Calcium and ROS: A mutual interplay

AbstractCalcium is an important second messenger involved in intra- and extracellular signaling cascades and plays an essential role in cell life and death decisions. The Ca2+ signaling network works in many different ways to regulate cellular processes that function over a wide dynamic range due to the action of buffers, pumps and exchangers on the plasma membrane as well as in internal stores. Calcium signaling pathways interact with other cellular signaling systems such as reactive oxygen species (ROS). Although initially considered to be potentially detrimental byproducts of aerobic metabolism, it is now clear that ROS generated in sub-toxic levels by different intracellular systems act as signaling molecules involved in various cellular processes including growth and cell death. Increasing evidence suggests a mutual interplay between calcium and ROS signaling systems which seems to have important implications for fine tuning cellular signaling networks. However, dysfunction in either of the systems might affect the other system thus potentiating harmful effects which might contribute to the pathogenesis of various disorders

Automated Volume Corrected Mitotic Index Calculation Through Annotation-Free Deep Learning using Immunohistochemistry as Reference Standard

The volume-corrected mitotic index (M/V-Index) was shown to provide prognostic value in invasive breast carcinomas. However, despite its prognostic significance, it is not established as the standard method for assessing aggressive biological behaviour, due to the high additional workload associated with determining the epithelial proportion. In this work, we show that using a deep learning pipeline solely trained with an annotation-free, immunohistochemistry-based approach, provides accurate estimations of epithelial segmentation in canine breast carcinomas. We compare our automatic framework with the manually annotated M/V-Index in a study with three board-certified pathologists. Our results indicate that the deep learning-based pipeline shows expert-level performance, while providing time efficiency and reproducibility

Deep learning-based Subtyping of Atypical and Normal Mitoses using a Hierarchical Anchor-Free Object Detector

Mitotic activity is key for the assessment of malignancy in many tumors. Moreover, it has been demonstrated that the proportion of abnormal mitosis to normal mitosis is of prognostic significance. Atypical mitotic figures (MF) can be identified morphologically as having segregation abnormalities of the chromatids. In this work, we perform, for the first time, automatic subtyping of mitotic figures into normal and atypical categories according to characteristic morphological appearances of the different phases of mitosis. Using the publicly available MIDOG21 and TUPAC16 breast cancer mitosis datasets, two experts blindly subtyped mitotic figures into five morphological categories. Further, we set up a state-of-the-art object detection pipeline extending the anchor-free FCOS approach with a gated hierarchical subclassification branch. Our labeling experiment indicated that subtyping of mitotic figures is a challenging task and prone to inter-rater disagreement, which we found in 24.89% of MF. Using the more diverse MIDOG21 dataset for training and TUPAC16 for testing, we reached a mean overall average precision score of 0.552, a ROC AUC score of 0.833 for atypical/normal MF and a mean class-averaged ROC-AUC score of 0.977 for discriminating the different phases of cells undergoing mitosis.Comment: 6 pages, 2 figures, 2 table

Transcriptomic Deconvolution of Neuroendocrine Neoplasms Predicts Clinically Relevant Characteristics

Pancreatic neuroendocrine neoplasms (panNENs) are a rare yet diverse type of neoplasia whose precise clinical–pathological classification is frequently challenging. Since incorrect classifications can affect treatment decisions, additional tools which support the diagnosis, such as machine learning (ML) techniques, are critically needed but generally unavailable due to the scarcity of suitable ML training data for rare panNENs. Here, we demonstrate that a multi-step ML framework predicts clinically relevant panNEN characteristics while being exclusively trained on widely available data of a healthy origin. The approach classifies panNENs by deconvolving their transcriptomes into cell type proportions based on shared gene expression profiles with healthy pancreatic cell types. The deconvolution results were found to provide a prognostic value with respect to the prediction of the overall patient survival time, neoplastic grading, and carcinoma versus tumor subclassification. The performance with which a proliferation rate agnostic deconvolution ML model could predict the clinical characteristics was found to be comparable to that of a comparative baseline model trained on the proliferation rate-informed MKI67 levels. The approach is novel in that it complements established proliferation rate-oriented classification schemes whose results can be reproduced and further refined by differentiating between identically graded subgroups. By including non-endocrine cell types, the deconvolution approach furthermore provides an in silico quantification of panNEN dedifferentiation, optimizing it for challenging clinical classification tasks in more aggressive panNEN subtypes.Peer Reviewe

Technical success and associated economic implications of conventional re-entry devices in subintimal recanalization of femoro-popliteal chronic total occlusions

PURPOSERe-entry devices contribute to the high success rate of subintimal recanalization of chronic total occlusions (CTO). However, to date, there are no studies comparing the available conventional re-entry devices concerning the impact of their technical success on economic aspects, as these devices differ greatly in their acquisition costs. This prospective observational study intends to contribute to this question.METHODSPrior to the start of the prospective study, all previous applications of the Outback® in femoro-popliteal CTO since its introduction to our hospital were analyzed retrospectively (n = 31). From June 2018 until January 2020, all patients with femoro-popliteal CTO treated with clear subintimal recanalization were included (n = 109). In the case of failed spontaneous re-entry, either the OffRoad® (study arm I, n = 20) or the Enteer® catheter (study arm II, n = 20) was used. If assisted re-entry failed, the Outback® device was used as a bailout. Baseline demographic and clinical data, morphologic characteristics, and technical success were documented. Additional per-patient costs due to the use of re-entry devices were analyzed.RESULTSA retrospective evaluation of all Outback® applications revealed a technical success rate of 97% (30/31). In the prospective study, 63% (68/109) were successfully treated without using re-entry devices. The overall procedural success was 95% (103/109). In study arm I, the OffRoad® achieved a success rate of 45% (9/20), with a subsequent successful application of the Outback® in 80% (8/10) of the failed cases. In study arm II, the Enteer® was successfully employed in 60% (12/20) of cases, and the Outback® was then used successfully in a further 62% (5/8) of cases. Too large a distance between the device and the target lumen was a knockout criterion for all tested devices, leading to a subgroup analysis with the exclusion of three cases, resulting in a success rate of 47% for the OffRoad® and 67% for the Enteer® device. Furthermore, in severe calcification, only the Outback® reliably enabled revascularization. Significant savings of almost €600 were only achieved in study arm II according to German prices.CONCLUSIONWith proper patient selection, a gradual approach with the Enteer® as the primarily used device, with the Outback® used additionally in case of failure, leads to significant savings and can be recommended. In severe calcification, the Outback® should be used as the primary device

EXACT: a collaboration toolset for algorithm-aided annotation of images with annotation version control

In many research areas, scientific progress is accelerated by multidisciplinary access to image data and their interdisciplinary annotation. However, keeping track of these annotations to ensure a high-quality multi-purpose data set is a challenging and labour intensive task. We developed the open-source online platform EXACT (EXpert Algorithm Collaboration Tool) that enables the collaborative interdisciplinary analysis of images from different domains online and offline. EXACT supports multi-gigapixel medical whole slide images as well as image series with thousands of images. The software utilises a flexible plugin system that can be adapted to diverse applications such as counting mitotic figures with a screening mode, finding false annotations on a novel validation view, or using the latest deep learning image analysis technologies. This is combined with a version control system which makes it possible to keep track of changes in the data sets and, for example, to link the results of deep learning experiments to specific data set versions. EXACT is freely available and has already been successfully applied to a broad range of annotation tasks, including highly diverse applications like deep learning supported cytology scoring, interdisciplinary multi-centre whole slide image tumour annotation, and highly specialised whale sound spectroscopy clustering

Pan-tumor CAnine cuTaneous Cancer Histology (CATCH) dataset

Due to morphological similarities, the differentiation of histologic sections of cutaneous tumors into individual subtypes can be challenging. Recently, deep learning-based approaches have proven their potential for supporting pathologists in this regard. However, many of these supervised algorithms require a large amount of annotated data for robust development. We present a publicly available dataset of 350 whole slide images of seven different canine cutaneous tumors complemented by 12,424 polygon annotations for 13 histologic classes, including seven cutaneous tumor subtypes. In inter-rater experiments, we show a high consistency of the provided labels, especially for tumor annotations. We further validate the dataset by training a deep neural network for the task of tissue segmentation and tumor subtype classification. We achieve a class-averaged Jaccard coefficient of 0.7047, and 0.9044 for tumor in particular. For classification, we achieve a slide-level accuracy of 0.9857. Since canine cutaneous tumors possess various histologic homologies to human tumors the added value of this dataset is not limited to veterinary pathology but extends to more general fields of application

Renal function stratified dose comparisons of eplerenone versus placebo in the EMPHASIS-HF trial.

BACKGROUND: Current heart failure guidelines recommend target eplerenone dose of 50 mg/day. We have examined the effect of different eplerenone doses based on pre-specified renal function stratification in the Eplerenone in Mild Patients Hospitalization and Survival Study in Heart Failure (EMPHASIS-HF). METHODS AND RESULTS: In EMPHASIS-HF, the target dose of eplerenone/placebo was stratified at randomization according to estimated glomerular filtration rate (eGFR): 50 mg/day if eGFR ≥ 50 mL/min/1.73 m2 and ≤ 25 mg/day if eGFR 30-49 mL/min/1.73 m2 . Patients remained within these dose ranges during the trial (as per stratification). The primary outcome was a composite of heart failure hospitalization or cardiovascular mortality. Eplerenone was superior to placebo within each respective eGFR stratum [eplerenone vs. placebo in the eGFR ≥ 50 mL/min/1.73 m2 stratum: hazard ratio (HR) 0.58, 95% confidence interval (CI) 0.45-0.74; and eplerenone vs. placebo in the eGFR 30-49 mL/min/1.73 m2 stratum: HR 0.62, 95% CI 0.49-0.78; Pinteraction  = 0.89]. Despite receiving lower eplerenone doses, patients in the eGFR 30-49 mL/min/1.73 m2 stratum more often had hyperkalaemia, renal failure events, and drug discontinuation. CONCLUSION: In EMPHASIS-HF the eplerenone dose was stratified according to renal function and the treatment effect was not influenced by renal function: 25 mg/day in patients with eGFR 30-49 mL/min/1.73 m2 was as effective as 50 mg/day in patients with eGFR > =50 mL/min/1.73 m2 . However, patients with impaired renal function experienced more adverse events, despite reveiving lower eplerenone doses. Current guidelines do not recommend tailoring the dose of eplereone according to renal function but the current data suggest they should