PubMed · 15507412
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D Visvikis, C Cheze-Le Rest, P Jarritt. 2004. PET technology: current trends and future developments.. https://doi.org/10.1259/bjr%2F32045866
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Specific estrogen receptor (ER) agonists have been used for hormone replacement therapy, contraception, osteoporosis prevention, and prostate cancer treatment. Some ER agonists and partial-agonists induce cancer and endocrine function disruption. Methods for predicting ER agonists are useful for facilitating drug discovery and chemical safety evaluation. Structure-activity relationships and rule-based decision forest models have been derived for predicting ER binders at impressive accuracies of 87.1-97.6% for ER binders and 80.2-96.0% for ER non-binders. However, these are not designed for identifying ER agonists and they were developed from a subset of known ER binders. This work explored several statistical learning methods (support vector machines, k-nearest neighbor, probabilistic neural network and C4.5 decision tree) for predicting ER agonists from comprehensive set of known ER agonists and other compounds. The corresponding prediction systems were developed and tested by using 243 ER agonists and 463 ER non-agonists, respectively, which are significantly larger in number and structural diversity than those in previous studies. A feature selection method was used for selecting molecular descriptors responsible for distinguishing ER agonists from non-agonists, some of which are consistent with those used in other studies and the findings from X-ray crystallography data. The prediction accuracies of these methods are comparable to those of earlier studies despite the use of significantly more diverse range of compounds. SVM gives the best accuracy of 88.9% for ER agonists and 98.1% for non-agonists. Our study suggests that statistical learning methods such as SVM are potentially useful for facilitating the prediction of ER agonists and for characterizing the molecular descriptors associated with ER agonists.
The following comments were presented to the 20th anniversary celebration of the geriatric medicine fellowship training program at the Baylor College of Medicine in the Texas Medical Center, Houston, Texas, on June 24, 2005. The author, who was the first graduate of the geriatric medicine training program at Baylor, proposes that geriatrics is at a critical juncture and that the voice and identity of geriatric medicine should be that of advocacy for our patients, and that in such a role geriatric medicine is well suited to become the "soul of medicine." She encourages all geriatricians to read the report of the Task Force on the Future of Geriatric Medicine and to look for opportunities to personally be involved in implementing the recommendations. She suggests that since our visions of the future are often only caricatures of our past, we must be creative and dream big dreams of what the future may hold. She challenges each of us to get involved in actively creating the future of geriatric medicine.
The nonaqueous-phase liquid (NAPL)-water interfacial area is an important parameter which influences the rate of NAPL dissolution in porous media. The aim of this study was to generate a set of baseline data for specific interfacial area for a two-phase-entrapped NAPL-water system in well-characterized porous media and subsequently use these data to evaluate two current theoretical models. The first model tested distributes entrapped NAPL over the pore classes based on Land's algorithm and assumes the resulting blobs to be spherical. The other model is thermodynamically based, assuming that reversible work done on the system results in an increase in interfacial area, such that the area between drainage and imbibition retention curves can be related to the interfacial area. Interfacial tracer tests (IFTT) were used to measure specific entrapped NAPL (hexadecane)-water interfacial areas in columns packed with four grades (12/20, 20/30, 30/40, 40/50) of silica sand. By use of the anionic surfactant dihexylsulfosuccinate (Aerosol MA80), IFTT gave specific interfacial areas between 58 cm(-1) for the finest sand and 16 cm(-1) for the coarsest, compared to values of between 33 and 7 cm(-1) for the first model and between 19 and 5 cm(-1) for the thermodynamic model. Results from the literature suggest that nonspherical blobs shapes occur relatively frequently; hence it is reasonable to suggest that the assumption of spherical NAPL blobs may explain the underprediction by the first model. The thermodynamic model underestimates the interfacial area because it assumes that entrapment occurs only within the largest pores. A modified version of the latter model, allowing entrapment across all pore classes, yielded values between 58 and 13 cm(-1). Of the models tested the modified thermodynamic model best predicts the interfacial area.