Publications by authors named "E Benjamin Randall"

215 Publications

Phenotyping heart failure using model-based analysis and physiology-informed machine learning.

J Physiol 2021 Sep 12. Epub 2021 Sep 12.

Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, MI, 48109, United States.

Key Points: Analysis of data from RHC and TTE of HF patients using a closed-loop model of the cardiovascular system identifies key parameters representing hemodynamic cardiovascular function in HFrEF and HFpEF patients. Analyzing optimized parameters representing cardiovascular function using machine learning shows mechanistic differences between HFpEF groups that are not seen analyzing clinical data alone. HFpEF groups presented here can be subdivided into 3 subgroups: HFpEF1 described as "HFrEF-like HFpEF", HFpEF2 as "pure HFpEF", and a third group of HFpEF patients that do not consistently cluster. Focusing purely on cardiac function consistently captures the underlying dysfunction in HFrEF, whereas HFpEF is better characterized by dysfunction in the entire cardiovascular system. Our methodology reveals that elevated left ventricular systolic and diastolic volumes are potential biomarkers for identifying HFpEF-like HFrEF patients.

Abstract: To phenotype mechanistic differences between heart failure with reduced (HFrEF) and preserved (HFpEF) ejection fraction, a closed-loop model of the cardiovascular system coupled with patient-specific transthoracic echocardiography (TTE) and right heart catheterization (RHC) data was used to identify key parameters representing hemodynamics. Thirty-one patient records (10 HFrEF, 21 HFpEF) were obtained from the Cardiovascular Health Improvement Project database at the University of Michigan. Model simulations were tuned to match RHC and TTE pressure, volume, and cardiac output measurements in each patient. The underlying physiological model parameters were plotted against model-based norms and compared between HFrEF and HFpEF. Our results confirm the main mechanistic parameter driving HFrEF is reduced left ventricular (LV) contractility, whereas HFpEF exhibits a heterogeneous phenotype. Conducting principal component analysis, means clustering, and hierarchical clustering on the optimized parameters reveal (i) a group of HFrEF-like HFpEF patients (HFpEF1), (ii) a "pure" HFpEF group (HFpEF2), and (iii) a group of HFpEF patients that do not consistently cluster (NCC). These subgroups cannot be distinguished from the clinical data alone. Increased LV active contractility (value<0.001) and LV passive stiffness (value<0.001) at rest are observed when comparing HFpEF2 to HFpEF1. Analyzing the clinical data of each subgroup reveals that elevated systolic and diastolic LV volumes seen in both HFrEF and HFpEF1 may be used as a biomarker to identify HFrEF-like HFpEF patients. These results suggest that modeling of the cardiovascular system and optimizing to standard clinical data can designate subgroups of HFpEF as separate phenotypes, possibly elucidating patient-specific treatment strategies. This article is protected by copyright. All rights reserved.
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http://dx.doi.org/10.1113/JP281845DOI Listing
September 2021

Interim clinical trial analysis of intraoperative mass spectrometry for breast cancer surgery.

NPJ Breast Cancer 2021 Sep 9;7(1):116. Epub 2021 Sep 9.

Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.

Optimal resection of breast tumors requires removing cancer with a rim of normal tissue while preserving uninvolved regions of the breast. Surgical and pathological techniques that permit rapid molecular characterization of tissue could facilitate such resections. Mass spectrometry (MS) is increasingly used in the research setting to detect and classify tumors and has the potential to detect cancer at surgical margins. Here, we describe the ex vivo intraoperative clinical application of MS using a liquid micro-junction surface sample probe (LMJ-SSP) to assess breast cancer margins. In a midpoint analysis of a registered clinical trial, surgical specimens from 21 women with treatment naïve invasive breast cancer were prospectively collected and analyzed at the time of surgery with subsequent histopathological determination. Normal and tumor breast specimens from the lumpectomy resected by the surgeon were smeared onto glass slides for rapid analysis. Lipidomic profiles were acquired from these specimens using LMJ-SSP MS in negative ionization mode within the operating suite and post-surgery analysis of the data revealed five candidate ions separating tumor from healthy tissue in this limited dataset. More data is required before considering the ions as candidate markers. Here, we present an application of ambient MS within the operating room to analyze breast cancer tissue and surgical margins. Lessons learned from these initial promising studies are being used to further evaluate the five candidate biomarkers and to further refine and optimize intraoperative MS as a tool for surgical guidance in breast cancer.
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http://dx.doi.org/10.1038/s41523-021-00318-5DOI Listing
September 2021

Self-reported radiation safety behaviors among veterinary specialists and residents performing fluoroscopic procedures on small animals.

J Am Vet Med Assoc 2021 Sep;259(5):518-527

Objective: To describe the radiation safety behaviors of veterinary specialists performing small animal fluoroscopic procedures and examine potential risk factors for these behaviors, including knowledge of radiation risk and training regarding machine operating parameters.

Sample: 197 veterinary specialists and residents in training.

Procedures: An electronic questionnaire was distributed to members of the American Colleges of Veterinary Internal Medicine (subspecialties of cardiology and small animal internal medicine), Veterinary Radiology, and Veterinary Surgery.

Results: The overall survey response rate was 6% (240/4,274 email recipients). Of the 240 respondents, 197 (82%) had operated an x-ray unit for a small animal fluoroscopic procedure in the preceding year and fully completed the questionnaire. More than 95% of respondents believed that radiation causes cancer, yet approximately 60% of respondents never wore hand or eye protection during fluoroscopic procedures, and 28% never adjusted the fluoroscopy machine operating parameters for the purpose of reducing their radiation dose. The most common reasons for not wearing eye shielding included no requirement to wear eyeglasses, poor fit, discomfort, and interference of eyeglasses with task performance. Respondents who had received training regarding machine operating parameters adjusted those parameters to reduce their radiation dose during procedures significantly more frequently than did respondents who had not received training.

Conclusions And Clinical Relevance: On the basis of the self-reported suboptimal radiation safety practices among veterinary fluoroscopy users, we recommend formal incorporation of radiation safety education into residency training programs. All fluoroscopy machine operators should be trained regarding the machine operating parameters that can be adjusted to reduce occupational radiation exposure.
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http://dx.doi.org/10.2460/javma.259.5.518DOI Listing
September 2021

Multimodal platform for assessing drug distribution and response in clinical trials.

Neuro Oncol 2021 Aug 12. Epub 2021 Aug 12.

Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA.

Background: Response to targeted therapy varies between patients for largely unknown reasons. Here, we developed and applied an integrative platform using mass spectrometry imaging (MSI), phosphoproteomics, and multiplexed tissue imaging for mapping drug distribution, target engagement, and adaptive response to gain insights into heterogeneous response to therapy.

Methods: Patient derived xenograft (PDX) lines of glioblastoma were treated with adavosertib, a Wee-1 inhibitor, and tissue drug distribution was measured with MALDI MSI. Phosphoproteomics was measured in the same tumors to identify biomarkers of drug target engagement and cellular adaptive response. Multiplexed tissue imaging was performed on sister sections to evaluate spatial co-localization of drug and cellular response. The integrated platform was then applied on clinical specimens from glioblastoma patients enrolled in a Phase 1 clinical trial.

Results: PDX tumors exposed to different doses of adavosertib revealed intra and inter-tumoral heterogeneity of drug distribution and integration of the heterogeneous drug distribution with phosphoproteomics and multiplexed tissue imaging revealed new markers of molecular response to adavosertib. Analysis of paired clinical specimens from patients enrolled in the Phase 1 clinical trial informed the translational potential of the identified biomarkers in studying patient's response to adavosertib.

Conclusions: The multimodal platform identified a signature of drug efficacy and patient-specific adaptive responses applicable to pre-clinical and clinical drug development. The information generated by the approach may inform mechanisms of success and failure in future early phase clinical trials, providing information for optimizing clinical trial design and guiding future application into clinical practice.
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http://dx.doi.org/10.1093/neuonc/noab197DOI Listing
August 2021

Quantitative analysis of mitochondrial ATP synthesis.

Math Biosci 2021 Oct 17;340:108646. Epub 2021 Jun 17.

Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, 48109, MI, USA. Electronic address:

We present a computational framework for analyzing and simulating mitochondrial ATP synthesis using basic thermodynamic and kinetic principles. The framework invokes detailed descriptions of the thermodynamic driving forces associated with the processes of the electron transport chain, mitochondrial ATP synthetase, and phosphate and adenine nucleotide transporters. Assembling models of these discrete processes into an integrated model of mitochondrial ATP synthesis, we illustrate how to analyze and simulate in vitro respirometry experiments and how models identified from in vitro experimental data effectively explain cardiac respiratory control in vivo. Computer codes for these analyses are embedded as Python scripts in a Jupyter Book to facilitate easy adoption and modification of the concepts developed here. This accessible framework may also prove useful in supporting educational applications. All source codes are available on at https://beards-lab.github.io/QAMAS_book/.
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http://dx.doi.org/10.1016/j.mbs.2021.108646DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8434986PMC
October 2021
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