Angiogenic and anti-inflammatory properties of micro-fragmented fat tissue and its derived mesenchymal stromal cells.

BACKGROUND: Adipose-derived mesenchymal stromal cells (Ad-MSCs) are a promising tool for advanced cell-based therapies. They are routinely obtained enzymatically from fat lipoaspirate (LP) as SVF, and may undergo prolonged ex vivo expansion, with significant senescence and decline in multipotency. Besides, these techniques have complex regulatory issues, thus incurring in the compelling requirements of GMP guidelines. Hence, availability of a minimally manipulated, autologous adipose tissue would have remarkable biomedical and clinical relevance. For this reason, a new device, named Lipogems® (LG), has been developed. This ready-to-use adipose tissue cell derivate has been shown to have in vivo efficacy upon transplantation for ischemic and inflammatory diseases. To broaden our knowledge, we here investigated the angiogenic and anti-inflammatory properties of LG and its derived MSC (LG-MSCs) population. METHODS: Human LG samples and their LG-MSCs were analyzed by immunohistochemistry for pericyte, endothelial and mesenchymal stromal cell marker expression. Angiogenesis was investigated testing the conditioned media (CM) of LG (LG-CM) and LG-MSCs (LG-MSCs-CM) on cultured endothelial cells (HUVECs), evaluating proliferation, cord formation, and the expression of the adhesion molecules (AM) VCAM-1 and ICAM-1. The macrophage cell line U937 was used to evaluate the anti-inflammatory properties, such as migration, adhesion on HUVECs, and release of RANTES and MCP-1. RESULTS: Our results indicate that LG contained a very high number of mesenchymal cells expressing NG2 and CD146 (both pericyte markers) together with an abundant microvascular endothelial cell (mEC) population. Substantially, both LG-CM and LG-MSC-CM increased cord formation, inhibited endothelial ICAM-1 and VCAM-1 expression following TNFα stimulation, and slightly improved HUVEC proliferation. The addition of LG-CM and LG-MSC-CM strongly inhibited U937 migration upon stimulation with the chemokine MCP-1, reduced their adhesion on HUVECs and significantly suppressed the release of RANTES and MCP-1. CONCLUSIONS: Our data indicate that LG micro-fragmented adipose tissue retains either per se, or in its embedded MSCs content, the capacity to induce vascular stabilization and to inhibit several macrophage functions involved in inflammation

Caltech Core-Collapse Project (CCCP) observations of type IIn supernovae: typical properties and implications for their progenitor stars

Type IIn Supernovae (SNe IIn) are rare events, constituting only a few percent of all core-collapse SNe, and the current sample of well observed SNe IIn is small. Here, we study the four SNe IIn observed by the Caltech Core-Collapse Project (CCCP). The CCCP SN sample is unbiased to the extent that object selection was not influenced by target SN properties. Therefore, these events are representative of the observed population of SNe IIn. We find that a narrow P-Cygni profile in the hydrogen Balmer lines appears to be a ubiquitous feature of SNe IIn. Our light curves show a relatively long rise time (>20 days) followed by a slow decline stage (0.01 to 0.15 mag/day), and a typical V-band peak magnitude of M_V=-18.4 +/- 1.0 mag. We measure the progenitor star wind velocities (600 - 1400 km/s) for the SNe in our sample and derive pre-explosion mass loss rates (0.026 - 0.12 solar masses per year). We compile similar data for SNe IIn from the literature, and discuss our results in the context of this larger sample. Our results indicate that typical SNe IIn arise from progenitor stars that undergo LBV-like mass-loss shortly before they explode.Comment: ApJ, submitte

Drug-releasing mesenchymal cells strongly suppress B16 lung metastasis in a syngeneic murine model

Mesenchymal stromal cells (MSCs) are considered an important therapeutic tool in cancer therapy. They possess intrinsic therapeutic potential and can also be in vitro manipulated and engineered to produce therapeutic molecules that can be delivered to the site of diseases, through their capacity to home pathological tissues. We have recently demonstrated that MSCs, upon in vitro priming with anti-cancer drug, become drug-releasing mesenchymal cells (Dr-MCs) able to strongly inhibit cancer cells growth

Acute Nonspecific Mesenteric Lymphadenitis: More Than (No Need for Surgery)

Acute nonspecific, or primary, mesenteric lymphadenitis is a self-limiting inflammatory condition affecting the mesenteric lymph nodes, whose presentation mimics appendicitis or intussusception. It typically occurs in children, adolescents, and young adults. White blood count and C-reactive protein are of limited usefulness in distinguishing between patients with and without mesenteric lymphadenitis. Ultrasonography, the mainstay of diagnosis, discloses 3 or more mesenteric lymph nodes with a short-axis diameter of 8 mm or more without any identifiable underlying inflammatory process. Once the diagnosis is established, supportive care including hydration and pain medication is advised. Furthermore, it is crucial to reassure patients and families by explaining the condition and stating that affected patients recover completely without residuals within 2-4 weeks

A Path to the Stars: The Evolution of the Species

During the last years, a number of telescopes have been dedicated to the followup of the GRBs. But after the Swift launch, the average observed intensity of the GRBs showed to be lower than thought before. Our experience with the robotic 60 cm REM telescope confirmed this evidence, with a large number oflostGRBs. Then, we proposed to study the feasibility of a 4 m fast pointing class telescope, equipped with a multichannel imagers, from Visible to Near Infrared. In this paper, we present the main result of the feasibility study we performed so far

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time, and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space. While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes, vast areas of the tropics remain understudied. In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity, but it remains among the least known forests in America and is often underrepresented in biodiversity databases. To worsen this situation, human-induced modifications may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge, it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost