Search

In this paper are presented our research about the magnetic nanoparticles with potential applications in malignant tumors diagnostic: cobalt nanoparticles, cobalt-nickel nanoparticles and mixed oxide particles, i.e. magnetite nanoparticles. Cobalt and nickel- cobalt nanoparticles have been prepared by co-reducing the corresponding salts. The reduction methods yield a dispersion of nanocrystals in liquids and need a ligand shell or a capping layer to prevent aggregation. The particles of magnetite were prepared by boiling in reflux of a mixture formed by γ-Fe2O3 and Fe(II) salt. The surfaces of magnetite nanoparticles were encapsulated by polymer, polyvinylpyrrolidone (PVP), and a saccharide (used for transportation the magnetic nanocomposite to malignant cells) [22]. TEM examination shows the average sizes of NiCo particles, 5-10nm and the average sizes of Co particles 2-5 nm. Magnetic measurements of nanoparticles were performed at room temperature using Vibrating Sample Magnetometer. Co, NiCo and magnetite nanocomposite exhibit a coercive field of 10-100 Oe, correlated with the sizes of nanoparticles. The magnetic properties of nanoparticles and core-shell nanocomposites have good quality for biomedical applications, for enhance the signal from magnetic resonance imaging examinations, as diagnostic tool of cancer tumors and targeting treatment of diseases. [ABSTRACT FROM AUTHOR]

Biomarkers in colorectal cancer (CRC) are of great interest in the current literature due to improvements in techniques such as liquid biopsy and next-generation sequencing (NGS). However, screening methods vary globally, with multi-target stool DNA (mt-sDNA) predominantly used in the USA and, more recently, the Cologuard Plus; biomarkers such as the Galectins family and septins show promise in early detection. Gut microbiome assessments, such as Fusobacterium nucleatum, are under intense exploration. Diagnostic tests, such as circulating DNA analysis via NGS, exhibit effectiveness and are being increasingly adopted. Circulating tumor cells emerge as potential alternatives to traditional methods in terms of diagnosis and prognosis. Predictive biomarkers are well established in guidelines; nonetheless, with the aid of machine learning and artificial intelligence, these biomarkers may be improved. This review critically explores the actual dynamic landscape of CRC biomarkers and future, promising biomarkers involved in screening, diagnosis, and prognosis.

In this paper, we present our system for the RSNA Intracranial Hemorrhage Detection challenge, which is based on the RSNA 2019 Brain CT Hemorrhage dataset. The proposed system is based on a lightweight deep neural network architecture composed of a convolutional neural network (CNN) that takes as input individual CT slices, and a Long Short-Term Memory (LSTM) network that takes as input multiple feature embeddings provided by the CNN. For efficient processing, we consider various feature selection methods to produce a subset of useful CNN features for the LSTM. Furthermore, we reduce the CT slices by a factor of 2×, which enables us to train the model faster. Even if our model is designed to balance speed and accuracy, we report a weighted mean log loss of 0.04989 on the final test set, which places us in the top 30 ranking (2%) from a total of 1345 participants. While our computing infrastructure does not allow it, processing CT slices at their original scale is likely to improve performance. In order to enable others to reproduce our results, we provide our code as open source. After the challenge, we conducted a subjective intracranial hemorrhage detection assessment by radiologists, indicating that the performance of our deep model is on par with that of doctors specialized in reading CT scans. Another contribution of our work is to integrate Grad-CAM visualizations in our system, providing useful explanations for its predictions. We therefore consider our system as a viable option when a fast diagnosis or a second opinion on intracranial hemorrhage detection are needed.

It is already known that electrostatic, magnetostatic, extremely low-frequency electric fields, and pulsed electric field could be utilized in cancer treatment. The healing effect depends on frequency and amplitude of electric field. In the present work, a simple theoretical model is developed to estimate the intensity of electrostatic field that damages a living cell during division. By this model, it is shown that magnification of electric field in the bottleneck of dividing cell is enough to break chemical bounds between molecules by an avalanche process. Our model shows that the externally applied electric field of 4 V/cm intensity is able to hurt a cancer cell at the dividing stage.