Cancer therapy combining the modalities of hyperthermia and chemotherapy: In vitro cellular response after rapid heat accumulation in the cancer cell

Hyperthermia is usually used at a sub-lethal level in cancer treatment to potentiate the effects of chemotherapy. The purpose of this study is to investigate the role of heating rate in achieving synergistic cell killing by chemotherapy and hyperthermia. For this purpose, in vitro cell culture experiments with a uterine cancer cell line (MES-SA) and its multidrug resistant (MDR) variant MES-SA/Dx5 were conducted. The cytotoxicity, mode of cell death, induction of thermal tolerance and P-gp mediated MDR following the two different modes of heating were studied. Doxorubicin (DOX) was used as the chemotherapy drug. Indocyanine green (ICG), which absorbs near infrared light at 808nm (ideal for tissue penetration), was chosen for achieving rapid rate hyperthermia. A slow rate hyperthermia was provided by a cell culture incubator. The results show that the potentiating effect of hyperthermia to chemotherapy can be maximized by increasing the rate of heating as evident by the results from the cytotoxicity assay. When delivered at the same thermal dose, a rapid increase in temperature from 37°C to 43°C caused more cell membrane damage than gradually heating the cells from 37°C to 43°C and thus allowed for more intracellular accumulation of the chemotherapeutic agents. Different modes of cell death are observed by the two hyperthermia delivery methods. The rapid rate laser-ICG hyperthermia @ 43°C caused cell necrosis whereas the slow rate incubator hyperthermia @ 43°C induced very mild apoptosis. At 43°C a positive correlation between thermal tolerance and the length of hyperthermia exposure is identified. This study shows that by increasing the rate of heating, less thermal dose is needed in order to overcome P-gp mediated MDR.

Spatial variability in Florida Bay particulate organic matter composition: combining flow cytometry with stable isotope analyses

Long-term management plans for restoration of natural flow conditions through the Everglades increase the importance of understanding potential nutrient impacts of increased freshwater delivery on Florida Bay biogeochemistry. Planktonic communities respond quickly to changes in water quality, thus spatial variability in community composition and relationships to nutrient parameters must be understood in order to evaluate future downstream impacts of modifications to Everglades hydrology. Here we present initial results combining flow cytometry analyses of phytoplankton and bacterial populations (0.1–50 μm size fraction) with measurements of δ13C and δ15N composition and dissolved inorganic nutrient concentrations to explore proxies for planktonic species assemblage compositions and nutrient cycling. Particulate organic material in the 0.1–50 μm size fraction was collected from five stations in Northeastern and Western Florida Bay to characterize spatial variability in species assemblage and stable isotopic composition. A dense bloom of the picocyanobacterium, Synechococcus elongatus, was observed at Western Florida Bay sites. Smaller Synechococcus sp. were present at Northeast sites in much lower abundance. Bacteria and detrital particles were also more abundant at Western Florida Bay stations than in the northeast region. The highest abundance of detritus occurred at Trout Creek, which receives freshwater discharge from the Everglades through Taylor Slough. In terms of nutrient availability and stable isotopic values, the S. elongatus population in the Western bay corresponded to low DIN (0.5 μM NH 4 + ; 0.2 μM NO 3 − ) concentrations and depleted δ15N signatures ranging from +0.3 to +0.8‰, suggesting that the bloom supported high productivity levels through N2-fixation. δ15N values from the Northeast bay were more enriched (+2.0 to +3.0‰), characteristic of N-recycling. δ13C values were similar for all marine Florida Bay stations, ranging from −17.6 to −14.4‰, however were more depleted at the mangrove ecotone station (−25.5 to −22.3‰). The difference in the isotopic values reflects differences in carbon sources. These findings imply that variations in resource availability and nutrient sources exert significant control over planktonic community composition, which is reflected by stable isotopic signatures.

Ensemble Stream Model for Data-Cleaning in Sensor Networks

Ensemble Stream Modeling and Data-cleaning are sensor information processing systems have different training and testing methods by which their goals are cross-validated. This research examines a mechanism, which seeks to extract novel patterns by generating ensembles from data. The main goal of label-less stream processing is to process the sensed events to eliminate the noises that are uncorrelated, and choose the most likely model without over fitting thus obtaining higher model confidence. Higher quality streams can be realized by combining many short streams into an ensemble which has the desired quality. The framework for the investigation is an existing data mining tool. First, to accommodate feature extraction such as a bush or natural forest-fire event we make an assumption of the burnt area (BA*), sensed ground truth as our target variable obtained from logs. Even though this is an obvious model choice the results are disappointing. The reasons for this are two: One, the histogram of fire activity is highly skewed. Two, the measured sensor parameters are highly correlated. Since using non descriptive features does not yield good results, we resort to temporal features. By doing so we carefully eliminate the averaging effects; the resulting histogram is more satisfactory and conceptual knowledge is learned from sensor streams. Second is the process of feature induction by cross-validating attributes with single or multi-target variables to minimize training error. We use F-measure score, which combines precision and accuracy to determine the false alarm rate of fire events. The multi-target data-cleaning trees use information purity of the target leaf-nodes to learn higher order features. A sensitive variance measure such as f-test is performed during each node’s split to select the best attribute. Ensemble stream model approach proved to improve when using complicated features with a simpler tree classifier. The ensemble framework for data-cleaning and the enhancements to quantify quality of fitness (30% spatial, 10% temporal, and 90% mobility reduction) of sensor led to the formation of streams for sensor-enabled applications. Which further motivates the novelty of stream quality labeling and its importance in solving vast amounts of real-time mobile streams generated today.