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A Piece of Shoe as a Foreign Body in a Patient with Type II Diabetes and Peripheral Neuropathy
No full textThere are approximately 38 million people in the United States who have diabetes. Half of diabetics have or will experience peripheral neuropathy (PN) predisposing them to foot injury with delays in diagnosis and care. Choice in proper footwear is crucial for this patient population. We present a 55-year-old male with a history of poorly-controlled diabetes and PN who presented to an urgent care after stepping on an unknown object that punctured the sole of his left foot. Initial X-ray revealed no abnormalities. The puncture site was sutured and antibiotics initiated. The next day, he presented to ED with swelling/erythema of the left dorsal foot reaching the mid-leg along with a 1.5cm wound to the plantar forefoot. Radiography revealed soft tissue gas in the dorsal foot without osseous abnormalities. Broad-spectrum antibiotics were started empirically, and the patient was admitted. General surgery was consulted who noted crepitus of the dorsal foot, prompting debridement in operating room (OR). The foot was opened widely on both surfaces which revealed a deep abscess through-and-through the foot between tendons on both surfaces. Additionally, 2 large pieces of mesh that appeared to be shoe were removed. The patient returned to the OR the next day for further debridement. The patient was discharged home on post-operative day (POD) #4, and he presented to outpatient wound care daily for dressing changes until he returned POD#13 for final debridement. At this time, the sole of the foot was closed, but a dorsal residual defect remained due to insufficient tissue coverage. This was treated with wet-to-dry dressing changes and followed until healed. Our case presents an unusual type of foreign body—the patient’s shoe. care. Careful consideration of proper footwear is encouraged for diabetic patients with PN. The patient was successfully treated with multiple debridements, delayed closure, and close follow up
Brand Media and Health Perceptions: A Thematic Analysis of Generational Differences
No full textThis thesis explores the influence of brand identity and media on shaping evolving perceptions of health across generations. The three generations considered are Generation X, Millennials, and Generation Z. The initial hypothesis predicted the popular media and brands that each generation interacts with are most likely to influence their perception. Additionally, the health brands most popular with each generation would influence their overall health perception, based on the identity and values of the brand. A qualitative study was conducted using focus groups to gather data about health perceptions, brand identity, media influence, and holistic health. The findings suggest that individuals reflect their health perceptions through the media content and brands they choose to interact. The insights uncovered in this study were used to develop suggested brand media implications for health brands. A secondary research question explored how health brands can effectively promote a shared holistic perception across all generations
Application of Deep Learning on Gage R&R for Anomaly Detection
Full text linkThis thesis explores the application of deep learning techniques, specifically autoencoder based models, to enhance anomaly detection within Gage Repeatability and Reproducibility (Gage R&R) studies—an essential component of Measurement System Analysis (MSA) in quality engineering. Traditional Gage R&R methodologies, while effective for linear and low-dimensional data, exhibit limitations in detecting subtle, nonlinear variations in complex measurement systems. To address this challenge, an unsupervised autoencoder was developed and trained on a synthetically generated dataset comprising 2,500 voltage measurements (5V and 33V) derived using Generative Adversarial Networks (GANs) based on real-world manufacturing data measurements.
The proposed autoencoder model achieved a 95th percentile-based anomaly detection threshold, successfully identifying anomalies with an overall accuracy of 94.6%, a precision of 92.1%, and a recall of 95.8%—surpassing the diagnostic effectiveness of traditional ANOVA-based Gage R&R methods. The reconstruction error analysis clearly differentiated anomalous measurements, providing a robust visualization of system inconsistencies. Statistical validation using ANOVA indicated minimal 2 operator-induced variability, affirming the system’s robustness in capturing part specific anomalies. The findings underscore the potential of integrating deep learning into Gage R&R analysis, offering a scalable, data-driven alternative for anomaly detection in high-dimensional measurement environments. This approach aligns with the increasing automation and precision demands of Industry 4.
Assessing the Utilization of Building Information Modelling (BIM) in the Preservation of Cultural Heritage: A Case Study of Ashe-Street Courthouse
Full text linkThis study examines how Building Information Modeling (BIM) was used in the Ashe Street Courthouse restoration, with an emphasis on how it contributes to heritage preservation. The study employs a case study methodology, following a systematic four-step approach: an initial review of relevant literature and historical records, semi-structured interviews with architects involved in the renovation, and analysis of project documentation. The study assesses the accuracy of documentation, the enhancement of collaboration and coordination among stakeholders, and the efficiency and time savings enabled by BIM by comparing BIM to conventional techniques. Results show that BIM allows for real-time adjustments while maintaining historical facts, greatly improving documentation accuracy. BIM also enhances collaboration by providing a centralized platform for architects, engineers, and conservation specialists, reducing errors and streamlining decision-making. BIM also cuts down on resource wastage and shortens project schedules, which results in significant time and cost savings. This study demonstrates how HBIM bridges modern technology with historical preservation, highlighting its potential for sustainable heritage conservation and offering valuable insights for future renovation projects
Access to Rehabilitation Services for Cardiovascular Patients: Rural vs. Urban Counties
Full text linkCardiovascular disease remains the leading cause of death in the United States, with rehabilitation services playing a critical role in secondary prevention and patient recovery. However, disparities in access to these services persist between urban and rural areas. This study examines geographical differences in access to rehabilitation facilities using county-level data from the Area Health Resources File (AHRF). A cross-sectional analysis of 3,142 U.S. counties was conducted to compare facility distribution using two measures: (1) rehabilitation facilities per 100,000 residents and (2) the average number of facilities per county. Facility availability was assessed based solely on the number of rehabilitation centers per 100,000 residents, without adjusting for other healthcare access factors. A two-sample t-test assuming unequal variances was performed to assess statistical significance. Findings reveal that urban areas have 2.27 rehabilitation facilities per 100,000 people, whereas rural areas have 7.90 per 100,000 people, suggesting a higher per capita facility count in rural regions. However, when analyzed at the county level, urban counties have an average of 4.58 facilities per county, while rural counties have 11.78 per county, indicating that services are concentrated in specific rural regions while other areas remain underserved. This uneven distribution may contribute to significant disparities in travel burden, workforce shortages, and access to care, limiting the availability of rehabilitation services for many rural residents. These findings challenge the assumption that rural areas generally experience lower access to rehabilitation services and instead reveal that geographic disparities within rural counties contribute to healthcare inequities. While some rural regions have sufficient facilities, others remain critically underserved, potentially leading to significant barriers such as extended travel distances, workforce shortages, and facility clustering
Exploring Predictors of Treatment Dropout in Primary Care Behavioral Health Using Andersen’s Behavioral Model of Health Services
No full textA recent meta-analysis indicated a 19.7% dropout rate from outpatient mental health care. Andersen’s Behavioral Model of Health Services identifies predisposing, enabling, and need factors as potential predictors of treatment dropout. The Primary Care Behavioral Health (PCBH) model aims to improve accessibility of mental health care but research on dropout in this setting is lacking. This study examined bivariate relationships between Andersen\u27s factors and dropout in PCBH, and interviewed participants about their intentions to attend subsequent PCBH visits. No statistically significant bivariate relationships were found, likely due to small sample size (n = 15). Reflexive Thematic Analysis identified two themes (i.e., facilitators and barriers) and four subthemes in participants’ responses about treatment attendance: patient-level facilitators, treatment-level facilitators, patient-level barriers, and external barriers. While quantitative findings were non-significant, qualitative themes highlight areas for improving PCBH treatment attendance and service delivery
Unraveling the Unknown: Characterization of a Novel Antimicrobial Compound Found in Klebsiella pneumoniae
No full textKlebsiella pneumoniae, a pathogenic bacterium resistant to numerous antimicrobials, is a worldwide health issue due to its ability to cause healthcare-associated diseases such as pneumonia and urinary tract infections. This study investigated an antimicrobial compound secreted by K. pneumoniae. The compound was found to possess inhibitory activity against Enterobacteriaceae members, including other Klebsiella pneumoniae strains, Citrobacter freundii, and Enterobacter cloacae. The inhibitory activity is mediated via a secreted protein that is active across a wide range of pH and temperature conditions. Higher concentrations of the compound enhanced antimicrobial activity. The antimicrobial protein suppressed biofilm formation during both the attachment and maturation phases. The antimicrobial protein gene was identified through transposon mutagenesis, sequencing, then PCR amplified and cloned into expression cells for purification. This research provides strong evidence that the antimicrobial protein is an uncharacterized bacteriocin and highlights its potential as a candidate in innovative treatments for antibiotic-resistant bacterial infections
