Challenges in Building of Deep Learning Models for Glioblastoma Segmentation: Evidence from Clinical Data.

In this article, we compare the performance of a state-of-the-art segmentation network (UNet) on two different glioblastoma (GB) segmentation datasets. Our experiments show that the same training procedure yields almost twice as bad results on the retrospective clinical data compared to the BraTS challenge data (in terms of Dice score). We discuss possible reasons for such an outcome, including inter-rater variability and high variability in magnetic resonance imaging (MRI) scanners and scanner settings.

CT-Based COVID-19 triage: Deep multitask learning improves joint identification and severity quantification.

The current COVID-19 pandemic overloads healthcare systems, including radiology departments. Though several deep learning approaches were developed to assist in CT analysis, nobody considered study triage directly as a computer science problem. We describe two basic setups: Identification of COVID-19 to prioritize studies of potentially infected patients to isolate them as early as possible; Severity quantification to highlight patients with severe COVID-19, thus direct them to a hospital or provide emergency medical care.

Optimizing Connectivity-Driven Brain Parcellation Using Ensemble Clustering.

This work addresses the problem of constructing a unified, topologically optimal connectivity-based brain atlas. The proposed approach aggregates an ensemble partition from individual parcellations without label agreement, providing a balance between sufficiently flexible individual parcellations and intuitive representation of the average topological structure of the connectome. The methods exploit a previously proposed dense connectivity representation, first performing graph-based hierarchical parcellation of individual brains, and subsequently aggregating the individual parcellations into a consensus parcellation.

Phytochemical characterization of an adaptogenic preparation from Rhodiola heterodonta.

The phytochemical constituents of a biologically active, standardized, 80% ethanol extract of Rhodiola heterodonta were characterized. The extract was fractionated over a Sephadex LH-20 column to afford two main fractions representing two classes of secondary metabolites: phenylethanoids and proanthocyanidins. This fractionation facilitated the identification and quantification of individual compounds in the fractions and sub-fractions using HPLC, and LC-MS.

Plant extracts from central Asia showing antiinflammatory activities in gene expression assays.

Plant natural products remain a good resource for the discovery of novel pharmaceuticals. A mouse macrophage-based quantitative, reverse transcription polymerase chain reaction (qRT-PCR) system was optimized to screen plant extracts for antiinflammatory activities using three well known genetic markers of inflammation. Plants used for extraction were taxonomically identified and vouchered species from two Central Asian countries, Uzbekistan and Kyrgyzstan, collected through the International Cooperative Biodiversity Groups (ICBG) program.

In vitro and in vivo anti-inflammatory activity of a seed preparation containing phenethylisothiocyanate.

Winter cress (Barbarea verna) seed preparations rich in phenethylisothiocyanate (PEITC) had strong in vivo and in vitro anti-inflammatory activity, significantly reducing the size of carrageenan-induced rat paw edema. This in vivo effect was comparable with that of the nonsteroidal anti-inflammatory drug aspirin. The seed preparation, in a concentration-dependent manner, reduced the mRNA levels of inflammation-related genes such as the inducible forms of cyclooxygenase and nitric-oxide synthase and the proinflammatory cytokine interleukin in lipopolysaccharide-stimulated mouse macrophage cell line RAW 264.