The significance of the ionophore-challenged acrosome reaction in the prediction of successful outcome of controlled ovarian stimulation and intrauterine insemination

BACKGROUND: This prospective study compared the acrosome reaction following ionophore challenge (ARIC) versus conventional sperm parameters and sperm velocities in predicting successful outcome following ovarian stimulation and intrauterine insemination. METHODS: All patients were offered a maximum of three treatment cycles. Conventional semen analysis was performed and sperm velocities were measured using computer-aided sperm analysis. Acrosome-reacted sperm were stained using chlortetracycline after ionophore challenge. Multiple logistic regression analysis and the receiver-operator characteristic curve analysis were applied to determine the best predictive variables and their cut-off values. RESULTS: ARIC score was the most significant variable in predicting pregnancy, followed by the percentage of induced acrosome-reacted sperm, serum estradiol levels on the day of hCG and sperm morphology by strict criteria. Higher spontaneous acrosome reaction had a negative relationship with pregnancy. ARIC score of 10% had a sensitivity of 85.3% and a specificity of 85.5%. The positive and negative predictive values were 64.2 and 96.6% respectively and the false positive and negative rates were 14.7 and 14.5% respectively. CONCLUSION: ARIC score was a better predictor of pregnancy than conventional sperm parameters and sperm velocities.     

A review on the use of DFT for the prediction of the properties of nanomaterials

Nanostructured materials have gained immense attraction because of their extraordinary properties compared to the bulk materials to be used in a plethora of applications in myriad fields. In this review article, we have discussed how the Density Functional Theory (DFT) calculation can be used to explain some of the properties of nanomaterials. With some specific examples here, it has been shown that how closely the different properties of nanomaterials (such as optical, optoelectronics, catalytic and magnetic) predicted by DFT calculations match well with the experimentally determined values. Some examples were discussed in detail to inspire the experimental scientists to conduct DFT-based calculations along with the experiments to derive a better understanding of the experimentally obtained results as well as to predict the properties of the nanomaterial. We have pointed out the challenges associated with DFT, and potential future perspectives of this new exciting field.

Depiction of unusual properties of nanomaterials through DFT studies.     

Comparison of synthetic surfactants and biosurfactants in enhancing biodegradation of polycyclic aromatic hydrocarbons

Polycyclic aromatic hydrocarbon (PAH) contamination of the environment represents a serious threat to the health of humans and ecosystems. Given the human health effects of PAHs, effective and cost-competitive remediation technologies are required. Bioremediation has shown promise as a potentially effective and low-cost treatment option, but concerns about the slow process rate and bioavailability limitations have hampered more widespread use of this technology. An option to enhance the bioavailability of PAHs is to add surfactants directly to soil in situ or ex situ in bioreactors. Surfactants increase the apparent solubility and desorption rate of the PAH to the aqueous phase. However, the results with some synthetic surfactants have shown that surfactant addition can actually inhibit PAH biodegradation via toxic interactions, stimulation of surfactant degraders, or sequestration of PAHs into surfactant micelles. Biosurfactants have been shown to have many of the positive effects of synthetic surfactants but without the drawbacks. They are biodegradable and nontoxic, and many biosurfactants do not produce true micelles, thus facilitating direct transfer of the surfactant-associated PAH to bacteria. The results with biosurfactants to date are promising, but further research to elucidate surfactant-PAH interactions in aqueous environments is needed to lead to predictive, mechanistic models of biosurfactant-enhanced PAH bioavailability and thus better bioremediation design.