Hospitalist workload influences faculty evaluations by internal medicine clerkship students.

BACKGROUND: The last decade has brought significant changes to internal medicine clerkships through resident work-hour restrictions and the widespread adoption of hospitalists as medical educators. These key medical educators face competing demands for quality teaching and clinical service intensity. OBJECTIVE: The study reported here was conducted to explore the relationship between clinical service intensity and teaching evaluations of hospitalists by internal medicine clerkship students. DESIGN: A retrospective correlation analysis of clinical service intensity and teaching evaluations of hospitalists by internal medicine clerkship students during the 2009 to 2013 academic years at Southern Illinois University School of Medicine was conducted. PARTICIPANTS: Internal medicine hospitalists who supervise the third-year inpatient experience for medical students during the 2009 to 2013 academic years participated in the study. MEASURES: Clinical service intensity data in terms of work relative value units (RVUs), patient encounters, and days of inpatient duty were collected for all members of the hospitalist service. Medical students rated hospitalists in the areas of patient rapport, enthusiasm about the profession, clinical skills, sharing knowledge and skills, encouraging the students, probing student knowledge, stimulating independent learning, providing timely feedback, providing constructive criticism, and observing patient encounters with students. RESULTS: Significant negative correlations between higher work RVU production, total patient encounters, duty days, and learner evaluation scores for enthusiasm about the profession, clinical skills, probing the student for knowledge and judgment, and observing a patient encounter with the student were identified. Higher duty days had a significant negative correlation with sharing knowledge/skills and encouraging student initiative. Higher work RVUs and total patient encounters were negatively correlated with timely feedback and constructive criticism. CONCLUSION: The results suggest that internal medicine clerkship student evaluations of hospitalist faculty are negatively influenced by high clinical service intensity measured in terms of annual work RVUs, patient encounters, and duty days.

Tablet computer use by medical students in the United States.

The value of tablet computer use in education is an area of considerable interest. Preliminary investigations shows that medical students feel that tablet computers were a positive addition to the preclinical curriculum. To better understand how and why medical students use tablet computers, we conducted an online survey of medical students in the United States This study shows frequent tablet computer use by medical students. Students in clinical years of medical school are the most frequent users of tablet computers. The high frequency of tablet computer use suggest that this may be an important area for medical educators to explore.

Influencing hospitalist clinical productivity with feedback.

Periodic clinical productivity feedback with peer comparison produces significant changes to CPT code distributions, which leads to improvements in per-encounter work relative value units (wRVUs) (+25%) and charges (+20%). Improved wRVU production and charges can lead to increased collections, thus improving a practice's financial outlook without increasing patient volume. These interventions have minimal cost or risk to a hospitalist practice.

Virtual design of chemical penetration enhancers for transdermal drug delivery.

Traditional drug design is a laborious and expensive process that often challenges the pharmaceutical industries. As a result, researchers have turned to computational methods for computer-assisted molecular design. Recently, genetic and evolutionary algorithms have emerged as efficient methods in solving combinatorial problems associated with computer-aided molecular design. Further, combining genetic algorithms with quantitative structure-property relationship analyses has proved effective in drug design. In this work, we have integrated a new genetic algorithm and nonlinear quantitative structure-property relationship models to develop a reliable virtual screening algorithm for the generation of potential chemical penetration enhancers. The genetic algorithms-quantitative structure-property relationship algorithm has been implemented successfully to identify potential chemical penetration enhancers for transdermal drug delivery of insulin. Validation of the newly identified chemical penetration enhancer molecular structures was conducted through carefully designed experiments, which elucidated the cytotoxicity and permeability of the chemical penetration enhancers.

Design of improved permeation enhancers for transdermal drug delivery.

One promising way to breach the skin's natural barrier to drugs is by the application of chemicals called penetration enhancers. However, identifying potential enhancers is difficult and time consuming. We have developed a virtual screening algorithm for generating potential chemical penetration enhancers (CPEs) by integrating nonlinear, theory-based quantitative structure-property relationship models, genetic algorithms, and neural networks. Our newly developed algorithm was used to identify seven potential CPE molecular structures. These chemical enhancers were tested for their toxicity on (a) mouse embryonic fibroblasts (MEFs) with MTT assay, and (b) porcine abdominal skin by histology using H/E staining at the end of a 48-h exposure period to the chemicals. Further, melatonin permeability in the presence of the enhancers was tested using porcine skin and Franz diffusion cells. Careful toxicity tests showed that four of the seven "general" CPEs were nontoxic candidate enhancers (menthone, 1-(1-adamantyl)-2-pyrrolidinone, R(+)-3-amino-1-hydroxy-2-pyrrolidinone, and 1-(4-nitro-phenyl)-pyrrolidine-2,5-dione). Further testing of these four molecules as potential melatonin-specific CPEs revealed that only menthone and 1-dodecyl-2-pyrrolidinone provided sufficient enhancement of the melatonin permeation. The results from our permeability and toxicity measurements provide validation of the efficacy and ability of our virtual screening algorithm for generating potential chemical enhancer structures by virtual screening algorithms, in addition to providing additional experimental data to the body of knowledge.

Nonlinear quantitative structure-property relationship modeling of skin permeation coefficient.

The permeation coefficient characterizes the ability of a chemical to penetrate the dermis, and the current study describes our efforts to develop structure-based models for the permeation coefficient. Specifically, we have integrated nonlinear, quantitative structure-property relationship (QSPR) models, genetic algorithms (GAs), and neural networks to develop a reliable model. Case studies were conducted to investigate the effects of structural attributes on permeation using a carefully characterized database. Upon careful evaluation, a permeation coefficient data set consisting of 333 data points for 258 molecules was identified, and these data were added to our extensive thermophysical database. Of these data, permeation values for 160 molecular structures were deemed suitable for our modeling efforts. We employed established descriptors and constructed new descriptors to aid the development of a reliable QSPR model for the permeation coefficient. Overall, our new nonlinear QSPR model had an absolute-average percentage deviation, root-mean-square error, and correlation coefficient of 8.0%, 0.34, and 0.93, respectively. Cause-and-effect analysis of the structural descriptors obtained in this study indicates that that three size/shape and two polarity descriptors accounted for approximately 70% of the permeation information conveyed by the descriptors.

Quantitative structure-property relationship modeling of skin sensitization: a quantitative prediction.

A quantitative structure-property relationship (QSPR) model for predicting the skin sensitization effects of chemical compounds has been developed. An extensive database of test results from three exclusive test procedures was used for QSPR model development. Since the experimental procedure and end-point ranking of data for local lymph node assay (LLNA), guinea pig maximization test (GPMT), and Federal Institute for Health Protection of Consumers and Veterinary Medicine (BgVV) are different, three separate QSPR models were developed. Effective non-linear regression models were used for QSPR model development. The predictive capability of the final QSPR models was further improved by using a combination of literature-recommended and structural descriptors. The resultant QSPR models are capable of predicting skin sensitization of the diverse set of molecules considered with accuracies of 90%, 95%, and 90% for the LLNA, GPMT, and BgVV datasets, respectively.

Quantitative structure-property relationships modeling of skin irritation.

Interest in developing procedures for estimating skin irritation potential of chemicals has been increasing as a result of concerns regarding animal welfare and costs involved in experimental irritation studies. In response to these concerns, a number of expert systems and quantitative structure-activity relationship (QSAR) models have been proposed for predicting the skin irritation potential of compounds. However, these models require as input independent estimates of several physiochemical properties. Hence, to predict skin irritation potential using these models often requires additional models capable of estimating the physiochemical properties of diverse structures; a requirement that most literature QSARs fail to meet. In the work reported here, we developed a skin irritation QSPR model based on rabbit Draize test data for 186 compounds, which included chemicals from diverse molecular classes. The effectiveness of using a combination of traditional, functional group and structural descriptors has been studied. Our non-linear QSPR model is capable of predicting the skin irritation potential of chemical compounds with an R2 of 0.78. Further, the final set of descriptors used to model skin irritation was analyzed to elucidate the effects of molecular size, reactivity and skin penetration on skin irritation.

Screening of chemical penetration enhancers for transdermal drug delivery using electrical resistance of skin.

PURPOSE: A novel technique is presented for identifying potential chemical penetration enhancers (CPEs) based on changes in the electrical resistance of skin. METHODS: Specifically, a multi-well resistance chamber was designed and constructed to facilitate more rapid determination of the effect of CPEs on skin resistance. The experimental setup was validated using nicotine and decanol on porcine skin in vitro. The multi-well resistance chambers were capable of operating at 37 degrees C in order to simulate the physiological temperature of the human body. Further, the utility of the multi-well resistance chamber technique was validated using standard Franz diffusion cells. Electrical resistance measurements were used to evaluate the potency of seven new potential CPEs, identified using virtual screening algorithms. From the resistance measurements, the chemicals 1-dodecyl-2-pyrrolidinone (P), menthone (M) and R(+)-3-amino-1-hydroxy-2-pyrrolidinone (C) were identified as the better penetration enhancers among the seven tested. Further, traditional permeation experiments were performed in Franz diffusion cells to confirm our findings. RESULTS: The permeation test results indicated that, of the three CPEs deemed potentially viable using the newly-developed resistance screening technique, both P and M increased the permeation of the test drug (melatonin) through skin in 48 h. CONCLUSION: In summary, this resistance technique can be used to effectively pre-evaluate potential CPEs, thereby reducing the time required to conduct the permeability studies.

Modeling high-pressure adsorption of gas mixtures on activated carbon and coal using a simplified local-density model.

The simplified local-density (SLD) theory was investigated regarding its ability to provide accurate representations and predictions of high-pressure supercritical adsorption isotherms encountered in coalbed methane (CBM) recovery and CO2 sequestration. Attention was focused on the ability of the SLD theory to predict mixed-gas adsorption solely on the basis of information from pure gas isotherms using a modified Peng-Robinson (PR) equation of state (EOS). An extensive set of high-pressure adsorption measurements was used in this evaluation. These measurements included pure and binary mixture adsorption measurements for several gas compositions up to 14 MPa for Calgon F-400 activated carbon and three water-moistened coals. Also included were ternary measurements for the activated carbon and one coal. For the adsorption of methane, nitrogen, and CO2 on dry activated carbon, the SLD-PR can predict the component mixture adsorption within about 2.2 times the experimental uncertainty on average solely on the basis of pure-component adsorption isotherms. For the adsorption of methane, nitrogen, and CO2 on two of the three wet coals, the SLD-PR model can predict the component adsorption within the experimental uncertainties on average for all feed fractions (nominally molar compositions of 20/80, 40/60, 60/40, and 80/20) of the three binary gas mixture combinations, although predictions for some specific feed fractions are outside of their experimental uncertainties.