A highly stable and magnetized citric acid (CA)-functionalized iron oxide aqueous colloidal solution (Fe3O4@CA) was synthesized by using a simple and rapid method of one-step co-participation via a chemical reaction between Fe3+ and Fe2+ in a NaOH solution at 65 °C, followed by CA addition to functionalize the Fe3O4 surface in 25 min. The NPs were synthesized at lower temperatures and shortened time compared with conventional methods. Surface functionalization is highly suggested because bare Fe3O4 nanoparticles (Fe3O4 NPs) are frequently deficient due to their low stability and hydrophilicity. Hence, 19 nm-sized Fe3O4 NPs coated with CA (Fe3O4@CA) were synthesized, and their microstructure, morphology, and magnetic properties were characterized using X-ray diffraction, transmission electron microscopy, Zeta potential, Fourier transform infrared spectroscopy, and vibrating sample magnetometer. CA successfully modified the Fe3O4 surface to obtain a stabilized (homogeneous and well dispersed) aqueous colloidal solution. The Zeta potential value of the as-prepared Fe3O4@CA increases from − 31 to − 45 mV. These CA-functionalized NPs with high magnetic saturation (54.8 emu/g) show promising biomedical applications.

Artificial Intelligence (AI) methods have significant potential for diagnosis and prognosis of COVID-19 infections. Rapid identification of COVID-19 and its severity in individual patients is expected to enable better control of the disease individually and at-large. There has been remarkable interest by the scientific community in using imaging biomarkers to improve detection and management of COVID-19. Exploratory tools such as AI-based models may help explain the complex biological mechanisms and provide better understanding of the underlying pathophysiological processes. The present review focuses on AI-based COVID-19 studies as applies to chest x-ray (CXR) and computed tomography (CT) imaging modalities, and the associated challenges. Explicit radiomics, deep learning methods, and hybrid methods that combine both deep learning and explicit radiomics have the potential to enhance the ability and usefulness of radiological images to assist clinicians in the current COVID-19 pandemic. The aims of this review are: first, to outline COVID-19 AI-analysis workflows, including acquisition of data, feature selection, segmentation methods, feature extraction, and multi-variate model development and validation as appropriate for AI-based COVID-19 studies. Secondly, existing limitations of AI-based COVID-19 analyses are discussed, highlighting potential improvements that can be made. Finally, the impact of AI and radiomics methods and the associated clinical outcomes are summarized. In this review, pipelines that include the key steps for AI-based COVID-19 signatures identification are elaborated. Sample size, non-standard imaging protocols, segmentation, availability of public COVID-19 databases, combination of imaging and clinical information and full clinical validation remain major limitations and challenges. We conclude that AI-based assessment of CXR and CT images has significant potential as a viable pathway for the diagnosis, follow-up and prognosis of COVID-19.

Gold nanoparticles (AuNPs) are becoming increasingly popular as drug carriers due to their unique properties such as size tenability, multivalency, low toxicity and biocompatibility. AuNPs have physical features that distinguish them from bulk materials, small molecules and other nanoscale particles. Their unique combination of characteristics is just now being fully realized in various biomedical applications. In this review, we focus on the research accomplishments and new opportunities in this field, and we describe the rising developments in the use of monodisperse AuNPs for diagnostic and therapeutic applications. This study addresses the key principles and the most recent published data, focusing on monodisperse AuNP synthesis, surface modifications, and future theranostic applications. Moving forward, we also consider the possible development of functionalized monodisperse AuNPs for theranostic applications based on these efforts. We anticipate that as research advances, flexible AuNPs will become a crucial platform for medical applications.

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One of the most widely used modalities of clinical imaging is computed tomography (CT). Recent reports of new contrast agents toward CT imaging have been numerous. The production of gold nanoparticles (AuNPs) as contrast agents for CT is primarily a topic of intense interest. AuNPs have beneficial features for this application, including excellent X-ray attenuation, flexible sizes and shapes, tailorable surface chemistry, excellent biocompatibility and high levels of contrast generating matter. AuNPs with a size of about 18.5 nm and semi-spherical shape were synthesized using a sonochemical method. The attenuation rate of X-rays as measured in Hounsfield units per unit concentration (HU/mg) was measured. Ultrasound treatment for a duration of five min has been shown to produce highly stable AuNPs in different media (AuNPs in water and phosphate-buffered saline (PBS) was −42.1 mV and −39.5 mV, respectively). The CT value (HU = 395) of the AuNPs increased linearly with an increase in the AuNP dosage. The results confirm the use of ultrasonic treatment for the production of metal nanostructures, particularly highly stable non-toxic AuNPs, with good morphology and high-quality crystal structure using an easy and fast method. Synthesized AuNPs have the potential to be used as a CT contrast agent in medical imaging applications.