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Biomedicines
Special Issues
Pathological Biomarkers in Precision Medicine
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Pathological Biomarkers in Precision Medicine

A special issue of Biomedicines (ISSN 2227-9059). This special issue belongs to the section "Cell Biology and Pathology".

Deadline for manuscript submissions: 31 March 2025 | Viewed by 6367

Special Issue Editors

Prof. Dr. Ovidiu Constantin Baltatu

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Guest Editor
Center of Innovation, Technology and Education (CITE) at Anhembi Morumbi University—Anima Institute, Sao Jose dos Campos, Brazil
Interests: cardiovascular disease; innovative diagnostics; disease prevention strategies
Special Issues, Collections and Topics in MDPI journals
Dr. Mirela A. Dobre

E-Mail Website
Guest Editor
Division of Nephrology and Hypertension, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
Interests: nephrology; kidney disease
Dr. Tudor Groza

E-Mail Website
Guest Editor
Rare Care Centre, Perth Children's Hospital, Nedlands, WA 6009, Australia
Interests: natural language processing; knowledge graphs; ontologies
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the dynamic landscape of precision medicine, this Special Issue seeks to comprehensively explore pathological biomarkers via multifaceted methods. The identification and validation of pathological biomarkers have emerged as critical components in tailoring therapeutic interventions for individual patients. This Special Issue invites contributions spanning a broad spectrum of research areas, including but not limited to the following:

  • Biomarker Discovery and Validation: Research focusing on the identification, validation, and clinical translation of pathological biomarkers for various diseases, such as cancer, cardiovascular disorders, neurodegenerative diseases, and infectious diseases.
  • Digital Biomarkers: Advancements in the identification and application of digital biomarkers, leveraging technologies for precise diagnostics and tailored therapeutic approaches.
  • Omics Data Integration: Innovations in integrating multi-omics data (genomics, transcriptomics, proteomics, metabolomics, and phenomics) and clinical data for a holistic understanding of disease mechanisms and personalized treatment strategies.
  • Digital Imaging Technologies: Advancements in medical imaging technologies, including radiomics, functional imaging, molecular imaging, computational pathology, and their role in refining diagnostics and biomarker detection.

We welcome original research articles and review papers that contribute to the deeper understanding of the importance of exploring more pathological biomarkers, with the ultimate goal of advancing precision medicine and improving patient outcomes.

Prof. Dr. Ovidiu Constantin Baltatu
Dr. Mirela A. Dobre
Dr. Tudor Groza
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. Biomedicines is an international peer-reviewed open access monthly 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

  • pathological biomarkers
  • precision medicine
  • artificial intelligence
  • medical imaging
  • omics data analysis
  • personalized medicine
  • biomarker discovery
  • digital pathology

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (5 papers)

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Research

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17 pages, 1030 KiB  
Article
Analysis of Brain Age Gap across Subject Cohorts and Prediction Model Architectures
by Lara Dular, Žiga Špiclin, for the Alzheimer’s Disease Neuroimaging Initiative and the Australian Imaging Biomarkers and Lifestyle Flagship Study of Ageing
Biomedicines 2024, 12(9), 2139; https://doi.org/10.3390/biomedicines12092139 - 20 Sep 2024
Cited by 1 | Viewed by 1599
Abstract
Background: Brain age prediction from brain MRI scans and the resulting brain age gap (BAG)—the difference between predicted brain age and chronological age—is a general biomarker for a variety of neurological, psychiatric, and other diseases or disorders. Methods: This study examined the differences [...] Read more.
Background: Brain age prediction from brain MRI scans and the resulting brain age gap (BAG)—the difference between predicted brain age and chronological age—is a general biomarker for a variety of neurological, psychiatric, and other diseases or disorders. Methods: This study examined the differences in BAG values derived from T1-weighted scans using five state-of-the-art deep learning model architectures previously used in the brain age literature: 2D/3D VGG, RelationNet, ResNet, and SFCN. The models were evaluated on healthy controls and cohorts with sleep apnea, diabetes, multiple sclerosis, Parkinson’s disease, mild cognitive impairment, and Alzheimer’s disease, employing rigorous statistical analysis, including repeated model training and linear mixed-effects models. Results: All five models consistently identified a statistically significant positive BAG for diabetes (ranging from 0.79 years with RelationNet to 2.13 years with SFCN), multiple sclerosis (2.67 years with 3D VGG to 4.24 years with 2D VGG), mild cognitive impairment (2.13 years with 2D VGG to 2.59 years with 3D VGG), and Alzheimer’s dementia (5.54 years with ResNet to 6.48 years with SFCN). For Parkinson’s disease, a statistically significant BAG increase was observed in all models except ResNet (1.30 years with 2D VGG to 2.59 years with 3D VGG). For sleep apnea, a statistically significant BAG increase was only detected with the SFCN model (1.59 years). Additionally, we observed a trend of decreasing BAG with increasing chronological age, which was more pronounced in diseased cohorts, particularly those with the largest BAG, such as multiple sclerosis (−0.34 to −0.2), mild cognitive impairment (−0.37 to −0.26), and Alzheimer’s dementia (−0.66 to −0.47), compared to healthy controls (−0.18 to −0.1). Conclusions: Consistent with previous research, Alzheimer’s dementia and multiple sclerosis exhibited the largest BAG across all models, with SFCN predicting the highest BAG overall. The negative BAG trend suggests a complex interplay of survival bias, disease progression, adaptation, and therapy that influences brain age prediction across the age spectrum. Full article
(This article belongs to the Special Issue Pathological Biomarkers in Precision Medicine)
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<p>Model architectures of five implemented brain age prediction models.</p> Full article ">Figure 2
<p>True age and prediction error for all five models on the HC subsample of the UK Biobank database. The blue line represents a fitted linear regression line, illustrating the relationship between age and prediction error for each model.</p> Full article ">Figure 3
<p>True age and prediction error for all five models for diseased subgroups and HC. The lines represent a fitted linear regression line for each subgroup, illustrating the relationship between age and prediction error for each model.</p> Full article ">

Review

Jump to: Research

17 pages, 805 KiB  
Review
Personalized Nutrition in Chronic Kidney Disease
by Nishigandha Pradhan, Jennifer Kerner, Luciana A. Campos and Mirela Dobre
Biomedicines 2025, 13(3), 647; https://doi.org/10.3390/biomedicines13030647 - 6 Mar 2025
Viewed by 216
Abstract
A personalized approach to nutrition in patients with chronic kidney disease (CKD) represents a promising paradigm shift in disease management, moving beyond traditional one-size-fits-all dietary recommendations. Patients with CKD often have other comorbidities and face unique nutritional challenges, including protein-energy wasting (PEW), sarcopenia, [...] Read more.
A personalized approach to nutrition in patients with chronic kidney disease (CKD) represents a promising paradigm shift in disease management, moving beyond traditional one-size-fits-all dietary recommendations. Patients with CKD often have other comorbidities and face unique nutritional challenges, including protein-energy wasting (PEW), sarcopenia, and impaired renal excretion of nutrients, which complicate dietary planning. Current guidelines focus primarily on nutrient restrictions—such as limiting protein, sodium, potassium, and phosphorus. However, these generalized recommendations often result in suboptimal adherence and outcomes. Personalized nutrition, which adapts dietary recommendations to individual characteristics, such as genotype, phenotype, and socio-cultural preferences, has gained traction across various chronic diseases. However, its application in nephrology remains underexplored, and despite promising results from studies such as Food4Me, questions remain about the real-world impact of such strategies. The aims of this review are (1) to summarize the evidence on the current state of nutritional recommendations in CKD, (2) to discuss the emerging role of multi-omics approaches in informing personalized nutrition advice in CKD, and (3) to provide an opinion on nutritional challenges faced by patients with CKD and the importance of collaboration with the renal dietician. We conclude that despite barriers, such as the cost and data integration, personalized nutrition holds the potential to improve CKD outcomes, enhance quality of life, and empower patients through tailored dietary strategies for better disease management. Full article
(This article belongs to the Special Issue Pathological Biomarkers in Precision Medicine)
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<p>Challenges to optimal nutrition in CKD. CKD = chronic kidney disease; LPD = low-protein diet; HPD = high-protein diet; PEW—protein-energy wasting.</p> Full article ">Figure 2
<p>Case study showing importance of medical nutrition therapy in managing complex co-morbidities. CKD = chronic kidney disease; DM = diabetes mellitus.</p> Full article ">
21 pages, 1883 KiB  
Review
Non-Invasive Retinal Biomarkers for Early Diagnosis of Alzheimer’s Disease
by Snježana Kaštelan, Antonela Gverović Antunica, Velibor Puzović, Ana Didović Pavičić, Samir Čanović, Petra Kovačević, Pia Antonia Franciska Vučemilović and Suzana Konjevoda
Biomedicines 2025, 13(2), 283; https://doi.org/10.3390/biomedicines13020283 - 24 Jan 2025
Viewed by 995
Abstract
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder of the brain associated with ageing and is the most prevalent form of dementia, affecting an estimated 55 million people worldwide, with projections suggesting this number will exceed 150 million by 2050. With its increasing [...] Read more.
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder of the brain associated with ageing and is the most prevalent form of dementia, affecting an estimated 55 million people worldwide, with projections suggesting this number will exceed 150 million by 2050. With its increasing prevalence, AD represents a significant global health challenge with potentially serious social and economic consequences. Diagnosing AD is particularly challenging as it requires timely recognition. Currently, there is no effective therapy for AD; however, certain medications may help slow its progression. Existing diagnostic methods such as magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), and biomarker analysis in cerebrospinal fluid tend to be expensive and invasive, making them impractical for widespread use. Consequently, research into non-invasive biomarkers that enable early detection and screening for AD is a crucial area of contemporary clinical investigation. One promising approach for the early diagnosis of AD may be retinal imaging. As an extension of the central nervous system, the retina offers a distinctive opportunity for non-invasive brain structure and function assessment. Considering their shared embryological origins and the vascular and immunological similarities between the eye and brain, alterations in the retina may indicate pathological changes in the brain, including those specifically related to AD. Studies suggest that structural and vascular changes in the retina, particularly within the neuronal network and blood vessels, may act as markers of cerebral changes caused by AD. These retinal alterations have the potential to act as biomarkers for early diagnosis. Since AD is typically diagnosed only after a significant neuronal loss has occurred, identifying early diagnostic markers could enable timely intervention and help prevent disease progression. Non-invasive retinal imaging techniques, such as optical coherence tomography (OCT) and OCT angiography, provide accessible methods for the early detection of changes linked to AD. This review article focuses on the potential of retinal imaging as a non-invasive biomarker for early diagnosis of AD. Investigating the ageing of the retina and its connections to neurodegenerative processes could significantly enhance the diagnosis, monitoring, and treatment of AD, paving the way for new diagnostic and therapeutic approaches. Full article
(This article belongs to the Special Issue Pathological Biomarkers in Precision Medicine)
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<p>Differential diagnosis in OCT and OCTA findings in Alzheimer’s disease.</p> Full article ">
18 pages, 4077 KiB  
Review
Mucins as Precision Biomarkers in Glioma: Emerging Evidence for Their Potential in Biospecimen Analysis and Outcome Prediction
by Anna Erickson, Luke R. Jackson, Kevin Camphausen and Andra V. Krauze
Biomedicines 2024, 12(12), 2806; https://doi.org/10.3390/biomedicines12122806 - 11 Dec 2024
Viewed by 966
Abstract
Despite attempts at improving survival by employing novel therapies, progression in glioma is nearly universal. Precision biomarkers are critical to advancing outcomes; however, biomarkers for glioma are currently unknown. Most data on which the field can draw for biomarker identification comprise tissue-based analysis [...] Read more.
Despite attempts at improving survival by employing novel therapies, progression in glioma is nearly universal. Precision biomarkers are critical to advancing outcomes; however, biomarkers for glioma are currently unknown. Most data on which the field can draw for biomarker identification comprise tissue-based analysis requiring the biospecimen to be removed from the tumor. Non-invasive specimen-based precision biomarkers are needed. Mucins are captured in tissue and blood and are increasingly studied in cancer, with several studies exploring their role as biomarkers to detect disease and monitor disease progression. CA125, also known as MUC16, is implemented as a biomarker in the clinic for ovarian cancer. Similarly, several mucins are membrane-bound, facilitating downstream signaling associated with tumor resistance and hallmarks of cancer. Evidence supports mucin expression in glioma cells with relationships to tumor detection, progression, resistance, and patient outcomes. The differential expression of mucins across tissues and organs could also provide a means of attributing signals measured in serum or plasma. In this review, we compiled existing research on mucins as candidate precision biomarkers in glioma, focusing on promising mucins in relationship to glioma and leading to a framework for mucin analysis in biospecimens as well as avenues for validation as data evolve. Full article
(This article belongs to the Special Issue Pathological Biomarkers in Precision Medicine)
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Figure 1

Figure 1
<p>Mucin classification, organ specificity, and potential candidate mucins as biomarkers in glioma. The blue left panel illustrates secreted mucins, both gel-forming and non-gel-forming, identified in heavily glycosylated layers such as the gastric epithelium and non-gel forming as identified in the respiratory epithelium. The right panel illustrates membrane-bound mucins, including MUC16, currently in use in ovarian cancer, as well as candidate mucins in glioma (MUC1, MUC4) [<a href="#B31-biomedicines-12-02806" class="html-bibr">31</a>,<a href="#B32-biomedicines-12-02806" class="html-bibr">32</a>,<a href="#B33-biomedicines-12-02806" class="html-bibr">33</a>,<a href="#B34-biomedicines-12-02806" class="html-bibr">34</a>,<a href="#B35-biomedicines-12-02806" class="html-bibr">35</a>].</p> Full article ">Figure 2
<p>(<b>A</b>). Tree diagram illustrating the presence of mucins in the literature based on Web of Science search [<a href="#B42-biomedicines-12-02806" class="html-bibr">42</a>] for each numbered mucin as an individual term and the presence of the term “glioma” in the same article. Several mucins did not have a literature presence in glioma and therefore, do not feature in the tree diagram. (<b>B</b>). Illustration of the mucins identified in connection with glioma based on data from Web of Science with average citations per item, sum of times cited and h-index as proxies for interest delegated in the field towards specific mucins.</p> Full article ">Figure 3
<p>(<b>A</b>). STRING diagram showcasing interactions of mucins identified in glioma with solid connections to EGFR and potential connections to IDH1, and MGMT as known glioma molecular markers [<a href="#B72-biomedicines-12-02806" class="html-bibr">72</a>]. (<b>B</b>). Promising mucins in glioma illustrating connections to pro-survival pathways and downstream effects. Precision markers may be identified amongst candidate mucins and proteomic and metabolomic mediators connected to molecular classification.</p> Full article ">Figure 4
<p>Heat map representing expression levels of mucins in various glial cells as identified and adapted from the Human Proteome Atlas [<a href="#B83-biomedicines-12-02806" class="html-bibr">83</a>]. The vertical dashed lines highlight glioma cells of origin (oligodendrocytes, astrocytes, oligodendrocyte precursors and microglial cells). Mucins (left vertical arrangement top to bottom from highest to lowest assigned number i.e., order of discovery) are displayed based on the fraction of highest expression in various cells of neural origin. OVGP1, EMCN and MCAM have alternate names in the mucin family: OVGP1 (MUC9), EMCN (MUC14) and MCAM (MUC18). MUC1 is exclusively expressed in astrocytes. MCAM (MUC18) is nearly exclusively expressed in oligodendrocytes, oligodendrocyte precursors and to a lesser extent microglial cells.</p> Full article ">Figure 5
<p>Proposed framework for mucin analysis as precision biomarkers in glioma illustrating potential steps that may be carried out concurrently or sequentially. (<b>1</b>). Identification of promising mucin molecules and the most promising upstream and downstream mediators or associated molecules based on existing data. (<b>2</b>). Characterization of mucins based on clinical and disease-related features such as tumor location and, specifically, biospecimen of origin. (<b>3</b>). Analyses aimed at distinguishing mucins as attributable to signal sources belonging to normal or tumor cells and biospecimen of origin. (<b>4</b>). Analyses aimed at distinguishing signal sources related to treatment effect comprised of wound healing postoperatively and fibrosis post chemo irradiation in glioma vs. tumor progression distinguished mucin signatures associated with active cancer cells [<a href="#B34-biomedicines-12-02806" class="html-bibr">34</a>]. The illustrations in this figure were created with the help of BioRender [<a href="#B99-biomedicines-12-02806" class="html-bibr">99</a>].</p> Full article ">
14 pages, 1568 KiB  
Review
Evaluation of PAGE-B Score for Hepatocellular Carcinoma Development in Chronic Hepatitis B Patients: Reliability, Validity, and Responsiveness
by Evanthia Tourkochristou, Maria Kalafateli, Christos Triantos and Ioanna Aggeletopoulou
Biomedicines 2024, 12(6), 1260; https://doi.org/10.3390/biomedicines12061260 - 5 Jun 2024
Cited by 1 | Viewed by 1583
Abstract
Chronic hepatitis B (CHB) constitutes a major global public health issue, affecting millions of individuals. Despite the implementation of robust vaccination programs, the hepatitis B virus (HBV) significantly influences morbidity and mortality rates. CHB emerges as one of the leading causes of hepatocellular [...] Read more.
Chronic hepatitis B (CHB) constitutes a major global public health issue, affecting millions of individuals. Despite the implementation of robust vaccination programs, the hepatitis B virus (HBV) significantly influences morbidity and mortality rates. CHB emerges as one of the leading causes of hepatocellular carcinoma (HCC), introducing a major challenge in the effective management of CHB patients. Therefore, it is of utmost clinical importance to diligently monitor individuals with CHB who are at high risk of HCC development. While various prognostic scores have been developed for surveillance and screening purposes, their accuracy in predicting HCC risk may be limited, particularly in patients under treatment with nucleos(t)ide analogues. The PAGE-B model, incorporating age, gender, and platelet count, has exhibited remarkable accuracy, validity, and reliability in predicting HCC occurrence among CHB patients receiving HBV treatment. Its predictive performance stands out, whether considered independently or in comparison to alternative HCC risk scoring systems. Furthermore, the introduction of targeted adjustments to the calculation of the PAGE-B score might have the potential to further improve its predictive accuracy. This review aims to evaluate the efficacy of the PAGE-B score as a dependable tool for accurate prediction of the development of HCC in CHB patients. The evidence discussed aims to provide valuable insights for guiding recommendations on HCC surveillance within this specific population. Full article
(This article belongs to the Special Issue Pathological Biomarkers in Precision Medicine)
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<p>Summary of the pivotal aspects examined within the study.</p> Full article ">Figure 2
<p>Accuracy of the PAGE-B score in predicting HCC (expressed as AUROC values) compared to modified PAGE-B scores.</p> Full article ">Figure 3
<p>Accuracy of PAGE-B scores in predicting HCC (expressed as AUROC values) compared to other HCC risk scores.</p> Full article ">Figure 4
<p>Accuracy of PAGE-B scores in predicting HCC (expressed as c-index) compared to other HCC risk scores.</p> Full article ">
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