Flow Cytometry of Breast Cancer Resistant Protein and microRNA in Breast Cancer Patients Post Metformin Effect

The goal of the present study is to investigate the role of metformin (MF) as a target of miRNAs in breast cancer resistant protein (BCRP) inhibition in an attempt to develop treatment strategies that may improve the response of breast cancer (BC) patients to chemotherapy (CT). In order to fulfill this target, non-diabetic female subjects were categorized into three groups: control group (group 1) (n=5), CT group of BC patients (group 2) (n=25) and CT plus MF group of BC patients (group 3) (n=25). All patients were subjected to full history taking, laboratory studies including mammogram, chest X-ray, pelvic-abdominal ultrasound and isotopic bone scan, in addition to ER and PR states. CT group was treated with neoadjuvent CT in the form of 5-FU (500 mg/m2), Adriamycin (50 mg/m2) and cyclophosphamide (500 mg/m2). Flow cytometry (FC) of BCRP and MiRNA was carried out on blood samples at every cycle of treatment for all partners. The results showed the presence of miRNA was higher than the presence of BCRP in the normal healthy control group. In most cases of CT and CT plus MF groups (group 2, 3) it was well noticed that the amount of BCRP in the blood samples exceeded that of miRNA illustrated the dysregulation of miRNA in BC patients and also to prove the basic role of BCRP as a multidrug resistance (MDR) for chemotherapeutic agents in patients with BC. It is concluded that the role of MF was well proved in targeting of miRNA to reinforce BC medication, so oncologists can be advised to use MF equivalent to CT in the recommended doses

NuCLS: A scalable crowdsourcing, deep learning approach and dataset for nucleus classification, localization and segmentation

High-resolution mapping of cells and tissue structures provides a foundation for developing interpretable machine-learning models for computational pathology. Deep learning algorithms can provide accurate mappings given large numbers of labeled instances for training and validation. Generating adequate volume of quality labels has emerged as a critical barrier in computational pathology given the time and effort required from pathologists. In this paper we describe an approach for engaging crowds of medical students and pathologists that was used to produce a dataset of over 220,000 annotations of cell nuclei in breast cancers. We show how suggested annotations generated by a weak algorithm can improve the accuracy of annotations generated by non-experts and can yield useful data for training segmentation algorithms without laborious manual tracing. We systematically examine interrater agreement and describe modifications to the MaskRCNN model to improve cell mapping. We also describe a technique we call Decision Tree Approximation of Learned Embeddings (DTALE) that leverages nucleus segmentations and morphologic features to improve the transparency of nucleus classification models. The annotation data produced in this study are freely available for algorithm development and benchmarking at: https://sites.google.com/view/nucls