The exploration of bio-inspired copper oxide nanoparticles: synthesis, characterization and in-vitro biological investigations.

The paper describes the synthesis and characterization of copper oxide nanoparticles (CuO NPs) using the mixture of plant rhizome extracts Ocimum sanctum and Saussurea lappa as a reducing agent. The prepared CuO nanoparticles are characterized and confirmed their formation based on data obtained from powder X-ray diffraction spectroscopy, Fourier Transmission Infrared, Ultraviolet-Visible spectra, Field Emission Scanning Electron Microscopy images, Energy Dispersive X-ray analysis and Dynamic light scattering techniques and data reveal that the average size of CuO Nps was 103.4 nm. The result of antibacterial and antifungal activities for concentrations 50, 100, and 170 ppm indicate that NPs may exhibit appreciable activity at higher (170 ppm) concentrations. The MTT cytotoxic assay studies of Chinese Hamster Ovary (CHO) cell lines showed a Half-maximal inhibitory concentration (IC50) value of 4.14 â€‹µg/mL.

Identification of a Grotthuss proton hopping mechanism at protonated polyhedral oligomeric silsesquioxane (POSS) - water interface.

The attachment and dissociation of a proton from a water molecule and the proton transfers at solid-liquid interfaces play vital roles in numerous biological, chemical processes and for the development of sustainable functional materials for energy harvesting and conversion applications. Using first-principles computational methodologies, we investigated the protonated forms of polyhedral oligomeric silsesquioxane (POSS-H+) interacting with water clusters (Wn, where n = 1-6) as a model to quantify the proton conducting and localization ability at solid-liquid interfaces. Successive addition of explicit water molecules to POSS-H+ shows that the assistance of at least three water molecules is required to dissociate the proton from POSS with the formation of an Eigen cation (H9O4+), whereas the presence of a fourth water molecule highly favors the formation of a Zundel ion (H5O2+). Reaction pathway and energy barrier analysis reveal that the formation of the Eigen cation requires significantly higher energy than the Zundel features. This confirms that the Zundel ion is destabilized and promptly converts in to Eigen ion at this interface. Moreover, we identified a Grotthuss-type mechanism for the proton transfer through a water chain close to the interface, where symmetrical and unsymmetrical arrangements of water molecules around H+ of protonated POSS-H+ are involved in the conduction of proton through water wires where successive Eigen-to-Zundel and Zundel-to-Eigen transformations are observed in quick succession.

Efficient artificial intelligence forecasting models for COVID-19 outbreak in Russia and Brazil.

COVID-19 is a new member of the Coronaviridae family that has serious effects on respiratory, gastrointestinal, and neurological systems. COVID-19 spreads quickly worldwide and affects more than 41.5 million persons (till 23 October 2020). It has a high hazard to the safety and health of people all over the world. COVID-19 has been declared as a global pandemic by the World Health Organization (WHO). Therefore, strict special policies and plans should be made to face this pandemic. Forecasting COVID-19 cases in hotspot regions is a critical issue, as it helps the policymakers to develop their future plans. In this paper, we propose a new short term forecasting model using an enhanced version of the adaptive neuro-fuzzy inference system (ANFIS). An improved marine predators algorithm (MPA), called chaotic MPA (CMPA), is applied to enhance the ANFIS and to avoid its shortcomings. More so, we compared the proposed CMPA with three artificial intelligence-based models include the original ANFIS, and two modified versions of ANFIS model using both of the original marine predators algorithm (MPA) and particle swarm optimization (PSO). The forecasting accuracy of the models was compared using different statistical assessment criteria. CMPA significantly outperformed all other investigated models.

Deep learning-based forecasting model for COVID-19 outbreak in Saudi Arabia.

COVID-19 outbreak has become a global pandemic that affected more than 200 countries. Predicting the epidemiological behavior of this outbreak has a vital role to prevent its spreading. In this study, long short-term memory (LSTM) network as a robust deep learning model is proposed to forecast the number of total confirmed cases, total recovered cases, and total deaths in Saudi Arabia. The model was trained using the official reported data. The optimal values of the model's parameters that maximize the forecasting accuracy were determined. The forecasting accuracy of the model was assessed using seven statistical assessment criteria, namely, root mean square error (RMSE), coefficient of determination (R2), mean absolute error (MAE), efficiency coefficient (EC), overall index (OI), coefficient of variation (COV), and coefficient of residual mass (CRM). A reasonable forecasting accuracy was obtained. The forecasting accuracy of the suggested model is compared with two other models. The first is a statistical based model called autoregressive integrated moving average (ARIMA). The second is an artificial intelligence based model called nonlinear autoregressive artificial neural networks (NARANN). Finally, the proposed LSTM model was applied to forecast the total number of confirmed cases as well as deaths in six different countries; Brazil, India, Saudi Arabia, South Africa, Spain, and USA. These countries have different epidemic trends as they apply different polices and have different age structure, weather, and culture. The social distancing and protection measures applied in different countries are assumed to be maintained during the forecasting period. The obtained results may help policymakers to control the disease and to put strategic plans to organize Hajj and the closure periods of the schools and universities.

Anti-corrosion and microstructural properties of Ni-W alloy coatings: effect of 3,4-Dihydroxybenzaldehyde.

In the present work impact of 3,4-Dihydroxybenzaldehyde on the microstructural and corrosion behavior of nanocrystalline Ni-W alloy coatings has been elucidated. A systematic investigation on the protection ability of Ni-W alloy coatings in 0.2 M H2SO4 solution was done with the aid of tafel polarization curves and electrochemical impedance spectroscopy (EIS) studies. Corrosion performance of the alloy films obtained in the absence and in the presence of different concentrations of 3,4-Dihydroxybenzaldehyde (0-500 ppm) in the bath was explained in the light of additive concentration. Compared to the blank and other concentrations of additive, 250 ppm of additive containing bath was predicted as the most promising one for the introduced citrate based Ni-W alloy electrodeposition. Low corrosion rate (0.06 mm/year) and high charge transfer resistance (2505.3 Ω cm2), for the electrodeposits, obtained from the bath containing 250 ppm of 3,4-Dihydroxybenzaldehyde supports for its high anticorrosion performance. The marked difference in the corrosion resistance property is ascribed to the formation of fine-grained deposits, smooth surface, and inclusion or adsorption of additive within the deposits in the presence of the additive (250 ppm) in the bath. Further, the adsorption of additive molecules on the metal surface was explored with the help of quantum chemical calculations based on DFT.

Colorectal specialization and survival in colorectal cancer.

AIM: It is recognized that higher surgeon volume is associated with improved survival in colorectal cancer. However, there is a paucity of national studies that have evaluated the relationship between surgical specialization and survival. METHOD: We used the Surveillance, Epidemiology, and End Results Medicare cancer registry to examine the association between colorectal specialization (CRS) and disease-specific survival (DSS) between 2001 and 2009. A total of 21,432 colon cancer and 5893 rectal cancer patients who underwent elective surgical resection between 2001 and 2009 were evaluated. Univariate and multivariate Cox survival analysis was used to identify the association between surgical specialization and cancer-specific survival. RESULTS: Colorectal specialists performed 16.3% of the colon and 27% of the rectal resections. On univariate analysis, specialization was associated with improved survival in Stage II and Stage III colon cancer and Stage II rectal cancer. In multivariate analysis, however, CRS was associated with significantly improved DSS only in Stage II rectal cancer [hazard ratio (HR) 0.70, P = 0.03]. CRS was not significantly associated with DSS in either Stage I (colon HR 1.14, P = 0.39; rectal HR 0.1.26, P = 0.23) or Stage III (colon HR 1.06, P = 0.52; rectal HR 1.08, P = 0.55) disease. When analysis was limited to high volume surgeons only, the relationship between CRS and DSS was unchanged. CONCLUSIONS: CRS is associated with improved DSS following resection of Stage II rectal cancer. A combination of factors may contribute to long-term survival in these patients, including appropriate surgical technique, multidisciplinary treatment decisions and guideline-adherent surveillance. CRS probably contributes positively to these factors resulting in improved survival.

Infantile cystinosis : the first reported case in Seychelles

The authors report here a case of infantile cystinosis which highlights some of the difficulties encountered in securing a diagnosis over a period of five years