Precise Quantitative Analysis of Cell Targeting by Particle-Based Agents Using Imaging Flow Cytometry and Convolutional Neural Network

Deep learning-based object detection algorithms in medical imaging: Systematic review

Over the past decade, Deep Learning (DL) techniques have demonstrated remarkable advancements across various domains, driving their widespread adoption. Particularly in medical image analysis, DL received greater attention for tasks like image segmentation, object detection, and classification. This paper provides an overview of DL-based object recognition in medical images, exploring recent methods and emphasizing different imaging techniques and anatomical applications. Utilizing a meticulous quantitative and qualitative analysis following PRISMA guidelines, we examined publications based on citation rates to explore into the utilization of DL-based object detectors across imaging modalities and anatomical domains. Our findings reveal a consistent rise in the utilization of DL-based object detection models, indicating unexploited potential in medical image analysis. Predominantly within Medicine and Computer Science domains, research in this area is most active in the US, China, and Japan. Notably, DL-based object detection methods have gotten significant interest across diverse medical imaging modalities and anatomical domains. These methods have been applied to a range of techniques including CR scans, pathology images, and endoscopic imaging, showcasing their adaptability. Moreover, diverse anatomical applications, particularly in digital pathology and microscopy, have been explored. The analysis underscores the presence of varied datasets, often with significant discrepancies in size, with a notable percentage being labeled as private or internal, and with prospective studies in this field remaining scarce. Our review of existing trends in DL-based object detection in medical images offers insights for future research directions. The continuous evolution of DL algorithms highlighted in the literature underscores the dynamic nature of this field, emphasizing the need for ongoing research and fitted optimization for specific applications.

Delivery of Theranostic Nanoparticles to Various Cancers by Means of Integrin-Binding Peptides

Active targeting of tumors is believed to be the key to efficient cancer therapy and accurate, early-stage diagnostics. Active targeting implies minimized off-targeting and associated cytotoxicity towards healthy tissue. One way to acquire active targeting is to employ conjugates of therapeutic agents with ligands known to bind receptors overexpressed onto cancer cells. The integrin receptor family has been studied as a target for cancer treatment for almost fifty years. However, systematic knowledge on their effects on cancer cells, is yet lacking, especially when utilized as an active targeting ligand for particulate formulations. Decoration with various integrin-targeting peptides has been reported to increase nanoparticle accumulation in tumors ≥ 3-fold when compared to passively targeted delivery. In recent years, many newly discovered or rationally designed integrin-binding peptides with excellent specificity towards a single integrin receptor have emerged. Here, we show a comprehensive analysis of previously unreviewed integrin-binding peptides, provide diverse modification routes for nanoparticle conjugation, and showcase the most notable examples of their use for tumor and metastases visualization and eradication to date, as well as possibilities for combined cancer therapies for a synergetic effect. This review aims to highlight the latest advancements in integrin-binding peptide development and is directed to aid transition to the development of novel nanoparticle-based theranostic agents for cancer therapy.

Pycard and BC017158 Candidate Genes of Irm1 Locus Modulate Inflammasome Activation for IL-1β Production

We identified Pycard and BC017158 genes as putative effectors of the Quantitative Trait locus (QTL) that we mapped at distal chromosome 7 named Irm1 for Inflammatory response modulator 1, controlling acute inflammatory response (AIR) and the production of IL-1β, dependent on the activation of the NLRP3 inflammasome. We obtained the mapping through genome-wide linkage analysis of Single Nucleotide Polymorphisms (SNPs) in a cross between High (AIRmax) and Low (AIRmin) responder mouse lines that we produced by several generations of bidirectional selection for Acute Inflammatory Response. A highly significant linkage signal (LOD score peak of 72) for ex vivo IL-1β production limited a 4 Mbp interval to chromosome 7. Sequencing of the locus region revealed 14 SNPs between “High” and “Low” responders that narrowed the locus to a 420 Kb interval. Variants were detected in non-coding regions of Itgam, Rgs10 and BC017158 genes and at the first exon of Pycard gene, resulting in an E19K substitution in the protein ASC (apoptosis associated speck-like protein containing a CARD) an adaptor molecule in the inflammasome complex. Silencing of BC017158 inhibited IL1-β production by stimulated macrophages and the E19K ASC mutation carried by AIRmin mice impaired the ex vivo IL-1β response and the formation of ASC specks in stimulated cells. IL-1β and ASC specks play major roles in inflammatory reactions and in inflammation-related diseases. Our results delineate a novel genetic factor and a molecular mechanism affecting the acute inflammatory response.

Highly Sensitive Nanomagnetic Quantification of Extracellular Vesicles by Immunochromatographic Strips: A Tool for Liquid Biopsy

Extracellular vesicles (EVs) are promising agents for liquid biopsy-a non-invasive approach for the diagnosis of cancer and evaluation of therapy response. However, EV potential is limited by the lack of sufficiently sensitive, time-, and cost-efficient methods for their registration. This research aimed at developing a highly sensitive and easy-to-use immunochromatographic tool based on magnetic nanoparticles for EV quantification. The tool is demonstrated by detection of EVs isolated from cell culture supernatants and various body fluids using characteristic biomarkers, CD9 and CD81, and a tumor-associated marker-epithelial cell adhesion molecules. The detection limit of 3.7 × 105 EV/µL is one to two orders better than the most sensitive traditional lateral flow system and commercial ELISA kits. The detection specificity is ensured by an isotype control line on the test strip. The tool's advantages are due to the spatial quantification of EV-bound magnetic nanolabels within the strip volume by an original electronic technique. The inexpensive tool, promising for liquid biopsy in daily clinical routines, can be extended to other relevant biomarkers.

Fully automated platelet differential interference contrast image analysis via deep learning

Platelets mediate arterial thrombosis, a leading cause of myocardial infarction and stroke. During injury, platelets adhere and spread over exposed subendothelial matrix substrates of the damaged blood vessel wall. The mechanisms which govern platelet activation and their interaction with a range of substrates are therefore regularly investigated using platelet spreading assays. These assays often use differential interference contrast (DIC) microscopy to assess platelet morphology and analysis performed using manual annotation. Here, a convolutional neural network (CNN) allowed fully automated analysis of platelet spreading assays captured by DIC microscopy. The CNN was trained using 120 generalised training images. Increasing the number of training images increases the mean average precision of the CNN. The CNN performance was compared to six manual annotators. Significant variation was observed between annotators, highlighting bias when manual analysis is performed. The CNN effectively analysed platelet morphology when platelets spread over a range of substrates (CRP-XL, vWF and fibrinogen), in the presence and absence of inhibitors (dasatinib, ibrutinib and PRT-060318) and agonist (thrombin), with results consistent in quantifying spread platelet area which is comparable to published literature. The application of a CNN enables, for the first time, automated analysis of platelet spreading assays captured by DIC microscopy.

Imaging flow cytometry data analysis using convolutional neural network for quantitative investigation of phagocytosis

Macrophages play an important role in the adaptive immune system. Their ability to neutralize cellular targets through Fc receptor-mediated phagocytosis has relied upon immunotherapy that has become of particular interest for the treatment of cancer and autoimmune diseases. A detailed investigation of phagocytosis is the key to the improvement of the therapeutic efficiency of existing medications and the creation of new ones. A promising method for studying the process is imaging flow cytometry (IFC) that acquires thousands of cell images per second in up to 12 optical channels and allows multiparametric fluorescent and morphological analysis of samples in the flow. However, conventional IFC data analysis approaches are based on a highly subjective manual choice of masks and other processing parameters that can lead to the loss of valuable information embedded in the original image. Here, we show the application of a Faster region-based convolutional neural network (CNN) for accurate quantitative analysis of phagocytosis using imaging flow cytometry data. Phagocytosis of erythrocytes by peritoneal macrophages was chosen as a model system. CNN performed automatic high-throughput processing of datasets and demonstrated impressive results in the identification and classification of macrophages and erythrocytes, despite the variety of shapes, sizes, intensities, and textures of cells in images. The developed procedure allows determining the number of phagocytosed cells, disregarding cases with a low probability of correct classification. We believe that CNN-based approaches will enable powerful in-depth investigation of a wide range of biological processes and will reveal the intricate nature of heterogeneous objects in images, leading to completely new capabilities in diagnostics and therapy.

Review: tomographic imaging flow cytometry

Within the last decades, conventional flow cytometry (FC) has evolved as a powerful measurement method in clinical diagnostics, biology, life sciences and healthcare. Imaging flow cytometry (IFC) extends the power of traditional FC by adding high resolution optical and spectroscopic information. However, the conventional IFC only provides a 2D projection of a 3D object. To overcome this limitation, tomographic imaging flow cytometry (tIFC) was developed to access 3D information about the target particles. The goal of tIFC is to visualize surfaces and internal structures in a holistic way. This review article gives an overview of the past and current developments in tIFC.

Radachlorin-Containing Microparticles for Photodynamic Therapy

Purpose: Reducing the undesirable systemic effect of photodynamic therapy (PDT) can be achieved by incorporating a photosensitizer in microparticles (MPs). This study is devoted to the preparation of biocompatible biodegradable MPs with the inclusion of the natural photosensitizer Radachlorin (RС) and an assessment of the possibility of their use for PDT. Methods: RC-containing MPs (RС MPs) with poly(lactic-co-glycolic acid) copolymer (PLGA) matrix were prepared by a double emulsion solvent evaporation methods. The size and morphology of RC MPs were surveyed using scanning electron microscopy, confocal laser scanning microscopy, and dynamic light scattering. The content of RC, its release from RC MPs, and singlet oxygen generation were evaluated by the optical spectroscopy. Cellular uptake and cytotoxic photodynamic effect of RC MPs were investigated with in vitro assays. Results: The average diameter of the prepared RC MPs was about 2-3 μm. The RC MPs prepared by the water/oil/oil method had a significantly higher inclusion of RC (1.74 μg/mg) then RC MPs prepared by the water/oil/water method (0.089 μg/mg). Exposure of the prepared RC MPs to PDT light radiation was accompanied by the singlet oxygen generation and a cytotoxic effect for tumor cells. The release of the RC from the RC MPs was prolonged and lasted at least two weeks. Conclusion: PLGA RC MPs were found to cause a photoactivated cytotoxic effect for tumor cells and can be used for local application in PDT of tumors.

Hematite Nanoparticles from Unexpected Reaction of Ferrihydrite with Concentrated Acids for Biomedical Applications

The development of synthetic ways to fabricate nanosized materials with a well-defined shape, narrow-sized distribution, and high stability is of great importance to a rapidly developing area of nanotechnology. Here, we report an unusual reaction between amorphous two-line ferrihydrite and concentrated sulfuric or other mineral and organic acids. Instead of the expected dissolution, we observed the formation of new narrow-distributed brick-red nanoparticles (NPs) of hematite. Different acids produce similar nanoparticles according to scanning (SEM) and transmission electron microscopy (TEM), selected area electron diffraction (SAED), X-ray diffraction (XRD), infrared spectroscopy (FTIR), and energy-dispersive X-ray spectroscopy (EDX). The reaction demonstrates new possibilities for the synthesis of acid-resistant iron oxide nanoparticles and shows a novel pathway for the reaction of iron hydroxide with concentrated acids. The biomedical potential of the fabricated nanoparticles is demonstrated by the functionalization of the particles with polymers, fluorescent labels, and antibodies. Three different applications are demonstrated: i) specific targeting of the red blood cells, e.g., for red blood cell (RBC)-hitchhiking; ii) cancer cell targeting in vitro; iii) infrared ex vivo bioimaging. This novel synthesis route may be useful for the development of iron oxide materials for such specificity-demanding applications such as nanosensors, imaging, and therapy.

Spindle-like MRI-active europium-doped iron oxide nanoparticles with shape-induced cytotoxicity from simple and facile ferrihydrite crystallization procedure

Nanoparticles (NPs) that can provide additional functionality to the nanoagents derived from them, e.g., cytotoxicity or imaging abilities, are in high demand in modern nanotechnology. Here, we report new spindle-like iron oxide nanoparticles doped with Eu3+ that feature magnetic resonance imaging (MRI) contrasting properties together with shape-related cytotoxicity (unusual for such low 2.4% Eu content). The NPs were prepared by a novel procedure for doping of iron oxide nanoparticles based on the crystallization of amorphous ferrihydrite in the presence of hydrated europium(iii) oxide and were thoroughly characterized. Cytotoxicity of low Eu-doped spindle-like hematite nanoparticles was confirmed by MTT assay and further studied in detail by imaging flow cytometry, optical and electron microscopies. Additionally, enhancement of MRI contrast properties of NPs upon doping with europium was demonstrated. According to the MRI using mice as an animal model and direct inductively coupled plasma mass spectrometry (ICP-MS) 153Eu biodistribution measurements, these particles accumulate in the liver and spleen. Therefore, NPs present a novel example of a multimodal component combining magnetic imaging and therapeutic (cytotoxic) abilities for development of theranostic nanoagents.