Long Bay Hypoxia Monitoring Consortium

In October 2011, the coastal municipalities of North Myrtle Beach, Myrtle Beach, Surfside, and Horry County signed a resolution, under the aegis of their Coastal Alliance of mayors, to develop and implement the Long Bay Hypoxia Monitoring Consortium. The goal of this consortium is to support monitoring and studies that further characterize hypoxia and its causes in Long Bay. The baseline data will enable assessments of water quality management efforts. Monitoring stations are to be maintained at three piers, Cherry Grove (NMB), Apache (Horry County), and Second Ave N. Pier (Myrtle Beach). Turbidity and chlorophyll sensors will be deployed at two piers and radon detectors at three piers. All piers will have weather stations. Data will be accessible via a real-time public website. Biological responses to low dissolved oxygen (DO) will be assessed via monitoring of larval recruitment and net plankton. The S.C. Department of Natural Resources (SCDNR) is also conducting creel surveys at the piers. These efforts are being coordinated with a marine education outreach campaign that includes signage at the piers, presentations at pier events, and web-based content

Rapid liquid chromatography–tandem mass spectrometry method for the determination of a broad mixture of pharmaceuticals in surface water

Herein, a new method for the detection of 13 different pharmaceuticals and one metabolite in surface water at low ng/L levels is described. The method utilizes ultra performance liquid chromatography–tandem mass spectrometry and a solid-phase extraction sample preparation. Mean method detection limits were low (4.10 ng/L) and overall solid-phase extraction recovery and reproducibility was adequate (mean recovery, 77.9%; mean RSD, 7.3%). The method allows for quick run times and minimal solvent use as compared with other previously reported high performance liquid chromatography-based methods. Application of this method for the detection of pharmaceuticals in Tennessee River surfacewater determined that caffeine, sulfamethoxazole, and carbamazepine were frequently detected (100% of samples). Trimethoprim was moderately detected (30% of samples); acetaminophen, atorvastatin, and lovastatin were infrequently detected (10% of samples); and ciprofloxacin, diltiazem, fluoxetine, levofloxacin, norfluoxetine, ranitidine, and sertraline were not detected. This study reports the first detection of lovastatin in surface water

Taxonomy of breast cancer based on normal cell phenotype predicts outcome

Accurate classification is essential for understanding the pathophysiology of a disease and can inform therapeutic choices. For hematopoietic malignancies, a classification scheme based on the phenotypic similarity between tumor cells and normal cells has been successfully used to define tumor subtypes; however, use of normal cell types as a reference by which to classify solid tumors has not been widely emulated, in part due to more limited understanding of epithelial cell differentiation compared with hematopoiesis. To provide a better definition of the subtypes of epithelial cells comprising the breast epithelium, we performed a systematic analysis of a large set of breast epithelial markers in more than 15,000 normal breast cells, which identified 11 differentiation states for normal luminal cells. We then applied information from this analysis to classify human breast tumors based on normal cell types into 4 major subtypes, HR0–HR3, which were differentiated by vitamin D, androgen, and estrogen hormone receptor (HR) expression. Examination of 3,157 human breast tumors revealed that these HR subtypes were distinct from the current classification scheme, which is based on estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2. Patient outcomes were best when tumors expressed all 3 hormone receptors (subtype HR3) and worst when they expressed none of the receptors (subtype HR0). Together, these data provide an ontological classification scheme associated with patient survival differences and provides actionable insights for treating breast tumors

Ovarian cancer

Ovarian cancer is not a single disease and can be subdivided into at least five different histological subtypes that have different identifiable risk factors, cells of origin, molecular compositions, clinical features and treatments. Ovarian cancer is a global problem, is typically diagnosed at a late stage and has no effective screening strategy. Standard treatments for newly diagnosed cancer consist of cytoreductive surgery and platinum-based chemotherapy. In recurrent cancer, chemotherapy, anti-angiogenic agents and poly(ADP-ribose) polymerase inhibitors are used, and immunological therapies are currently being tested. High-grade serous carcinoma (HGSC) is the most commonly diagnosed form of ovarian cancer and at diagnosis is typically very responsive to platinum-based chemotherapy. However, in addition to the other histologies, HGSCs frequently relapse and become increasingly resistant to chemotherapy. Consequently, understanding the mechanisms underlying platinum resistance and finding ways to overcome them are active areas of study in ovarian cancer. Substantial progress has been made in identifying genes that are associated with a high risk of ovarian cancer (such as BRCA1 and BRCA2), as well as a precursor lesion of HGSC called serous tubal intraepithelial carcinoma, which holds promise for identifying individuals at high risk of developing the disease and for developing prevention strategies

Hypoxia in the Nearshore Coastal Waters of South Carolina Along the Grand Strand

2010 S.C. Water Resources Conference - Science and Policy Challenges for a Sustainable Futur

Long Bay Hypoxia Monitoring Consortium

2012 S.C. Water Resources Conference - Exploring Opportunities for Collaborative Water Research, Policy and Managemen

Taxonomy of breast cancer based on normal cell phenotype predicts outcome

Accurate classification is essential for understanding the pathophysiology of a disease and can inform therapeutic choices. For hematopoietic malignancies, a classification scheme based on the phenotypic similarity between tumor cells and normal cells has been successfully used to define tumor subtypes; however, use of normal cell types as a reference by which to classify solid tumors has not been widely emulated, in part due to more limited understanding of epithelial cell differentiation compared with hematopoiesis. To provide a better definition of the subtypes of epithelial cells comprising the breast epithelium, we performed a systematic analysis of a large set of breast epithelial markers in more than 15,000 normal breast cells, which identified 11 differentiation states for normal luminal cells. We then applied information from this analysis to classify human breast tumors based on normal cell types into 4 major subtypes, HR0–HR3, which were differentiated by vitamin D, androgen, and estrogen hormone receptor (HR) expression. Examination of 3,157 human breast tumors revealed that these HR subtypes were distinct from the current classification scheme, which is based on estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2. Patient outcomes were best when tumors expressed all 3 hormone receptors (subtype HR3) and worst when they expressed none of the receptors (subtype HR0). Together, these data provide an ontological classification scheme associated with patient survival differences and provides actionable insights for treating breast tumors

Taxonomy of breast cancer based on normal cell phenotype predicts outcome

Accurate classification is essential for understanding the pathophysiology of a disease and can inform therapeutic choices. For hematopoietic malignancies, a classification scheme based on the phenotypic similarity between tumor cells and normal cells has been successfully used to define tumor subtypes; however, use of normal cell types as a reference by which to classify solid tumors has not been widely emulated, in part due to more limited understanding of epithelial cell differentiation compared with hematopoiesis. To provide a better definition of the subtypes of epithelial cells comprising the breast epithelium, we performed a systematic analysis of a large set of breast epithelial markers in more than 15,000 normal breast cells, which identified 11 differentiation states for normal luminal cells. We then applied information from this analysis to classify human breast tumors based on normal cell types into 4 major subtypes, HR0–HR3, which were differentiated by vitamin D, androgen, and estrogen hormone receptor (HR) expression. Examination of 3,157 human breast tumors revealed that these HR subtypes were distinct from the current classification scheme, which is based on estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2. Patient outcomes were best when tumors expressed all 3 hormone receptors (subtype HR3) and worst when they expressed none of the receptors (subtype HR0). Together, these data provide an ontological classification scheme associated with patient survival differences and provides actionable insights for treating breast tumors