AC Biosusceptometry (ACB) was previously employed towards recording gastrointestinal motility. Our data show a reliable and successful evaluation of gastrointestinal transit of liquid and solid meals in rats, considering the methods scarcity and number of experiments needed to endorsement of drugs and medicinal plants. ACB permits real time and simultaneous experiments using the same animal, preserving the physiological conditions employing both meals with simplicity and accuracy.

We introduce and demonstrate that the AC biosusceptometry (ACB) technique enables real-time monitoring of magnetic nanoparticles (MNPs) in the bloodstream. We present an ACB system as a simple, portable, versatile, non-invasive, and accessible tool to study pharmacokinetic parameters of MNPs, such as circulation time, in real time. We synthesized and monitored manganese doped iron oxide nanoparticles in the bloodstream of Wistar rats using two different injection protocols. Aiming towards a translational approach, we also simultaneously evaluated cardiovascular parameters, including mean arterial pressure, heart rate, and episodes of arrhythmia in order to secure the well-being of all animals. We found that serial injections increased the circulation time compared with single injections. Immediately after each injection, we observed a transitory drop in arterial pressure, a small drop in heart rate, and no episodes of arrhythmia. Although some cardiovascular effects were observed, they were transitory and easily recovered in both protocols. These results indicate that the ACB system may be a valuable tool for in vivo, real-time MNP monitoring that allows associations with other techniques, such as pulsatile arterial pressure and electrocardiogram recordings, helping ensuring the protocol safety, which is a fundamental step towards clinical applications.

Once administered in an organism, the physiological parameters of magnetic nanoparticles (MNPs) must be addressed, as well as their possible interactions and retention and elimination profiles. Alternating current biosusceptometry (ACB) is a biomagnetic detection system used to detect and quantify MNPs. The aims of this study were to evaluate the biodistribution and clearance of MNPs profiles through long-time in vivo analysis and determine the elimination time carried out by the association between the ACB system and MnFe₂O₄ nanoparticles. The liver, lung, spleen, kidneys, and heart and a blood sample were collected for biodistribution analysis and, for elimination analysis, and over 60 days. During the period analyzed, the animal’s feces were also collectedd. It was possible to notice a higher uptake by the liver and the spleen due to their characteristics of retention and uptake. In 60 days, we observed an absence of MNPs in the spleen and a significant decay in the liver. We also determined the MNPs’ half-life through the liver and the spleen elimination. The data indicated a concentration decay profile over the 60 days, which suggests that, in addition to elimination via feces, there is an endogenous mechanism of metabolization or possible agglomeration of MNPs, resulting in loss of ACB signal intensity.

Background: Neuroimaging strategies are essential to locate, to elucidate the etiology, and to the follow up of brain disease patients. Magnetic resonance imaging (MRI) provides good cerebral soft-tissue contrast detection and diagnostic sensitivity. Inflammatory lesions and tumors are common brain diseases that may present a similar pattern of a cerebral ring enhancing lesion on MRI, and non-enhancing core (which may reflect cystic components or necrosis) leading to misdiagnosis. Texture analysis (TA) and machine learning approaches are computer-aided diagnostic tools that can be used to assist radiologists in such decisions. Methods: In this study, we combined texture features with machine learning (ML) methods aiming to differentiate brain tumors from inflammatory lesions in magnetic resonance imaging. Retrospective examination of 67 patients, with a pattern of a cerebral ring enhancing lesion, 30 with inflammatory, and 37 with tumoral lesions were selected. Three different MRI sequences and textural features were extracted using gray level co-occurrence matrix and gray level run length. All diagnoses were confirmed by histopathology, laboratorial analysis or MRI. Results: The features extracted were processed for the application of ML methods that performed the classification. T1-weighted images proved to be the best sequence for classification, in which the differentiation between inflammatory and tumoral lesions presented high accuracy (0.827), area under ROC curve (0.906), precision (0.837), and recall (0.912). Conclusion: The algorithm obtained textures capable of differentiating brain tumors from inflammatory lesions, on T1-weghted images without contrast medium using the Random Forest machine learning classifier.