SPIONs Conjugate Supported Anticancer Drug Doxorubicin's Delivery: Current Status, Challenges, and Prospects

Considerable efforts have been directed towards development of nano-structured carriers to overcome the limitations of anticancer drug, doxorubicin's, delivery to various cancer sites. The drug's severe toxicity to cardio and hepatic systems, low therapeutic outcomes, inappropriate dose-demands, metastatic and general resistance, together with non-selectivity of the drug have led to the development of superparamagnetic iron oxide nanoparticles (SPIONs)-based drug delivery modules. Nano-scale polymeric co-encapsulation of the drug, doxorubicin, with SPIONs, the SPIONs surface end-groups' cappings with small molecular entities, as well as structural modifications of the SPIONs' surface-located functional end-groups, to attach the doxorubicin, have been achieved through chemical bonding by conjugation and cross-linking of natural and synthetic polymers, attachments of SPIONs made directly to the non-polymeric entities, and attachments made through mediation of molecular-spacer as well as non-spacer mediated attachments of several types of chemical entities, together with the physico-chemical bondings of the moieties, e.g., peptides, proteins, antibodies, antigens, aptamers, glycoproteins, and enzymes, etc. to the SPIONs which are capable of targeting multiple kinds of cancerous sites, have provided stable and functional SPIONs-based nano-carriers suitable for the systemic, and in vitro deliveries, together with being suitable for other biomedical/biotechnical applications. Together with the SPIONs inherent properties, and ability to respond to magnetic resonance, fluorescence-directed, dual-module, and molecular-level tumor imaging; as well as multi-modular cancer cell targeting; magnetic-field-inducible drug-elution capacity, and the SPIONs' magnetometry-led feasibility to reach cancer action sites have made sensing, imaging, and drug and other payloads deliveries to cancerous sites for cancer treatment a viable option. Innovations in the preparation of SPIONs-based delivery modules, as biocompatible carriers; development of delivery route modalities; approaches to enhancing their drug delivery-cum-bioavailability have explicitly established the SPIONs' versatility for oncological theranostics and imaging. The current review outlines the development of various SPIONs-based nano-carriers for targeted doxorubicin delivery to different cancer sites through multiple methods, modalities, and materials, wherein high-potential nano-structured platforms have been conceptualized, developed, and tested for, both, in vivo and in vitro conditions. The current state of the knowledge in this arena have provided definite dose-control, site-specificity, stability, transport feasibility, and effective onsite drug de-loading, however, with certain limitations, and these shortcomings have opened the field for further advancements by identifying the bottlenecks, suggestive and plausible remediation, as well as more clear directions for future development.

Intelligent Diagnostic Prediction and Classification Models for Detection of Kidney Disease

Kidney disease is a major public health concern that has only recently emerged. Toxins are removed from the body by the kidneys through urine. In the early stages of the condition, the patient has no problems, but recovery is difficult in the later stages. Doctors must be able to recognize this condition early in order to save the lives of their patients. To detect this illness early on, researchers have used a variety of methods. Prediction analysis based on machine learning has been shown to be more accurate than other methodologies. This research can help us to better understand global disparities in kidney disease, as well as what we can do to address them and coordinate our efforts to achieve global kidney health equity. This study provides an excellent feature-based prediction model for detecting kidney disease. Various machine learning algorithms, including k-nearest neighbors algorithm (KNN), artificial neural networks (ANN), support vector machines (SVM), naive bayes (NB), and others, as well as Re-cursive Feature Elimination (RFE) and Chi-Square test feature-selection techniques, were used to build and analyze various prediction models on a publicly available dataset of healthy and kidney disease patients. The studies found that a logistic regression-based prediction model with optimal features chosen using the Chi-Square technique had the highest accuracy of 98.75 percent. White Blood Cell Count (Wbcc), Blood Glucose Random (bgr), Blood Urea (Bu), Serum Creatinine (Sc), Packed Cell Volume (Pcv), Albumin (Al), Hemoglobin (Hemo), Age, Sugar (Su), Hypertension (Htn), Diabetes Mellitus (Dm), and Blood Pressure (Bp) are examples of these traits.

Recent advances in medical image processing for the evaluation of chronic kidney disease

Assessment of renal function and structure accurately remains essential in the diagnosis and prognosis of Chronic Kidney Disease (CKD). Advanced imaging, including Magnetic Resonance Imaging (MRI), Ultrasound Elastography (UE), Computed Tomography (CT) and scintigraphy (PET, SPECT) offers the opportunity to non-invasively retrieve structural, functional and molecular information that could detect changes in renal tissue properties and functionality. Currently, the ability of artificial intelligence to turn conventional medical imaging into a full-automated diagnostic tool is widely investigated. In addition to the qualitative analysis performed on renal medical imaging, texture analysis was integrated with machine learning techniques as a quantification of renal tissue heterogeneity, providing a promising complementary tool in renal function decline prediction. Interestingly, deep learning holds the ability to be a novel approach of renal function diagnosis. This paper proposes a survey that covers both qualitative and quantitative analysis applied to novel medical imaging techniques to monitor the decline of renal function. First, we summarize the use of different medical imaging modalities to monitor CKD and then, we show the ability of Artificial Intelligence (AI) to guide renal function evaluation from segmentation to disease prediction, discussing how texture analysis and machine learning techniques have emerged in recent clinical researches in order to improve renal dysfunction monitoring and prediction. The paper gives a summary about the role of AI in renal segmentation.

Image registration in dynamic renal MRI-current status and prospects

Magnetic resonance imaging (MRI) modalities have achieved an increasingly important role in the clinical work-up of chronic kidney diseases (CKD). This comprises among others assessment of hemodynamic parameters by arterial spin labeling (ASL) or dynamic contrast-enhanced (DCE-) MRI. Especially in the latter, images or volumes of the kidney are acquired over time for up to several minutes. Therefore, they are hampered by motion, e.g., by pulsation, peristaltic, or breathing motion. This motion can hinder subsequent image analysis to estimate hemodynamic parameters like renal blood flow or glomerular filtration rate (GFR). To overcome motion artifacts in time-resolved renal MRI, a wide range of strategies have been proposed. Renal image registration approaches could be grouped into (1) image acquisition techniques, (2) post-processing methods, or (3) a combination of image acquisition and post-processing approaches. Despite decades of progress, the translation in clinical practice is still missing. The aim of the present article is to discuss the existing literature on renal image registration techniques and show today’s limitations of the proposed techniques that hinder clinical translation. This paper includes transformation, criterion function, and search types as traditional components and emerging registration technologies based on deep learning. The current trend points towards faster registrations and more accurate results. However, a standardized evaluation of image registration in renal MRI is still missing.