Digital Commons Network^™

Machine Learning As A Tool For Early Detection: A Focus On Late-Stage Colorectal Cancer Across Socioeconomic Spectrums, Hadiza Galadima, Rexford Anson-Dwamena, Ashley Johnson, Ghalib Bello, Georges Adunlin, James Blando

Community & Environmental Health Faculty Publications

Purpose: To assess the efficacy of various machine learning (ML) algorithms in predicting late-stage colorectal cancer (CRC) diagnoses against the backdrop of socio-economic and regional healthcare disparities. Methods: An innovative theoretical framework was developed to integrate individual- and census tract-level social determinants of health (SDOH) with sociodemographic factors. A comparative analysis of the ML models was conducted using key performance metrics such as AUC-ROC to evaluate their predictive accuracy. Spatio-temporal analysis was used to identify disparities in late-stage CRC diagnosis probabilities. Results: Gradient boosting emerged as the superior model, with the top predictors for late-stage CRC diagnosis being anatomic site, …

Msdrp: A Deep Learning Model Based On Multisource Data For Predicting Drug Response, Haochen Zhao, Xiaoyu Zhang, Qichang Zhao, Yaohang Li, Jianxin Wang

Computer Science Faculty Publications

Motivation: Cancer heterogeneity drastically affects cancer therapeutic outcomes. Predicting drug response in vitro is expected to help formulate personalized therapy regimens. In recent years, several computational models based on machine learning and deep learning have been proposed to predict drug response in vitro. However, most of these methods capture drug features based on a single drug description (e.g. drug structure), without considering the relationships between drugs and biological entities (e.g. target, diseases, and side effects). Moreover, most of these methods collect features separately for drugs and cell lines but fail to consider the pairwise interactions between drugs and cell …

Foundations Of Plasmas For Medical Applications, T. Von Woedtke, Mounir Laroussi, M. Gherardi

Electrical & Computer Engineering Faculty Publications

Plasma medicine refers to the application of nonequilibrium plasmas at approximately body temperature, for therapeutic purposes. Nonequilibrium plasmas are weakly ionized gases which contain charged and neutral species and electric fields, and emit radiation, particularly in the visible and ultraviolet range. Medically-relevant cold atmospheric pressure plasma (CAP) sources and devices are usually dielectric barrier discharges and nonequilibrium atmospheric pressure plasma jets. Plasma diagnostic methods and modelling approaches are used to characterize the densities and fluxes of active plasma species and their interaction with surrounding matter. In addition to the direct application of plasma onto living tissue, the treatment of liquids …

Non-Contact Trapping And Stretching Of Biological Cells Using Dual-Beam Optical Stretcher On Microfluidic Platform, Aotuo Dong, Balaadithya Uppalapati, Shariful Islam, Brandon Gibbs, Ganesan Kamatchi, Sacharia Albin, Makarand Deo

Electrical & Computer Engineering Faculty Publications

Optical stretcher is a tool in which two counter-propagating, slightly diverging, and identical laser beams are used to trap and axially stretch microparticles in the path of light. In this work, we utilized the dual-beam optical stretcher setup to trap and stretch human embryonic kidney (HEK) cells and mammalian breast cancer (MBC) cells. Experiments were performed by exposing the HEK cells to counter-propagating laser beams for 30 seconds at powers ranging from 100 mW to 561 mW. It was observed that the percentage of cell deformation increased from 16.7% at 100 mW to 40.5% at 561 mW optical power. The …

Inactivation Of Myeloma Cancer Cells By Helium And Argon Plasma Jets: The Effect Comparison And The Key Reactive Species, Zeyu Chen, Qingjie Cui, Chen Chen, Dehui Xu, Dingxin Liu, H. L. Chen, Michael G. Kong

Bioelectrics Publications

In plasma cancer therapy, the inactivation of cancer cells under plasma treatment is closely related to the reactive oxygen and nitrogen species (RONS) induced by plasmas. Quantitative study on the plasma-induced RONS that related to cancer cells apoptosis is critical for advancing the research of plasma cancer therapy. In this paper, the effects of several reactive species on the inactivation of LP-1 myeloma cancer cells are comparatively studied with variable working gas composition, surrounding gas composition, and discharge power. The results show that helium plasma jet has a higher cell inactivation efficiency than argon plasma jet under the same discharge …

Race, Ethnicity, Psychosocial Factors, And Telomere Length In A Multicenter Setting, Shannon M. Lynch, M. K. Peek, Nandita Mitra, Krithika Ravichandran, Charles Branas, Elaine Spangler, Wenting Zhou, Electra D. Paskett, Sarah Gehlert, Cecilia Degraffinreid, Timothy R. Rebbeck, Harold Riethman

School of Medical Diagnostics & Translational Sciences Faculty Publications

Background

Leukocyte telomere length(LTL) has been associated with age, self-reported race/ethnicity, gender, education, and psychosocial factors, including perceived stress, and depression. However, inconsistencies in associations of LTL with disease and other phenotypes exist across studies. Population characteristics, including race/ethnicity, laboratory methods, and statistical approaches in LTL have not been comprehensively studied and could explain inconsistent LTL associations.

Methods

LTL was measured using Southern Blot in 1510 participants from a multi-ethnic, multi-center study combining data from 3 centers with different population characteristics and laboratory processing methods. Main associations between LTL and psychosocial factors and LTL and race/ethnicity were evaluated and then …

Adjacent Slice Prostate Cancer Prediction To Inform Maldi Imaging Biomarker Analysis, Shao-Hui Chuang, Xiaoyan Sun, Lisa Cazares, Julius Nyalwidhe, Dean Troyer, O. John Semmes, Jiang Li, Frederic D. Mckenzie, Nico Karssemeijer (Ed.), Ronald M. Summers (Ed.)

Electrical & Computer Engineering Faculty Publications

Prostate cancer is the second most common type of cancer among men in US [1]. Traditionally, prostate cancer diagnosis is made by the analysis of prostate-specific antigen (PSA) levels and histopathological images of biopsy samples under microscopes. Proteomic biomarkers can improve upon these methods. MALDI molecular spectra imaging is used to visualize protein/peptide concentrations across biopsy samples to search for biomarker candidates. Unfortunately, traditional processing methods require histopathological examination on one slice of a biopsy sample while the adjacent slice is subjected to the tissue destroying desorption and ionization processes of MALDI. The highest confidence tumor regions gained from the …