A Machine Learning Approach for Predicting Clinical Trial Patient Enrollment in Drug Development Portfolio Demand Planning

One of the biggest challenges the clinical research industry currently faces is the accurate forecasting of patient enrollment (namely if and when a clinical trial will achieve full enrollment), as the stochastic behavior of enrollment can significantly contribute to delays in the development of new drugs, increases in duration and costs of clinical trials, and the over- or under- estimation of clinical supply. This study proposes a Machine Learning model using a Fully Convolutional Network (FCN) that is trained on a dataset of 100,000 patient enrollment data points including patient age, patient gender, patient disease, investigational product, study phase, blinded vs. unblinded, sponsor CRO selection, enrollment quarter, and enrollment country values to predict patient enrollment characteristics in clinical trials. The model was tested using a dataset consisting of 5,000 data points and yielded a high level of accuracy. This development in patient enrollment prediction will optimize portfolio demand planning and help avoid costs associated with inaccurate patient enrollment forecasting

Clinical Evaluation of Denture Retention by Multi-suction Cup and Denture Adhesive

AIM: The aim of the study was to compare the retention of two modalities: Multi-suction cup denture, and denture adhesive and to evaluate the change of retention by different time intervals. PATIENTS AND METHODS: Twelve completely edentulous patients were selected. The patients received two dentures: One conventional denture, and the other with multi-suction cups. The retention was measured by a universal testing machine at insertion, 15 min, 30 min, 1 h, 2 h, and 4 h. All values were recorded in Newtons. Statistical analysis was carried out using two-way analysis of variance with post hoc Tukey’s test. RESULTS: Retention was higher in denture adhesive than multi-suction cup, and the change of retention was not statistically significant by time. CONCLUSION: Denture adhesive showed better retention clinically and simplified laboratory procedures than multi-suction denture

Polydimethylsiloxane (PDMS)/Carbon Nanofiber Nanocomposite with Piezoresistive Sensing Functions

Flexible material that can be deployed for sensing a wide range of pressure and strain is an active research area due to potential applications in engineering and biomedical devices. Current load sensing materials such as metals, semiconductor, and piezo ceramics have limitations in certain applications, due to their heavy density and small maximum measurable strain. In order to overcome those issues, this thesis delves into an alternative material class based on polydimethyl siloxane (PDMS) and carbon nanofiber (CNF) nanocomposites. Although silica and carbon nanoparticles have been traditionally used to reinforce mechanical properties in PDMS matrix nanocomposites, this study focuses on novel sensing systems with high sensitivity and wide load ranges. A series of nanocomposites with different CNF and silica concentrations were synthesized and characterized to understand their thermal, electrical, and sensing capabilities. The thermal properties, such as thermal stability and thermal diffusivity, of the developed nanocomposites were studied using thermogravimetirc and laser flash techniques, respectively. The electrical volume conductivity of each type of nanocomposite was measured using the four-probe method to eliminate the effects of contact electrical resistance during measurement. Scanning electron microscopy (SEM) was used at different length scales which showed uniform dispersion. Experimental results showed that both CNFs and silica were able to impact on the overall properties of the synthesized PDMS/CNF nanocomposites. The pressure sensing functions were achieved by correlating the piezoresistance variations of the materials to the applied load on the sensing area. Due to the conductive network formed by carbon nanofibers (CNFs) and the tunneling effect between neighboring CNFs, the experimental results showed a clear correlation between piezoresistance and the loading conditions. The proposed nanocomposite based sensor materials were experimentally characterized under both quasi-static and cyclic tensile and compressive loading conditions. The optimal nanocomposite formulation was identified by choosing materials with the highest sensing gauge factors under the required load ranges. The ideal material were employed to sense strain as high as 30% and pressures up to 50, 100, and 150 psi, which was a significant improvement compared to current off-the-shelf similar sensors. The sensing capability and sensitivity of the identified nanocomposites were further optimized using advanced optimization algorithms and finite element analysis method. Three different shapes including cylinder, conical, and truncated pyramid shaped sensing units were designed, fabricated, and characterized. Cyclic compression tests verified that the optimized sensor units enhanced the sensing capability by obtaining higher gauge factors. Finally, optimized sensing units were assembly in array forms for the continuous monitoring of pressure in a large area. The prototypes of sensor arrays successfully demonstrated their sensing capability under both static and cyclic pressure conditions in the desired pressure range

The Effect of Advertising on Male Body Image Disturbance: A Content Analysis of Male Models in \u3ci\u3eEsquire\u3c/i\u3e Magazine Ads from 1955-2005

This study examines the transformation of the male body ideal in magazine ads over time. A content analysis of 218 male models in Esquire ads from 1955 to 2005 were coded for level of male models’ fat, muscularity, and nudity levels; whether the ad was photographed or illustrated; and product category. Findings reveal (1) a significant decrease in male models’ fat levels over time, (2) a significant increase in male models’ muscularity levels over time, and (3) a significant increase in nudity over time. Male models in photographed advertisements were found to have higher levels of muscularity and nudity than those in illustrated advertisements. The majority of product categories differed significantly on the fat, muscularity, and nudity scales. Practical and theoretical implications of the results are considered

Automated Grain Boundary (GB) Segmentation and Microstructural Analysis in 347H Stainless Steel Using Deep Learning and Multimodal Microscopy

Austenitic 347H stainless steel offers superior mechanical properties and corrosion resistance required for extreme operating conditions such as high temperature. The change in microstructure due to composition and process variations is expected to impact material properties. Identifying microstructural features such as grain boundaries thus becomes an important task in the process-microstructure-properties loop. Applying convolutional neural network (CNN) based deep-learning models is a powerful technique to detect features from material micrographs in an automated manner. Manual labeling of the images for the segmentation task poses a major bottleneck for generating training data and labels in a reliable and reproducible way within a reasonable timeframe. In this study, we attempt to overcome such limitations by utilizing multi-modal microscopy to generate labels directly instead of manual labeling. We combine scanning electron microscopy (SEM) images of 347H stainless steel as training data and electron backscatter diffraction (EBSD) micrographs as pixel-wise labels for grain boundary detection as a semantic segmentation task. We demonstrate that despite producing instrumentation drift during data collection between two modes of microscopy, this method performs comparably to similar segmentation tasks that used manual labeling. Additionally, we find that na\"ive pixel-wise segmentation results in small gaps and missing boundaries in the predicted grain boundary map. By incorporating topological information during model training, the connectivity of the grain boundary network and segmentation performance is improved. Finally, our approach is validated by accurate computation on downstream tasks of predicting the underlying grain morphology distributions which are the ultimate quantities of interest for microstructural characterization

Circulating microRNA and automated motion analysis as novel methods of assessing chemotherapy-induced peripheral neuropathy in mice.

Chemotherapy-induced peripheral neuropathy (CiPN) is a serious adverse effect in the clinic, but nonclinical assessment methods in animal studies are limited to labor intensive behavioral tests or semi-quantitative microscopic evaluation. Hence, microRNA (miRNA) biomarkers and automated in-life behavioral tracking were assessed for their utility as non-invasive methods. To address the lack of diagnostic biomarkers, we explored miR-124, miR-183 and miR-338 in a CiPN model induced by paclitaxel, a well-known neurotoxic agent. In addition, conventional and Vium's innovative Digital Vivarium technology-based in-life behavioral tests and postmortem microscopic examination of the dorsal root ganglion (DRG) and the sciatic nerve were performed. Terminal blood was collected on days 8 or 16, after 20 mg/kg paclitaxel was administered every other day for total of 4 or 7 doses, respectively, for plasma miRNA quantification by RT-qPCR. DRG and sciatic nerve samples were collected from mice sacrificed on day 16 for miRNA quantification. Among the three miRNAs analyzed, only miR-124 was statistically significantly increased (5 fold and 10 fold on day 8 and day 16, respectively). The increase in circulating miR-124 correlated with cold allodynia and axonal degeneration in both DRG and sciatic nerve. Automated home cage motion analysis revealed for the first time that nighttime motion was significantly decreased (P < 0.05) in paclitaxel-dosed animals. Although both increase in circulating miR-124 and decrease in nighttime motion are compelling, our results provide positive evidence warranting further testing using additional peripheral nerve toxicants and diverse experimental CiPN models