Analytical, Diagnostic and Therapeutic Techniques and Equipment Commons^™

Disseminated Coccidioidomycosis Discovered Through Skin Biopsy In A Pregnant Patient From Mexico, Henry Lim, Christina Guo, Marshall Hall, Christian Scheufele, Christopher M. Wong, Michael Carletti, Stephen Weis

North Texas GME Research Forum 2024

Introduction: Coccidioidomycosis is an infection caused by the organism Coccidioides immitis, a fungus endemic to the southwestern United States, Mexico, Central and South America. The presentation of coccidioidomycosis can range from symptoms resembling a simple upper respiratory infection, such as cough, to more severe systemic symptoms including fever, malaise, and chills. Cutaneous lesions of coccidioidomycosis demonstrate a large heterogeneity of clinical manifestations but are significant as they may be the presenting sign of disseminated disease. While usually confined to the lungs, extrapulmonary coccidioidomycosis can occur in about 1 in 200 patients, most commonly associated with immunocompromised status. Pregnancy is an …

Retrospective Review Of Adverse Events After Treatment Of Non-Melanoma Skin Cancer With Image-Guided Superficial Radiation Therapy, Marshall Hall, Henry Lim, Jason Pham, Alyssa Forsyth, Wenqin Du

North Texas GME Research Forum 2024

No abstract provided.

Geometric Favre-Racouchot Syndrome Following Image-Guided Superficial Radiation Therapy, Marshall Hall, Cecilia Nguyen, Christian Scheufele, Christopher M. Wong, Stephen E. Weis, Michael Carletti, Henry Lim

North Texas GME Research Forum 2024

Introduction: Favre-Racouchot Syndrome is a cutaneous disease characterized by nodules, cysts, and comedones located in sun-exposed areas of the face. It is most commonly found on the bilateral temporal and periorbital skin. Favre-Racouchot Syndrome is most associated with chronic sun exposure. It occurs more commonly in cigarette smokers. It has been rarely associated with radiation therapy. It has not been reported after image-guided superficial radiation therapy or in a geometric pattern. Case presentation: We present the first two recorded cases of Favre-Racouchot Syndrome occurring in the setting of image-guided superficial radiation therapy. Both patients presented with well-demarcated geometric plaques with …

Improving Lesion Diameter Reporting On Skin Biopsy Requisition Forms: A Quality Improvement Project, Christopher Wong, Christian Scheufele, Marshall Hall, Henry Lim, Daniel A. Nguyen, Stephen Weis, Michael Carletti, Dustin Wilkes

North Texas GME Research Forum 2024

Background: Skin biopsy requisition forms (SBRFs) are the primary communication tool between dermatologists and dermatopathologists. Diameters of biopsied skin lesions are frequently omitted on SBRFs. This quality improvement project aimed to increase the rate of reporting diameters of neoplasms on SBRFs from an academic dermatology outpatient clinic to greater than 65% within three years. Methods: The Plan-Do-Study-Act model was utilized. An initial audit was performed for SBRFs of biopsies obtained between July 1, 2021, and February 4, 2022 (“Cycle 1”). On February 4, 2022, the authors discussed societal guidelines for lesion diameter reporting on SBRFs (“Intervention A”). Cycle 2 prospectively …

A Konnyaku Jelly Model For Ultrasound-Guided Fascia Iliaca Compartment Block, Arthraj J. Vyas, Matthew C. Lo, Arthur Au, Md

Alpha Omega Alpha Research Symposium Posters

Purpose

Konnyaku Jelly:

•Previously used as effective ultrasound-guided IV access phantom

•Withstands multiple needle piercings without phantom deterioration

•<$3 per pack

Employing A Machine Learning Approach In Precision Oncology To Predict Pik3ca Functional Status And Identify Phenocopying Variants Of Deleterious Pik3ca Mutations, Javier Castillo

CMC Senior Theses

The following proposal describes a modular machine learning approach that detects malfunctioning genes and pathways in cancer using the transcriptome of cancer patients. The transcriptome is underused in precision oncology and, combined with machine learning, can aid in the identification of hidden responders. Applied to the PI3K/AKT/mTOR pathway, this method can be used to predict PIK3CA functional status and identify phenocopying variants of deleterious PIK3CA mutations. The classifier will do this by integrating RNA-seq, copy number, and mutation data from tumors to determine the functional status of PIK3CA using a set of learned gene-specific weights. The classifier will then be …

Caries Risk Prediction In Pediatric Patients Using Machine Learning Techniques: A Retrospective Study, Nagarathna P J, Vishnu Priya Veeraraghavan, Aysha Jebin A, Shikar Daniel, Kaladhar Reddy Aileni, Santosh R Patil

International Arab Journal of Dentistry

Introduction: Dental caries is a multifactorial disease prevalent in children and is often influenced by behavioral, environmental, and genetic factors. Traditional caries risk assessment methods have a limited accuracy and objectivity. Machine learning (ML) models provide a data-driven approach for predicting caries risk, thereby enabling targeted interventions.

Objectives: The main objective of this study is to develop and validate ML models for predicting caries risk in pediatric patients aged 6–12 years by incorporating clinical, behavioral, dietary, and socioeconomic factors.

Methods: This retrospective observational study included 148 children aged 6–12 years. Data, including demographic details, Decayed Missing Filling …

Utilizing Ai Integrated Neuroimaging Technology To Expand Upon Machine Learning In Positron Emission Tomography Technology With The Aim Of Detecting Amyloid Beta Biomarkers Early In The Onset Of Alzheimer's., Ethan S. Terman

UROP Posters

Early intervention in Alzheimer's is vital for treatment. The earlier a professional can detect symptoms and make a diagnosis the earlier a prognosis can be implemented. With the prevalence of data in our day-to-day world combined with Artificial intelligence (AI), utilizing both for machine learning can pave the way for more accurate and efficient detection of Alzheimer's and other neurodegenerative diseases. AI combined with Machine learning (ML) increases diagnostic efficiency and reduces human errors, making it a valuable resource for physicians and clinicians alike. With the increasing amount of data processing and image interpretation required, the ability to use AI …

Tim/Tam Receptors: A Potential Biomarker For Predicting Sensitivity To Zika Virus-Induced Oncolysis In Non-Small Cell Lung Cancers, Shankari Somasekar

Honors Undergraduate Theses

Non-small cell lung cancers (NSCLC) constitute 80-85% of lung cancers and are the leading cause of cancer-related deaths globally. The most common cause is prolonged smoking. Current treatment options for NSCLC include surgery, radiation, chemotherapy, targeted drug therapy, and immunotherapy. Although these medications are effective in the short term, patients often face issues of drug resistance and debilitating side effects with prolonged use. Currently, the use of Zika virus (ZIKV) is being researched as a possible alternative treatment for cancer, which minimizes side effects and the risk of drug resistance. TIM/TAM proteins are identified as the putative ZIKV receptors on …

Reliability And Minimal Detectable Change Of The Mx3 Hydration Testing System, Ian Winter, Josie Burdin, Patrick B. Wilson

Exercise Science Faculty Publications

Assessing hydration status outside of laboratories can be challenging given that most hydration measures are invasive, stationary, costly, or have questionable validity. This study investigated the within-day, test-retest reliability, and minimal detectable change (MDC) of the MX3 Hydration Testing System (MX3 Diagnostics), a relatively low cost, noninvasive, and portable method to measure saliva osmolality. Seventy-five adults (44 men, 31 women; 29.6±10.8 yr, 171.1±9.2 cm, 79.1±15.4 kg) presented two saliva samples approximately 3 to 5 minutes apart. Fluid intake was avoided for at least 5 minutes prior to sample collections. For each sample collection, a researcher used the MX3 to tap …

Adversarial Training Based Domain Adaptation Of Skin Cancer Images, Syed Qasim Gilani, Muhammad Umair, Maryam Naqvi, Oge Marques, Hee-Cheol Kim

Electrical & Computer Engineering Faculty Publications

Skin lesion datasets used in the research are highly imbalanced; Generative Adversarial Networks can generate synthetic skin lesion images to solve the class imbalance problem, but it can result in bias and domain shift. Domain shifts in skin lesion datasets can also occur if different instruments or imaging resolutions are used to capture skin lesion images. The deep learning models may not perform well in the presence of bias and domain shift in skin lesion datasets. This work presents a domain adaptation algorithm-based methodology for mitigating the effects of domain shift and bias in skin lesion datasets. Six experiments were …

Investigation Of The Effect Of Preparation Parameters On The Structural And Mechanical Properties Of Gelatin/Elastin/Sodium Hyaluronate Scaffolds Fabricated By The Combined Foaming And Freeze-Drying Techniques, Mansour Qamash, S. Misagh Imani, Meisam Omidi, Ciara Glancy, Lobat Tayebi

Electrical & Computer Engineering Faculty Publications

This paper aimed to evaluate the effects of different preparation parameters, including agitation speed, agitation time, and chilling temperature, on the structural and mechanical properties of a novel gelatin/elastin/sodium hyaluronate tissue engineering scaffold, recently developed by our research group. Fabricated using a combination of foaming and freeze-drying techniques, the scaffolds were assessed to understand how these parameters influence their morphology, internal microstructure, porosity, mechanical properties, and degradation behavior. The fabrication process used in this study involved preparing a homogeneous aqueous solution containing 8% gelatin, 2% elastin, and 0.5% sodium hyaluronate (w/v), which was then subjected to mechanical agitation at speeds …

Ai And Ml-Based Risk Assessment Of Chemicals: Predicting Carcinogenic Risk From Chemical-Induced Genomic Instability, Ajay Vikram Singh, Preeti Bhardwaj, Peter Laux, Prachi Pradeep, Madleen Busse, Andreas Luch, Akihiko Hirose, Christopher J. Osgood, Michael W. Stacey

Biological Sciences Faculty Publications

Chemical risk assessment plays a pivotal role in safeguarding public health and environmental safety by evaluating the potential hazards and risks associated with chemical exposures. In recent years, the convergence of artificial intelligence (AI), machine learning (ML), and omics technologies has revolutionized the field of chemical risk assessment, offering new insights into toxicity mechanisms, predictive modeling, and risk management strategies. This perspective review explores the synergistic potential of AI/ML and omics in deciphering clastogen-induced genomic instability for carcinogenic risk prediction. We provide an overview of key findings, challenges, and opportunities in integrating AI/ML and omics technologies for chemical risk assessment, …

Classification Of Vaginal Cleanliness Grades Through Surface-Enhanced Raman Spectral Analysis Via The Deep-Learning Variational Autoencoder: Long Short-Term Memory Model, Jia Wei Tang, Xin Ru Wen, Hui Min Chen, Jie Chen, Kun Hui Hong, Quan Yuan, Muhammad Usman, Liang Wang

Research outputs 2022 to 2026

In this study, it is aimed to establish a novel method based on a deep-learning-guided surface-enhanced Raman spectroscopy (SERS) technique to achieve rapid and accurate classification of vaginal cleanliness levels. We proposed a variational autoencoder (VAE) approach to enhance spectral quality, coupled with a deep learning algorithm long short-term memory (LSTM) neural network to analyze SERS spectra produced by vaginal secretions. The performance of various machine learning (ML) algorithms is assessed using multiple evaluation metrics. Finally, the reliability of the optimal model is tested using blind test data (N = 10/group for each cleanliness level). The data quality of the …

Rapid Analysis Of N-Nitrosamines In Urine Using Ultra High-Pressure Liquid Chromatography-Mass Spectrometry, S. Shinde, K. D. Croft, J. M. Hodgson, C. P. Bondonno

Research outputs 2022 to 2026

N-Nitrosamines, carcinogenic compounds present in dietary and environmental sources and formed endogenously, are believed to be linked with the presence of nitrate and nitrite, both within dietary sources and after intake. To fully evaluate this potential threat to human health, an accurate analytical method to measure N-nitrosamines in biological matrices is necessary. We report a simple, fast, selective mass spectrometry method to detect N-nitrosamines in human urine. Analysis of seven N-nitrosamines, N-nitrosodimethylamine (NDMA), N-nitrosomethylethylamine (NMEA), N-nitrosodiethylamine (NDEA), N-nitrosopiperdine (NPIP), N-nitrosopyrrolidine (NPYR), N-nitrosodi-N-propylamine (NDPA) and N-nitrosodi-N-butylamine (NDBA) in urine was quantitated using Ultra High-Pressure Liquid Chromatography-tandem Mass spectrometry (UHPLC-MS/MS). A Sorbent …

A Scoping Review Of Population Diversity In The Common Genomic Aberrations Of Clear Cell Renal Cell Carcinoma, Sean S. Kumar, Ninad Khandekar, Komal Dani, Saina R. Bhatt, Vinay Duddalwar, Anishka D'Souza

Department of Medicine Faculty Publications

Introduction: Previous literature has shown that clear cell renal cell carcinoma (ccRCC) is becoming a more prevalent diagnosis and that the incidence and mortality differ both regionally and racially. While the molecular profiles for ccRCC are studied regionally through biopsy and sequencing techniques, the genomic landscape and ccRCC diversity data are not well-studied. We conducted a review of the known genomic data on 6 of the most clinically relevant DNA biomarkers in ccRCC: Von Hippel-Landau (vHL), Polybromo-1 (PBRM1), Breast Cancer Gene 1-Associated Protein 1 (BAP1), Histone-Lysine N-Methyltransferase Domain-Containing 2 (SETD2), Mammalian Target of Rapamycin (mTOR), and Lysine-Specific Demethylase 5C (KDM5C). …

The Validity And Reliability Of Ultrasound As A Tool For Assessing Skeletal Muscle Characteristics In The Pediatric Population, Elizabeth Seewer

Theses and Dissertations (ETD)

Annually, around 5 million people in the U.S. are admitted to Intensive Care Units (ICU) due to critical conditions, with pediatric patients comprising 21% of these admissions. Approximately 40% of pediatric ICU (PICU) patients require mechanical ventilation and extended stays, and about 6% result in death. Survivors often face long-term disabilities, including loss of skeletal muscle mass (SMM), a risk factor for morbidity and mortality. Abdominal computed tomography (CT) scans, often performed during PICU admissions, can assess muscle mass but pose risks of increased cost and radiation exposure. While CT and Magnetic Resonance Imaging (MRI) are gold standards for muscle …

Positive Airway Pressure And Mask Factors Affective Adherence In Patients With Obstructive Sleep Apnea, Rutvi Sheth, Michel Audette, Soham Sheth

Electrical & Computer Engineering Faculty Publications

Purpose: Sleep apnea is a major risk factor for cardiovascular disorders. CPAP (Continuous Positive Airway Pressure) therapy is the main treatment for sleep apnea. However, adherence with CPAP remains a concern. Although there are various factors attributed to failure of CPAP therapy, mask related factors are not well studied.

Methods: The study recruited patients with obstructive sleep apnea using CPAP from neurology outpatient clinic at Progressive Neurology and Sleep Center. An IRB (Institutional Review Board) approved questionnaire was administered to evaluate factors affecting CPAP compliance.

Results: The study recruited 24 patients. It showed that almost 50% of the patients did …

Advancing Chronic Kidney Disease Prediction Through Machine Learning And Deep Learning With Feature Analysis, Shiddarth Dey Tusar, S. M. Ahad Ali Chowdhury, Md. Jalal Uddin Chowdhury, Rana M. Pir, H. M. Nur A. Alam, Muhammad Rezaur Rahman, Md. Nural Absar Siddiky, Muhammad Enayetur Rahman

Electrical & Computer Engineering Faculty Publications

Chronic Kidney Diesease (CKD) is a significant health issue, ranking as the fourth leading cause of mortality worldwide. The traditional diagnosis and treatment process, reliant on medical experts, is time-consuming. Therefore, thereis an urgent need for more efficient diagnostic methods to improve patient outcomes and reduce mortality rates. In this study, we employ Machine Learning (ML) and Deep Learning (DL) techniques to predict CKD based on important features. Feature analysis was performed using a correlation matrix and the LASSO algo-rithm to identify the most relevant features for model training. We evaluated several ML and DL classifiers, including Logistic Regression (LR), …

Integrated Machine Learning And Deep Learning Models For Cardiovascular Disease Risk Prediction: A Comprehensive Comparative Study, Shadman Mahmood Khan Pathan, Sakan Binte Imran

Electrical & Computer Engineering Faculty Publications

Cardiovascular Diseases (CVDs) pose a significant global health challenge, necessitating accurate risk prediction for effective preventive measures. This comprehensive comparative study explores the performance of traditional Machine Learning (ML) and Deep Learning (DL) models in predicting CVD risk, utilizing a meticulously curated dataset derived from health records. Rigorous preprocessing, including normalization and outlier removal, enhances model robustness. Diverse ML models (Logistic Regression, Random Forest, Support Vector Machine, K-Nearest Neighbor, Decision Tree, and Gradient Boosting) are compared with a Long Short-Term Memory (LSTM) neural network for DL. Evaluation metrics include accuracy, ROC AUC, computation time, and memory usage. Results identify the …