Current Issue : April-June Volume : 2024 Issue Number : 2 Articles : 5 Articles
Diabetic macular edema (DME) and age-related macular degeneration (AMD) are two common eye diseases. They are often undiagnosed or diagnosed late. This can result in permanent and irreversible vision loss. Therefore, early detection and treatment of these diseases can prevent vision loss, save money, and provide a better quality of life for individuals. Optical coherence tomography (OCT) imaging is widely applied to identify eye diseases, including DME and AMD. In this work, we developed automatic deep learning-based methods to detect these pathologies using SD-OCT scans. The convolutional neural network (CNN) from scratch we developed gave the best classification score with an accuracy higher than 99% on Duke dataset of OCT images....
Although the World Health Organization has declared the end of the COVID-19 pandemic, doctors continue to register new cases of the disease among both adults and children. Unfortunately, the course of COVID-19 in children can have a severe form, with death being a potential outcome. The absence of published works discussing the pathological morphology of COVID-19 in children prevents the objective analysis of the disease’s pathogenesis, including among the adult population. In this vein, the objective of our study is to identify the morphological features of the lungs’ involvement and evaluate virus–host interactions in the case of COVID-19 in patients at a pediatric medical practice. We present the results of the study of the lungs of three children who died due to COVID-19, highlighting the predominant involvement of their respiratory organs at different stages of the disease (5, 21, and 50 days). This article presents data obtained from histopathological and immunohistochemical investigations, taking into account the results of clinical and laboratory indicators and intravital and postmortem SARS-CoV-2 PCR investigations. The common finding of all of the examined COVID-19 cases is the involvement of the endothelium in microcirculation vessels, which are considered to be a primary target of various pathogenic influencing factors. We also discuss both the significance of apoptosis as a result of virus–host interactions and the most likely cause of endothelium cell destruction. The results of this study could be useful for the development of endothelium-protective therapy to prevent the progression of disseminated intravascular coagulation syndrome....
Purpose Although neural networks have shown remarkable performance in medical image analysis, their translation into clinical practice remains difficult due to their lack of interpretability. An emerging field that addresses this problem is Explainable AI. Methods Here, we aimed to investigate the ability of Convolutional Neural Networks (CNNs) to classify head and neck cancer histopathology. To this end, we manually annotated 101 histopathological slides of locally advanced head and neck squamous cell carcinoma. We trained a CNN to classify tumor and non-tumor tissue, and another CNN to semantically segment four classes - tumor, non-tumor, non-specified tissue, and background. We applied Explainable AI techniques, namely Grad-CAM and HR-CAM, to both networks and explored important features that contributed to their decisions. Results The classification network achieved an accuracy of 89.9% on previously unseen data. Our segmentation network achieved a class-averaged Intersection over Union score of 0.690, and 0.782 for tumor tissue in particular. Explainable AI methods demonstrated that both networks rely on features agreeing with the pathologist’s expert opinion. Conclusion Our work suggests that CNNs can predict head and neck cancer with high accuracy. Especially if accompanied by visual explanations, CNNs seem promising for assisting pathologists in the assessment of cancer sections....
Background Histologic evaluation of the mucosal changes associated with celiac disease is important for establishing an accurate diagnosis and monitoring the impact of investigational therapies. While the Marsh-Oberhuber classification has been used to categorize the histologic findings into discrete stages (i.e., Type 0-3c), significant variability has been documented between observers using this ordinal scoring system. Therefore, we evaluated whether pathologist- trained machine learning classifiers can be developed to objectively quantitate the pathological changes of villus blunting, intraepithelial lymphocytosis, and crypt hyperplasia in small intestine endoscopic biopsies. Methods A convolutional neural network (CNN) was trained and combined with a secondary algorithm to quantitate intraepithelial lymphocytes (IEL) with 5 classes on CD3 immunohistochemistry whole slide images (WSI) and used to correlate feature outputs with ground truth modified Marsh scores in a total of 116 small intestine biopsies. Results Across all samples, median %CD3 counts (positive cells/enterocytes) from villous epithelium (VE) increased with higher Marsh scores (Type 0%CD3 VE = 13.4; Type 1–3%CD3 VE = 41.9, p < 0.0001). Indicators of villus blunting and crypt hyperplasia were also observed (Type 0–2 villous epithelium/lamina propria area ratio = 0.81; Type 3a-3c villous epithelium/lamina propria area ratio = 0.29, p < 0.0001), and Type 0–1 crypt/villous epithelial area ratio = 0.59; Type 2–3 crypt/villous epithelial area ratio = 1.64, p < 0.0001). Using these individual features, a combined feature machine learning score (MLS) was created to evaluate a set of 28 matched pre- and post-intervention biopsies captured before and after dietary gluten restriction. The disposition of the continuous MLS paired biopsy result aligned with the Marsh score in 96.4% (27/28) of the cohort. Conclusions Machine learning classifiers can be developed to objectively quantify histologic features and capture additional data not achievable with manual scoring. Such approaches should be further investigated to improve biopsy evaluation, especially for clinical trials....
High-atomic-number (Z) nanoparticles produce a cascade of low-energy secondary electrons and characteristic X-rays when ionized by X-ray irradiation. These secondary particles deposit their energy in the vicinity of the nanoparticles and, provided that the latter are selectively accumulated within tumor cells, this results in increased DNA damage and tumor cell deaths. This study reviews the utilization of high-Z nanoparticles in the treatment of soft tissue sarcomas (STS). Both in vitro and in vivo experiments demonstrated that the dose is enhanced by approximately 1.2 when polyethelyne glycol (PEG)-modified gold nanoparticles, and from 1.4 to 1.8 when hafnium oxide nanoparticles (NBTXR3, Nanobiotix SA, France) are introduced into tumor cells and activated by X-ray beams. In a phase 2/3 clinical trial investigating the therapeutic benefit of using nanoparticles in preoperative external beam radiotherapy for locally advanced STS, the proportion of patients with a pathological complete response in their resected tumor was doubled when NBTXR3 nanoparticles were used. Additionally, a higher percentage of patients with complete tumor resection was observed in the NBTXR3 plus radiotherapy group. Similar toxicity profiles were found for both the NBTXR3 plus radiotherapy and the radiotherapy alone patient groups. The incorporation of radio-sensitizing nanoparticles in the preoperative radiotherapy of STS could enhance treatment outcomes....
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