Personalized Medicine Research Articles

The neurobiological impact of oxytocin in mental health disorders: a comprehensive review

Oxytocin, a neuropeptide, plays a significant role in modulating social behavior and has been widely studied for its potential impact on mental health disorders. This review examines the neurobiological mechanisms through which oxytocin influences mental health and its therapeutic potential in conditions such as autism spectrum disorder, schizophrenia, PTSD, anxiety, and depression. Oxytocin enhances social bonding, trust, and empathy by modulating neural circuits linked to social interactions. Studies indicate that oxytocin’s ability to regulate the hypothalamic-pituitary-adrenal (HPA) axis plays a vital role in stress response and emotional regulation. Therapeutic applications, particularly intranasal administration of oxytocin, have shown promise in alleviating symptoms and improving patient outcomes. However, personalized approaches are essential to optimize treatment effectiveness. Despite its potential, challenges remain in understanding the mechanisms underlying its effects and identifying the patient populations that would benefit most from such therapies. Future research should focus on elucidating these mechanisms, exploring the long-term efficacy of oxytocin-based interventions, and advancing personalized medicine to maximize its clinical utility.

Developing Trustworthy Artificial Intelligence Models to Predict Vascular Disease Progression: the VASCUL-AID-RETRO Study Protocol.

Abdominal aortic aneurysms (AAAs) and peripheral artery disease (PAD) are two vascular diseases with a significant risk of major adverse cardiovascular events and mortality. A challenge in current disease management is the unpredictable disease progression in individual patients. The VASCUL-AID-RETRO study aims to develop trustworthy multimodal predictive artificial intelligence (AI) models for multiple tasks including risk stratification of disease progression and cardiovascular events in patients with AAA and PAD. The VASCUL-AID-RETRO study will collect data from 5000 AAA and 6000 PAD patients across multiple European centers of the VASCUL-AID consortium using electronic health records from 2015 to 2024. This retrospectively-collected data will be enriched with additional data from existing biobanks and registries. Multimodal data, including clinical records, radiological imaging, proteomics, and genomics, will be collected to develop AI models predicting disease progression and cardiovascular risks. This will be done while integrating the international ethics guidelines and legal standards for trustworthy AI, to ensure a socially-responsible data integration and analysis. A consensus-based variable list of clinical parameters and core outcome set for both diseases will be developed through meetings with key opinion leaders. Blood, plasma, and tissue samples from existing biobanks will be analyzed for proteomic and genomic variations. AI models will be trained on segmented AAA and PAD artery geometries for estimation of hemodynamic parameters to quantify disease progression. Initially, risk prediction models will be developed for each modality separately, and subsequently, all data will be combined to be used as input to multimodal prediction models. During all processes, data security, data quality, and ethical guidelines and legal standards will be carefully considered. As a next step, the developed models will be further adjusted with prospective data and internally validated in a prospective cohort (VASCUL-AID-PRO study). The VASCUL-AID-RETRO study will utilize advanced AI techniques and integrate clinical, imaging, and multi-omics data to predict AAA and PAD progression and cardiovascular events. The VASCUL-AID-RETRO study is registered at www.clinicaltrials.gov under the identification number NCT06206369. The VASCUL-AID-RETRO study aims to improve clinical practice of vascular surgery by developing artificial intelligence-driven multimodal predictive models for patients with abdominal aortic aneurysms or peripheral artery disease, enhancing personalized medicine. By integrating comprehensive data sets including clinical, imaging, and multi-omics data, these models have the potential to provide accurate risk stratification for disease progression and cardiovascular events. An innovation lies in the extensive European data set in combination with multimodal analyses approaches, which enables the development of advanced models to facilitate better understanding of disease mechanisms and progression. For clinicians, this means that more precise, individualized treatment plans can be established, ultimately aiming to improve patient outcomes.

ESPClust: Unsupervised identification of modifiers for the effect size profile in omics association studies.

High-throughput omics technologies have revolutionised the identification of associations between individual traits and underlying biological characteristics, but still use 'one effect-size fits all' approaches. While covariates are often used, their potential as effect modifiers often remains unexplored. We propose ESPClust, a novel unsupervised method designed to identify covariates that modify the effect size of associations between sets of omics variables and outcomes. By extending the concept of moderators to encompass multiple exposures, ESPClust analyses the effect size profile (ESP) to identify regions in covariate space with different ESP, enabling the discovery of subpopulations with distinct associations. Applying ESPClust to synthetic data, insulin resistance and COVID-19 symptom manifestation, we demonstrate its versatility and ability to uncover nuanced effect size modifications that traditional analyses may overlook. By integrating information from multiple exposures, ESPClust identifies effect size modifiers in datasets that are too small for traditional univariate stratified analyses. This method provides a robust framework for understanding complex omics data and holds promise for personalised medicine. The source code ESPClust is available at https://github.com/fjpreche/ESPClust.git.It can be installed via Python package repositories as `pip install ESPClust==1.1.0`. Supplementary data are available at Bioinformatics online.

Smart Health Records: Integrating AI for Efficient Data Management

The rapid evolution of healthcare demands innovative solutions for managing the vast quantities of data generated daily. This paper examines the concept of Smart Health Records (SHRs) and their integration with Artificial Intelligence (AI) to improve healthcare delivery. SHRs represent an advanced iteration of electronic health records, equipped with AI-powered tools to ensure real-time data sharing, predictive analytics, and automated administrative processes. AI’s capabilities, including natural language processing, machine learning, and predictive modeling, streamline data management and enhance clinical decision-making. However, the transition to SHRs is not without challenges, such as data privacy, algorithm accuracy, and resistance to technological adoption. This paper discusses best practices for implementing AI-driven SHRs and examines future trends, emphasizing their potential to revolutionize healthcare by fostering patient-centered care, improving efficiency, and enabling personalized medicine. Keywords: Smart Health Records, Artificial Intelligence, Electronic Health Records, Healthcare Data Management, Predictive Analytics

Personalized Medicine: Leave no Patient Behind; Report From the 2024 Coffey-Holden Prostate Cancer Academy Meeting.

The 11th Annual 2024 Coffey - Holden Prostate Cancer Academy (CHPCA) Meeting, was themed "Personalized Medicine: Leave No Patient Behind," and was held from June 20 to 23, 2024 at the University of California, Los Angeles, Luskin Conference Center, in Los Angeles, CA. The CHPCA Meeting is an academy-styled annual conference organized by the Prostate Cancer Foundation, to focus discussion on the most critical emerging research that have the greatest potential to advance knowledge of prostate cancer biology and treatment. The 2024 CHPCA Meeting was attended by 75 academic investigators and included 37 talks across 8 sessions. The meeting sessions focused on: novel human, mouse and systems biology research models, novel immunotherapies for prostate cancer, efforts to overcome treatment resistance, the role of metabolism and diet in prostate cancer biology and as a therapeutic target, mechanisms that drive differentiation into neuroendocrine cancer subtypes, the evolving prostate cancer epigenome in disease progression and treatment resistance, and machine learning and advanced computational approaches for precision oncology. This article summarizes the presentations and discussions from the 2024 CHPCA Meeting. We hope that sharing this knowledge will inspire and accelerate research into new discoveries and solutions for prostate cancer.

Integrated analysis of gene expressions and targeted mirnas for explaining crosstalk between oral and esophageal squamous cell carcinomas through an interpretable machine learning approach.

This study explores the bidirectional relation of esophageal squamous cell carcinoma (ESCC) and oral squamous cell carcinoma (OSCC), examining shared risk factors and underlying molecular mechanisms. By employing random forest (RF) classifier, enhanced with interpretable machine learning (IML) through SHapley Additive exPlanations (SHAP), we analyzed gene expression from two GEO datasets (GSE30784 and GSE44021). The GSE30784 dataset comprises 167 OSCC samples and 45 control group, whereas the GSE44021 dataset encompasses 113 ESCC samples and 113 control group. Our analysis led to identification of 20 key genes, such as XBP1, VGLL1, and RAD1, which are significantly associated with development of ESCC and OSCC. Further investigations were conducted using tools like NetworkAnalyst 3.0, Single Cell Portal, and miRNET 2.0, which highlighted complex interactions between these genes and specific miRNA targets including hsa-mir-124-3p and hsa-mir-1-3p. Our model achieved high precision in identifying genes linked to crucial processes like programmed cell death and cancer pathways, suggesting new avenues for diagnosis and treatment. This study confirms the bidirectional relationship between OSCC and ESCC, laying groundwork for targeted therapeutic approaches. This study helps to identify shared biological pathways and genetic factors of these conditions for designing personalized medicine strategies and to improve disease management.

Mass spectrometry-based analysis of eccrine sweat supports predictive, preventive and personalised medicine in a cohort of breast cancer patients in Austria

Mass spectrometry-based analysis of eccrine sweat supports predictive, preventive and personalised medicine in a cohort of breast cancer patients in Austria

Pharmacogenomics and its Role in Cardiovascular Diseases: A Narrative Literature Review.

Pharmacogenomics has transformed the way we approach the treatment of the most common diseases worldwide, especially cardiovascular. In this article, we highlight the main categories of drugs involved in major cardiovascular diseases (CVD), related genetic variability and their effects on metabolism in each case of contrastive operability. This not only explains disparities in treatment outcomes but also unfolds customised management based on genomic studies to improve efficiency and limit side effects. Genetic variations have been identified that impact the efficacy, safety, and adverse effects of drugs commonly used in the treatment of CVDs, such as Angiotensin converting Enzyme Inhibitor (ACEI), Angiotensin Receptor Blocker (ARBs), calcium channel blockers, antiplatelet agents, diuretics, statins, beta-blockers, and anticoagulants. It discusses the impact of genetic polymorphisms on drug metabolism, efficacy, and adverse reactions, highlighting the importance of genetic testing in optimizing treatment outcomes. Pharmacogenomics holds immense potential for revolutionizing the management of CVDs by enabling personalized medicine approaches tailored to individual genetic profiles. However, challenges such as clinical implementation, cost-effectiveness, and ethical considerations need to be addressed to completely incorporate pharmacogenomic testing into standard clinical practice. Continued research and clinical diligence are required for the utilization of pharmacogenomics to improve therapeutic outcomes and reduce the burden of CVD globally.

MiCML: a causal machine learning cloud platform for the analysis of treatment effects using microbiome profiles.

The treatment effects are heterogenous across patients due to the differences in their microbiomes, which in turn implies that we can enhance the treatment effect by manipulating the patient's microbiome profile. Then, the coadministration of microbiome-based dietary supplements/therapeutics along with the primary treatment has been the subject of intensive investigation. However, for this, we first need to comprehend which microbes help (or prevent) the treatment to cure the patient's disease. In this paper, we introduce a cloud platform, named microbiome causal machine learning (MiCML), for the analysis of treatment effects using microbiome profiles on user-friendly web environments. MiCML is in particular unique with the up-to-date features of (i) batch effect correction to mitigate systematic variation in collective large-scale microbiome data due to the differences in their underlying batches, and (ii) causal machine learning to estimate treatment effects with consistency and then discern microbial taxa that enhance (or lower) the efficacy of the primary treatment. We also stress that MiCML can handle the data from either randomized controlled trials or observational studies. We describe MiCML as a useful analytic tool for microbiome-based personalized medicine. MiCML is freely available on our web server ( http://micml.micloud.kr ). MiCML can also be implemented locally on the user's computer through our GitHub repository ( https://github.com/hk1785/micml ).

Recent Progress in PDMS-Based Microfluidics Toward Integrated Organ-on-a-Chip Biosensors and Personalized Medicine

The organ-on-a-chip (OoC) technology holds significant promise for biosensors and personalized medicine by enabling the creation of miniature, patient-specific models of human organs. This review studies the recent advancements in the application of polydimethylsiloxane (PDMS) microfluidics for OoC purposes. It underscores the main fabrication technologies of PDMS microfluidic systems, such as photolithography, injection molding, hot embossing, and 3D printing. The review also highlights the crucial role of integrated biosensors within OoC platforms. These electrochemical, electrical, and optical sensors, integrated within the microfluidic environment, provide valuable insights into cellular behavior and drug response. Furthermore, the review explores the exciting potential of PDMS-based OoC technology for personalized medicine. OoC devices can forecast drug effectiveness and tailor therapeutic strategies for patients by incorporating patient-derived cells and replicating individual physiological variations, helping the healing process and accelerating recovery. This personalized approach can revolutionize healthcare by offering more precise and efficient treatment options. Understanding OoC fabrication and its applications in biosensors and personalized medicine can play a pivotal role in future implementations of multifunctional OoC biosensors.

Revolutionising Osseous Biopsy: The Impact of Artificial Intelligence in the Era of Personalised Medicine.

In a rapidly evolving healthcare environment, artificial intelligence (AI) is transforming diagnostic techniques and personalised medicine. This is also seen in osseous biopsies. AI applications in radiomics, histopathology, predictive modelling, biopsy navigation, and interdisciplinary communication are reshaping how bone biopsies are conducted and interpreted. We provide a brief review of AI in image- guided biopsy of bone tumours (primary and secondary) and specimen handling, in the era of personalised medicine. This paper explores AI's role in enhancing diagnostic accuracy, improving safety in biopsies, and enabling more precise targeting in bone lesion biopsies, ultimately contributing to better patient outcomes in personalised medicine. We dive into various AI technologies applied to osseous biopsies, such as traditional machine learning, deep learning, radiomics, simulation and generative models. We explore their roles in tumour board meetings, communication between clinicians, radiologists, and pathologists. Additionally, we inspect ethical considerations associated with the integration of AI in bone biopsy procedures, technical limitations, and we delve into health equity, generalisability, deployment issues, and reimbursement challenges in AI-powered healthcare. Finally, we explore potential future developments and offer a list of open-source AI tools and algorithms relevant to bone biopsies, which we include to encourage further discussion and research.

Palliative Care, Psychological Interventions, Personalized Medicine: The Triple "P" Hypothesis For Enhancing Quality of Life in Palliative Care

Patients in palliative care face complex and multidimensional challenges, including physical, psychological, and spiritual difficulties, that significantly impact their quality of life (QoL). Traditional care often addresses these needs through a holistic lens, yet the diversity in patient characteristics demands a more personalized approach. The Triple “P” Model integrates Palliative Care, Psychological Interventions, and Personalized Medicine to create a tailored framework that accounts for individual factors such as personality, special needs, biological profiles, and emotional demands. This model emphasizes the use of tools like biomarkers, genetic counseling, and neuroimaging to guide precise and effective interventions, while also addressing the psychological and existential concerns of patients. By transitioning from a generalized holistic view to an individualized approach, the Triple “P” Model aims to improve care outcomes and QoL in palliative care settings. This article explores the conceptual foundation of the Triple “P” Model, its alignment with the Biopsychosocial-Spiritual framework, and its potential to reshape palliative care through a more comprehensive and patient-centered strategy.

Molecular insight into histone methylation as a novel target for oral squamous cell carcinoma: future hope in personalised medicine

Molecular insight into histone methylation as a novel target for oral squamous cell carcinoma: future hope in personalised medicine

Precision Medicine for Peritoneal Carcinomatosis—Current Advances in Organoid Drug Testing and Clinical Applicability

Peritoneal carcinomatosis from gastrointestinal tumours is considered a poor prognostic factor, with a median overall survival of six to nine months in the absence of intervention. The advent of patient-derived organoid cultures (PDOs) has provided a breakthrough in personalised medicine, allowing researchers and clinicians to model the complexity and heterogeneity of individual tumours in vitro. PDOs hold great promise in this field, as variations in the management of peritoneal carcinomatosis due to differences in the method of delivery of chemotherapeutics, drug selection, exposure duration, and tumour pathology make it impractical to use a single, standardised treatment regimen. We aim to summarise the methodologies and limitations of studies encapsulating organoids derived from peritoneal metastases to encourage design considerations that may improve future clinical relevance, standardise protocols, and address translational challenges in personalising treatment strategies.

Connecting electronic health records to a biomedical knowledge graph to link clinical phenotypes and molecular endotypes in atopic dermatitis

Precision medicine is defined by the U.S. Food & Drug Administration as “an innovative approach to tailoring disease prevention and treatment that considers differences in people’s genes, environments, and lifestyles”. To succeed in providing personalized medicine to patients, it will be necessary to integrate medical, biological and molecular data in order to identify all complex disease subtypes and understand their pathobiological mechanism. Since biomedical knowledge graphs (BKGs) are limited to the integration of prior knowledge data and do not integrate real-world data (RWD) that would allow for the incorporation of patient level information, we propose a first step towards using RWD, BKGs and graph machine learning (ML) to enable a fully integrated precision medicine strategy. In this study, we established a link between RWD and a BKG. Our methodology introduced a novel patient representation using graph ML applied to the BKG. This approach facilitated the interpretation and extension of ML findings, particularly in disease subtype identification with molecular data contained in the BKG. We applied our innovative methodology to deepen our understanding of atopic dermatitis, a condition with a complex underlying pathophysiological mechanism. Through our analysis, we identified seven subgroups of patients each characterized by clinical and genomic characteristics.

AI in Personalized Medicine: Transforming Treatment Plans through Precision Health

AI in Personalized Medicine: Transforming Treatment Plans through Precision Health

Personalised Care in CKD: Moving Beyond Traditional Biomarkers.

The ultimate goal of precision medicine is to tailor treatment to specific disease processes, thereby optimising patient outcomes. This approach moves beyond the one-size-fits-all model, recognising at an individual level the unique combinations of molecular, genetic, and environmental factors determining disease progression and treatment response. Chronic kidney disease (CKD) exemplifies the need for precision medicine, given its complex and heterogeneous nature. Traditional biomarkers, such as estimated glomerular filtration rate (eGFR) and urine albumin-to-creatinine ratio (uACR), have long been the cornerstone of CKD diagnosis and management. However, these markers homogenise a diverse group of distinct conditions within CKD with separate pathophysiologies and progression rates. The standardisation of CKD definition has improved clarity and consistency but has inadvertently led to a generic classification system, which categorises patients with CKD based on these non-specific markers and fails to capture the nuances of individual patient conditions. As a result, there is a critical need for novel biomarkers that can more accurately represent specific aetiologies and mechanisms of CKD progression. By identifying and utilising novel biomarkers, the field of nephrology could better understand individual mechanisms of CKD progression and move towards tailored risk prediction and treatment strategies, ultimately improving patient outcomes. This review is not intended to be a comprehensive review of all biomarkers in CKD but a proposal to the nephrology community to think more pathophysiologically about CKD, recognise the importance of distinct primary kidney diseases, and start working towards a more personalised medicine approach.

Harnessing Radionuclides: Unveiling the Promising Role of Radiopharmaceuticals in Cancer Theranostics and Palliative Care.

In the context of modern oncology, radiogenic elements have emerged as pivotal tools for targeted cancer therapies. Elements like Iodine-131 and Yttrium-90 offer unique radiological properties that allow precise treatment delivery. This article explores their growing importance and potential in reshaping the landscape of cancer therapy. Utilizing a systematic literature search, relevant studies, clinical trials, and research articles were collected from databases. The selected material was scrutinized to extract insights into the mechanisms, applications, advantages, and challenges of radiogenic elements. These results are combined in the study to give a perceptive picture of how contemporary oncology treatment is developing. The article reveals a comprehensive analysis of the outcomes derived from the study of radiogenic elements in contemporary cancer treatment. The results highlight the diverse applications of radionuclides like Iodine-131, Yttrium-90, and actinides in targeted therapies. It showcases their ability to selectively damage cancer cells while sparing healthy tissues, emphasizing precision and efficacy. The review underscores the increasing importance of personalized medicine, combination therapies, and the potential of emerging alpha-particle-based treatments. Furthermore, the results shed light on the challenges posed by radiation safety and potential side effects, prompting a need for vigilant management. This comprehensive examination of results provides a nuanced understanding of the pivotal role that radiogenic elements play in shaping the future of modern oncology therapy. The article examines the role of radiogenic elements in contemporary cancer treatment. It highlights the significance of elements like 131I, 90Y, and actinides in targeted therapies, discussing their mechanisms and applications. The article emphasizes personalized medicine, combination therapies, and emerging alpha-particle-based treatments. Challenges, including radiation safety and side effects, are also addressed. The review anticipates a promising future where radiogenic elements contribute to precise, effective, and patient-centered cancer care.

Virtual Biopsy for the Prediction of MGMT Promoter Methylation in Gliomas: A Comprehensive Review of Radiomics and Deep Learning Approaches Applied to MRI

Background/Objectives: The methylation status of the O6-methylguanine-DNA methyltransferase (MGMT) promoter in gliomas has emerged as a critical biomarker for prognosis and treatment response. Conventional methods for assessing MGMT promoter methylation, such as methylation-specific PCR, are invasive and require tissue sampling. Methods: A comprehensive literature search was performed in compliance with the updated PRISMA 2020 guidelines within electronic databases MEDLINE/PubMed, Scopus, and IEEE Xplore. Search terms, including “MGMT”, “methylation”, “glioma”, “glioblastoma”, “machine learning”, “deep learning”, and “radiomics”, were adopted in various MeSH combinations. Original studies in the English, Italian, German, and French languages were considered for inclusion. Results: This review analyzed 34 studies conducted in the last six years, focusing on assessing MGMT methylation status using radiomics (RD), deep learning (DL), or combined approaches. These studies utilized radiological data from the public (e.g., BraTS, TCGA) and private institutional datasets. Sixteen studies focused exclusively on glioblastoma (GBM), while others included low- and high-grade gliomas. Twenty-seven studies reported diagnostic accuracy, with fourteen achieving values above 80%. The combined use of DL and RD generally resulted in higher accuracy, sensitivity, and specificity, although some studies reported lower minimum accuracy compared to studies using a single model. Conclusions: The integration of RD and DL offers a powerful, non-invasive tool for precisely recognizing MGMT promoter methylation status in gliomas, paving the way for enhanced personalized medicine in neuro-oncology. The heterogeneity of study populations, data sources, and methodologies reflected the complexity of the pipeline and machine learning algorithms, which may require general standardization to be implemented in clinical practice.

P0687 Development and validation of a novel multimodal model based on histology of colonoscopic biopsy to predict primary non-response to infliximab in patients with Crohn’s disease

Abstract Background Crohn’s disease (CD) is a chronic inflammatory condition of the gastrointestinal tract, and while biologics like infliximab (IFX) offer significant benefits, 10-30% of patients remain non-responders initially. This study aims to refine the prediction of treatment responses using artificial intelligence to integrate multimodal information, potentially enhancing treatment precision. Methods We conducted a retrospective analysis of clinical data from Crohn’s disease patients treated at The First Affiliated Hospital of Sun Yat-sen University from October 2016 to October 2021. The endpoint event was defined as primary non-response within 14 weeks of treatment. This study included clinical, laboratory, endoscopic, imaging, and histological data from colonoscopic biopsies. The data were split into a test set and an internal validation set in a 7:3 ratio. A deep learning (DL) model was developed using a multi-instance learning framework to incorporate this multimodal information for predicting primary non-response to IFX. Finally, the multimodal model was validated in an independent external validation cohort.[Figure1] Results The study included 170 patients, with 93 in the internal training set, 38 in the test set, and 39 in the external validation set. The DL model utilizing pathological features achieved an area under the receiver operating characteristic (AUROC) of 0.79 in 5-fold cross-validation. When combining pathological and radiological features, the AUC was 0.76, and integrating clinical features raised the AUC to 0.82. External validation upheld the efficacy of model with the highest AUC at 0.73 for the multimodal model. Conclusion The study developed several DL models to predict primary non-response to IFX with high accuracy. The most effective model integrated pathology, radiomics, and clinical data, supporting targeted treatment strategies and advancing personalized medicine for CD.