Karpinski Score under Digital Investigation: A Fully Automated Segmentation Algorithm to Identify Vascular and Stromal Injury of Donors’ Kidneys

In kidney transplantations, the evaluation of the vascular structures and stromal areas is crucial for determining kidney acceptance, which is currently based on the pathologist's visual evaluation. In this context, an accurate assessment of the vascular and stromal injury is fundamental to assessing the nephron status. In the present paper, the authors present a fully automated algorithm, called RENFAST (Rapid EvaluatioN of Fibrosis And vesselS Thickness), for the segmentation of kidney blood vessels and fibrosis in histopathological images. The proposed method employs a novel strategy based on deep learning to accurately segment blood vessels, while interstitial fibrosis is assessed using an adaptive stain separation method. The RENFAST algorithm is developed and tested on 350 periodic acid-Schiff (PAS) images for blood vessel segmentation and on 300 Massone's trichrome (TRIC) stained images for the detection of renal fibrosis. In the TEST set, the algorithm exhibits excellent segmentation performance in both blood vessels (accuracy: 0.8936) and fibrosis (accuracy: 0.9227) and outperforms all the compared methods. To the best of our knowledge, the RENFAST algorithm is the first fully automated method capable of detecting both blood vessels and fibrosis in digital histological images. Being very fast (average computational time 2.91 s), this algorithm paves the way for automated, quantitative, and real-time kidney graft assessments

Karpinski Score under Digital Investigation: A Fully Automated Segmentation Algorithm to Identify Vascular and Stromal Injury of Donors’ Kidneys

In kidney transplantations, the evaluation of the vascular structures and stromal areas is crucial for determining kidney acceptance, which is currently based on the pathologist’s visual evaluation. In this context, an accurate assessment of the vascular and stromal injury is fundamental to assessing the nephron status. In the present paper, the authors present a fully automated algorithm, called RENFAST (Rapid EvaluatioN of Fibrosis And vesselS Thickness), for the segmentation of kidney blood vessels and fibrosis in histopathological images. The proposed method employs a novel strategy based on deep learning to accurately segment blood vessels, while interstitial fibrosis is assessed using an adaptive stain separation method. The RENFAST algorithm is developed and tested on 350 periodic acid–Schiff (PAS) images for blood vessel segmentation and on 300 Massone’s trichrome (TRIC) stained images for the detection of renal fibrosis. In the TEST set, the algorithm exhibits excellent segmentation performance in both blood vessels (accuracy: 0.8936) and fibrosis (accuracy: 0.9227) and outperforms all the compared methods. To the best of our knowledge, the RENFAST algorithm is the first fully automated method capable of detecting both blood vessels and fibrosis in digital histological images. Being very fast (average computational time 2.91 s), this algorithm paves the way for automated, quantitative, and real-time kidney graft assessments

CD19-Targeted Immunotherapies for Diffuse Large B-Cell Lymphoma

Surgical resection, chemotherapy and radiotherapy were, for many years, the only available cancer treatments. Recently, the use of immune checkpoint inhibitors and adoptive cell therapies has emerged as promising alternative. These cancer immunotherapies are aimed to support or harness the patient\u2019s immune system to recognize and destroy cancer cells. Preclinical and clinical studies, based on the use of T cells and more recently NK cells genetically modified with chimeric antigen receptors retargeting the adoptive cell therapy towards tumor cells, have already shown remarkable results. In this review, we outline the latest highlights and progress in immunotherapies for the treatment of Diffuse Large B-cell Lymphoma (DLBCL) patients, focusing on CD19-targeted immunotherapies. We also discuss current clinical trials and opportunities of using immunotherapies to treat DLBCL patients

Identification of Tree Species in Japanese Forests based on Aerial Photography and Deep Learning

Natural forests are complex ecosystems whose tree species distribution and their ecosystem functions are still not well understood. Sustainable management of these forests is of high importance because of their significant role in climate regulation, biodiversity, soil erosion and disaster prevention among many other ecosystem services they provide. In Japan particularly, natural forests are mainly located in steep mountains, hence the use of aerial imagery in combination with computer vision are important modern tools that can be applied to forest research. Thus, this study constitutes a preliminary research in this field, aiming at classifying tree species in Japanese mixed forests using UAV images and deep learning in two different mixed forest types: a black pine (Pinus thunbergii)-black locust (Robinia pseudoacacia) and a larch (Larix kaempferi)-oak (Quercus mongolica) mixed forest. Our results indicate that it is possible to identify black locust trees with 62.6 % True Positives (TP) and 98.1% True Negatives (TN), while lower precision was reached for larch trees (37.4% TP and 97.7% TN).Comment: Proc. of EnviroInfo 2020, Nicosia, Cyprus, September 202

Minimal residual disease after transplantation or lenalidomide-based consolidation in myeloma patients: a prospective analysis

We analyzed 50 patients who achieved at least a very good partial response in the RV-MM-EMN-441 study. Patients received consolidation with autologous stem-cell transplantation (ASCT) or cyclophosphamide-lenalidomide-dexamethasone (CRD), followed by Lenalidomide-based maintenance. We assessed minimal residual disease (MRD) by multi-parameter flow cytometry (MFC) and allelic-specific oligonucleotide real-time quantitative polymerase chain reaction (ASO-RQ-PCR) after consolidation, after 3 and 6 courses of maintenance, and thereafter every 6 months until progression. By MFC analysis, 19/50 patients achieved complete response (CR) after consolidation, and 7 additional patients during maintenance. A molecular marker was identified in 25/50 patients, 4/25 achieved molecular-CR after consolidation, and 3 additional patients during maintenance. A lower MRD value by MFC was found in ASCT patients compared with CRD patients (p = 0.0134). Tumor burden reduction was different in patients with high-risk vs standard-risk cytogenetics (3.4 vs 5.2, ln-MFC; 3 vs 6 ln-PCR, respectively) and in patients who relapsed vs those who did not (4 vs 5, ln-MFC; 4.4 vs 7.8 ln-PCR). MRD progression anticipated clinical relapse by a median of 9 months while biochemical relapse by a median of 4 months. MRD allows the identification of a low-risk group, independently of response, and a better characterization of the activity of treatments