Glutamine-synthetase (GS), the glutamine-synthesizing enzyme from glutamate, controls important events, including the release of inflammatory mediators, mammalian target of rapamycin (mTOR) activation, and autophagy. However, its role in macrophages remains elusive. We report that pharmacologic inhibition of GS skews M2-polarized macrophages toward the M1-like phenotype, characterized by reduced intracellular glutamine and increased succinate with enhanced glucose flux through glycolysis, which could be partly related to HIF1α activation. As a result of these metabolic changes and HIF1α accumulation, GS-inhibited macrophages display an increased capacity to induce T cell recruitment, reduced T cell suppressive potential, and an impaired ability to foster endothelial cell branching or cancer cell motility. Genetic deletion of macrophagic GS in tumor-bearing mice promotes tumor vessel pruning, vascular normalization, accumulation of cytotoxic T cells, and metastasis inhibition. These data identify GS activity as mediator of the proangiogenic, immunosuppressive, and pro-metastatic function of M2-like macrophages and highlight the possibility of targeting this enzyme in the treatment of cancer metastasis.

We have evaluated the best method to assess the ovarian reserve and the ovarian protective effect of GnRH-analog (GnRH-a), in 29 women with Hodgkin's disease (HD) treated with chemotherapy (CHT). The ovarian reserve was studied by measuring the serum levels of follicle stimulating hormone (FSH), luteinizing hormone (LH), inhibin B, antimullerian hormone (AMH) and the ultrasound antral follicular count (AFC). The patients were randomly treated with or without GnRH-a. At the time of study menstrual function was normal in 21 cases (72.4%), but absent in 8 (27.5%). Mean basal values of FSH, LH, AMH, inhibin Band AFC were normal in patients less than 30 years old and in the group treated four years or less before observation. AFC appeared to be the best marker of reduced ovarian reserve and a combination of AFC–AMH or inhibin B appeared the best predictor. In the GnRH-a group, no women had amenorrhoea, although ovarian reserve assessment was not significantly different from those who were not treated. The time-interval from CHT was the only significant predictor of ovarian function in GnRH-a treated patients. In conclusion, ovarian reserve evaluation, in young patients treated by CHT, can be performed by AFC. GnRH-a treatment does not have a protective effect, but could delay the development of ovarian failure.

Anemia is a condition in which the oxygen-carrying capacity of red blood cells is insufficient to meet the body's physiological needs. It affects billions of people worldwide. An early diagnosis of this disease could prevent the advancement of other disorders. Traditional methods used to detect anemia consist of venipuncture, which requires a patient to frequently undergo laboratory tests. Therefore, anemia diagnosis using noninvasive and cost-effective methods is an open challenge. The pallor of the fingertips, palms, nail beds, and eye conjunctiva can be observed to establish whether a patient suffers from anemia. This article addresses the above challenges by presenting a novel intelligent system, based on machine learning, that supports the automated diagnosis of anemia. This system is innovative from different points of view. Specifically, it has been trained on a dataset that contains eye conjunctiva photos of Indian and Italian patients. This dataset, which was created using a very strict experimental set, is now made available to the Scientific Community. Moreover, compared to previous systems in the literature, the proposed system uses a low-cost device, which makes it suitable for widespread use. The performance of the learning algorithms utilizing two different areas of the mucous membrane of the eye is discussed. In particular, the RUSBoost algorithm, when appropriately trained on palpebral conjunctiva images, shows good performance in classifying anemic and nonanemic patients. The results are very robust, even when considering different ethnicities.

Purpose: To determine a “gene/molecular fingerprint” of multiple myeloma endothelial cells and identify vascular mechanisms governing the malignant progression from quiescent monoclonal gammopathy of undetermined significance. Experimental Design: Comparative gene expression profiling of multiple myeloma endothelial cells and monoclonal gammopathy of undetermined significance endothelial cells with the Affymetrix U133A Arrays was carried out in patients at diagnosis; expression and function of selective vascular markers was validated by real-time reverse transcriptase-PCR, Western blot, and small interfering RNA analyses. Results: Twenty-two genes were found differentially expressed (14 down-regulated and eight up-regulated) at relatively high stringency in multiple myeloma endothelial cells compared with monoclonal gammopathy of undetermined significance endothelial cells. Functional annotation revealed a role of these genes in the regulation of extracellular matrix formation and bone remodeling, cell adhesion, chemotaxis, angiogenesis, resistance to apoptosis, and cell-cycle regulation. Validation was focused on six genes (DIRAS3, SERPINF1, SRPX, BNIP3, IER3, and SEPW1) not previously found to be functionally correlated to the overangiogenic phenotype of multiple myeloma endothelial cells in active disease. The small interfering RNA knockdown of BNIP3, IER3, and SEPW1 genes affected critical multiple myeloma endothelial cell functions correlated with the overangiogenic phenotype. Conclusions: The distinct endothelial cell gene expression profiles and vascular phenotypes detected in this study may influence remodeling of the bone marrow microenvironment in patients with active multiple myeloma. A better understanding of the linkage between plasma cells and endothelial cells in multiple myeloma could contribute to the molecular classification of the disease and thus pinpoint selective gene targets for more effective antiangiogenic treatments. (Clin Cancer Res 2009;15(17):5369–78)