The Socioeconomic Impact on Presentation and Clinical Course of Celiac Disease

Introduction Celiac Disease (CD) is a chronic autoimmune condition primarily affecting the small intestine. CD is triggered by ingestion of gluten and the only effective treatment for CD involves strict and lifelong elimination of dietary gluten. Compliance with the gluten free diet (GFD) relies on purchasing gluten-free foods. Studies have shown the cost of a GFD to be from 76% to 518% more expensive than gluten containing counterparts. Because of this, the economic burden that CD patients face may be substantial, placing these patients at high risk for dietary neglect. Financial limitation aside, GFD availability also varies by differing neighborhoods, resulting in economic food deserts across the country.https://jdc.jefferson.edu/gastrohepposters/1006/thumbnail.jp

How Do Surgeon Preferences and Technique Variances Affect Outcome?

The goal of the research project is to create a blue-print of a robot-assisted hysterectomy procedure to support design and evaluation of technology to enhance system performance. To create this blue-print, we will conduct a task analysis, model the cognitive task flow and decision making, and develop a simulation of the hysterectomy procedure. The surgical simulation will be used as a platform to train surgeons on robotic-assisted hysterectomies, as well as to assess learning and performance. Additionally, it will be used to design and develop techniques and novel technology to support surgeons in their performance of the surgery. Current research efforts are focused on the task analysis step. Data collection included observations in the hospital operating room, interviews with surgeons and nurses, analysis of surgery instructional videos and textbooks. A hierarchical task decomposition has been conducted. Thus far, results of the task analysis reveal several different types of hysterectomies and large variance in surgical techniques based on each surgeon’s preference. These findings will be validated by expert surgeons, and supplemented with a cognitive task analysis. In the next phase of the research project, we will identify several critical decision points within the surgical procedure that include variations in the use of surgical tools or variations in the sequence of actions. For example, the use of a uterine manipulator during the hysterectomy procedure seems to have an impact on the surgeon’s ease, speed, and accuracy while performing the procedure. These variations will be modeled and incorporated into the surgical simulation during development. Ultimately, the simulator will be used to train and assess the physician’s performance. It will also allow us to analyze the difference in techniques and how that affects patient outcome. A surgical simulation that has been designed and developed based on a systematic task analysis and cognitive model will allow us to more accurately study the requirements and constraints of the surgical environment, and support future innovate to enhance surgical performance and patient safety.https://corescholar.libraries.wright.edu/urop_celebration/1142/thumbnail.jp

Advancing Brain Tumor Detection: A Thorough Investigation of CNNs, Clustering, and SoftMax Classification in the Analysis of MRI Images

Brain tumors pose a significant global health challenge due to their high prevalence and mortality rates across all age groups. Detecting brain tumors at an early stage is crucial for effective treatment and patient outcomes. This study presents a comprehensive investigation into the use of Convolutional Neural Networks (CNNs) for brain tumor detection using Magnetic Resonance Imaging (MRI) images. The dataset, consisting of MRI scans from both healthy individuals and patients with brain tumors, was processed and fed into the CNN architecture. The SoftMax Fully Connected layer was employed to classify the images, achieving an accuracy of 98%. To evaluate the CNN's performance, two other classifiers, Radial Basis Function (RBF) and Decision Tree (DT), were utilized, yielding accuracy rates of 98.24% and 95.64%, respectively. The study also introduced a clustering method for feature extraction, improving CNN's accuracy. Sensitivity, Specificity, and Precision were employed alongside accuracy to comprehensively evaluate the network's performance. Notably, the SoftMax classifier demonstrated the highest accuracy among the categorizers, achieving 99.52% accuracy on test data. The presented research contributes to the growing field of deep learning in medical image analysis. The combination of CNNs and MRI data offers a promising tool for accurately detecting brain tumors, with potential implications for early diagnosis and improved patient care

Boosting Stock Price Prediction with Anticipated Macro Policy Changes

Prediction of stock prices plays a significant role in aiding the decision-making of investors. Considering its importance, a growing literature has emerged trying to forecast stock prices with improved accuracy. In this study, we introduce an innovative approach for forecasting stock prices with greater accuracy. We incorporate external economic environment-related information along with stock prices. In our novel approach, we improve the performance of stock price prediction by taking into account variations due to future expected macroeconomic policy changes as investors adjust their current behavior ahead of time based on expected future macroeconomic policy changes. Furthermore, we incorporate macroeconomic variables along with historical stock prices to make predictions. Results from this strongly support the inclusion of future economic policy changes along with current macroeconomic information. We confirm the supremacy of our method over the conventional approach using several tree-based machine-learning algorithms. Results are strongly conclusive across various machine learning models. Our preferred model outperforms the conventional approach with an RMSE value of 1.61 compared to an RMSE value of 1.75 from the conventional approach

Time Value Analysis of Inpatient Endoscopy Workflow

Aims for Improvement Our aim was to evaluate the variables involved in room turnover and identify any delays that lead to the time difference found between inpatient and outpatient rooms. We planned to design an intervention to improve efficiency in the endoscopy suite and complete more inpatient cases. We predicted that completion of more inpatient cases would subsequently decrease the need for cancellation and rescheduling of medically necessary cases on a daily basis

Sustained epidermal powder drug delivery via skin microchannels

Transdermal delivery of hydrophilic drugs is challenging. This study presents a novel sustained epidermal powder delivery technology (sEPD) for safe, efficient, and sustained delivery of hydrophilic drugs across the skin. sEPD is based on coating powder drugs into high-aspect-ratio, micro-coating channels (MCCs) followed by topical application of powder drug-coated array patches onto ablative fractional laser-generated skin MCs to deliver drugs into the skin. We found sEPD could efficiently deliver chemical drugs without excipients and biologics drugs in the presence of sugar excipients into the skin with a duration of ~ 12 h. Interestingly the sEPD significantly improved zidovudine bioavailability by ~ 100% as compared to oral gavage delivery. sEPD of insulin was found to maintain blood glucose levels in normal range for at least 6 h in chemical-induced diabetes mice, while subcutaneous injection failed to maintain blood glucose levels in normal range. sEPD of anti-programmed death-1 antibody showed more potent anti-tumor efficacy than intraperitoneal injection in B16F10 melanoma models. Tiny skin MCs and ‘bulk’ drug powder inside relatively deep MCCs are crucial to induce the sustained drug release. The improved bioavailability and functionality warrants further development of the novel sEPD for clinical use