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Evaluating Phosphatidylinositide as Lipid Biomarkers for Hyperglycemia-related Triple-negative Breast Cancer Aggressiveness
No full textIUITriple-negative breast cancer (TNBC) is the most aggressive breast cancer subtype, marked by limited treatment options and poor patient outcomes. Clinical and epidemiological studies suggest that systemic metabolic conditions, particularly high blood glucose associated with Type 2 Diabetes, may worsen TNBC progression. However, there are no current biomarker for detecting TNBC, especially in hyperglycemic patients.
This thesis investigates how elevated glucose levels influence TNBC behavior and whether lipid signaling molecules at the cell membrane, known as phosphatidylinositol lipids (PIs), can serve as biomarkers of disease progression. To address this, we combined functional assays that measure key cancer traits, including cell migration, attachment to the extracellular matrix (ECM), and cell accumulation, with lipidomic analyses of cell membranes under normal and high-glucose conditions.
Our findings demonstrate that hyperglycemia enhances aggressive traits in TNBC cells, promoting increased movement and growth, but no effect on ECM attachment. In parallel, lipid profiling revealed alterations in PI molecules that regulate growth and survival pathways, with differences observed across cell models. These results highlight a direct connection between metabolic stress and cancer progression. By linking systemic metabolic dysregulation to cell signaling in TNBC, this work identifies PI4P, PI(4,5)P2 and PIP3 lipids as potential biomarkers and points toward new strategies for risk stratification and therapeutic intervention
Machine reading and recovery of colors for hemoglobin-related bioassays and bioimaging
No full textDespite advances in machine learning and computer vision for biomedical imaging, machine reading and learning of colors remain underexplored. Color consistency in computer vision, color constancy in human perception, and color accuracy in biomedical imaging are intertwined, complicating digital color-based diagnostics. Existing color reference charts and correction algorithms are inadequate for mobile health (mHealth) and telemedicine in digital health applications where detecting subtle color changes is critical. We present a machine reading and learning platform for color recognition and quantification to extract diagnostic information from colors. A unique combination of spectroscopic gamut determination, reference color optimization, nonsubjective quantification metrics, and neural network-based color recovery retrieves absolute colors of biological tissue. Studies on inflammation bioimaging of photocarcinogenesis and mHealth blood hemoglobin assessment demonstrate accuracy and precision in color recovery across diverse acquisition scenarios. The reported framework overcomes limitations of conventional colorimetric detection, enabling machine-compatible color-based bioassays and bioimaging, advancing digital diagnostics
Practice Patterns in Management of Low- to Intermediate-Grade Salivary Gland Carcinoma: A Multi-Institutional Study
No full textObjectives: To characterize practice patterns and outcomes in the management of low- and intermediate-grade salivary gland carcinoma prior to the existence of treatment guidelines.
Methods: Analysis of a registry of patients who underwent parotid and submandibular gland resections for low-and intermediate-grade carcinomas between 2010 and 2019.
Results: Of all 786 patients included in the study, 726 (92%) had preoperative imaging and 653 (83%) had preoperative biopsy. Of the 729 patients with parotid gland cancer, the majority underwent superficial (n = 384, 53%) or total (n = 254, 35%) parotidectomy. In patients with facial nerve preservation, total parotidectomy was associated with a significant increase in transient facial weakness (72/177 (41%) vs. 82/311 (26%), RR 0.65, 95% CI 0.50-0.84, p < 0.05) and permanent facial nerve weakness (23/176 (13%) vs. 16/301 (5%), RR 0.41, 95% CI 0.22-0.75, p < 0.05) compared to superficial parotidectomy. Adjuvant radiation therapy (RT) was delivered to 285 (36%) patients. The proportion of patients receiving adjuvant RT declined significantly over the time period from 2015 to 2019 compared to 2010 to 2014 at 162/504 (32%) and 123/282 (44%), respectively (RR 0.74, 95% CI 0.61-0.89, p < 0.05). When comparing the time periods from 2015 to 2019 and 2010 to 2014, there was no significant difference in local control rates (RR 0.52, 95% CI 0.26-1.04, p = 0.06) or regional control rates (RR 0.75, 95% CI 0.26-2.13, p = 0.58).
Conclusions: Management of low- and intermediate-grade salivary cancer from 2010 to 2019 was variable, which is expected given the rarity and heterogeneity of the disease and the lack of treatment guidelines prior to 2021. Most patients with parotid malignancies underwent superficial or total parotidectomy. The extent of parotidectomy had an impact on facial nerve function outcomes. Delivery of adjuvant radiation trended down with time. The data presented here will support dissemination of the guidelines and provide data that could inform future trials
2026 Behavioral Health Board License Renewal Information Fields
No full textThis document outlines the data fields included in the 2026 Indiana Behavioral Health Board license renewal survey, applicable to a range of behavioral health professionals including social workers, counselors, and therapists. It collects demographic, educational, and employment information, as well as details on practice settings, service types, supervision roles, telehealth usage, and populations served. The survey is designed to inform workforce planning, licensure oversight, and behavioral health policy development across the state
Analyzing Participant Feedback on various training components to enhance future HANDS Intensive trainings (2006-2025)
No full textThis project analyzed nearly two decades of participant feedback from HANDS in Autism® Intensive Trainings conducted between 2006 and 2025. The goal was to identify satisfaction trends and improvement opportunities in training logistics, content, communication, and participant engagement. Using REDCap datasets, the data was cleaned, standardized, and analyzed using Python, Power BI, and Excel. Results revealed consistently high satisfaction scores (averaging 4.8/5), with increased engagement over the course of each training week. Top-rated components included speaker knowledge and small group activities, while lecture engagement showed room for improvement. The project demonstrated the value of health informatics in translating large-scale feedback into actionable insights and highlighted the importance of data-driven strategies to enhance the delivery of autism-focused professional training programs
Improving Personalized Meal Planning with Large Language Models: Identifying and Decomposing Compound Ingredients
No full textBackground/objectives: Identifying and decomposing compound ingredients within meal plans presents meal customization and nutritional analysis challenges. It is essential for accurately identifying and replacing problematic ingredients linked to allergies or intolerances and helping nutritional evaluation.
Methods: This study explored the effectiveness of three large language models (LLMs)-GPT-4o, Llama-3 (70B), and Mixtral (8x7B), in decomposing compound ingredients into basic ingredients within meal plans. GPT-4o was used to generate 15 structured meal plans, each containing compound ingredients. Each LLM then identified and decomposed these compound items into basic ingredients. The decomposed ingredients were matched to entries in a subset of the USDA FoodData Central repository using API-based search and mapping techniques. Nutritional values were retrieved and aggregated to evaluate accuracy of decomposition. Performance was assessed through manual review by nutritionists and quantified using accuracy and F1-score. Statistical significance was tested using paired t-tests or Wilcoxon signed-rank tests based on normality.
Results: Results showed that large models-both Llama-3 (70B) and GPT-4o-outperformed Mixtral (8x7B), achieving average F1-scores of 0.894 (95% CI: 0.84-0.95) and 0.842 (95% CI: 0.79-0.89), respectively, compared to an F1-score of 0.690 (95% CI: 0.62-0.76) from Mixtral (8x7B).
Conclusions: The open-source Llama-3 (70B) model achieved the best performance, outperforming the commercial GPT-4o model, showing its superior ability to consistently break down compound ingredients into precise quantities within meal plans and illustrating its potential to enhance meal customization and nutritional analysis. These findings underscore the potential role of advanced LLMs in precision nutrition and their application in promoting healthier dietary practices tailored to individual preferences and needs
Open, laparoscopic, and robotic radical nephroureterectomy for upper tract urothelial carcinoma: Comparing outcomes and the tetrafecta as a composite marker of surgery quality
No full textIntroduction: The purpose of this study was to compare surgical outcomes and costs between robotic radical nephroureterectomy (RNU), laparoscopic radical nephroureterectomy (LNU), and open radical nephroureterectomy (ONU), and to assess the relevance of the tetrafecta as a composite outcome on survival parameters after nephroureterectomy (NU).
Methods: Operative and oncologic followup data was retrospectively collected on patients who underwent NU from 2006-2022 at our institution. The tetrafecta was defined as a true bladder cuff, lymph node dissection, negative surgical margins, and no postoperative complications. Cox proportional hazards regression was used to assess the impact of surgical approach on survival outcomes.
Results: A total of 248 patients were included in the analysis (145 RNU, 61 LNU, and 42 ONU). The complication rate differed by approach and was lowest in RNU (p<0.01). Cancer-specific survival (CSS) differed between ONU and RNU patients, with ONU patients 2.51 times as likely to die from their cancer. Retroperitoneal recurrence-free survival (RPFS) differed between ONU and RNU patients, with ONU patients 7.22 times more likely to experience a retroperitoneal recurrence (p=0.0013). Variable surgical costs were lower in LNU compared to ONU (p=0.028) and direct inpatient hospital costs were lowest with RNU (p<0.01). Eighty-one patients met criteria for the tetrafecta. RNU patients were more likely to achieve the tetrafecta compared to LNU (p<0.01) and ONU (p<0.01) patients. No differences in survival parameters existed between patients who did and did not achieve the tetrafecta.
Conclusions: Most oncologic outcomes after NU do not differ by approach on long-term followup; however, CSS and RPFS appear to differ between RNU and ONU. ONU has traditionally been considered the approach with the lowest cost; however, our analysis demonstrates both RNU and LNU require lower costs than ONU, depending on the cost parameter analyzed. Among all approaches, the tetrafecta is best achieved with RNU
Harnessing C. elegans as a Biosensor: Integrating Microfluidics, Image Analysis, and Machine Learning for Environmental Sensing
No full textEnvironmental contamination is becoming an increasingly evident risk to human health worldwide. The small, free-living nematode Caenorhabditis elegans (C. elegans) has become a compelling model organism for environmental toxicity studies in recent years, owing to its numerous advantages, including its transparent body, small size, well-characterized biology, genetic tractability, short lifespan, and ease of culture. Several assays have been developed using C. elegans to enable a better understanding of toxicant effects, from whole-animal to single-cell levels. While these methods can be extremely useful, they can be time-consuming and cumbersome to perform on a large scale. Recent advances in microfluidics have adapted many of these assays to enable high-throughput analysis of C. elegans, greatly reducing time and resource consumption while increasing efficiency and scalability. Further integration of these microfluidic platforms with machine learning expands their analytical capabilities and accuracy, revolutionizing what can be achieved with this model organism. This article will review the physiological basis of C. elegans as a model organism for environmental toxicity studies, and recent advances in integrating microfluidics and machine learning which could lead to using C. elegans as a promising living biosensor for environmental sensing