fully investigated and could lead to the availability of tailored models that accurately describe these novel complex systems. In this work, we aim to demonstrate that a more in-depth knowledge about the interference between macromolecules and MRI CAs and an understanding of their physicochemical properties can significantly to impact in the design strategies of the nanostructures and, consequently, to overcome the limitations of clinically used MRI CAs, particularly linked to the low relaxivity. In this perspective, it is of primary importance to study the main phenomena involved in the formation of polymer matrices and how their properties can influence the relaxivity of MRI CAs. For this reason, we proposed a general strategy based on formation of nanostructures for boosting the efficacy of commercial Gd-based CAs by using FDA approved biopolymers, providing also tissue specificity and reduced nephrotoxicity. Indeed, we want to take advantage not only by the use of nanotechnologies for enhanced MRI but only by their capability to reach a specific target and to accumulate only in the site of interest. The implemented strategy has consisted in the control of the relaxometric properties by tuning the water dynamics, the physicochemical interactions and, therefore, the polymer conformation. Effectively, we primary investigated, in bulk, the impact of hydrogel solutions on the relaxometric properties of commercial CAs, highlighting the key role of hydrogel structural parameters (mesh size and crosslink density) in the relaxation enhancement. In this part, chemical and thermodynamic interactions involved in the complexation between biopolymers and CAs have been investigated through Isothermal Titration Calorimetry. Furthermore, characterizations of water dynamics and mobility and measurement of the relaxometric properties in hydrogel solutions containing CAs have been carried out by NMR and Time-Domain relaxometer. The main outputs were summarized in a concept called Hydrodenticity and defined as the equilibrium between the water osmotic pressure, the elastodynamic forces of the polymer chains and the hydration degree of the CA which is able to increase the relaxivity of the CA itself. Indeed, hydrogel nanostructures made of hydrophilic polymer chains held together by chemical or physical crosslinking, have the ability to swell in water, forming elastic gels that retain a large quantity of fluid in their mesh-like structures. The presence of hydrophilic polymer interfaces and the control of water behaviour in hydrogels play a fundamental role in the relaxation enhancement of the Gadolinium-based CAs by influencing the characteristic correlation times defined by the theory of Solomon and Bloembergen. Then, starting from the acquired knowledge, we moved to observe the role of Hydrodenticity in the design of biopolymer nanostructures for enhanced MRI. For the nanostructures’ synthesis, we used two different processing techniques: (1) High Pressure Homogenization; (2) Microfluidic Flow Focusing. These techniques were selected because of their ability to control process parameters enabling the tuning of the interaction between the biopolymers and the CA. Indeed, by easily adjusting concentrations, pressure of the Homogenizer and/or flow rates of the Microfluidic platform, we can modulate the crosslinking degree of the nanostructures and tune their hydrophilicity, size, shape and surface charge, impacting on the relaxometric properties. These approaches allow us to load MRI CAs into functional nanostructures and obtain nanocarries with tunable relaxometric properties. The powerful aspect and the novelty of our approach lies in the definition of Hydrodenticity and in its application to several architectures, biopolymers, lipids and mixture of them., preserving the main properties of nanoparticles for drug delivery. As future perspective, the nanostructures can also be engineered to carry more than one agent, accumulate in specific tissues or to act as probes for simultaneous diagnosis and therapy (theranostic or multimodal imaging agents), thereby facilitating targeted treatments and precision medicine

Multiparametric Investigation of Dynamics in Fetal Heart Rate Signals

In the field of electronic fetal health monitoring, computerized analysis of fetal heart rate (FHR) signals has emerged as a valid decision-support tool in the assessment of fetal wellbeing. Despite the availability of several approaches to analyze the variability of FHR signals (namely the FHRV), there are still shadows hindering a comprehensive understanding of how linear and nonlinear dynamics are involved in the control of the fetal heart rhythm. In this study, we propose a straightforward processing and modeling route for a deeper understanding of the relationships between the characteristics of the FHR signal. A multiparametric modeling and investigation of the factors influencing the FHR accelerations, chosen as major indicator of fetal wellbeing, is carried out by means of linear and nonlinear techniques, blockwise dimension reduction, and artificial neural networks. The obtained results show that linear features are more influential compared to nonlinear ones in the modeling of HRV in healthy fetuses. In addition, the results suggest that the investigation of nonlinear dynamics and the use of predictive tools in the field of FHRV should be undertaken carefully and limited to defined pregnancy periods and FHR mean values to provide interpretable and reliable information to clinicians and researchers

Impact of biopolymer matrices on relaxometric properties of contrast agents

Properties of water molecules at the interface between contrast agents (CAs) for magnetic resonance imaging and macromolecules could have a valuable impact on the effectiveness of metal chelates. Recent studies, indeed, demonstrated that polymer architectures could influence CAs' relaxivity by modifying the correlation times of the metal chelate. However, an understanding of the physico-chemical properties of polymer/CA systems is necessary to improve the efficiency of clinically used CAs, still exhibiting low relaxivity. In this context, we investigate the impact of hyaluronic acid (HA) hydrogels on the relaxometric properties of Gd-DTPA, a clinically used CA, to understand better the determining role of the water, which is crucial for both the relaxation enhancement and the polymer conformation. To this aim, water self-diffusion coefficients, thermodynamic interactions and relaxometric properties of HA/Gd-DTPA solutions are studied through time-domain NMR relaxometry and isothermal titration calorimetry. We observed that the presence of Gd-DTPA could alter the polymer conformation and the behaviour of water molecules at the HA/Gd-DTPA interface, thus modulating the relaxivity of the system. In conclusion, the tunability of hydrogel structures could be exploited to improve magnetic properties of metal chelates, inspiring the development of new CAs as well as metallopolymer complexes with applications as sensors and memory devices

Impact of the Number of Needle Tip Bevels on the Exerted Forces and Energy in Insulin Pen Injections

Patients affected with type 1 diabetes and a non-negligible number of patients with type 2 diabetes are insulin dependent. Both the injection technique and the choice of the most suitable needle are fundamental for allowing them to have a good injection experience. The needles may differ in several parameters, from the length and diameter, up to the forces required to perform the injection and to some geometrical parameters of the needle tip (e.g., number of facets or bevels). The aim of the research is to investigate whether an increased number of bevels could decrease forces and energy involved in the insertion–extraction cycle, thus potentially allowing patients to experience lower pain. Two needle variants, namely, 31 G 5 mm and 32 G 4 mm, are considered, and experimental tests are carried out to compare 3-bevels with 5-bevels needles for both the variants. The analysis of the forces and energy for both variants show that the needles with 5 bevels require a statistically significant lower drag or sliding force (p-value = 0.040 for the 31 G 5 mm needle and p-value < 0.001 for 32 G 4 mm), extraction force (p-value < 0.001 for both variants), and energy (p-value < 0.001 for both variants) during the insertion–extraction cycle. As a result, 3-bevels needles do not have the same functionality of 5-bevels needles, show lower capacity of drag and extraction, and can potentially be related to more painful injection experience for patients

A Comprehensive Review of Techniques for Processing and Analyzing Fetal Heart Rate Signals

The availability of standardized guidelines regarding the use of electronic fetal monitoring (EFM) in clinical practice has not effectively helped to solve the main drawbacks of fetal heart rate (FHR) surveillance methodology, which still presents inter- and intra-observer variability as well as uncertainty in the classification of unreassuring or risky FHR recordings. Given the clinical relevance of the interpretation of FHR traces as well as the role of FHR as a marker of fetal wellbeing autonomous nervous system development, many different approaches for computerized processing and analysis of FHR patterns have been proposed in the literature. The objective of this review is to describe the techniques, methodologies, and algorithms proposed in this field so far, reporting their main achievements and discussing the value they brought to the scientific and clinical community. The review explores the following two main approaches to the processing and analysis of FHR signals: traditional (or linear) methodologies, namely, time and frequency domain analysis, and less conventional (or nonlinear) techniques. In this scenario, the emerging role and the opportunities offered by Artificial Intelligence tools, representing the future direction of EFM, are also discussed with a specific focus on the use of Artificial Neural Networks, whose application to the analysis of accelerations in FHR signals is also examined in a case study conducted by the authors

Modelling the length of hospital stay after knee replacement surgery through Machine Learning and Multiple Linear Regression at San Giovanni di Dio e Ruggi daAragonaa University Hospital

Knee arthroplasty is one of the most commonly performed procedures within a hospital. The progressive aging of the population and the spread of clinical conditions such as obesity will lead to an increasing use of this procedure. Therefore, being able to make the process related to this procedure more effective and efficient becomes strategic within hospitals, subject to increasingly stringent clinical and financial pressures. A useful parameter for this purpose is the length of stay (LOS), whose early prediction allows for better bed management and resource allocation, models patient expectations and facilitates discharge planning. In this work, the data of 124 patients who underwent knee surgery in the two-year period 2019-2020 at the San Giovanni di Dio and Ruggi d’Aragona university hospital were studied using multiple linear regression and machine learning algorithms in order to evaluate and predict how patient data affect LOS

Application of DMAIC Cycle and Modeling as Tools for Health Technology Assessment in a University Hospital

Background. The Health Technology Assessment (HTA) is used to evaluate health services, manage healthcare processes more efficiently, and compare medical technologies. The aim of this paper is to carry out an HTA study that compares two pharmacological therapies and provides the clinicians with two models to predict the length of hospital stay (LOS) of patients undergoing oral cavity cancer surgery on the bone tissue. Methods. The six Sigma method was used as a tool of HTA; it is a technique of quality management and process improvement that combines the use of statistics with a five-step procedure: “Define, Measure, Analyze, Improve, Control” referred to in the acronym DMAIC. Subsequently, multiple linear regression has been used to create two models. Two groups of patients were analyzed: 45 were treated with ceftriaxone while 48 were treated with the combination of cefazolin and clindamycin. Results. A reduction of the overall mean LOS of patients undergoing oral cavity cancer surgery on bone was observed of 40.9% in the group treated with ceftriaxone. Its reduction was observed in all the variables of the ceftriaxone group. The best results are obtained in younger patients (−54.1%) and in patients with low oral hygiene (−52.4%) treated. The regression results showed that the best LOS predictors for cefazolin/clindamycin are ASA score and flap while for ceftriaxone, in addition to these two, oral hygiene and lymphadenectomy are the best predictors. In addition, the adjusted R squared showed that the variables considered explain most of the variance of LOS. Conclusion. SS methodology, used as an HTA tool, allowed us to understand the performance of the antibiotics and provided variables that mostly influence postoperative LOS. The obtained models can improve the outcome of patients, reducing the postoperative LOS and the relative costs, consequently increasing patient safety, and improving the quality of care provided

Water-Mediated Nanostructures for Enhanced MRI: Impact of Water Dynamics on Relaxometric Properties of Gd-DTPA

Recently, rational design of a new class of contrast agents (CAs), based on biopolymers (hydrogels), have received considerable attention in Magnetic Resonance Imaging (MRI) diagnostic field. Several strategies have been adopted to improve relaxivity without chemical modification of the commercial CAs, however, understanding the MRI enhancement mechanism remains a challenge. Methods: A multidisciplinary approach is used to highlight the basic principles ruling biopolymer-CA interactions in the perspective of their influence on the relaxometric properties of the CA. Changes in polymer conformation and thermodynamic interactions of CAs and polymers in aqueous solutions are detected by isothermal titration calorimetric (ITC) measurements and later, these interactions are investigated at the molecular level using NMR to better understand the involved phenomena. Water molecular dynamics of these systems is also studied using Differential Scanning Calorimetry (DSC). To observe relaxometric properties variations, we have monitored the MRI enhancement of the examined structures over all the experiments. The study of polymer-CA solutions reveals that thermodynamic interactions between biopolymers and CAs could be used to improve MRI Gd-based CA efficiency. High-Pressure Homogenization is used to obtain nanoparticles. Results: The effect of the hydration of the hydrogel structure on the relaxometric properties, called Hydrodenticity and its application to the nanomedicine field, is exploited. The explanation of this concept takes place through several key aspects underlying biopolymer-CA's interactions mediated by the water. In addition, Hydrodenticity is applied to develop Gadolinium-based polymer nanovectors with size around 200 nm with improved MRI relaxation time (10-times). Conclusions: The experimental results indicate that the entrapment of metal chelates in hydrogel nanostructures offers a versatile platform for developing different high performing CAs for disease diagnosis