Digital Commons Network^™

Access To Digital Health Technologies: Personalized Framework And Global Perspectives, Sanjiv M Narayan, Mina K Chung, Demilade Adedinsewo, Luisa C C Brant, Leslie L Davis, David Duncker, Jennifer L Hall, Janet K Han, Carolyn S P Lam, Eldrin Lewis, Joseph Loscalzo, Manlio F Márquez, Vasiliki Rahimzadeh, Fatima Rodriguez, Prashanthan Sanders, Emma Svennberg, Kenneth Stein, Mintu Turakhia, Clyde Yancy, Antonis A Armoundas

Center for Medical Ethics and Health Policy Staff Publications

The emergence and rapid adoption of digital health technologies (DHT) present unprecedented opportunities to democratize and reduce disparities in health care by monitoring health and disease at the point of care in all patients. However, limited access to DHT is becoming a major obstacle to realizing these goals. Access to DHT is influenced not only by well-recognized social determinants of health, but also by digital determinants of health, such as digital literacy and the need for broad access to digital infrastructure, as well as commercial and economic factors. Addressing these challenges and designing unbiased systems of care are essential to …

Pd-L1-Centric Whole Blood-Based Immune Signature Profiles Of Tuberculosis Patients During Therapy, Johanna Eggeling, Martina Sester, Christoph Lange, Jan Heyckendorf, Barbara Kalsdorf, Anna M Mandalakas, Andrew R Dinardo, David Lewinsohn, Dagmar Schaub, Tina Schmidt, Eva Tolosa, Maja Reimann, Patricia M Sánchez Carballo

Faculty, Staff and Students Publications

This prospective study investigated whole blood-based immune cell biomarkers for pulmonary tuberculosis (TB) immunoprofiling. Blood samples from 34 healthy controls and 51 tuberculosis patients were analyzed at three timepoints: Prior to therapy (T0), after 14 days of therapy (T1), and at the end of treatment (Te). Using multiparameter flow cytometry, 386 immune cell populations were analyzed. Predictive models were developed using two machine learning algorithms. A TB5LF change score, which was based on five cell populations, effectively distinguished tuberculosis patients from controls (AUC = 0.89) and tuberculosis patients before and at the end of treatment (AUC = 0.92). Similarly, the …

Unraveling Posttranslational Modification Complexity: Advances In Quantitative Histone Proteoform Mass Spectrometry, Karl F Poncha, Alyssa T Paparella, Nicolas L Young

Faculty, Staff and Students Publications

Histone proteins and their posttranslational modifications are central to chromatin structure and function. These modifications often occur in combinations, generating a diverse array of histone proteoforms that contribute to the dynamic regulation of chromatin architecture. Advancements in mass spectrometry-based proteomics, particularly top-down and middle-down approaches, have significantly enhanced our ability to characterize these proteoforms and elucidate PTM crosstalk. This review provides an analysis of the epigenetic machinery involved in the addition, recognition, and removal of histone PTMs, emphasizing the complexity introduced by histone variants and combinatorial PTM patterns. We examine the challenges and limitations of traditional antibody-based methods for PTM …

Evolving Burden Of Metabolic Dysfunction-Associated Steatotic Liver Disease And Its Complications In A Us Nationwide Healthcare System, Basim Ali, Ronald Samuel, Jennifer R Kramer, Liang Li, Xian Yu, Yumei Cao, Roxanne Desiderio, George Cholankeril, Steven M Asch, Hashem B El-Serag, Fasiha Kanwal

Faculty, Staff and Students Publications

Background: Metabolic dysfunction-associated steatotic liver disease (MASLD) is a growing public health problem. Measuring the burden of cirrhosis and its complications in people with MASLD is important to inform health care delivery.

Methods: We aimed to quantify the changes in the burden of MASLD-related cirrhosis and hepatocellular carcinoma (HCC) in a nationwide U.S. healthcare system. We used data from the Veterans Health Administration to examine temporal trends between 2010 and 2021 in the annual incidence and prevalence of cirrhosis and HCC in a large cohort of patients with MASLD, and to compare them to those in patients with hepatitis C …

Highly Variable Expressivity Of A Cnv Deletion Involving Tbx4 In Three Deceased Siblings With Lung Developmental Disorder And Their Mildly Affected Mother And Grandfather, Przemyslaw Szafranski, Tomasz Gambin, Michal Kadlof, Michał Denkiewicz, Dariusz Plewczynski, Hyun Jeong Kim, Gail Deutsch, Nahir Cortes-Santiago, Salmo Raskin, Paweł Stankiewicz

Faculty, Staff and Students Publications

Single nucleotide variants (SNVs) and copy-number variant (CNV) deletions involving TBX4 have been associated with pulmonary arterial hypertension, ischiocoxopodopatellar syndrome, and lethal lung developmental disorders (LLDDs). Thus far, all large CNV deletions encompassing entire TBX4 have been found to have arisen de novo. Here, we present a three-generation family with three neonate siblings who died within 35-66 days due to histopathologically diagnosed LLDD. Whole-genome sequencing identified an ~108-kb CNV deletion encompassing TBX4 in all three infants. The deletion was also found in their mother with a history of pneumonia and persistent thick upper airway secretions and in the maternal grandfather …

Predictive Modeling Of Hematoma Expansion From Non-Contrast Computed Tomography In Spontaneous Intracerebral Hemorrhage Patients, Natasha Ironside, Shinjini Kundu, Et Al.

2020-Current year OA Pubs

Hematoma expansion is a consistent predictor of poor neurological outcome and mortality after spontaneous intracerebral hemorrhage (ICH). An incomplete understanding of its biophysiology has limited early preventative intervention. Transport-based morphometry (TBM) is a mathematical modeling technique that uses a physically meaningful metric to quantify and visualize discriminating image features that are not readily perceptible to the human eye. We hypothesized that TBM could discover relationships between hematoma morphology on initial Non-Contrast Computed Tomography (NCCT) and hematoma expansion. 170 spontaneous ICH patients enrolled in the multi-center international Virtual International Trials of Stroke Archive (VISTA-ICH) with time-series NCCT data were used for …

Measurable Imaging-Based Changes In Enhancement Of Intrahepatic Cholangiocarcinoma After Radiotherapy Reflect Physical Mechanisms Of Response, Brian De, Prashant Dogra, Mohamed Zaid, Dalia Elganainy, Kevin Sun, Ahmed M Amer, Charles Wang, Michael K Rooney, Enoch Chang, Hyunseon C Kang, Zhihui Wang, Priya Bhosale, Bruno C Odisio, Timothy E Newhook, Ching-Wei D Tzeng, Hop S Tran Cao, Yun S Chun, Jean-Nicholas Vauthey, Sunyoung S Lee, Ahmed Kaseb, Kanwal Raghav, Milind Javle, Bruce D Minsky, Sonal S Noticewala, Emma B Holliday, Grace L Smith, Albert C Koong, Prajnan Das, Vittorio Cristini, Ethan B Ludmir, Eugene J Koay

Faculty, Staff and Student Publications

Escalated doses of radiotherapy associate with improved local control and overall survival (OS) in intrahepatic cholangiocarcinoma (iCCA), but personalization remains limited because conventional size-based CT criteria correlate poorly with outcomes. We hypothesized that quantitative enhancement measurements would better predict clinical outcomes and guide individualized RT optimization. In a retrospective cohort of 154 patients, we analyzed pre- and post-RT CT scans using quantitative European Association for Study of Liver (qEASL) to derive viable tumor volumes, comparing enhancement-based metrics with size-based RECIST and linking them to outcomes via survival and mathematical modeling. Change in enhancement volume was strongly associated with OS after …

Can Cta-Based Machine Learning Identify Patients For Whom Successful Endovascular Stroke Therapy Is Insufficient?, Jerome A Jeevarajan, Yingjun Dong, Anjan Ballekere, Sergio Salazar Marioni, Arash Niktabe, Rania Abdelkhaleq, Sunil A Sheth, Luca Giancardo

Faculty, Staff and Student Publications

Background and purpose: Despite advances in endovascular stroke therapy (EST) devices and techniques, many patients are left with substantial disability, even if the final infarct volumes (FIVs) remain small. Here, we evaluate the performance of a machine learning (ML) approach by using pretreatment CTA to identify this cohort of patients that may benefit from additional interventions.

Materials and methods: We identified consecutive subjects with large vessel occlusion (LVO) acute ischemic stroke (AIS) who underwent EST with successful reperfusion in a multicenter prospective registry cohort. We included only subjects with FIV < 30 mL and recorded 90-day outcome (mRS). A deep learning model was pretrained and then fine-tuned to predict 90-day mRS 0-2 by using pretreatment CTA images (DeepsymNet-v3 model pretrained on radiology reports and fine-tuned on detection of unexpected clinical outcomes using brain CTA images [DSN-CTA] model). The primary outcome was the predictive performance of the DSN-CTA model compared with a logistic regression model with clinical variables, measured by the area under the receiver operating characteristic curve (AUROC).

Results: The DSN-CTA model was pretrained on 1542 subjects and …

Categorizing Weight Growth Of Infants Born Before 32 Weeks' Gestation Using The 2023 Postnatal Growth Charts For Preterm Infants., Fu-Sheng Chou, Hung-Wen Yeh, Crystal Hsueh, Jing Zhang, Maria Fe B. Villosis, Karine Barseghyan, Ashwini Lakshmanan, Reese H. Clark

Manuscripts, Articles, Book Chapters and Other Papers

OBJECTIVE: To categorize growth and correlate growth categories with morbidities in infants born before 32 weeks of gestation.

STUDY DESIGN: This retrospective study categorized weight growth by correlating mean growth velocity (GV) with growth trajectory z-score changes (ΔGTZ ), as measured using 2023 Postnatal Growth Charts for Preterm Infants. The associations between weight categories and morbidities were assessed.

RESULTS: Weight gain of infants without morbidities was categorized into three groups based on correlating ΔGTZ with mean GV: slower ( ΔGTZ < -0.1), in-parallel ( ΔGTZ -0.1 to 0.3), and faster ( ΔGTZ > 0.3). The proportions of infants with and without morbidities in these categories were evaluated across two distinct cohorts, revealing similar …

Dual-Level Pattern Tree For Visual Field Improves Glaucoma Progression And Polygenic Risk Prediction, Luo Song, Lucy Q. Shen, Louis R. Pasquale, Michael V. Boland, Sarah R. Wellik, Carlos Gustavo De Moraes, Ayellet Segre, Janey L. Wiggs, Constance Turman, Jonathan S. Myers, Tobias Elze, Nazlee Zebardast, David S. Friedman, Jae H. Kang, Mengyu Wang

Wills Eye Hospital Papers

PURPOSE: To develop a dual-level pattern tree to characterize visual field (VF) loss subtypes that can be used to better predict glaucoma progression and glaucoma polygenic risk scores (PRSs).

METHODS: This study included 113,030 patients from three datasets, each used for a specific purpose: (1) model training, (2) progression forecasting, and (3) PRS correlations. We applied archetypal analysis to cluster 24-2 VFs into trunk patterns and their branch patterns. The Cox regression model was used to forecast VF progression using mean deviation (MD) slope, MD-fast slope, total deviation (TD) pointwise slope, and visual field index (VFI) slope. Multivariable regression analyses …

Short-Term Preeclampsia Prediction: Cutoff Variations For Sflt-1/Plgf In U.S. Patients With Or Without Hypertensive Disorders, Yaxin Li, Kristen Cagino, Jim Yee, Caroline Andy, Dajana Borova, Ayush Shah, Isla Racine, Tracy Grossman, Zhen Zhao

Student Papers, Posters & Projects

BACKGROUND: Preeclampsia (PE) is a complex disorder with significant maternal and fetal risks. The soluble fms-like tyrosine kinase-1 (sFlt-1) and placental growth factor (PlGF) ratio shows promise as a diagnostic tool, but its adoption in the U.S. remains limited due to the lack of accessible testing platforms, U.S.-based studies, and evidence-based implementation guidelines.

PATIENTS/MATERIALS AND METHODS: We conducted a cohort study to evaluate the sFlt-1/PlGF ratio for predicting PE within two weeks among pregnant individuals ≥18 years, ≥20 weeks gestation. Serum samples were obtained from routine prenatal visits or triage evaluations. sFlt-1/PlGF ratios were measured using Roche Elecsys assays, and …

Detection Of Phase-Binning And Interpolation Artifacts In 4-Dimensional Computed Tomography Imaging Using Deep Learning And Rule-Based Approaches, Jorge Cisneros, Nathan H. Feldt, Yevgeniy Vinogradskiy, Richard Castillo, Edward Castillo

Department of Radiation Oncology Faculty Papers

BACKGROUND: Four-dimensional computed tomography (4DCT) imaging is a crucial component to lung cancer radiotherapy planning and enables CT-ventilation-based functional avoidance planning to mitigate radiation toxicity. However, 4DCT scans are frequently impaired by acquisition artifacts that corrupt downstream analyses that depend on lung segmentation and deformable image registration, such as CT-ventilation and dose accumulation.

PURPOSE: This study develops 3D deep learning models to identify phase-binning artifacts at the voxel level and a heuristic, rule-based method to identify interpolation slices within 4DCT images.

METHODS: We introduce a generator that systematically inserts synthetic phase-binning and interpolation artifacts into any artifact-free breathing phase obtained …

Personalized Risk Prediction For Cancer Survivors: A Generalized Bayesian Semi-Parametric Model Of Recurrent Events With Competing Outcomes, Nam Hoai Nguyen, Seung Jun Shin, Elissa Dodd-Eaton, Jing Ning, Wenyi Wang

Faculty, Staff and Student Publications

Multiple primary cancers are increasingly more frequent due to improved survival of cancer patients. Characteristics of the first primary cancer largely impact the risk of developing subsequent primary cancers. Hence, model-based risk characterization of cancer survivors that captures patient-specific variables is needed for healthcare policy making. We propose a Bayesian semi-parametric framework, where the occurrence processes of the competing cancer types follow independent non-homogeneous Poisson processes and adjust for covariates including the type and age at diagnosis of the first primary. Applying this framework to a historically collected cohort with families presenting a highly enriched history of multiple primary tumors …

Forecasting Chemoradiation Response Midtreatment For High-Grade Gliomas Through Patient-Specific Biology-Based Modeling, David A Hormuth, Maguy Farhat, Bikash Panthi, Holly Langshaw, Mihir D Shanker, Wasif Talpur, Sara Thrower, Jodi Goldman, Sophia Ty, Calliope Custer, Jeanne Kowalski, Thomas E Yankeelov, Caroline Chung

Faculty, Staff and Student Publications

Purpose: The entire course of radiation therapy (RT) for high-grade glioma (HGG) is currently derived from pre-RT magnetic resonance imaging (MRI). Although it is possible to adapt RT during the course of treatment, it is often guided only by anatomical changes to the tumor. This study seeks to determine if a biology-based mathematical model, parameterized by patient-specific, multiparametric MRI (mpMRI) data, can accurately forecast HGG response during RT.

Methods and materials: Twenty one patients with HGG planned for 6 weeks of concurrent RT and chemotherapy were imaged weekly with mpMRI during RT and at 1, 2, and 3 months post-RT. …

Normalization Of Temperature Effects For Quality Assurance Of Quantitative Prostate Apparent Diffusion Coefficient Imaging Across Multiple Sites, Ken-Pin Hwang, Joshua Yung, R Jason Stafford, Caroline Chung, Aradhana M Venkatesan

Faculty, Staff and Student Publications

Background: Apparent Diffusion Coefficient (ADC) as measured by diffusion weighted imaging is known to negatively correlate with prostate tumor aggressiveness. Heterogeneity in system and protocol performance causes potential variability in ADC acquired across a large scanner network, prompting a need to evaluate quantitative ADC from a prostate-specific MR diffusion protocol as part of quality assurance (QA). Due to the temperature dependence of ADC, repeatability and reproducibility assessments typically require phantoms to maintain a temperature of 0°C, imposing a considerable burden when assessing large numbers of scanners.

Purpose: To develop a QA procedure at room temperature for assessing the reproducibility of …

From Standard To Stratified: Modeling Ntcp And Ear To Personalize Daily Mv-Cbct In Radiotherapy., Duong Thanh Tai, Luong Tien Phat, Tran Trung Kien, Duong Tuan Linh, Nguyen Ngoc Anh, Nguyen Quang Hung, Peter Sandwall, Parham Alaei, David Bradley, James C L Chow

Oncology Articles

PURPOSE: To evaluate the cumulative radiobiological impact of daily megavoltage cone-beam computed tomography (MV-CBCT) imaging dose based on normal tissue complication probability (NTCP) and excess absolute risk (EAR) of secondary malignancies among radiotherapy patients treated for breast, pelvic, and head & neck cancers. This study investigated whether MV-CBCT imaging dose warrants protocol personalization according to patient age, anatomical treatment site, and organ-specific radiosensitivity.

METHODS: This retrospective study included cohorts of breast (n = 30), pelvic (n = 17), and head & neck (n = 20) cancer patients undergoing radiotherapy with daily MV-CBCT. Imaging dose distributions employing two common MV-CBCT protocols …

Smags-Lasso: A Novel Feature Selection Method For Sensitivity Maximization In Early Cancer Detection, Hamid Khoshfekr Rudsari, Sara Khorami-Sarvestani, Johannes F Fahrmann, James P Long, Samir Hanash, Kim-Anh Do, Ehsan Irajizad

Faculty, Staff and Student Publications

Background: Sensitivity and specificity are foundational metrics for cancer detection tools. However, most machine learning algorithms prioritize overall accuracy during optimization, which fails to align with clinical priorities of early detection. We aim to develop a feature selection machine learning algorithm while maximizing sensitivity at a given specificity.

Methods: We developed SMAGS-LASSO, a machine learning algorithm that combines our developed Sensitivity Maximization at a Given Specificity (SMAGS) framework with L1 regularization for feature selection. This approach simultaneously optimizes sensitivity at user-defined specificity thresholds while performing feature selection. SMAGS-LASSO utilizes a custom loss function with L1 regularization and multiple parallel optimization …

Knowledge Mapping And Visualized Analysis Of Research Progress In Onconephrology: A Bibliometric Analysis, Yiwei Wang, Shuling Fan, Wei Wang

Faculty, Staff and Student Publications

Objectives: Onconephrology is an expanding subspecialty focused on the management of cancer patients with renal injury. This study used a comprehensive bibliometric analysis to emphasize the need for cooperation between oncologists and nephrologists, exploring current trends and future research areas in onconephrology.

Methods: Relevant literature on onconephrology published between 1 January 2000 and 27 April 2024 was retrieved from the Science Citation Index Expanded of the Web of Science Core Collection, followed by manual screening. Bibliometric analyses were performed using CiteSpace, VOSviewer, and Bibliometrix software.

Results: A total of 1,853 publications, including 1,647 articles and 206 reviews, by 11,606 authors …

Muc15 Ectodomain Architecture Regulates Integrin Clustering To Control Cancer Metastasis, Simei Zhang, Guy M Genin, Et Al.

2020-Current year OA Pubs

Cancer metastasis is governed by physical cues at the cell-matrix interface, with matrix stiffness, ligand density, and topography established as key determinants. Here, a fourth critical factor in cancer metastasis, the architecture of the cell-surface glycocalyx is identified. Using MUC15 as a representative small glycoprotein, mathematical modeling and domain truncation experiments are combined to show that glycoprotein size distribution governs integrin adhesion states and metastatic outcomes. MUC15 localizes to focal adhesions and interact with integrins, while larger glycoproteins such as MUC1 are sterically excluded. These physical effects, rather than intracellular signaling, dictate adhesion state transitions: removing MUC15's ectodomain eliminated its …

Does Health Insurance Coverage Improve Cardiometabolic Risk Factor Levels? Quasi-Experimental Evidence From India, Kavita Singh, Anubha Agarwal, Mark D Huffman, Et Al.

2020-Current year OA Pubs

BACKGROUND: Chronic conditions cause notable health and economic burdens. While health insurance enables access to healthcare, its effects on chronic care outcomes remain under-explored.

OBJECTIVE: To examine the association between health insurance coverage and cardiometabolic risk factors among people with chronic conditions in India.

METHODS: Data from the Centre for Cardiometabolic Risk Reduction in South Asia (CARRS) and Solan studies, including 2,926 adults with chronic conditions were analyzed using propensity score weighting to evaluate the associations between health insurance and cardiometabolic risk factors (HbA1c, low-density lipoprotein cholesterol [LDLc], and blood pressure [BP]) and self-reported health status (measured using European Quality …

Predicting The Response Of Triple Negative Breast Cancer To Neoadjuvant Systemic Therapy Via Biology-Based Modeling And Habitat Analysis, Casey E Stowers, Chengyue Wu, Clinton Yam, Jingfei Ma, Gaiane M Rauch, Thomas E Yankeelov

Faculty, Staff and Student Publications

Despite being the standard-of-care treatment, neoadjuvant therapy (NAT) attains a complete response only in approximately half of the patients with triple negative breast cancer. Thus, methods to predict and optimize patient response to NAT are needed. Previously, we employed patient-specific MRI data to calibrate a biology-based mathematical model that describes cell movement, proliferation, and death due to drug at the tumor level and cell proliferation at an image voxel level. We now extend our approach by using MRI data to group voxels into "habitats" whereby tumor cells of a habitat share the same proliferation. With this approach, we now calibrate …

Turning Patients' Open-Ended Narratives Of Chronic Pain Into Quantitative Measures: Natural Language Processing Study, Raquel Norel, Jennifer Gewandter, Zhengwu Zhang, Anika Tahsin, Chadi G Abdallah, John Markman, Zhiyao Duan, Guillermo Cecchi, Paul Geha

Faculty, Staff and Students Publications

Background: Subjective report of pain remains the gold standard for assessing symptoms in patients with chronic pain and their response to analgesics. This subjectivity underscores the importance of understanding patients' personal narratives, as they offer an accurate representation of the illness experience.

Objective: In this pilot study involving 20 patients with chronic low back pain (CLBP), we applied emerging tools from natural language processing (NLP) to derive quantitative measures that captured patients' pain narratives.

Methods: Patients' narratives were collected during recorded semistructured interviews in which they spoke about their lives in general and their experiences with CLBP. Given that NLP …

Estimated Impact Of 2022–2023 Influenza Vaccines On Annual Hospital Burden In The United States, Kaiming Bi, Shraddha Ramdas Bandekar, Anass Bouchnita, Annalise Cramer, Spencer J Fox, Rebecca K Borchering, Matthew Biggerstaff, Lauren Ancel Meyers

Faculty, Staff and Student Publications

During the COVID-19 pandemic early years, infection prevention measures suppressed transmission of seasonal influenza and other respiratory viruses. The early onset and moderate severity of the US 2022-2023 influenza season may have resulted from reduced use of nonpharmaceutical interventions or lower population immunity after 2 y of limited influenza virus circulation. We used a mathematical model of influenza virus transmission that incorporates vaccine-derived protection against both infection and severe disease to estimate the impact of influenza vaccines on healthcare burden. Assuming reported levels of past vaccine effectiveness (VE) against infection and hospitalization, we estimate that influenza vaccines prevented 69,886 (95% …

Use And Outcomes Of The Medical Hybrid Procedure For Stage 1 Palliation In Infants With Hypoplastic Left Heart Syndrome And Variants., Daniel N. Beauchamp, Christopher J. Statile, Huaiyu Zang, David A. Parra, Justin Godown, Natalie Jayaram, Matthew L. Moehlmann, Garick D. Hill

Manuscripts, Articles, Book Chapters and Other Papers

BACKGROUND: Staged palliation of hypoplastic left heart syndrome and variants begins with the Norwood or hybrid procedure. Hybrid palliation is used in a minority of cases and often reserved for high-risk patients. Stented hybrid (SH) comprises bilateral pulmonary artery bands and ductal stenting, and medical hybrid (MH) comprises bilateral pulmonary artery bands and prostaglandins. MH use and outcomes have not been well described. We sought to compare MH, SH, and surgical stage 1 (SS1) using a national database.

METHODS: Patients from the National Pediatric Cardiology Quality Improvement Collaborative database born between 2016 and 2021 were categorized by initial intervention: MH, …

Validation Of A Risk Score For Cancer-Associated Thrombosis Using Nationwide Ehr Data, Ang Li, Omid Jafari, Barbara D Lam, Jun Y Jiang, Rock Bum Kim, Shengling Ma, Emily Zhou, Joyce W Tiong, Elizabeth C Chiang, Justine Ryu, Christopher I Amos, Jennifer La, Nathanael R Fillmore

Faculty, Staff and Student Publications

Importance: Venous thromboembolism (VTE) is associated with increased mortality and morbidity in patients with cancer. Existing risk prediction models are typically validated within individual sites, a fragmented approach that limits clinical adoption.

Objective: To validate the electronic health record cancer-associated thrombosis (EHR-CAT) score compared with the benchmark Khorana score in a contemporary cohort of patients with cancer across the nation, before and after treatment, excluding those at high risk of bleeding.

Design, setting, and participants: This prognostic study included patients in a nationwide longitudinal EHR database from January 2018 to December 2023 with follow-up continuing to April 2025. Patients with …

Modeling Cell Differentiation In Neuroblastoma: Insights Into Development, Malignancy, And Treatment Relapse., Simon F. Martina-Perez, Luke A. Heirene, Jennifer C. Kasemeier, Paul M. Kulesa, Ruth E. Baker

Manuscripts, Articles, Book Chapters and Other Papers

Neuroblastoma is a paediatric extracranial solid cancer that arises from the developing sympathetic nervous system and is characterised by an abnormal distribution of cell types in tumours compared to healthy infant tissues. In this paper, we propose a new mathematical model of cell differentiation during sympathoadrenal development. By performing Bayesian inference of the model parameters using clinical data from patient samples, we show that the model successfully accounts for the observed differences in cell type heterogeneity among healthy adrenal tissues and four common types of neuroblastomas. Using a phenotypically structured model, we show that alterations in healthy differentiation dynamics are …

‘Earth System Engineers’ And The Cumulative Impact Of Organisms In Deep Time, Simon A. F. Darroch, Michelle M. Casey, Alison T. Cribb, Amanda E. Bates, Matthew E. Clapham, Dori L. Contreras, Matthew Craffey, Ivo A. P. Duijnstee, William Gearty, Nicholas J. Gotelli, Marcus J. Hamilton, Riley F. Hayes, Pincelli M. Hull, Daniel E. Ibarra, V. A. Korasidis, Jaemin Lee, Cindy V. Looy, Tyler R. Lyson, Benjamin Muddiman, Peter D. Roopnarine, Alex B. Shupinski, Felisa A. Smith, Alycia L. Stigall, Catalina P. Tomé, Katherine A. Turk, Amelia Villaseñor, Peter J. Wagner, Steve C. Wang, S. Kathleen Lyons

College of Life Sciences Faculty Papers

Understanding the role of humans as 'ecosystem engineers' requires a deep-time perspective rooted in evolutionary history and the fossil record. However, no conceptual framework exists for studying the rise of ecosystem engineering in deep time, requiring us to consider effects that fall outside the scope of traditional definitions. Here, we present a new framework applicable to both modern and ancient engineering-type effects. We propose a new term - 'Earth system engineering' - to describe biological processes that alter the structure and function of planetary spheres, and which combines core tenets of ecosystem engineering, niche construction, and legacy effects. We illustrate …

Concerns Regarding The Standard Deviation Of Individual Responses For Assessing Treatment Response Heterogeneity, Aaron R Caldwell, David B Allison, Andrew W Brown, Gary L Gadbury, Thirupathi Reddy Mokalla, R Drew Sayer, Andrew D Vigotsky

Children’s Nutrition Research Center Staff Publications

The estimation of treatment response heterogeneity (TRH) is increasingly important as medicine moves toward personalized approaches. While various statistical methods have been proposed to quantify TRH in parallel-group trials, the standard deviation of individual responses (SDIR) has gained prominence within physiological research. This method is intended to quantify individual response variation by comparing standard deviations of change scores between intervention and control groups. We acknowledge that SDIR represents an improvement over many other flawed approaches that often involve responder counting. However, SDIR has critical limitations: 1) it cannot overcome the fundamental problem of causal inference because the correlation between potential …

Probabilistic Template Matching For Detecting Resting-State Functional Mri Language Network In Brain Tumor Patients, Jian Ming Teo, Vinodh A Kumar, Alexander M Khalaf, Kyle R Noll, Sherise D Ferguson, Chibawanye I Ene, Sujit S Prabhu, Max Wintermark, Ho-Ling Liu

Faculty, Staff and Student Publications

Background: Intersubject variation among patients with brain tumors complicates the template matching process for detecting the resting-state (rs) functional MRI (fMRI) language network when using independent component analysis (ICA).

Purpose: This study aimed to develop methods that use a probabilistic language atlas to incorporate intersubject variation in brain tumor patients into the template matching process.

Methods: This retrospective study included 79 patients with brain tumors (average age, 50 ± 15 years) who underwent presurgical task-based (tb)-fMRI and rs-fMRI at clinical 3T scanners. At varying template generation thresholds (τ), binary and probabilistic templates were obtained from the language atlas. A binary …

Dysregulated Mitochondrial Energy Metabolism Drives The Progression Of Mucosal Field Effects To Invasive Bladder Cancer, Sangkyou Lee, Sung Yun Jung, Pawel Kuś, Jolanta Bondaruk, June Goo Lee, Roman Jaksik, Nagireddy Putluri, Khanh N Dinh, David Cogdell, Huiqin Chen, Yishan Wang, Jiansong Chen, Neema Navai, Colin Dinney, Cathy Mendelsohn, David Mcconkey, Richard R Behringer, Charles C Guo, Peng Wei, Marek Kimmel, Bogdan Czerniak

Faculty, Staff and Students Publications

Multiplatform mutational and gene expression profiling complemented with proteomic and metabolomic spatial mapping were used on the whole-organ scale to identify the molecular profile of bladder cancer evolution from field effects. Analysis of the mutational landscape identified three types of mutations, referred to as α, β, and γ. Time modeling of the mutations revealed that carcinogenesis may span 30 years and can be divided into dormant and progressive phases. The α mutations developed in the dormant phase. The progressive phase lasted 5 years and was signified by expanding β mutations, but it was driven to invasive cancer by γ mutations. …