DGSAT: Dwarf Galaxy Survey with Amateur Telescopes II. A catalogue of isolated nearby edge-on disk galaxies and the discovery of new low surface brightness systems

The connection between the bulge mass or bulge luminosity in disk galaxies and the number, spatial and phase space distribution of associated dwarf galaxies is a discriminator between cosmological simulations related to galaxy formation in cold dark matter and generalized gravity models. Here, a nearby sample of isolated Milky Way class edge-on galaxies is introduced, to facilitate observational campaigns to detect the associated families of dwarf galaxies at low surface brightness. Three galaxy pairs with at least one of the targets being edge-on are also introduced. About 60% of the catalogued isolated galaxies contain bulges of different size, while the remaining objects appear to be bulge-less. Deep images of NGC 3669 (small bulge, with NGC 3625 at the edge of the image) and NGC 7814 (prominent bulge), obtained with a 0.4-m aperture, are also presented, resulting in the discovery of two new dwarf galaxy candidates, NGC3669-DGSAT-3 and NGC7814-DGSAT-7. Eleven additional low surface brightness galaxies are identified, previously notified with low quality measurement flags in the Sloan Digital Sky Survey (SDSS). Integrated magnitudes, surface brightnesses, effective radii, Sersic indices, axis ratios, and projected distances to their putative major hosts are displayed. At least one of the galaxies, NGC3625-DGSAT-4, belongs with a surface brightness of approximately 26 mag per arcsec^2 and effective radius >1.5 kpc to the class of ultra-diffuse galaxies (UDGs). NGC3669-DGSAT-3, the galaxy with lowest surface brightness in our sample, may also be an UDG.Comment: 12 pages including 6 figures, 4 tables, a brief appendix, accepted for publication in Astronomy & Astrophysics (A&A). Paper slightly modified after A&A language editing, updating very few references and correcting a small typo at the start of the Appendi

Tachyarrhythmia in patients with congenital heart disease:inevitable destiny?

Contains fulltext : 171611.pdf (publisher's version ) (Open Access)The prevalence of patients with congenital heart disease (CHD) has increased over the last century. As a result, the number of CHD patients presenting with late, postoperative tachyarrhythmias has increased as well. The aim of this review is to discuss the present knowledge on the mechanisms underlying both atrial and ventricular tachyarrhythmia in patients with CHD and the advantages and disadvantages of the currently available invasive treatment modalities

International consensus guidelines for scoring the histopathological growth patterns of liver metastasis

BACKGROUND: Liver metastases present with distinct histopathological growth patterns (HGPs), including the desmoplastic, pushing and replacement HGPs and two rarer HGPs. The HGPs are defined owing to the distinct interface between the cancer cells and the adjacent normal liver parenchyma that is present in each pattern and can be scored from standard haematoxylin-and-eosin-stained (H&E) tissue sections. The current study provides consensus guidelines for scoring these HGPs. METHODS: Guidelines for defining the HGPs were established by a large international team. To assess the validity of these guidelines, 12 independent observers scored a set of 159 liver metastases and interobserver variability was measured. In an independent cohort of 374 patients with colorectal liver metastases (CRCLM), the impact of HGPs on overall survival after hepatectomy was determined. RESULTS: Good-to-excellent correlations (intraclass correlation coefficient >0.5) with the gold standard were obtained for the assessment of the replacement HGP and desmoplastic HGP. Overall survival was significantly superior in the desmoplastic HGP subgroup compared with the replacement or pushing HGP subgroup (P=0.006). CONCLUSIONS: The current guidelines allow for reproducible determination of liver metastasis HGPs. As HGPs impact overall survival after surgery for CRCLM, they may serve as a novel biomarker for individualised therapies

c-Rel orchestrates energy-dependent epithelial and macrophage reprogramming in fibrosis

Fibrosis is a common pathological feature of chronic disease. Deletion of the NF-κB subunit c-Rel limits fibrosis in multiple organs, although the mechanistic nature of this protection is unresolved. Using cell-specific gene-targeting manipulations in mice undergoing liver damage, we elucidate a critical role for c-Rel in controlling metabolic changes required for inflammatory and fibrogenic activities of hepatocytes and macrophages and identify Pfkfb3 as the key downstream metabolic mediator of this response. Independent deletions of Rel in hepatocytes or macrophages suppressed liver fibrosis induced by carbon tetrachloride, while combined deletion had an additive anti-fibrogenic effect. In transforming growth factor-β1-induced hepatocytes, c-Rel regulates expression of a pro-fibrogenic secretome comprising inflammatory molecules and connective tissue growth factor, the latter promoting collagen secretion from HMs. Macrophages lacking c-Rel fail to polarize to M1 or M2 states, explaining reduced fibrosis in RelΔLysM mice. Pharmacological inhibition of c-Rel attenuated multi-organ fibrosis in both murine and human fibrosis. In conclusion, activation of c-Rel/Pfkfb3 in damaged tissue instigates a paracrine signalling network among epithelial, myeloid and mesenchymal cells to stimulate fibrogenesis. Targeting the c-Rel–Pfkfb3 axis has potential for therapeutic applications in fibrotic disease

Author Correction: c-Rel orchestrates energy-dependent epithelial and macrophage reprogramming in fibrosis

Correction to: Nature Metabolism https://doi.org/10.1038/s42255-020-00306-2, published online 9 November 2020. In the version of this article initially published, in the ×40 diseased human kidney images in Supplementary Fig. 1, the FSGS image duplicated the DN image. The error has been corrected in the HTML version of the article

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing