Estimation of Physiologic Pressures: Invasive and Non-Invasive Techniques, AI Models, and Future Perspectives.

The measurement of physiologic pressure helps diagnose and prevent associated health complications. From typical conventional methods to more complicated modalities, such as the estimation of intracranial pressures, numerous invasive and noninvasive tools that provide us with insight into daily physiology and aid in understanding pathology are within our grasp. Currently, our standards for estimating vital pressures, including continuous BP measurements, pulmonary capillary wedge pressures, and hepatic portal gradients, involve the use of invasive modalities. As an emerging field in medical technology, artificial intelligence (AI) has been incorporated into analyzing and predicting patterns of physiologic pressures. AI has been used to construct models that have clinical applicability both in hospital settings and at-home settings for ease of use for patients. Studies applying AI to each of these compartmental pressures were searched and shortlisted for thorough assessment and review. There are several AI-based innovations in noninvasive blood pressure estimation based on imaging, auscultation, oscillometry and wearable technology employing biosignals. The purpose of this review is to provide an in-depth assessment of the involved physiologies, prevailing methodologies and emerging technologies incorporating AI in clinical practice for each type of compartmental pressure measurement. We also bring to the forefront AI-based noninvasive estimation techniques for physiologic pressure based on microwave systems that have promising potential for clinical practice.

Machine Learning-Based Classification of Abnormal Liver Tissues Using Relative Permittivity.

The search for non-invasive, fast, and low-cost diagnostic tools has gained significant traction among many researchers worldwide. Dielectric properties calculated from microwave signals offer unique insights into biological tissue. Material properties, such as relative permittivity (εr) and conductivity (σ), can vary significantly between healthy and unhealthy tissue types at a given frequency. Understanding this difference in properties is key for identifying the disease state. The frequency-dependent nature of the dielectric measurements results in large datasets, which can be postprocessed using artificial intelligence (AI) methods. In this work, the dielectric properties of liver tissues in three mouse models of liver disease are characterized using dielectric spectroscopy. The measurements are grouped into four categories based on the diets or disease state of the mice, i.e., healthy mice, mice with non-alcoholic steatohepatitis (NASH) induced by choline-deficient high-fat diet, mice with NASH induced by western diet, and mice with liver fibrosis. Multi-class classification machine learning (ML) models are then explored to differentiate the liver tissue groups based on dielectric measurements. The results show that the support vector machine (SVM) model was able to differentiate the tissue groups with an accuracy up to 90%. This technology pipeline, thus, shows great potential for developing the next generation non-invasive diagnostic tools.

Author Correction: Cellulose Mediated Transferrin Nanocages for Enumeration of Circulating Tumor Cells for Head and Neck Cancer.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Cellulose Mediated Transferrin Nanocages for Enumeration of Circulating Tumor Cells for Head and Neck Cancer.

Herein we report a hierarchically organized, water-dispersible 'nanocage' composed of cellulose nanocrystals (CNCs), which are magnetically powered by iron oxide (Fe3O4) nanoparticles (NPs) to capture circulating tumor cells (CTCs) in blood for head and neck cancer (HNC) patients. Capturing CTCs from peripheral blood is extremely challenging due to their low abundance and its account is clinically validated in progression-free survival of patients with HNC. Engaging multiple hydroxyl groups along the molecular backbone of CNC, we co-ordinated Fe3O4 NPs onto CNC scaffold, which was further modified by conjugation with a protein - transferrin (Tf) for targeted capture of CTCs. Owing to the presence of Fe3O4 nanoparticles, these nanocages were magnetic in nature, and CTCs could be captured under the influence of a magnetic field. Tf-CNC-based nanocages were evaluated using HNC patients' blood sample and compared for the CTC capturing efficiency with clinically relevant Oncoviu platform. Conclusively, we observed that CNC-derived nanocages efficiently isolated CTCs from patient's blood at 85% of cell capture efficiency to that of the standard platform. Capture efficiency was found to vary with the concentration of Tf and Fe3O4 nanoparticles immobilized onto the CNC scaffold. We envision that, Tf-CNC platform has immense connotation in 'liquid biopsy' for isolation and enumeration of CTCs for early detection of metastasis in cancer.