Somatostatin modulates dopamine release in rat retina

The aim of the present study was to determine the possible role of somatostatin as a modulator of dopamine release in rat retina. Basal release of dopamine, and how this is influenced by somatostatin receptor (sst) selective ligands, was examined ex vivo in rat retinal explants. Dopamine levels were quantified by high-pressure liquid chromatography (HPLC) with electrochemical detection. Basal levels of dopamine were measured over 120 min of tissue incubation and found to be 1.17 ± 0.35 ng/ml. Somatostatin (10 -6, 10-5, 10-4 M) increased dopamine levels in a concentration-dependent manner, while the sst2 antagonist CYN154806 (10-4 M) reversed its actions. BIM23014 (sst2 agonist) increased dopamine levels in a statistically significant manner only at the concentration of 10-5 M. The sst1 agonist L797.591 (10-5, 10-4 M) also increased dopamine levels, while activation of the sst3 receptor (sst3 agonist, L796.778, 10-4 M) had no effect. These data substantiate a neuromodulatory role for sst1 and sst2 somatostatin receptors in the retina and show for the first time somatostatin's influence on dopamine release. © 2005 Elsevier Ireland Ltd. All rights reserved

The Roles of Hyaluronan/RHAMM/CD44 and Their Respective Interactions along the Insidious Pathways of Fibrosarcoma Progression

Fibrosarcomas are rare malignant mesenchymal tumors originating from fibroblasts. Importantly, fibrosarcoma cells were shown to have a high content and turnover of extracellular matrix (ECM) components including hyaluronan (HA), proteoglycans, collagens, fibronectin, and laminin. ECMs are complicated structures that surround and support cells within tissues. During cancer progression, significant changes can be observed in the structural and mechanical properties of the ECM components. Importantly, hyaluronan deposition is usually higher in malignant tumors as compared to benign tissues, predicting tumor progression in some tumor types. Furthermore, activated stromal cells are able to produce tissue structure rich in hyaluronan in order to promote tumor growth. Key biological roles of HA result from its interactions with its specific CD44 and RHAMM (receptor for HA-mediated motility) cell-surface receptors. HA-receptor downstream signaling pathways regulate in turn cellular processes implicated in tumorigenesis. Growth factors, including PDGF-BB, TGFβ2, and FGF-2, enhanced hyaluronan deposition to ECM and modulated HA-receptor expression in fibrosarcoma cells. Indeed, FGF-2 through upregulation of specific HAS isoforms and hyaluronan synthesis regulated secretion and net hyaluronan deposition to the fibrosarcoma pericellular matrix modulating these cells’ migration capability. In this paper we discuss the involvement of hyaluronan/RHAMM/CD44 mediated signaling in the insidious pathways of fibrosarcoma progression

The Motile Breast Cancer Phenotype Roles of Proteoglycans/Glycosaminoglycans.

The consecutive stages of cancer growth and dissemination are obligatorily perpetrated through specific interactions of the tumor cells with their microenvironment. Importantly, cell-associated and tumor microenvironment glycosaminoglycans (GAGs)/proteoglycan (PG) content and distribution are markedly altered during tumor pathogenesis and progression. GAGs and PGs perform multiple functions in specific stages of the metastatic cascade due to their defined structure and ability to interact with both ligands and receptors regulating cancer pathogenesis. Thus, GAGs/PGs may modulate downstream signaling of key cellular mediators including insulin growth factor receptor (IGFR), epidermal growth factor receptor (EGFR), estrogen receptors (ERs), or Wnt members. In the present review we will focus on breast cancer motility in correlation with their GAG/PG content and critically discuss mechanisms involved. Furthermore, new approaches involving GAGs/PGs as potential prognostic/diagnostic markers or as therapeutic agents for cancer-related pathologies are being proposed

Deep learning identifies inflamed fat as a risk factor for lymph node metastasis in early colorectal cancer

The spread of early-stage (T1 and T2) adenocarcinomas to locoregional lymph nodes is a key event in disease progression of colorectal cancer (CRC). The cellular mechanisms behind this event are not completely understood and existing predictive biomarkers are imperfect. Here, we used an end-to-end deep learning algorithm to identify risk factors for lymph node metastasis (LNM) status in digitized histopathology slides of the primary CRC and its surrounding tissue. In two large population-based cohorts, we show that this system can predict the presence of more than one LNM in pT2 CRC patients with an area under the receiver operating curve (AUROC) of 0.733 (0.67-0.758) and patients with any LNM with an AUROC of 0.711 (0.597-0.797). Similarly, in pT1 CRC patients, the presence of more than one LNM or any LNM was predictable with an AUROC of 0.733 (0.644-0.778) and 0.567 (0.542-0.597), respectively. Based on these findings, we used the deep learning system to guide human pathology experts towards highly predictive regions for LNM in the whole slide images. This hybrid human observer and deep learning approach identified inflamed adipose tissue as the highest predictive feature for LNM presence. Our study is a first proof of concept that artificial intelligence (AI) systems may be able to discover potentially new biological mechanisms in cancer progression. Our deep learning algorithm is publicly available and can be used for biomarker discovery in any disease setting. (c) 2021 The Pathological Society of Great Britain and Ireland

Adhesion of adipose-derived mesenchymal stem cells to glycosaminoglycan surfaces with different protein patterns

Uncorrected proofProteins and glycosaminoglycans (GAGs) are main constituents of the extracellular matrix (ECM). They act in synergism and are equally critical for the development, growth, function or survival of an organism. In this work, we developed surfaces that display these two classes of biomacromolecules, namely GAGs and proteins, in spatially controlled fashion. The generated surfaces can be used as a minimalistic but straightforward model aiding the elucidation of cell-ECM interactions. GAGs (hyaluronic acid and heparin) were covalently bound to amino func-tionalized surfaces and albumin or fibronectin were patterned by micro-contact printing on top of them. We demonstrate that adipose-derived stem cells (ASCs) can adhere either on protein or GAG pattern as a function of the patterned molecules. ASCs found on the GAG pattern had different morphology and expressed different surface markers than the cells adherent on the protein pattern. ASCs morphology and spreading were also dependent on the size of the pattern. These results show that the developed supports can be also used for ASCs differentiation into different lineages.This work was carried out under the scope of the EU seventh Framework Programme (FP7/2007-2013) under grant agreement no. NMP4-SL-2009-229292 (Find&Bind). D.S.C. and I.P. acknowledge the Portuguese Foundation for Science and Technology (FCT) for their grants (BPD/85790/2012 and IF/00032/2013)