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Background Advances in data collection provide opportunities to use population samples in identifying risk factors for urinary incontinence (UI), which occurs in up to 71% of men with prostate cancer following prostatectomy. Most studies on patient-centered outcomes use surveys or manual chart abstraction for data collection, which can be costly and difficult to scale. We sought to evaluate rates of and risk factors for UI following prostatectomy using natural language processing on electronic health record (EHR) data. Methods We conducted a retrospective analysis of patients undergoing prostatectomy for prostate cancer between January 2008 and August 2018 using EHR data from an academic medical center. UI incidence for each patient in the cohort was assessed using natural language processing from clinical notes generated pre- and postoperatively. Multivariable logistic regression was used to evaluate potential risk factors for postoperative UI at various time points within 2 years following surgery. Results We identified 3792 patients who underwent prostatectomy for prostate cancer. We found a significant association between preoperative UI and UI in the first (odds ratio [OR], 2.30; 95% confidence interval [CI], 1.24-4.28) and second (OR 2.24, 95% CI 1.04-4.83) years following surgery. Preoperative body mass index was also associated with UI in the second postoperative year (OR 1.11, 95% CI 1.02-1.21). Conclusion We show that a natural language processing approach using clinical narratives can be used to assess risk for UI in prostate cancer patients. Unstructured clinical narrative text can help advance future population-level research in patient-centered outcomes and quality of care.

Dermal absorption is a critical part in the risk assessment of complex mixtures such as agrochemical formulations. To reduce the number of in vivo or in vitro absorption experiments, the present study aimed to develop an in silico prediction model that considers mixture-related effects. Therefore, an experimental 'real-world' dataset derived from regulatory in vitro studies with human and rat skin was processed. Overall, 56 test substances applied in more than 150 mixtures were used. Descriptors for the substances as well as the mixtures were generated and used for multiple linear regression analysis. Considering the heterogeneity of the underlying data set, the final model provides a good fit (r² = 0.75) and is able to estimate the influence of a newly composed formulation on dermal absorption of a well-known substance (predictivity Q²Ext = 0.73). Application of this model would reduce animal and non-animal testings when used for the optimization of formulations in early developmental stages, or would simplify the registration process, if accepted for read-across.

Quantitative structure-activity relationship (QSAR) models have been widely used to study the permeability of chemicals or solutes through skin. Among the various QSAR models, Abraham's linear free-energy relationship (LFER) model is often employed. However, when the experimental conditions are complex, it is not always appropriate to use Abraham's LFER model with a single set of regression coefficients. In this paper, we propose an expanded model in which one set of partial slopes is defined for each experimental condition, where conditions are defined according to solvent: water, synthetic oil, semi-synthetic oil, or soluble oil. This model not only accounts for experimental conditions but also improves the ability to conduct rigorous hypothesis testing. To more adequately evaluate the predictive power of the QSAR model, we modified the usual leave-one-out internal validation strategy to employ a leave-one-solute-out strategy and accordingly adjust the Q(2) LOO statistic. Skin permeability was shown to have the rank order: watersyntheticsemi-syntheticsoluble oil. In addition, fitted relationships between permeability and solute characteristics differ according to solvents. We demonstrated that the expanded model (r(2) = 0.70) improved both the model fit and the predictive power when compared with the simple model (r(2) = 0.21).

This special article seeks to provide balance and clarity to the confusion brought about by the conclusions resulting from a recent study published in Archives of Internal Medicine by Mursu et al. (2011). An examination of three key limitations of the study provides context to why additional research is needed: (a) Nonusers were poorly defined; (b) supplement users were healthier than nonusers; and (c) the number of supplement users increased throughout the study. Although the literature is limited, other similar observational studies have also shown positive effects on the risk mortality for both multivitamins and single-nutrient supplements. Observational trials are an essential component of evidence-based nutrition but do not offer certainty because other data, such as the one generated from randomized controlled trials, are equally important in regard to the totality of evidence. The Senior Scientific Advisory Committee for the Council for Responsible Nutrition, an industry trade group, feels that the conclusions of the study by Mursu et al. (2011) are overstated and suggests that researchers analyze cohort(s) designed to specifically examine vitamin and mineral supplements free of confounding from factors, such as hormone replacement therapy, to better assess their benefits to the general population.

Quantitative structure-activity relationship (QSAR) models are being used increasingly in skin permeation studies. The main idea of QSAR modelling is to quantify the relationship between biological activities and chemical properties, and thus to predict the activity of chemical solutes. As a key step, the selection of a representative and structurally diverse training set is critical to the prediction power of a QSAR model. Early QSAR models selected training sets in a subjective way and solutes in the training set were relatively homogenous. More recently, statistical methods such as D-optimal design or space-filling design have been applied but such methods are not always ideal. This paper describes a comprehensive procedure to select training sets from a large candidate set of 4534 solutes. A newly proposed 'Baynes' rule', which is a modification of Lipinski's 'rule of five', was used to screen out solutes that were not qualified for the study. U-optimality was used as the selection criterion. A principal component analysis showed that the selected training set was representative of the chemical space. Gas chromatograph amenability was verified. A model built using the training set was shown to have greater predictive power than a model built using a previous dataset [1].

Occupational and environmental exposures to chemicals are major potential routes of exposure for direct skin toxicity and for systemic absorption. The majority of these exposures are to complex mixtures, yet most experimental studies to assess topical chemical absorption are conducted neat or in simple aqueous vehicles. A component of many industrial mixtures is surfactants that solubilize ingredients and stabilize mixtures of oily components when present in aqueous vehicles. The purpose of this series of experiments was to use two well-developed experimental techniques to assess how solution interactions present in a pure nonbiological in vitro system (membrane coated fibers, MCF) compare to those seen in a viable ex vivo biological preparation (isolated perfused porcine skin flap, IPPSF). Two widely encountered anionic surfactants, sodium lauryl sulfate (SLS) and linear alkylbenzene sulfonate (LAS), were studied in 10% solutions. The rank orders of absorption were: water: pentachlorophenol (PCP) > 4-nitrophenol (PNP) > parathion > fenthion > simazine > propazine; SLS: PNP > PCP > parathion > simazine > fenthion > propazine; and LAS: PNP > PCP > simazine > parathion > fenthion > propazine. For all penetrants, absorption was greater in SLS compared to LAS mixtures, a finding consistent with smaller micelle sizes seen with SLS. For these low-water-solubility compounds, absorption was greater from aqueous solutions in nearly every case. The inert three-fiber MCF array predicted absorptive fluxes seen in the ex vivo IPPSF, suggesting lack of any biological effects of the surfactants on skin.

Significant progress has been made on predicting dermal absorption/penetration of topically applied compounds by developing QSPR models based on linear free energy relations (LFER). However, all of these efforts have employed compounds applied to the skin in aqueous or single solvent systems, a dosing scenario that does not mimic occupational, environmental or pharmaceutical exposure. We have explored using hybrid QSPR equations describing individual compound penetration based on the molecular descriptors for the compound modified by a mixture factor (MF) which accounts for the physicochemical properties of the vehicle/mixture components. The MF is calculated based on percentage composition of the vehicle/mixture components and physical chemical properties selected using principal components analysis. This model has been applied to 12 different compounds in 24 mixtures for a total of 288 treatment combinations obtained from flow-through porcine skin diffusion cells and in an additional dataset of 10 of the same compounds in five mixtures for a total of 50 treatment combinations in the ex vivo isolated perfused porcine skin flap. The use of the MF in combination with a classic LFER based on penetrant properties significantly improved the ability to predict dermal absorption of compounds dosed in complex chemical mixtures.

Exposure to N,N-diethyl-m-toluamide (DEET) commonly occurs in the general population and has been implicated as a contributory factor to the Gulf War Illness. The focus of the present studies was to determine the effect of coexposure factors, potentially encountered in a military environment, that could modulate transdermal flux of topically applied DEET. Factors investigated were vehicle, dose, coexposure to permethrin, low-level sulfur mustard, occlusion, and simultaneous systemic exposure to pyridostigmine bromide and the nerve agent stimulant diisopropylfluorophosphate (DFP). Studies were conducted using the isolated perfused porcine skin flap (IPPSF), with a few mechanistically oriented studies conducted using in vitro porcine skin and silastic membrane diffusion cells. DEET was quantitated using high-performance liquid chromatography. The vehicle-control transdermal DEET flux in the IPPSF was approximately 2 micrograms/cm2/h for both 7.5 and 75% DEET concentrations, a value similar to that reported in humans. DEET absorption was enhanced by coinfusion of pyridostigmine bromide and DFP, by the presence of sulfur mustard, or by dosing under complete occlusion. The greatest increase in baseline flux was fivefold. In vitro diffusion cell studies indicated that silastic membranes had two orders of magnitude greater permeability than porcine skin, and showed vehicle effects on flux that were not detected in the IPPSF. These results suggest that coexposure to a number of chemicals that potentially could be encountered in a military environment may modulate the percutaneous absorption of topically applied DEET beyond that seen for normal vehicles at typically applied concentrations.

Skin contact with nonylphenol ethoxylates (NPE), a group of widely used surfactants, is the primary source of human exposure. Previous studies have shown that the absorption of NPE through human and animal skin in vitro is limited (

The experimental determination of dermal absorption of volatile chemicals is fraught with difficulties. The isolated perfused porcine skin flap (IPPSF) is a biologically intact, perfused skin preparation that has been employed to predict dermal absorption of chemicals in humans. The purpose of this work was to explore various experimental dosing strategies for volatile chemicals using dichlorobenzene (DCB) and chloropentafluorobenzene (CPFB) as model compounds. Effects of complete occlusion and various strategies of vapor trapping, vapor dosing, and solvent effects were explored. The results suggest that dosing methodology is a major determinant of dermal absorption and could easily skew results obtained from different systems. A biologically sensitive system such as the IPPSF is particularly sensitive to the manipulations required to ensure precise dosing of these compounds. An interesting finding was that the effects of solvents on compound absorption that are routinely described in liquid dosing scenar...