The Impact of Polycystic Ovarian Syndrome, a Potential Risk Factor to Endometrial Cancer, on the Quality of Sleep

Abstract Polycystic ovarian syndrome (PCOS) is a common endocrine disorder in women during their reproductive age. Recent studies showed that PCOS may be a risk factor to the development of endometrial cancer. This risk factor may be associated with sleep disorders including sleep-disordered breathing and excessive daytime sleepiness. The mechanisms leading to this high prevalence of sleep disorders in PCOS have not yet been identified. However, possible causes include alterations in body fat composition due to excess androgen levels and/or the effects of the metabolic syndrome. These effects on sleep disorders may have an impact on daily physical activities

Fabrication and Characterization of Effective Biochar Biosorbent Derived from Agricultural Waste to Remove Cationic Dyes from Wastewater

The main aim of this work is to treat sugarcane bagasse agricultural waste and prepare an efficient, promising, and eco-friendly adsorbent material. Biochar is an example of such a material, and it is an extremely versatile and eco-friendly biosorbent to treat wastewater. Crystal violet (CV)-dye and methylene blue (MB)-dye species are examples of serious organic pollutants. Herein, biochar was prepared firstly from sugarcane bagasse (SCB), and then a biochar biosorbent was synthesized through pyrolysis and surface activation with NaOH. SEM, TEM, FTIR, Raman, surface area, XRD, and EDX were used to characterize the investigated materials. The reuse of such waste materials is considered eco-friendly in nature. After that, the adsorption of MB and CV-species from synthetically prepared wastewater using treated biochar was investigated under various conditions. To demonstrate the study’s effectiveness, it was attempted to achieve optimum effectiveness at an optimum level by working with time, adsorbent dose, dye concentration, NaCl, pH, and temperature. The number of adsorbed dyes reduced as the dye concentrations increased and marginally decreased with NaCl but increased with the adsorbent dosage, pH, and temperature of the solution increased. Furthermore, it climbed for around 15 min before reaching equilibrium, indicating that all pores were almost full. Under the optimum condition, the removal perecentages of both MB and CV-dyes were ≥98%. The obtained equilibrium data was represented by Langmuir and Freundlich isotherm models. Additionally, the thermodynamic parameters were examined at various temperatures. The results illustrated that the Langmuir isotherm was utilized to explain the experimental adsorption processes with maximum adsorption capacities of MB and CV-dyes were 114.42 and 99.50 mgg$_{−1}$, respectively. The kinetic data were estimated by pseudo-first and pseudo-second-order equations. The best correlation coefficients of the investigated adsorption processes were described by the pseudo-second-order kinetic model. Finally, the data obtained were compared with some works published during the last four years

Antifungal activity of the bark and leaf oils of Cinnamomum verum J.S. Presl. alone and in combination against Various Fungi

The leaf and bark oils of Cinnamomum verum J.S. Presl. were examined for their antifungal activity against 6 dermatophytes (Trichophyton rubrum, T. mentagrophytes, T. tonsurans, Microsporum canis, M. gypseum and M. audouini), one filamentous fungi(Aspergillus fumigatus) and 5 strains of yeasts (Candida albicans, Ca. glabrata, Ca.tropicalis, Ca. parapsilosis and Crytococcus neoformans) by using the broth microdilution method. The antifungal activities of 4 standard compounds(cinnamaldehyde, eugenol, linalool and α-terpineol) which were major constituents in the oils were also investigated in an effort to correlate the effectiveness of the oils with those of the components of the oils. The combined antifungal effect of the oils against M.canis, M. gypseum and Cr. neoformans was investigated by the checkerboard assay. Isobolograms were constructed and Fractional Inhibitory Concentrations Index (FICI) were calculated to determine the combination effects between the oils. The chemical composition of the oils was analyzed by gas chromatography (GC) and gaschromatography- mass spectrometry (GC-MS). The oils showed strong activity against all the tested fungi with Minimum Inhibition Concentration (MIC) values ranging from 0.04 to 0.31 mg/ml. Cinnamaldehyde which was the most abundant component of the bark oil of C. verum showed the strongest activity against all the fungi studied. Based on the results of the assay on standard samples, it may be that the high levels of cinnamaldehyde and eugenol in the oils and in combination with the minor components could be responsible for the high antifungal activity of the oils. The antifungal effect of the leaf and bark oils of C. verum in combination against the tested fungi was not synergistic. However, the effect was additive against M. gypseum and antagonistic against Cr. neoformans and M. canis

Fabrication and Characterization of Effective Biochar Biosorbent Derived from Agricultural Waste to Remove Cationic Dyes from Wastewater

The main aim of this work is to treat sugarcane bagasse agricultural waste and prepare an efficient, promising, and eco-friendly adsorbent material. Biochar is an example of such a material, and it is an extremely versatile and eco-friendly biosorbent to treat wastewater. Crystal violet (CV)-dye and methylene blue (MB)-dye species are examples of serious organic pollutants. Herein, biochar was prepared firstly from sugarcane bagasse (SCB), and then a biochar biosorbent was synthesized through pyrolysis and surface activation with NaOH. SEM, TEM, FTIR, Raman, surface area, XRD, and EDX were used to characterize the investigated materials. The reuse of such waste materials is considered eco-friendly in nature. After that, the adsorption of MB and CV-species from synthetically prepared wastewater using treated biochar was investigated under various conditions. To demonstrate the study’s effectiveness, it was attempted to achieve optimum effectiveness at an optimum level by working with time, adsorbent dose, dye concentration, NaCl, pH, and temperature. The number of adsorbed dyes reduced as the dye concentrations increased and marginally decreased with NaCl but increased with the adsorbent dosage, pH, and temperature of the solution increased. Furthermore, it climbed for around 15 min before reaching equilibrium, indicating that all pores were almost full. Under the optimum condition, the removal perecentages of both MB and CV-dyes were ≥98%. The obtained equilibrium data was represented by Langmuir and Freundlich isotherm models. Additionally, the thermodynamic parameters were examined at various temperatures. The results illustrated that the Langmuir isotherm was utilized to explain the experimental adsorption processes with maximum adsorption capacities of MB and CV-dyes were 114.42 and 99.50 mgg−1, respectively. The kinetic data were estimated by pseudo-first and pseudo-second-order equations. The best correlation coefficients of the investigated adsorption processes were described by the pseudo-second-order kinetic model. Finally, the data obtained were compared with some works published during the last four years

The Role of Big Data in Improving E-Learning Transition

Abstract Educational organizations are operating in a dynamic competitive environment. Online learning, education, emerging technologies, and the number of learners are growing very fast and produce big data that contain meaningful information to be extracted and processed. The idea of applying the big data in e-learning supports developing and analyzing the students as well as the educational organization’s stakeholders. A big data framework architecture, methods, and tools in the e-learning platform are introduced. The paper studies briefly the opportunities of big data deployment in the educational sector and the value that the students and organizations gain. However, it also highlights the challenges and future research directions as the tool’s selection and value extraction from complex educational data sets.</jats:p