Academic literature on the topic 'AromaScan'

Microbiological assessment, sensory evaluation, and electronic nose (AromaScan) analysis were performed on yellowfin tuna stored at 0, 4, 10, and 22°C for 0, 1, 3, 5, and 9 days. Fish color, texture, appearance, and odor were evaluated by a trained sensory panel, while aroma-odor properties were evaluated using an AromaScan. Bacterial enumeration was performed using plate count agar containing 1.5% NaCl. Tuna fillets stored at 22°C for 3 days or longer had a bacterial load of over 107 CFU/g and were rated not acceptable for consumption (grade C) by the sensory panel. Tuna fillets stored at 4°C for 9 days or 10°C for over 5 days were rated as grade C products and also had a bacterial load of over 107 CFU/g. The change in fish quality as determined by AromaScan followed increases in microbiological counts in tuna fillets, indicating that bacterial load can serve as a useful and objective indicator of gross spoilage. Electronic nose devices can be used in conjunction with microbial counts and sensory panels to evaluate the degree of decomposition in tuna during storage.

Ziel jeder Hormontherapie des Mammakarzinoms ist die Ausschaltung der Östrogenbedingten Wachstumsstimulation. Die dazu neben Antiöstrogenen eingesetzten Aromatase- Inhibitoren vermindern die Östrogen-Produktion, indem sie die Umwandlung (Aromatisierung) von Androgenen in Östrogen unterdrücken. Die Aromatase-Hemmer der dritten Generation wie das steroidale Exemestan (Aromasin®) wirken sehr effektiv und hoch selektiv, sodass deutlich weniger unerwünschte Nebenwirkungen wie etwa Arthralgien und Knochendichteverluste mit der Folge von Frakturen auftreten, erläuterte Prof. Marc Sütterlin, Mannheim.

Ziel jeder Hormontherapie des Mammakarzinoms ist die Ausschaltung der ÖstrogenbedingtenWachstumsstimulation. Die dazu neben Antiöstrogenen eingesetzten Aromatase- Inhibitoren vermindern die Östrogen-Produktion, indem sie die Umwandlung (Aromatisierung) von Androgenen in Östrogen unterdrücken. Die Aromatase-Hemmer der dritten Generation wie das steroidale Exemestan (Aromasin®) wirken sehr effektiv und hoch selektiv, sodass deutlich weniger unerwünschte Nebenwirkungen wie etwa Arthralgien und Knochendichteverluste mit der Folge von Frakturen auftreten, erläuterte Prof. Marc Sütterlin, Mannheim.

Ziel jeder Hormontherapie des Mammakarzinoms ist die Ausschaltung der ÖstrogenbedingtenWachstumsstimulation. Die dazu neben Antiöstrogenen eingesetzten Aromatase- Inhibitoren vermindern die Östrogen-Produktion, indem sie die Umwandlung (Aromatisierung) von Androgenen in Östrogen unterdrücken. Die Aromatase-Hemmer der dritten Generation wie das steroidale Exemestan (Aromasin®) wirken sehr effektiv und hoch selektiv, sodass deutlich weniger unerwünschte Nebenwirkungen wie etwa Arthralgien und Knochendichteverluste mit der Folge von Frakturen auftreten, erläuterte Prof. Marc Sütterlin, Mannheim.

Background: The breast cancer takes the first place among women cancer diagnosed worldwide. Objective: Based on the preferential multi-targeted approach on cancer therapy, we, in this study, aimed to design in silico drug candidates possessing multi-targeted bioactivity to cope with multi-drug resistance using the known drug structures, molecular modeling, and ADME parameters. Materials and Methods: We first evaluated the bioactivity score of the approved breast cancer drugs across the top-three drug targets GPCR, kinase, and nuclear receptors and calculated their physicochemical properties to see their drug-likeness profiles. Among 29 approved drugs, Aromasin and Capecitabine showed the broadest bioactivity across the targets listed. By using molecular modeling and bioisosteric modifications, and applying two filtering approaches, we investigated thirty-one analogues of Aromasin and Capecitabine. Results : Software prediction resulted in that the compounds A14, C4, and C13 replaced with B(OH)2 and/or Si(CH3)3 showed a broader spectrum of biological activity with a multi-targeted manner than even the approved analogs. Conclusion: The interesting point of these new design molecules is to have either silicon and/or boron incorporation. The increased bioactivity effect of Silicon and Boron incorporation is also seen in the recently approved drug list of FDA and in clinical trials ongoing. Our new design boron and silicon-based molecules appeared to be promising candidates for breast cancer treatment to be tested in vitro, in vivo, and in the clinic for further pharmacological investigations.

Odours caused by intensive piggery operations have become a major environmental issue in the piggery industry in Australia. Effluent ponds are the major source of odours in typical piggeries. It is assumed that the odour emissions from ponds are mainly driven by pond loading rate. However, there are few data to corroborate this concept. Allied to this is the need for a convenient and low cost method of odour measurement, which can be used as an alternative method for current olfactometry. The present odour measurement methods using olfactometry is time-consuming, expensive and often impractical because of its fundamental problem of using subjective human panels. In addition, one of the major problems in odour measurement lies in the air sampling method. Wind tunnels have been accepted as a preferred method for the sampling of odour from area sources. However, current wind tunnels do not consider meteorological factors, which directly affect the odour emission rates. A machine-based odour quantification method and a novel wind tunnel were developed and evaluated in this Ph D study. These methods were then used in a demonstration trial to investigate the effects of pond loading rate on odour emissions. The AromaScan A32S electronic nose, and an artificial neural network were used to develop the machine based odour quantification method. The sensor data analysed by the AromaScan were used to train an ANN, to correlate the responses to the actual odour concentration provided by a human olfactometry panel. Preprocessing techniques and different network architectures were evaluated through network simulation to find an optimal artificial neural network model. The simulation results showed that the two-layer back-propagation neural network can be trained to predict piggery odour concentrations correctly with a low mean squared error. The trained ANN was able to predict the odour concentration of nine unknown air samples with a value for the coefficient of correlation, r2 of 0.59. A novel wind tunnel was developed for odour sampling. The USQ wind tunnel was designed to have a capability to control wind speed and airflow rate. The tunnel was evaluated in terms of the aerodynamics of the airflow inside the tunnel, nd the gas recovery efficiency rate, in order to further improve the performance of the wind tunnel. The USQ wind tunnel showed that sample recovery efficiencies ranging from 61.7 to 106.8%, while the average result from the entire trial was 81.1%. The optimal sample recovery efficiency of the tunnel was observed to be 88.9% from statistical analysis. Consequently, it can be suggested that the tunnel will give estimates of the odour emission rate with significant level of precision. However, the tunnel needs to be calibrated to compensate for the error caused by different airflow rates and odour emission rates. In addition, the installation of a perforated baffle upstream of the sampling section was suggested to improve its performance. To investigate the relationship between the pond loading rate and odour emission rate, replicable experimental studies were conducted using a novel experimental facility and the machine based odour quantification method. The experimental facility consisted of reactor vessels to simulate the operation of effluent ponds and the USQ wind tunnel for odour sampling. A strong relationship between organic loading rate (OLR) and physical and chemical parameters was observed except pH and NH3-N. The pH was not affected by OLR due to the buffering capacity of piggery effluent. EC and COD were suggested as indicators to estimate the operating condition of the piggery effluent ponds because the regression results show that these two parameters can be predicted accurately by OLR. The time averaged odour emission rates from the reactor vessels showed a strong relationship with OLR. Consequently, it can be concluded that heavily loaded effluent ponds would produce more odours. The effect of hydraulic retention time (HRT) was examined. The HRT was increased from 30 days to 60 days, resulting in a significant decrease in odour emission rates from the reactor vessels. This decrease ranged from 59.1% to 54.9%, with an average of 57.1%. Therefore, it can be concluded that the increasing HRT will decrease the odour emission rate. This trial confirmed the value of the project methodology in obtaining unambiguous data on odour emission processes. However, more data are required for a wider range of OLR, HRT and other pertained variables before a usable model can be formulated.