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Integrating Clinical and Translational Research Networks—Building Team Medicine: 3rd Edition
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Integrating Clinical and Translational Research Networks—Building Team Medicine: 3rd Edition

A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Clinical Laboratory Medicine".

Deadline for manuscript submissions: closed (30 April 2025) | Viewed by 8904

Special Issue Editors

Prof. Dr. Ravi Salgia

E-Mail Website
Guest Editor
Chairman, Department of Medical Oncology, City of Hope National Medical Center, Duarte, CA, USA
Interests: lung cancer; thoracic oncology
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Prakash Kulkarni

E-Mail Website
Guest Editor
Department of Medical Oncology, City of Hope National Medical Center, Duarte, CA, USA
Interests: cancer systems biology; evolution; non-genetic mechanisms; intrinsically disordered proteins
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The previous Special Issue Series of the Journal of Clinical Medicine, entitled ‘Integrating Clinical and Translational Research Networks—Building Team Medicine’, highlighted our collective experience from the City of Hope and was well received. Thus far, the two Series published in 2021 and 2023, which included a total of 33 articles, have thus far received over 75,000 views; these have articles have been collectively cited more than 135 times.

Buoyed by the enthusiastic response from our peers and colleagues, we have embarked on establishing Volume 3. The basic theme is the same, namely integrating academic medical centers with community hospitals to ensure that all patients regardless of their physical proximity to major medical institutions can benefit from recent clinical advances. However, in Volume 3, we have ventured to include our colleagues internationally. Our aim here is to highlight translational research approaches that leverage the combined knowledge, skills, experience, expertise, and vision of clinicians in academic medical centers and their affiliated community centers and hospitals, together with those of basic research scientists, computational scientists, bioinformaticians and data scientists. We look forward to sharing our Team Medicine experience with our colleagues and trust that they will find the approach described in the articles embodied in this Special Issue helpful in guiding their approach to treating cancer patients.

Prof. Dr. Ravi Salgia
Prof. Dr. Prakash Kulkarni
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Clinical Medicine is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

 

Keywords

  • translational research
  • team medicine
  • health care reform
  • clinical advances
  • emerging inter- and cross-disciplinary
  • team-oriented culture

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Related Special Issue

Published Papers (6 papers)

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Research

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25 pages, 1360 KiB  
Article
Teams, Tools, Processes and Resources to Manage Oncologic Clinical Decision Support: Lessons Learned from City of Hope’s Multistate, Academic, and Community Oncology Enterprise
by Linda D. Bosserman, YiHsuan Lin, Sepideh Shayani, Brian Moore, Denise Morse, Emmanuel Enwere, Vijay Trisal and Wafa Samara
J. Clin. Med. 2025, 14(6), 2048; https://doi.org/10.3390/jcm14062048 - 17 Mar 2025
Viewed by 505
Abstract
Background/Objectives: Clinical decision support systems (CDSSs) consisting of Computerized Physician Order Entry (CPOE) and oncology pathways serve as the foundation of high-quality cancer care. However, the resources needed to develop and maintain these systems have not been characterized for oncology enterprises. Methods: Executive [...] Read more.
Background/Objectives: Clinical decision support systems (CDSSs) consisting of Computerized Physician Order Entry (CPOE) and oncology pathways serve as the foundation of high-quality cancer care. However, the resources needed to develop and maintain these systems have not been characterized for oncology enterprises. Methods: Executive leadership appointed a medical director and clinical pharmacist to develop and lead a Pathways and Protocols Program for the City of Hope (COH) enterprise. This involved developing a program charter and governance committee and a business case for resources to support CPOE in our Epic Beacon treatment orders. Missing CPOEs for oncology treatments were identified for treatments in COH’s Elsevier ClinicalPath treatment pathways and for those few diseases not in the pathways for medical oncology and hematology. New FDA oncology drug approvals were used to estimate ongoing CPOE build needs. Time estimates for Beacon analysts to build Beacon protocols were developed from a prior CPOE catch-up project, from informal surveys of our clinical pharmacists and Beacon leads, and surveys of staff leads at two other large, multisite cancer programs using Epic. Informal surveys of oncology clinicians and pharmacists were carried out to understand the time they were using to build Beacon orders that were not in the COH system. This information was used to build a business case for additional project management and staffing to catch up on building 400 missing Beacon orders, to maintain Beacon orders as new therapies and regimens are needed, and to provide required regulatory oversight of Beacon orders. Given these standards had not been shared by others, this work was gathered into a manuscript to help others evaluate and support needed resources to manage oncology pathway programs and CPOE to improve efficiencies, safety, and quality of care for medical oncology and hematology programs. Results: A Pathways and Protocols program was developed with a governance committee, a program charter, and a charge for disease committees to prioritize, approve, and oversee the regulation of COH’s Beacon treatment orders. CPOE resources to catch up and maintain COH’s Beacon treatment orders were developed and shared with COH’s executive leadership. Informal surveys were completed to benchmark Beacon resources with COH and two other Beacon enterprises as well as to estimate the time used by COH clinicians to build Beacon orders for orders not in the system. Conclusions: The resources for managing clinical oncology pathways and CPOE for an enterprise have not previously been published. Work components identified from our work at COH are shared so that other oncology leaders might have a starting framework to evaluate their own CDSS needs for oncology pathways and CPOE. Full article
(This article belongs to the Special Issue Integrating Clinical and Translational Research Networks—Building Team Medicine: 3rd Edition)
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18 pages, 2519 KiB  
Article
Electronic-Medical-Record-Driven Machine Learning Predictive Model for Hospital-Acquired Pressure Injuries: Development and External Validation
by Kim-Anh-Nhi Nguyen, Dhavalkumar Patel, Masoud Edalati, Maria Sevillano, Prem Timsina, Robert Freeman, Matthew A. Levin, David L. Reich and Arash Kia
J. Clin. Med. 2025, 14(4), 1175; https://doi.org/10.3390/jcm14041175 - 11 Feb 2025
Viewed by 1438
Abstract
Background: Hospital-acquired pressure injuries (HAPIs) affect approximately 2.5 million patients annually in the United States, leading to increased morbidity and healthcare costs. Current rule-based screening tools, such as the Braden Scale, lack sensitivity, highlighting the need for improved risk prediction methods. Methods: We [...] Read more.
Background: Hospital-acquired pressure injuries (HAPIs) affect approximately 2.5 million patients annually in the United States, leading to increased morbidity and healthcare costs. Current rule-based screening tools, such as the Braden Scale, lack sensitivity, highlighting the need for improved risk prediction methods. Methods: We developed and externally validated a machine learning model to predict HAPI risk using longitudinal electronic medical record (EMR) data. This study included adult inpatients (2018–2023) across five hospitals within a large health system. An automated pipeline was built for EMR data curation, labeling, and integration. The model employed XGBoost with recursive feature elimination to identify 35 optimal clinical variables and utilized time-series analysis for dynamic risk prediction. Results: Internal validation and multi-center external validation on 5510 hospitalizations demonstrated AUROC values of 0.83–0.85. The model outperformed the Braden Scale in sensitivity and F1-score and showed superior performance compared to previous predictive models. Conclusions: This is the first externally validated, cross-institutional HAPI prediction model using longitudinal EMR data and automated pipelines. The model demonstrates strong generalizability, scalability, and real-time applicability, offering a novel bioengineering approach to improve HAPI prevention, patient care, and clinical operations. Full article
(This article belongs to the Special Issue Integrating Clinical and Translational Research Networks—Building Team Medicine: 3rd Edition)
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Review

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38 pages, 2291 KiB  
Review
Operationalizing Team Science at the Academic Cancer Center Network to Unveil the Structure and Function of the Gut Microbiome
by Kevin J. McDonnell
J. Clin. Med. 2025, 14(6), 2040; https://doi.org/10.3390/jcm14062040 - 17 Mar 2025
Viewed by 811
Abstract
Oncologists increasingly recognize the microbiome as an important facilitator of health as well as a contributor to disease, including, specifically, cancer. Our knowledge of the etiologies, mechanisms, and modulation of microbiome states that ameliorate or promote cancer continues to evolve. The progressive refinement [...] Read more.
Oncologists increasingly recognize the microbiome as an important facilitator of health as well as a contributor to disease, including, specifically, cancer. Our knowledge of the etiologies, mechanisms, and modulation of microbiome states that ameliorate or promote cancer continues to evolve. The progressive refinement and adoption of “omic” technologies (genomics, transcriptomics, proteomics, and metabolomics) and utilization of advanced computational methods accelerate this evolution. The academic cancer center network, with its immediate access to extensive, multidisciplinary expertise and scientific resources, has the potential to catalyze microbiome research. Here, we review our current understanding of the role of the gut microbiome in cancer prevention, predisposition, and response to therapy. We underscore the promise of operationalizing the academic cancer center network to uncover the structure and function of the gut microbiome; we highlight the unique microbiome-related expert resources available at the City of Hope of Comprehensive Cancer Center as an example of the potential of team science to achieve novel scientific and clinical discovery. Full article
(This article belongs to the Special Issue Integrating Clinical and Translational Research Networks—Building Team Medicine: 3rd Edition)
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Other

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15 pages, 1186 KiB  
Perspective
Single-Cell Transcriptomics Sheds Light on Tumor Evolution: Perspectives from City of Hope’s Clinical Trial Teams
by Patrick A. Cosgrove, Andrea H. Bild, Thanh H. Dellinger, Behnam Badie, Jana Portnow and Aritro Nath
J. Clin. Med. 2024, 13(24), 7507; https://doi.org/10.3390/jcm13247507 - 10 Dec 2024
Viewed by 1242
Abstract
Tumor heterogeneity is a significant factor influencing cancer treatment effectiveness and can arise from genetic, epigenetic, and phenotypic variations among cancer cells. Understanding how tumor heterogeneity impacts tumor evolution and therapy response can lead to more effective treatments and improved patient outcomes. Traditional [...] Read more.
Tumor heterogeneity is a significant factor influencing cancer treatment effectiveness and can arise from genetic, epigenetic, and phenotypic variations among cancer cells. Understanding how tumor heterogeneity impacts tumor evolution and therapy response can lead to more effective treatments and improved patient outcomes. Traditional bulk genomic approaches fail to provide insights into cellular-level events, whereas single-cell RNA sequencing (scRNA-seq) offers transcriptomic analysis at the individual cell level, advancing our understanding of tumor growth, progression, and drug response. However, implementing single-cell approaches in clinical trials involves challenges, such as obtaining high-quality cells, technical variability, and the need for complex computational analysis. Effective implementation of single-cell genomics in clinical trials requires a collaborative “Team Medicine” approach, leveraging shared resources, expertise, and workflows. Here, we describe key technical considerations in implementing the collection of research biopsies and lessons learned from integrating scRNA-seq into City of Hope’s clinical trial design, highlighting collaborative efforts between computational and clinical teams across breast, brain, and ovarian cancer studies to understand the composition, phenotypic state, and underlying resistance mechanisms within the tumor microenvironment. Full article
(This article belongs to the Special Issue Integrating Clinical and Translational Research Networks—Building Team Medicine: 3rd Edition)
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17 pages, 1769 KiB  
Perspective
Nanoengineering Solutions for Cancer Therapy: Bridging the Gap between Clinical Practice and Translational Research
by Pankaj Garg, Siddhika Pareek, Prakash Kulkarni, Ravi Salgia and Sharad S. Singhal
J. Clin. Med. 2024, 13(12), 3466; https://doi.org/10.3390/jcm13123466 - 13 Jun 2024
Cited by 3 | Viewed by 1819
Abstract
Nanoengineering has emerged as a progressive method in cancer treatment, offering precise and targeted delivery of therapeutic agents while concurrently reducing overall toxicity. This scholarly article delves into the innovative strategies and advancements in nanoengineering that bridge the gap between clinical practice and [...] Read more.
Nanoengineering has emerged as a progressive method in cancer treatment, offering precise and targeted delivery of therapeutic agents while concurrently reducing overall toxicity. This scholarly article delves into the innovative strategies and advancements in nanoengineering that bridge the gap between clinical practice and research in the field of cancer treatment. Various nanoengineered platforms such as nanoparticles, liposomes, and dendrimers are scrutinized for their capacity to encapsulate drugs, augment drug efficacy, and enhance pharmacokinetics. Moreover, the article investigates research breakthroughs that drive the progression and enhancement of nanoengineered remedies, encompassing the identification of biomarkers, establishment of preclinical models, and advancement of biomaterials, all of which are imperative for translating laboratory findings into practical medical interventions. Furthermore, the integration of nanotechnology with imaging modalities, which amplify cancer detection, treatment monitoring, and response assessment, is thoroughly examined. Finally, the obstacles and prospective directions in nanoengineering, including regulatory challenges and issues related to scalability, are examined. This underscores the significance of fostering collaboration among various entities in order to efficiently translate nanoengineered interventions into enhanced cancer therapies and patient management. Full article
(This article belongs to the Special Issue Integrating Clinical and Translational Research Networks—Building Team Medicine: 3rd Edition)
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15 pages, 3544 KiB  
Perspective
Leveraging Cancer Phenotypic Plasticity for Novel Treatment Strategies
by Sravani Ramisetty, Ayalur Raghu Subbalakshmi, Siddhika Pareek, Tamara Mirzapoiazova, Dana Do, Dhivya Prabhakar, Evan Pisick, Sagun Shrestha, Srisairam Achuthan, Supriyo Bhattacharya, Jyoti Malhotra, Atish Mohanty, Sharad S. Singhal, Ravi Salgia and Prakash Kulkarni
J. Clin. Med. 2024, 13(11), 3337; https://doi.org/10.3390/jcm13113337 - 5 Jun 2024
Cited by 3 | Viewed by 1988
Abstract
Cancer cells, like all other organisms, are adept at switching their phenotype to adjust to the changes in their environment. Thus, phenotypic plasticity is a quantitative trait that confers a fitness advantage to the cancer cell by altering its phenotype to suit environmental [...] Read more.
Cancer cells, like all other organisms, are adept at switching their phenotype to adjust to the changes in their environment. Thus, phenotypic plasticity is a quantitative trait that confers a fitness advantage to the cancer cell by altering its phenotype to suit environmental circumstances. Until recently, new traits, especially in cancer, were thought to arise due to genetic factors; however, it is now amply evident that such traits could also emerge non-genetically due to phenotypic plasticity. Furthermore, phenotypic plasticity of cancer cells contributes to phenotypic heterogeneity in the population, which is a major impediment in treating the disease. Finally, plasticity also impacts the group behavior of cancer cells, since competition and cooperation among multiple clonal groups within the population and the interactions they have with the tumor microenvironment also contribute to the evolution of drug resistance. Thus, understanding the mechanisms that cancer cells exploit to tailor their phenotypes at a systems level can aid the development of novel cancer therapeutics and treatment strategies. Here, we present our perspective on a team medicine-based approach to gain a deeper understanding of the phenomenon to develop new therapeutic strategies. Full article
(This article belongs to the Special Issue Integrating Clinical and Translational Research Networks—Building Team Medicine: 3rd Edition)
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