Advances in Personalized Medicine for Cardiovascular Care

A special issue of Journal of Personalized Medicine (ISSN 2075-4426). This special issue belongs to the section "Personalized Therapy and Drug Delivery".

Deadline for manuscript submissions: 20 April 2025 | Viewed by 4078

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Centro de Referência de Cardiopatias Congénitas do Centro Hospital e Universitário de Lisboa Central, Lisbon, Portugal
Interests: transient loss of consciousness; syncope; heart rate variability; wavelet; Hilbert–Huang; autonomic nervous system; tilt table test; atrial fibrillation (AF); signal analysis; computational models; computational medicine; cardiac rehabilitation
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Special Issue Information

Dear Colleagues,

This Special Issue is dedicated to “Advances in Personalized Medicine for Cardiovascular Care”, focusing on the transformative role that personalized approaches can play in the diagnosis, management, and treatment of cardiovascular diseases. Conventional modalities including imaging techniques and pharmacotherapy continue to be invaluable; however, personalized medicine has introduced an unparalleled depth of patient-specific care. Groundbreaking technologies such as artificial intelligence, augmented reality, and digital twins are enabling clinicians to predict disease patterns, tailor treatments, and even simulate surgical outcomes before intervention. Moreover, advances in genomics are helping to identify individual predispositions to cardiovascular diseases, allowing for early, targeted interventions.

This Issue invites submissions that delve into both the traditional and Personalized approaches in cardiovascular medicine. We encourage contributions that discuss diagnostic advancements, therapeutic innovations, and the integration of emerging technologies to expand the existing knowledge base in this vital medical field.

Dr. Sérgio Laranjo
Guest Editor

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.

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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

  • personalized medicine
  • cardiovascular medicine
  • artificial intelligence
  • augmented reality
  • digital twins
  • advanced imaging
  • genomics
  • early diagnosis
  • tailored treatments
  • clinical trials
  • pharmacogenetics
  • pharmacogenomics

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Published Papers (3 papers)

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14 pages, 2849 KiB  
Article
Prediction of Acute Cardiac Rejection Based on Gene Expression Profiles
by Bulat Abdrakhimov, Emmanuel Kayewa and Zhiwei Wang
J. Pers. Med. 2024, 14(4), 410; https://doi.org/10.3390/jpm14040410 - 12 Apr 2024
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Abstract
Acute cardiac rejection remains a significant challenge in the post-transplant period, necessitating meticulous monitoring and timely intervention to prevent graft failure. Thus, the goal of the present study was to identify novel biomarkers involved in acute cardiac rejection, paving the way for personalized [...] Read more.
Acute cardiac rejection remains a significant challenge in the post-transplant period, necessitating meticulous monitoring and timely intervention to prevent graft failure. Thus, the goal of the present study was to identify novel biomarkers involved in acute cardiac rejection, paving the way for personalized diagnostic, preventive, and treatment strategies. A total of 809 differentially expressed genes were identified in the GSE150059 dataset. We intersected genes selected by analysis of variance, recursive feature elimination, least absolute shrinkage and selection operator, and random forest classifier to identify the most relevant genes involved in acute cardiac rejection. Thus, HCP5, KLRD1, GZMB, PLA1A, GNLY, and KLRB1 were used to train eight machine learning models: random forest, logistic regression, decision trees, support vector machines, gradient boosting machines, K-nearest neighbors, XGBoost, and neural networks. Models were trained, tested, and validated on the GSE150059 dataset (MMDx-based diagnosis of rejection). Eight algorithms achieved great performance in predicting acute cardiac rejection. However, all machine learning models demonstrated poor performance in two external validation sets that had rejection diagnosis based on histology: merged GSE2596 and GSE4470 dataset and GSE9377 dataset, thus highlighting differences between these two methods. According to SHAP and LIME, KLRD1 and HCP5 were the most impactful genes. Full article
(This article belongs to the Special Issue Advances in Personalized Medicine for Cardiovascular Care)
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14 pages, 3666 KiB  
Article
The Interplay between Dyslipidemia and Neighboring Developments in Coronary Artery Disease Progression: A Personalized Approach
by Tomasz Urbanowicz, Krzysztof Skotak, Anna Olasińska-Wiśniewska, Krzysztof J. Filipiak, Jakub Bratkowski, Beata Krasińska, Zbigniew Krasiński, Andrzej Tykarski and Marek Jemielity
J. Pers. Med. 2024, 14(3), 237; https://doi.org/10.3390/jpm14030237 - 23 Feb 2024
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Abstract
(1) Background: Estimates suggest that up to 10% of global annual cardiovascular deaths could be related to environmental factors. Not only air pollution components, but also noise exposure and climate changes, are highlighted as nontraditional causes of cardiovascular morbidity. The aim of this [...] Read more.
(1) Background: Estimates suggest that up to 10% of global annual cardiovascular deaths could be related to environmental factors. Not only air pollution components, but also noise exposure and climate changes, are highlighted as nontraditional causes of cardiovascular morbidity. The aim of this study was to identify possible urbanization risk factors for the progression of coronary artery disease in a group of patients with chronic coronary syndrome. (2) Method: There were 77 patients (50 (65%) males and 27 (35%) females) with a median age of 70 (60–74) years who underwent repetitive angiography due to chronic coronary syndrome between 2018 and 2022. The Gensini score was calculated for assessment of coronary artery disease advancement. Environmental factors including neighboring developments were taken into account in this analysis, including housing, commercial, and industrial developments within 300, 500, and 700 m distances (buffer) from the place of habitation. (3) Results: The multivariable analysis results for prediction of Gensini score progression in relation to 700 m buffer urbanization pointed out the significance of hyperlipidemia (OR: 4.24, 95% CI 1.34–13.39, p = 0.014), initial Gensini score (OR: 1.02, 95% CI 1.00–1.05, p = 0.112), and neighborhood housing (OR: 0.03, 95% CI 0.01–0.49, p = 0.025). (4) Conclusions: Hyperlipidemia and housing neighborhood can be regarded as possible factors for coronary disease progression in patients with chronic coronary syndrome with the use of optimal medical therapy. Full article
(This article belongs to the Special Issue Advances in Personalized Medicine for Cardiovascular Care)
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14 pages, 468 KiB  
Systematic Review
Morphological Aspects of the Aberrant Right Subclavian Artery—A Systematic Review of the Literature
by Alin Horatiu Nedelcu, Ancuta Lupu, Marius Constantin Moraru, Cristina Claudia Tarniceriu, Cristinel Ionel Stan, Simona Alice Partene Vicoleanu, Ana Maria Haliciu, Gabriel Statescu, Manuela Ursaru, Ciprian Danielescu, Ileana Ioniuc, Razvan Tudor Tepordei and Vasile Valeriu Lupu
J. Pers. Med. 2024, 14(4), 335; https://doi.org/10.3390/jpm14040335 - 22 Mar 2024
Cited by 2 | Viewed by 1203
Abstract
Background: The aberrant origin of the right subclavian artery (ARSA), also known as the lusoria artery, is a congenital malformation with an incidence of 0.5–4.4%. Most cases are incidental due to minimal clinical manifestations. Computer tomography (CT) is important in diagnosing and evaluating [...] Read more.
Background: The aberrant origin of the right subclavian artery (ARSA), also known as the lusoria artery, is a congenital malformation with an incidence of 0.5–4.4%. Most cases are incidental due to minimal clinical manifestations. Computer tomography (CT) is important in diagnosing and evaluating these patients. Materials and Methods: We conduct a computerized search in two databases, PubMed and EMBASE, for articles published between 1 January 2022 and 31 December 2023, PROSPERO code: CRD42024511791. Eligible for inclusion were case reports and case series that presented the aberrant origin of the right subclavian artery. The main outcome was the highlighting of the morphological types of ARSA. In this context, we proposed a new classification system of this anomaly. The secondary outcome was the evaluation of the demographic distribution of the lusoria artery. Results: Our search identified 47 articles describing 51 patients with ARSA. The typical course for ARSA is retroesophageal, being registered in 49 out of 51 patients. This malformation is frequently associated with Kommerell diverticulum (15 out of 51), troncus bicaroticus (7 out of 51), and aberrant origins of the right vertebral artery (7 out of 51). We observed a higher incidence of the condition among women (32 out of 51) compared to men (19 out of 51). From a demographic point of view, ARSA is more frequent in the “44 to 57 years” and “58 to 71 years” age ranges. Conclusions: ARSA is a congenital malformation resulting from a defect in the development of the aortic arches. The imaging studies such as computer tomography play a defined diagnostic role. Full article
(This article belongs to the Special Issue Advances in Personalized Medicine for Cardiovascular Care)
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