Pyrazole-Enriched Cationic Nanoparticles Induced Early- and Late-Stage Apoptosis in Neuroblastoma Cells at Sub-Micromolar Concentrations.

Neuroblastoma (NB) is a severe form of tumor occurring mainly in young children and originating from nerve cells found in the abdomen or next to the spine. NB needs more effective and safer treatments, as the chance of survival against the aggressive form of this disease are very small. Moreover, when current treatments are successful, they are often responsible for unpleasant health problems which compromise the future and life of surviving children. As reported, cationic macromolecules have previously been found to be active against bacteria as membrane disruptors by interacting with the negative constituents of the surface of cancer cells, analogously inducing depolarization and permeabilization, provoking lethal damage to the cytoplasmic membrane, and cause loss of cytoplasmic content and consequently, cell death. Here, aiming to develop new curative options for counteracting NB cells, pyrazole-loaded cationic nanoparticles (NPs) (BBB4-G4K and CB1H-P7 NPs), recently reported as antibacterial agents, were assayed against IMR 32 and SHSY 5Y NB cell lines. Particularly, while BBB4-G4K NPs demonstrated low cytotoxicity against both NB cell lines, CB1H-P7 NPs were remarkably cytotoxic against both IMR 32 and SHSY 5Y cells (IC50 = 0.43-0.54 µM), causing both early-stage (66-85%) and late-stage apoptosis (52-65%). Interestingly, in the nano-formulation of CB1H using P7 NPs, the anticancer effects of CB1H and P7 were increased by 54-57 and 2.5-4-times, respectively against IMR 32 cells, and by 53-61 and 1.3-2 times against SHSY 5Y cells. Additionally, based on the IC50 values, CB1H-P7 was also 1-12-fold more potent than fenretinide, an experimental retinoid derivative in a phase III clinical trial, with remarkable antineoplastic and chemopreventive properties. Collectively, due to these results and their good selectivity for cancer cells (selectivity indices = 2.8-3.3), CB1H-P7 NPs represent an excellent template material for developing new treatment options against NB.

Detection of Necrosis in Digitised Whole-Slide Images for Better Grading of Canine Soft-Tissue Sarcomas Using Machine-Learning.

The definitive diagnosis of canine soft-tissue sarcomas (STSs) is based on histological assessment of formalin-fixed tissues. Assessment of parameters, such as degree of differentiation, necrosis score and mitotic score, give rise to a final tumour grade, which is important in determining prognosis and subsequent treatment modalities. However, grading discrepancies are reported to occur in human and canine STSs, which can result in complications regarding treatment plans. The introduction of digital pathology has the potential to help improve STS grading via automated determination of the presence and extent of necrosis. The detected necrotic regions can be factored in the grading scheme or excluded before analysing the remaining tissue. Here we describe a method to detect tumour necrosis in histopathological whole-slide images (WSIs) of STSs using machine learning. Annotated areas of necrosis were extracted from WSIs and the patches containing necrotic tissue fed into a pre-trained DenseNet161 convolutional neural network (CNN) for training, testing and validation. The proposed CNN architecture reported favourable results, with an overall validation accuracy of 92.7% for necrosis detection which represents the number of correctly classified data instances over the total number of data instances. The proposed method, when vigorously validated represents a promising tool to assist pathologists in evaluating necrosis in canine STS tumours, by increasing efficiency, accuracy and reducing inter-rater variation.

Effect of nanovesicular surface-functionalization via chitosan and/or PEGylation on cytotoxicity of tamoxifen in induced-breast cancer model.

AIMS: The effect of surface-modification of Tamoxifen (Tam)-loaded-niosomes on drug cytotoxicity and bio-distribution, via functionalization with chitosan and/or PEGylation, was investigated. MATERIALS AND METHODS: Tam-loaded hybrid-nanocarriers (Tam-loaded niosomes, chitosomes, PEGylated niosomes, and PEGylated chitosomes) were formulated and characterized. KEY FINDINGS: Chitosanization with/without PEGylation proved to selectively enhance Tam-release at the cancerous-acidic micromilieu. Cytotoxic activity study showed that Tam-loaded PEGylated niosomes had a lower IC50 value on MCF-7 cell line (0.39, 0.35, and 0.27 times) than Tam-loaded PEGylated chitosomes, Tam-loaded niosomes, and Tam-loaded chitosomes, respectively. Cell cycle analysis showed that PEGylation and/or Chitosanization significantly impact Tam efficiency in inducing apoptosis, with a preferential influence of PEGylation over chitosanization. The assay of Annexin-V/PI double staining revealed that chitosanized-nanocarriers had a significant role in increasing the incidence of apoptosis over necrosis. Besides, PEGylated-nanocarriers increased apoptosis, as well as total death and necrosis percentages more than what was shown from free Tam. Moreover, the average changes in both Bax/Bcl-2 ratio and Caspase 9 were best improved in cells treated by Tam-loaded PEGylated niosomes over all other formulations. The in-vivo study involving DMBA-induced-breast cancer rats revealed that PEGylation made the highest tumor-growth inhibition (84.9 %) and breast tumor selectivity, while chitosanization had a lower accumulation tendency in the blood (62.3 ng/ml) and liver tissues (103.67 ng/ml). The histopathological specimens from the group treated with Tam-loaded PEGylated niosomes showed the best improvement over other formulations. SIGNIFICANCE: All these results concluded the crucial effect of both PEGylation and chitosan-functionalization of Tam-loaded niosomes in enhancing effectiveness, targetability, and safety.

Deep convolutional neural networks for automatic classification of gastric carcinoma using whole slide images in digital histopathology.

Deep learning using convolutional neural networks is an actively emerging field in histological image analysis. This study explores deep learning methods for computer-aided classification in H&E stained histopathological whole slide images of gastric carcinoma. An introductory convolutional neural network architecture is proposed for two computerized applications, namely, cancer classification based on immunohistochemical response and necrosis detection based on the existence of tumor necrosis in the tissue. Classification performance of the developed deep learning approach is quantitatively compared with traditional image analysis methods in digital histopathology requiring prior computation of handcrafted features, such as statistical measures using gray level co-occurrence matrix, Gabor filter-bank responses, LBP histograms, gray histograms, HSV histograms and RGB histograms, followed by random forest machine learning. Additionally, the widely known AlexNet deep convolutional framework is comparatively analyzed for the corresponding classification problems. The proposed convolutional neural network architecture reports favorable results, with an overall classification accuracy of 0.6990 for cancer classification and 0.8144 for necrosis detection.

MRI Brain Tumor Segmentation and Necrosis Detection Using Adaptive Sobolev Snakes.

Brain tumor segmentation in brain MRI volumes is used in neurosurgical planning and illness staging. It is important to explore the tumor shape and necrosis regions at different points of time to evaluate the disease progression. We propose an algorithm for semi-automatic tumor segmentation and necrosis detection. Our algorithm consists of three parts: conversion of MRI volume to a probability space based on the on-line learned model, tumor probability density estimation, and adaptive segmentation in the probability space. We use manually selected acceptance and rejection classes on a single MRI slice to learn the background and foreground statistical models. Then, we propagate this model to all MRI slices to compute the most probable regions of the tumor. Anisotropic 3D diffusion is used to estimate the probability density. Finally, the estimated density is segmented by the Sobolev active contour (snake) algorithm to select smoothed regions of the maximum tumor probability. The segmentation approach is robust to noise and not very sensitive to the manual initialization in the volumes tested. Also, it is appropriate for low contrast imagery. The irregular necrosis regions are detected by using the outliers of the probability distribution inside the segmented region. The necrosis regions of small width are removed due to a high probability of noisy measurements. The MRI volume segmentation results obtained by our algorithm are very similar to expert manual segmentation.