Plasma insulin-like growth factor-I, CA-125, estrogen, and progesterone in women with leiomyomas

OBJECTIVE: To determine plasma levels of insulin-like growth factor-I (IGF-I), CA-125, estrone (E1), E2, and P in women with uterine leiomyomas compared with normal women. DESIGN: Women with leiomyomas were compared with normal women (control). SETTING: University Department of Obstetrics and Gynecology. PATIENTS: Fifty-one premenopausal women with uterine myomas > 14 weeks gestation and 30 normal fertile women (controls) were studied. Peripheral blood samples were obtained before myomectomy or hysterectomy and during the nonmenstruating phase in the controls. MAIN OUTCOME MEASURES: Plasma levels of E1, E2, P, CA-125, and IGF-I were determined by specific and sensitive RIAs and immunoradiometric assays. RESULTS: Plasma IGF-I levels were 2,006 +/- 185 mU/mL (mean +/- SEM, n = 35) and 2,335 +/- 287 mU/mL (n = 16) in women with leiomyomas during the follicular and luteal phases, respectively, whereas the corresponding values for normal women were 1,702 +/- 120 (n = 30) and 1,774 +/- 239 mU/mL (n = 30). Similarly, plasma CA-125 levels were unchanged in women with leiomyomas (myomas: 18.8 +/- 2.4, 21.5 +/- 3.7 U/mL; normal: 15.9 +/- 1.5, 15.8 +/- 1.3 U/mL during follicular and luteal phases, respectively). Women with leiomyomas had plasma E1, E2, and P levels during the follicular phase (91.9 +/- 11.5 pg/mL; conversion factor to SI unit, 3.699; 94.6 +/- 19.0 pg/mL; conversion factor to SI unit, 3.671; and 1.5 +/- 0.4 ng/mL; conversion factor to SI unit, 3.180, respectively) and the luteal phase (105.8 +/- 11.2 pg/mL; conversion factor to SI unit, 3.699; 128.7 +/- 24.8 pg/mL; conversion factor to SI unit, 3.671; and 9.6 +/- 1.6 ng/mL; conversion factor to SI unit, 3.180) similar to normal women. CONCLUSION: Plasma levels of IGF-I, CA-125, E1, E2, and P are normal in women with leiomyomas.     

Morphological and mechanical studies of surface treated mica reinforced high density polyethylene

Morphological studies of high density polyethylene (HDPE) reinforced by mica flakes has been investigated. The mica flakes were surface treated by silane and titanate coupling agents. Effects of these treatments on the polymer-mica interface is discussed. It is found that surface treatment, particularly silane treated composites, have better adhesion between the reinforcing agent and the polymer matrix. This effect can only be detected when the mica concentration is higher than 20 weight percent. This interfacial adhesion could not be seen across the surface of the sample but only when the measurements were made parallel to the surface.     

Light weight model for intra mode selection in HEVC

Multimedia Tools and Applications - The High Efficiency Video Coding (HEVC) efficiently reduces the size of the multimedia contents, but at the cost of high computation complexity. In order to make...     

Modeling and Analysis of Temperature Compensation for Multi-temperature Zone Sintering Furnace Temperature Sensing

International Journal of Control, Automation and Systems - The surface temperature of workpieces in a multi-temperature zone sintering furnace is an important parameter to characterize the...     

Sediment flux from Lesser Zab River in Dokan Reservoir: Implications for the sustainability of long‐term water resources in Iraq

Prudent management of Iraqi water resources under climate change conditions requires plans to be based on actual figures of the storage capacity of existing reservoirs. With the absence of sediment flushing measures, the actual storage capacity of Dokan Reservoir (operated since 1959) has been affected by the amount of sediment delivered during its operational life leading to an undetermined reduction in its storage capacity. In consequence, there has not been an update on the dam's operational storage capacity curves. In this research, new operational curves were established for the reservoir based on a recent bathymetric survey undertaken in 2014. The reduction in reservoir capacity during the period between 1959 and 2014 was calculated by the mean of the difference between the designed storage capacity and the storage capacity which was concluded from the 2014 bathymetric survey. Moreover, the rate of sediment transported to the reservoir was calculated based on the overall quantities of accumulated sediment and the water discharge of the Lesser Zab River into the reservoir. The results indicate that the dam capacity is reduced by 25% due to sedimentation of an estimated volume of 367 million cubic metres at water level 480 m.a.s.l. The annual sedimentation rate was about 6.6 million cubic metres, and the sediment yield was estimated to be 701.2 t?km^?3?year.     

Synthesis and characterization of eugenol-based silicone modified waterborne polyurethane with excellent properties

A eugenol-based silicone-containing monomer 4,4′-(1,1,3,3-tetramethyldisiloxane-1,3-dipropyl)bis-2-methoxyphenol(EUSi) was synthesized from eugenol and 1,1,3,3-tetramethyldisiloxane via the hydrosilylation reaction. And waterborne polyurethane (WPU) with excellent properties was obtained by using EUSi as a type of diol chain extender. The unique combination of rigidity and flexibility in the chemical structure of EUSi greatly facilitated the mechanical properties, thermal properties, and water resistance of WPU. With only a 3% dosage of EUSi, the maximum tensile strength was increased from 6.2 to 22.4 MPa, while the water absorption was decreased from 31.3% to a surprisingly 7.6%. Our work provides a new convenient strategy for the preparation of organosilicon-modified WPU with improved performance.     

Adaptive infill sampling criterion for multi-fidelity optimization based on Gappy-POD

This paper presents a reformulation of the “Gappy Proper Orthogonal Decomposition” (Gappy-POD) multi-fidelity modeling approach and proposes an enrichment criterion associated with an adaptive infill algorithm. The latter is here applied to the study of the flight domain of the RAE-2822 transonic airfoil at two different levels of accuracy to demonstrate its ability to detect areas in a two-dimensional design space where the surrogate model needs improvement to better drive the optimization process.     

A Framework of Deep Learning and Selection-Based Breast Cancer Detection from Histopathology Images

Breast cancer (BC) is a most spreading and deadly cancerous malady which is mostly diagnosed in middle-aged women worldwide and effecting beyond a half-million people every year. The BC positive newly diagnosed cases in 2018 reached 2.1 million around the world with a death rate of 11.6% of total cases. Early diagnosis and detection of breast cancer disease with proper treatment may reduce the number of deaths. The gold standard for BC detection is biopsy analysis which needs an expert for correct diagnosis. Manual diagnosis of BC is a complex and challenging task. This work proposed a deep learning-based (DL) solution for the early detection of this deadly disease from histopathology images. To evaluate the robustness of the proposed method a large publically available breast histopathology image database containing a total of 277524 histopathology images is utilized. The proposed automatic diagnosis of BC detection and classification mainly involves three steps. Initially, a DL model is proposed for feature extraction. Secondly, the extracted feature vector (FV) is passed to the proposed novel feature selection (FS) framework for the best FS. Finally, for the classification of BC into invasive ductal carcinoma (IDC) and normal class different machine learning (ML) algorithms are used. Experimental outcomes of the proposed methodology achieved the highest accuracy of 92.7% which shows that the proposed technique can successfully be implemented for BC detection to aid the pathologists in the early and accurate diagnosis of BC.