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New Applications in the Diagnosis and Therapy of Diseases, 2nd Edition

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A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Pathology, Diagnostics, and Therapeutics".

Deadline for manuscript submissions: 20 March 2025 | Viewed by 1861

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Special Issue Editors

Dr. Miguel Hueso

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Guest Editor

Department of Nephrology, Hospital Universitari de Bellvitge, 08907 L’Hospitalet de Llobregat, Spain
Interests: kidney pathology; dialysis; vascular pathology; molecular medicine; predictive medicine; personalized medicine; inflammation; ncRNAs; organs-on-chip; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Dr. Alfredo Vellido

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IDEAI_UPC Research Center, Universitat Politècnica de Catalunya (UPC BarcelonaTech), 08034 Barcelona, Spain
Interests: machine learning; data science; medical applications of artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Personalized medicine is an emerging field that promotes the use of novel diagnostic tests to offer the right treatment at the right time for each patient. Personalized medicine unavoidably leads us towards a data-centric view of health, as data from expensive multicenter, randomized, controlled clinical trials (evidence-based medicine) are required to demonstrate that a treatment is effective, but the results can be disappointing in population minorities. Therefore, an analysis of retrospective data collected in large databases (Big Data) can help to explore usefulness in population minorities and benefit patients and health systems. However, data must usually be obtained from heterogeneous sources and in different modalities (individual medical history, high-resolution images, omics data, diagnostic tests, etc.) and their analysis may become complex due to non-linear behaviour or changing dynamics. Therefore, modelling such complexity is a major challenge that requires powerful new tools and technologies. This is a natural challenge and pursuit for data science in the form of data collection, storage, curation of databases, processing, and analysis. We argue that the knowledge generated by data science approaches will have a positive impact on clinical practice through personalized treatments, and we would like to dedicate a Special Issue to collecting experiences of the diagnosis and personalized treatment of diseases from a data science and molecular perspective. Review papers and work in progress putting forward new and groundbreaking ideas are also welcomed.

Dr. Miguel Hueso
Dr. Alfredo Vellido
Guest Editors

Manuscript Submission Information

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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. International Journal of Molecular Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

personalized medicine
systems biology
high-throughput technologies
molecular imaging
omics
pharmacogenomics
monitoring responses
data visualization
theranostics

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Published Papers (2 papers)

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Research

12 pages, 2871 KiB

Open AccessArticle

Comparison Length of Linker in Compound for Nuclear Medicine Targeting Fibroblast Activation Protein as Molecular Target

by Kentaro Hisada, Kazuko Kaneda-Nakashima, Yoshifumi Shirakami, Yuichiro Kadonaga, Atsuko Saito, Tadashi Watabe, Sifan Feng, Kazuhiro Ooe, Xiaojie Yin, Hiromitsu Haba, Masashi Murakami, Atsushi Toyoshima, Jens Cardinale, Frederik L. Giesel and Koichi Fukase

Int. J. Mol. Sci. 2024, 25(22), 12296; https://doi.org/10.3390/ijms252212296 - 15 Nov 2024

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Abstract

Novel nuclear medicine therapeutics are being developed by labeling medium-molecular-weight compounds with short-lived alpha-emitting radionuclides. Fibroblast activation protein α (FAPα) is recognized as a highly useful molecular target, and its inhibitor, FAPI, is a compound capable of theranostics, both therapeutic and diagnostic, for cancer treatment. In this study, we compared the functions of two compounds that target FAPα: ²¹¹At-FAPI1 and ²¹¹At-FAPI2. First, in vitro screening procedures are generally accepted because of the low endogenous expression of FAPα. We suggest the usefulness of this 3D culture system for in vitro screening. Second, when FAPIs are used therapeutically, the expected therapeutic effects are often not achieved. Therefore, we compared the accumulation and excretion in tumor tissues and the anti-tumor effects based on the length of the linker in the compounds. The compounds were rapidly labeled using the Shirakami reaction. Doubling the linker length increased tumor retention. Additionally, the excretion pathway was altered, suggesting a potential reduction in toxicity. Although no significant differences were observed in the anti-tumor effects of ²¹¹At-FAPI1 and ²¹¹At-FAPI2, it was confirmed that the linker length affects the biological half-life. Full article

(This article belongs to the Special Issue New Applications in the Diagnosis and Therapy of Diseases, 2nd Edition)

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33 pages, 13307 KiB

Open AccessArticle

A Study on the Robustness and Stability of Explainable Deep Learning in an Imbalanced Setting: The Exploration of the Conformational Space of G Protein-Coupled Receptors

by Mario A. Gutiérrez-Mondragón, Alfredo Vellido and Caroline König

Int. J. Mol. Sci. 2024, 25(12), 6572; https://doi.org/10.3390/ijms25126572 - 14 Jun 2024

Viewed by 955

Abstract

G-protein coupled receptors (GPCRs) are transmembrane proteins that transmit signals from the extracellular environment to the inside of the cells. Their ability to adopt various conformational states, which influence their function, makes them crucial in pharmacoproteomic studies. While many drugs target specific GPCR states to exert their effects—thereby regulating the protein’s activity—unraveling the activation pathway remains challenging due to the multitude of intermediate transformations occurring throughout this process, and intrinsically influencing the dynamics of the receptors. In this context, computational modeling, particularly molecular dynamics (MD) simulations, may offer valuable insights into the dynamics and energetics of GPCR transformations, especially when combined with machine learning (ML) methods and techniques for achieving model interpretability for knowledge generation. The current study builds upon previous work in which the layer relevance propagation (LRP) technique was employed to interpret the predictions in a multi-class classification problem concerning the conformational states of the

β 2

-adrenergic (

β_{2}

AR) receptor from MD simulations. Here, we address the challenges posed by class imbalance and extend previous analyses by evaluating the robustness and stability of deep learning (DL)-based predictions under different imbalance mitigation techniques. By meticulously evaluating explainability and imbalance strategies, we aim to produce reliable and robust insights. Full article