Precision and Personalized Medicine: How Genomic Approach Improves the Management of Cardiovascular and Neurodegenerative Disease

A Copernican revolution of multigenic analysis: A retrospective study on clinical exome sequencing in unclear genetic disorders

Despite the inevitable shift in medical practice towards a deeper understanding of disease etiology and progression through multigenic analysis, the profound historical impact of Mendelian diseases cannot be overlooked. These diseases, such as cystic fibrosis and thalassemia, are characterized by a single variant in a single gene leading to clinical conditions, and have significantly shaped our medical knowledge and treatments. In this respect, the monogenic approach inevitably results in the underutilization of Next-Generation Sequencing (NGS) data. Herein, a retrospective study was performed to assess the diagnostic value of the clinical exome in 32 probands with specific phenotypic characteristics (patients with autoinflammation and immunological dysregulation, N = 20; patients diagnosed with Hemolytic uremic syndrome N = 9; and patients with Waldenström macroglobulinemia, N = 3). A gene enrichment analysis was performed using the *. VCF file generated by SOPHiA-DDM-v4. This analysis selected a subset of genes containing pathogenic or likely pathogenic variants with autosomal dominant (AD) inheritance. In addition, all variants of uncertain significance (VUS) were included, filtered by AD inheritance mode, the presence of compound heterozygotes, and a minor allele frequency (MAF) cutoff of 0.05 %. The aim of the pipeline described here is based on a perspective shift that focuses on analyzing patients' gene assets, offering new light on the complex interplay between genetics and disease presentation. Integrating this approach into clinical practices could significantly enhance the management of patients with rare genetic disorders.

Polyglutamine (PolyQ) Diseases: Navigating the Landscape of Neurodegeneration

Polyglutamine (polyQ) diseases are a group of inherited neurodegenerative disorders caused by expanded cytosine-adenine-guanine (CAG) repeats encoding proteins with abnormally expanded polyglutamine tract. A total of nine polyQ disorders have been identified, including Huntington's disease, six spinocerebellar ataxias, dentatorubral pallidoluysian atrophy (DRPLA), and spinal and bulbar muscular atrophy (SBMA). The diseases of this class are each considered rare, yet polyQ diseases constitute the largest group of monogenic neurodegenerative disorders. While each subtype of polyQ diseases has its own causative gene, certain pathologic molecular attributes have been implicated in virtually all of the polyQ diseases, including protein aggregation, proteolytic cleavage, neuronal dysfunction, transcription dysregulation, autophagy impairment, and mitochondrial dysfunction. Although animal models of polyQ disease are available helping to understand their pathogenesis and access disease-modifying therapies, there is neither a cure nor prevention for these diseases, with only symptomatic treatments available. In this paper, we analyze data from the CAS Content Collection to summarize the research progress in the class of polyQ diseases. We examine the publication landscape in the area in effort to provide insights into current knowledge advances and developments. We review the most discussed concepts and assess the strategies to combat these diseases. Finally, we inspect clinical applications of products against polyQ diseases with their development pipelines. The objective of this review is to provide a broad overview of the evolving landscape of current knowledge regarding the class of polyQ diseases, to outline challenges, and evaluate growth opportunities to further efforts in combating the diseases.

Genetic Polymorphisms in Cardiovascular Disease: Effects Across Three Generations Exposed to Radiation from the Semipalatinsk Nuclear Test Site

The population in the areas neighboring the Semipalatinsk Nuclear Test Site (SNTS) in the eastern region of Kazakhstan faces increased cardiovascular disease (CVD) risk. Previous research has not explored gene polymorphisms related to CVD in this population. Therefore, the present study examines the prevalence of six CVD-associated genotypes in three generations exposed to SNTS radiation. The genotyping of ApoE Leu28 → Pro, AGT Met174 → Thr, AGT Met235 → Thr, eNOS T786 → C, PON1 Gln192 → Arg, and EDN 1 Lys198 → Asn was performed using real-time polymerase chain reaction. The present study encompassed a cohort of 218 participants with a familial history of arterial hypertension and/or carotid artery disease spanning at least three generations. The analysis unveiled significant disparities in the prevalence of ApoE Leu28 → Pro, eNOS T786 → C, and PON1 Gln192 → Arg genotypes across different generations. Furthermore, a substantial variation in the distribution of the eNOS T786 → C genotype was observed between individuals of Kazakh and Russian ethnicities. Nevertheless, no significant discrepancies were detected in the frequencies of the investigated genotypes between genders. Further research in this area is warranted to enhance the understanding of the genetic factors contributing to CVD in the population exposed to radiation from the SNTS. Specifically, future studies should broaden the scope of genetic polymorphisms investigated and include representatives of healthy individuals who have not been exposed to radiation as controls.

Investigating the effects of artificial intelligence on the personalization of breast cancer management: a systematic study

BACKGROUND

Providing appropriate specialized treatment to the right patient at the right time is considered necessary in cancer management. Targeted therapy tailored to the genetic changes of each breast cancer patient is a desirable feature of precision oncology, which can not only reduce disease progression but also potentially increase patient survival. The use of artificial intelligence alongside precision oncology can help physicians by identifying and selecting more effective treatment factors for patients.

METHOD

A systematic review was conducted using the PubMed, Embase, Scopus, and Web of Science databases in September 2023. We performed the search strategy with keywords, namely: Breast Cancer, Artificial intelligence, and precision Oncology along with their synonyms in the article titles. Descriptive, qualitative, review, and non-English studies were excluded. The quality assessment of the articles and evaluation of bias were determined based on the SJR journal and JBI indices, as well as the PRISMA2020 guideline.

RESULTS

Forty-six studies were selected that focused on personalized breast cancer management using artificial intelligence models. Seventeen studies using various deep learning methods achieved a satisfactory outcome in predicting treatment response and prognosis, contributing to personalized breast cancer management. Two studies utilizing neural networks and clustering provided acceptable indicators for predicting patient survival and categorizing breast tumors. One study employed transfer learning to predict treatment response. Twenty-six studies utilizing machine-learning methods demonstrated that these techniques can improve breast cancer classification, screening, diagnosis, and prognosis. The most frequent modeling techniques used were NB, SVM, RF, XGBoost, and Reinforcement Learning. The average area under the curve (AUC) for the models was 0.91. Moreover, the average values for accuracy, sensitivity, specificity, and precision were reported to be in the range of 90-96% for the models.

CONCLUSION

Artificial intelligence has proven to be effective in assisting physicians and researchers in managing breast cancer treatment by uncovering hidden patterns in complex omics and genetic data. Intelligent processing of omics data through protein and gene pattern classification and the utilization of deep neural patterns has the potential to significantly transform the field of complex disease management.

TLE4 downregulation identified by WGCNA and machine learning algorithm promotes papillary thyroid carcinoma progression via activating JAK/STAT pathway

Background: Papillary Thyroid Carcinoma (PTC), a common type of thyroid cancer, has a pathogenesis that is not fully understood. This study utilizes a range of public databases, sophisticated bioinformatics tools, and empirical approaches to explore the key genetic components and pathways implicated in PTC, particularly concentrating on the Transducin-Like Enhancer of Split 4 (TLE4) gene. Methods: Public databases such as TCGA and GEO were utilized to conduct differential gene expression analysis in PTC. Hub genes were identified using Weighted Gene Co-expression Network Analysis (WGCNA), and machine learning techniques, including Random Forest, LASSO regression, and SVM-RFE, were employed for biomarker identification. The clinical impact of the TLE4 gene was assessed in terms of diagnostic accuracy, prognostic value, and its functional enrichment analysis in PTC. Additionally, the study focused on understanding the role of TLE4 in the dynamics of immune cell infiltration, gene function enhancement, and behaviors of PTC cells like growth, migration, and invasion. To complement these analyses, in vivo studies were performed using a xenograft mouse model. Results: 244 genes with significant differential expression across various databases were identified. WGCNA indicated a strong link between specific gene modules and PTC. Machine learning analysis brought the TLE4 gene into focus as a key biomarker. Bioinformatics studies verified that TLE4 expression is lower in PTC, linking it to immune cell infiltration and the JAK-STAT signaling pathways. Experimental data revealed that decreased TLE4 expression in PTC cell lines leads to enhanced cell growth, migration, invasion, and activates the JAK/STAT pathway. In contrast, TLE4 overexpression in these cells inhibited tumor growth and metastasis. Conclusions: This study sheds light on TLE4's crucial role in PTC pathogenesis, positioning it as a potential biomarker and target for therapy. The integration of multi-omics data and advanced analytical methods provides a robust framework for understanding PTC at a molecular level, potentially guiding personalized treatment strategies.

Role of Artificial Intelligence in Multinomial Decisions and Preventative Nutrition in Alzheimer's Disease

Alzheimer's disease (AD) affects 50 million people worldwide, an increase of 35 million since 2015, and it is known for memory loss and cognitive decline. Considering the morbidity associated with AD, it is important to explore lifestyle elements influencing the chances of developing AD, with special emphasis on nutritional aspects. This review will first discuss how dietary factors have an impact in AD development and the possible role of Artificial Intelligence (AI) and Machine Learning (ML) in preventative care of AD patients through nutrition. The Mediterranean-DASH diets provide individuals with many nutrient benefits which assists the prevention of neurodegeneration by having neuroprotective roles. Lack of micronutrients, protein-energy, and polyunsaturated fatty acids increase the chance of cognitive decline, loss of memory, and synaptic dysfunction among others. ML software has the ability to design models of algorithms from data introduced to present practical solutions that are accessible and easy to use. It can give predictions for a precise medicine approach to evaluate individuals as a whole. There is no doubt the future of nutritional science lies on customizing diets for individuals to reduce dementia risk factors, maintain overall health and brain function.

Advancing Precision Medicine: Algebraic Topology and Differential Geometry in Radiology and Computational Pathology

Precision medicine aims to provide personalized care based on individual patient characteristics, rather than guideline-directed therapies for groups of diseases or patient demographics. Images-both radiology- and pathology-derived-are a major source of information on presence, type, and status of disease. Exploring the mathematical relationship of pixels in medical imaging ("radiomics") and cellular-scale structures in digital pathology slides ("pathomics") offers powerful tools for extracting both qualitative and, increasingly, quantitative data. These analytical approaches, however, may be significantly enhanced by applying additional methods arising from fields of mathematics such as differential geometry and algebraic topology that remain underexplored in this context. Geometry's strength lies in its ability to provide precise local measurements, such as curvature, that can be crucial for identifying abnormalities at multiple spatial levels. These measurements can augment the quantitative features extracted in conventional radiomics, leading to more nuanced diagnostics. By contrast, topology serves as a robust shape descriptor, capturing essential features such as connected components and holes. The field of topological data analysis was initially founded to explore the shape of data, with functional network connectivity in the brain being a prominent example. Increasingly, its tools are now being used to explore organizational patterns of physical structures in medical images and digitized pathology slides. By leveraging tools from both differential geometry and algebraic topology, researchers and clinicians may be able to obtain a more comprehensive, multi-layered understanding of medical images and contribute to precision medicine's armamentarium.

404-error "Disease not found": Unleashing the translational potential of -omics approaches beyond traditional disease classification in heart failure research

The emergence of personalized medicine, facilitated by the progress in -omics technologies, has initiated a new era in medical diagnostics and treatment. This review examines the potential of -omics approaches in heart failure, a condition that has not yet fully capitalized on personalized strategies compared to other medical fields like cancer therapy. Here, we argue that integrating multi-omics technology with systems medicine approaches could fundamentally transform heart failure management, moving away from the traditional paradigm of 'one size fits all'. Our review examines how omics can enhance understanding of heart failure's molecular foundations and contribute to a more comprehensive disease classification. We draw attention to the current state of medical practice that only relies on clinical evidence and a number of standard laboratory tests. At the same time, we propose a shift towards a universal approach that uses quantitative data from multi-omics to unravel complex molecular interactions. The discussion centres around the potential of the transition as a means to enhance individual risk assessment and emphasizes management within clinical settings. While the use of omics in cardiovascular research is not recent, many past studies have focused only on a single omics approach. In order to achieve a better understanding of disease mechanisms, we explore more holistic approaches using genomics, transcriptomics, epigenomics, and proteomics. This review concludes with a call to action to adopt multi-omics in clinical trials and practice to pave the way for more personalized disease management and more effective heart failure interventions.

A novel approach to generate enzyme-free single cell suspensions from archived tissues for miRNA sequencing

Obtaining high-quality omics data at the single-cell level from archived human tissue samples is crucial for gaining insights into cellular heterogeneity and pushing the field of personalized medicine forward. In this technical brief we present a comprehensive methodological framework for the efficient enzyme-free preparation of tissue-derived single cell suspensions and their conversion into single-cell miRNA sequencing libraries. The resulting data from this study have the potential to deepen our understanding of miRNA expression at the single-cell level and its relevance in the context of the examined tissues. The workflow encompasses tissue collection, RNALater immersion, storage, thawing, TissueGrinder-mediated dissociation, miRNA lysis, library preparation, sequencing, and data analysis. Quality control measures ensure reliable miRNA data, with specific attention to sample quality. The UMAP analysis reveals tissue-specific cell clustering, while miRNA diversity reflects tissue variations. The presented workflow effectively processes preserved tissues, extending opportunities for retrospective analysis and biobank utilization.

Envisioning the Future of Personalized Medicine: Role and Realities of Digital Twins

Digital twins have emerged as a groundbreaking concept in personalized medicine, offering immense potential to transform health care delivery and improve patient outcomes. It is important to highlight the impact of digital twins on personalized medicine across the understanding of patient health, risk assessment, clinical trials and drug development, and patient monitoring. By mirroring individual health profiles, digital twins offer unparalleled insights into patient-specific conditions, enabling more accurate risk assessments and tailored interventions. However, their application extends beyond clinical benefits, prompting significant ethical debates over data privacy, consent, and potential biases in health care. The rapid evolution of this technology necessitates a careful balancing act between innovation and ethical responsibility. As the field of personalized medicine continues to evolve, digital twins hold tremendous promise in transforming health care delivery and revolutionizing patient care. While challenges exist, the continued development and integration of digital twins hold the potential to revolutionize personalized medicine, ushering in an era of tailored treatments and improved patient well-being. Digital twins can assist in recognizing trends and indicators that might signal the presence of diseases or forecast the likelihood of developing specific medical conditions, along with the progression of such diseases. Nevertheless, the use of human digital twins gives rise to ethical dilemmas related to informed consent, data ownership, and the potential for discrimination based on health profiles. There is a critical need for robust guidelines and regulations to navigate these challenges, ensuring that the pursuit of advanced health care solutions does not compromise patient rights and well-being. This viewpoint aims to ignite a comprehensive dialogue on the responsible integration of digital twins in medicine, advocating for a future where technology serves as a cornerstone for personalized, ethical, and effective patient care.

Prioritization of genes for translation: a computational approach

INTRODUCTION

Gene identification for genetic diseases is critical for the development of new diagnostic approaches and personalized treatment options. Prioritization of gene translation is an important consideration in the molecular biology field, allowing researchers to focus on the most promising candidates for further investigation.

AREAS COVERED

In this paper, we discussed different approaches to prioritize genes for translation, including the use of computational tools and machine learning algorithms, as well as experimental techniques such as knockdown and overexpression studies. We also explored the potential biases and limitations of these approaches and proposed strategies to improve the accuracy and reliability of gene prioritization methods. Although numerous computational methods have been developed for this purpose, there is a need for computational methods that incorporate tissue-specific information to enable more accurate prioritization of candidate genes. Such methods should provide tissue-specific predictions, insights into underlying disease mechanisms, and more accurate prioritization of genes.

EXPERT OPINION

Using advanced computational tools and machine learning algorithms to prioritize genes, we can identify potential targets for therapeutic intervention of complex diseases. This represents an up-and-coming method for drug development and personalized medicine.

Nuclear Cardiology in the Era of Precision Medicine: Tailoring Treatment to the Individual Patient

Nuclear cardiology, employing advanced imaging technologies like positron emission tomography (PET) and single photon emission computed tomography (SPECT), is instrumental in diagnosing, risk stratifying, and managing heart diseases. Concurrently, precision medicine advocates for treatments tailored to each patient's genetic, environmental, and lifestyle specificities, promising a revolution in personalized cardiovascular care. This review explores the synergy between nuclear cardiology and precision medicine, highlighting advancements, potential enhancements in patient outcomes, and the challenges and opportunities of this integration. We examined the evolution of nuclear cardiology technologies, including PET and SPECT, and their role in cardiovascular diagnostics. We also delved into the principles of precision medicine, focusing on genetic and molecular profiling, data analytics, and individualized treatment strategies. The integration of these domains aims to optimize diagnostic accuracy, therapeutic interventions, and prognostic evaluations in cardiovascular care. Advancements in molecular imaging and the application of artificial intelligence in nuclear cardiology have significantly improved the precision of diagnostics and treatment plans. The adoption of precision medicine principles in nuclear cardiology enables the customization of patient care, leveraging genetic information and biomarkers for enhanced therapeutic outcomes. However, challenges such as data integration, accessibility, cost, and the need for specialized expertise persist. The confluence of nuclear cardiology and precision medicine offers a promising pathway toward revolutionizing cardiovascular healthcare, providing more accurate, effective, and personalized patient care. Addressing existing challenges and fostering interdisciplinary collaboration is crucial for realizing the full potential of this integration in improving patient outcomes.

Genomic Insights into Dementia: Precision Medicine and the Impact of Gene-Environment Interaction

The diagnosis, treatment, and management of dementia provide significant challenges due to its chronic cognitive impairment. The complexity of this condition is further highlighted by the impact of gene-environment interactions. A recent strategy combines advanced genomics and precision medicine methods to explore the complex genetic foundations of dementia. Utilizing the most recent research in the field of neurogenetics, the importance of precise genetic data in explaining the variation seen in dementia patients can be investigated. Gene-environment interactions are important because they influence genetic susceptibilities and aid in the development and progression of dementia. Modified to each patient's genetic profile, precision medicine has the potential to detect groups at risk and make previously unheard-of predictions about the course of diseases. Precision medicine techniques have the potential to completely transform treatment and diagnosis methods. Targeted medications that target genetic abnormalities will probably appear, providing the possibility for more efficient and customized medical interventions. Investigating the relationship between genes and the environment may lead to preventive measures that would enable people to change their surroundings and minimize the risk of dementia, leading to the improved lifestyle of affected people. This paper provides a comprehensive overview of the genomic insights into dementia, emphasizing the pivotal role of precision medicine, and gene-environment interactions.

Epigenetic biomarkers for disease susceptibility and preventative medicine

The development of molecular biomarkers for disease makes it possible for preventative medicine approaches to be considered. Therefore, therapeutics, treatments, or clinical management can be used to delay or prevent disease development. The problem with genetic mutations as biomarkers is the low frequency with genome-wide association studies (GWASs), generally at best a 1% association of the patients with the disease. In contrast, epigenetic alterations have a high-frequency association of greater than 90%-95% of individuals with pathology in epigenome-wide association studies (EWASs). A wide variety of human diseases have been shown to have epigenetic biomarkers that are disease specific and that detect pathology susceptibility. This review is focused on the epigenetic biomarkers for disease susceptibility, and it distinct from the large literature on epigenetics of disease etiology or progression. The development of efficient epigenetic biomarkers for disease susceptibility will facilitate a paradigm shift from reactionary medicine to preventative medicine.

A Review of the Common Neurodegenerative Disorders: Current Therapeutic Approaches and the Potential Role of Bioactive Peptides

Neurodegenerative disorders, which include Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's disease (HD), and amyotrophic lateral sclerosis (ALS), represent a significant and growing global health challenge. Current therapies predominantly focus on symptom management rather than altering disease progression. In this review, we discuss the major therapeutic strategies in practice for these disorders, highlighting their limitations. For AD, the mainstay treatments are cholinesterase inhibitors and N-methyl-D-aspartate (NMDA) receptor antagonists. For PD, dopamine replacement therapies, including levodopa, are commonly used. HD is managed primarily with symptomatic treatments, and reusable extends survival in ALS. However, none of these therapies halts or substantially slows the neurodegenerative process. In contrast, this review highlights emerging research into bioactive peptides as potential therapeutic agents. These naturally occurring or synthetically designed molecules can interact with specific cellular targets, potentially modulating disease processes. Preclinical studies suggest that bioactive peptides may mitigate oxidative stress, inflammation, and protein misfolding, which are common pathological features in neurodegenerative diseases. Clinical trials using bioactive peptides for neurodegeneration are limited but show promising initial results. For instance, hemiacetal, a γ-secretase inhibitor peptide, has shown potential in AD by reducing amyloid-beta production, though its development was discontinued due to side effects. Despite these advancements, many challenges remain, including identifying optimal peptides, confirming their mechanisms of action, and overcoming obstacles related to their delivery to the brain. Future research should prioritize the discovery and development of novel bioactive peptides and improve our understanding of their pharmacokinetics and pharmacodynamics. Ultimately, this approach may lead to more effective therapies for neurodegenerative disorders, moving beyond symptom management to potentially modify the course of these devastating diseases.

Advancing personalized medicine in brain cancer: exploring the role of mRNA vaccines

Advancing personalized medicine in brain cancer relies on innovative strategies, with mRNA vaccines emerging as a promising avenue. While the initial use of mRNA vaccines was in oncology, their stunning success in COVID-19 resulted in widespread attention, both positive and negative. Regardless of politically biased opinions, which relate more to the antigenic source than form of delivery, we feel it is important to objectively review this modality as relates to brain cancer. This class of vaccines trigger robust immune responses through MHC-I and MHC-II pathways, in both prophylactic and therapeutic settings. The mRNA platform offers advantages of rapid development, high potency, cost-effectiveness, and safety. This review provides an overview of mRNA vaccine delivery technologies, tumor antigen identification, combination therapies, and recent therapeutic outcomes, with a particular focus on brain cancer. Combinatorial approaches are vital to maximizing mRNA cancer vaccine efficacy, with ongoing clinical trials exploring combinations with adjuvants and checkpoint inhibitors and even adoptive cell therapy. Efficient delivery, neoantigen identification, preclinical studies, and clinical trial results are highlighted, underscoring mRNA vaccines' potential in advancing personalized medicine for brain cancer. Synergistic combinatorial therapies play a crucial role, emphasizing the need for continued research and collaboration in this area.

Unraveling the Link Between Serum Homocysteine Levels and Nutrient Deficiency in Subfertility: A Comprehensive Review

Subfertility, a prevalent and emotionally taxing condition affecting couples worldwide, has garnered increasing attention in reproductive health. While its etiology remains multifaceted, emerging research has explored the role of serum homocysteine levels and nutrient deficiencies in influencing subfertility. This comprehensive review synthesizes current knowledge, beginning with an introduction to subfertility and the significance of investigating serum homocysteine levels. It proceeds to elucidate the role of nutrient deficiencies, particularly folate and vitamin B12, in homocysteine metabolism and examines existing research linking homocysteine to subfertility. The review explores potential mechanisms underlying this relationship, addressing the variability in study findings and their contributing factors. Implications for clinical practice, including assessing serum homocysteine levels, nutritional interventions, and personalized medicine, are discussed. Moreover, the review underscores the importance of ongoing research. It offers a call to action for advancing our understanding of subfertility and improving the lives of individuals and couples navigating reproduction challenges.

Mastering the Art of Dermatosurgery: Aesthetic Alchemy in Medical Excellence

Dermatosurgery, a specialized branch within dermatology, has traversed an extraordinary journey through time, shaped by ancient practices, technological leaps, and shifting societal perceptions. This review explores the evolution of dermatosurgery, highlighting its profound transformation from addressing solely medical concerns to seamlessly integrating aesthetics. From its roots in ancient civilizations, where cultural traditions laid the foundation for modern techniques, to the twentieth-century technological renaissance, marked by innovative tools and enhanced understanding of skin anatomy, dermatosurgery has emerged as a dynamic field. Societal notions of beauty and health have significantly influenced dermatosurgery, blurring the lines between medical necessity and elective aesthetic procedures. The delicate balance between satisfying aesthetic desires and upholding medical ethics is a central challenge that dermatosurgeons face today. Open dialogue between practitioners and patients as well as psychological support plays a pivotal role in navigating this terrain. The training and ethics associated with dermatosurgery have evolved to meet the increasing demand for specialized procedures. Maintaining a focus on patient safety and satisfaction remains paramount as commercial pressures and disparities in access to care loom. Upholding best practices and standards in the field is essential for ensuring consistent, high-quality care for all patients. Looking ahead, dermatosurgery stands on the brink of a transformative era, marked by non-invasive techniques, artificial intelligence (AI) integration, and personalized medicine. The field's ability to harmonize medical science with aesthetic artistry is evident in various case studies, showcasing the intricate balance dermatosurgeons strike between addressing medical concerns and fulfilling aesthetic desires. As dermatosurgery continues to evolve, it promises to provide patients with even more precise, tailored treatments that enhance both their physical well-being and aesthetic satisfaction.

Exploring the Risks, Benefits, Advances, and Challenges in Internet Integration in Medicine With the Advent of 5G Technology: A Comprehensive Review

The integration of 5G technology in the healthcare sector is poised to bring about transformative changes, offering numerous advantages such as enhanced telemedicine services, expedited data transfer for medical records, improved remote surgery capabilities, real-time monitoring and diagnostics, advancements in wearable medical devices, and the potential for precision medicine. However, this technological shift is not without its concerns, including potential health implications related to 5G radiation exposure, heightened cybersecurity risks for medical devices and data systems, potential system failures due to technology dependence, and privacy issues linked to data breaches in healthcare. We are striking a balance between harnessing these benefits and addressing the associated risks. Achieving this equilibrium requires the establishment of a robust regulatory framework, ongoing research into the health impacts of 5G radiation, the implementation of stringent cybersecurity measures, education and training for healthcare professionals, and the development of ethical standards. The future of 5G in the medical field holds immense promise, but success depends on our ability to navigate this evolving landscape while prioritizing patient safety, privacy, and ethical practice.

Enhancement of Classifier Performance Using Swarm Intelligence in Detection of Diabetes from Pancreatic Microarray Gene Data

In this study, we focused on using microarray gene data from pancreatic sources to detect diabetes mellitus. Dimensionality reduction (DR) techniques were used to reduce the dimensionally high microarray gene data. DR methods like the Bessel function, Discrete Cosine Transform (DCT), Least Squares Linear Regression (LSLR), and Artificial Algae Algorithm (AAA) are used. Subsequently, we applied meta-heuristic algorithms like the Dragonfly Optimization Algorithm (DOA) and Elephant Herding Optimization Algorithm (EHO) for feature selection. Classifiers such as Nonlinear Regression (NLR), Linear Regression (LR), Gaussian Mixture Model (GMM), Expectation Maximum (EM), Bayesian Linear Discriminant Classifier (BLDC), Logistic Regression (LoR), Softmax Discriminant Classifier (SDC), and Support Vector Machine (SVM) with three types of kernels, Linear, Polynomial, and Radial Basis Function (RBF), were utilized to detect diabetes. The classifier's performance was analyzed based on parameters like accuracy, F1 score, MCC, error rate, FM metric, and Kappa. Without feature selection, the SVM (RBF) classifier achieved a high accuracy of 90% using the AAA DR methods. The SVM (RBF) classifier using the AAA DR method for EHO feature selection outperformed the other classifiers with an accuracy of 95.714%. This improvement in the accuracy of the classifier's performance emphasizes the role of feature selection methods.

Model Systems to Study the Mechanism of Vascular Aging

Cardiovascular diseases are the leading cause of death globally. Within cardiovascular aging, arterial aging holds significant importance, as it involves structural and functional alterations in arteries that contribute substantially to the overall decline in cardiovascular health during the aging process. As arteries age, their ability to respond to stress and injury diminishes, while their luminal diameter increases. Moreover, they experience intimal and medial thickening, endothelial dysfunction, loss of vascular smooth muscle cells, cellular senescence, extracellular matrix remodeling, and deposition of collagen and calcium. This aging process also leads to overall arterial stiffening and cellular remodeling. The process of genomic instability plays a vital role in accelerating vascular aging. Progeria syndromes, rare genetic disorders causing premature aging, exemplify the impact of genomic instability. Throughout life, our DNA faces constant challenges from environmental radiation, chemicals, and endogenous metabolic products, leading to DNA damage and genome instability as we age. The accumulation of unrepaired damages over time manifests as an aging phenotype. To study vascular aging, various models are available, ranging from in vivo mouse studies to cell culture options, and there are also microfluidic in vitro model systems known as vessels-on-a-chip. Together, these models offer valuable insights into the aging process of blood vessels.

Bridging the gap between tumor-on-chip and clinics: a systematic review of 15 years of studies

Over the past 15 years, the field of oncology research has witnessed significant progress in the development of new cell culture models, such as tumor-on-chip (ToC) systems. In this comprehensive overview, we present a multidisciplinary perspective by bringing together physicists, biologists, clinicians, and experts from pharmaceutical companies to highlight the current state of ToC research, its unique features, and the challenges it faces. To offer readers a clear and quantitative understanding of the ToC field, we conducted an extensive systematic analysis of more than 300 publications related to ToC from 2005 to 2022. ToC offer key advantages over other in vitro models by enabling precise control over various parameters. These parameters include the properties of the extracellular matrix, mechanical forces exerted on cells, the physico-chemical environment, cell composition, and the architecture of the tumor microenvironment. Such fine control allows ToC to closely replicate the complex microenvironment and interactions within tumors, facilitating the study of cancer progression and therapeutic responses in a highly representative manner. Importantly, by incorporating patient-derived cells or tumor xenografts, ToC models have demonstrated promising results in terms of clinical validation. We also examined the potential of ToC for pharmaceutical industries in which ToC adoption is expected to occur gradually. Looking ahead, given the high failure rate of clinical trials and the increasing emphasis on the 3Rs principles (replacement, reduction, refinement of animal experimentation), ToC models hold immense potential for cancer research. In the next decade, data generated from ToC models could potentially be employed for discovering new therapeutic targets, contributing to regulatory purposes, refining preclinical drug testing and reducing reliance on animal models.

Clinical Perspectives of Gut Microbiota in Patients with Chronic Kidney Disease and End-Stage Kidney Disease: Where Do We Stand?

The gut microbiota (GM) plays a vital role in human health, with increasing evidence linking its imbalance to chronic kidney disease and end-stage kidney disease. Although the exact methods underlying kidney-GM crosstalk are not fully understood, interventions targeting GM were made and lay in three aspects: diagnostic, predictive, and therapeutic interventions. While these interventions show promising results in reducing uremic toxins and inflammation, challenges remain in the form of patient-specific GM variability, potential side effects, and safety concerns. Our understanding of GMs role in kidney disease is still evolving, necessitating further research to elucidate the causal relationship and mechanistic interactions. Personalized interventions focusing on specific GM signatures could enhance patient outcomes. However, comprehensive clinical trials are needed to validate these approaches' safety, efficacy, and feasibility.

Exploring the Potential of Antidiabetic Agents as Therapeutic Approaches for Alzheimer's and Parkinson's Diseases: A Comprehensive Review

Alzheimer's and Parkinson's are two prevalent neurodegenerative disorders with significant societal and healthcare burdens. The search for effective therapeutic approaches to combat these diseases has led to growing interest in exploring the potential of antidiabetic agents. This comprehensive review aims to provide a detailed overview of the current literature on using antidiabetic agents as therapeutic interventions for Alzheimer's and Parkinson's diseases. We discuss the underlying pathological mechanisms of these neurodegenerative diseases, including protein misfolding, inflammation, oxidative stress, and mitochondrial dysfunction. We then delve into the potential mechanisms by which antidiabetic agents may exert neuroprotective effects, including regulation of glucose metabolism and insulin signaling, anti-inflammatory effects, modulation of oxidative stress, and improvement of mitochondrial function and bioenergetics. We highlight in vitro, animal, and clinical studies that support the potential benefits of antidiabetic agents in reducing disease pathology and improving clinical outcomes. However, we also acknowledge these agents' limitations, variability in treatment response, and potential side effects. Furthermore, we explore emerging therapeutic targets and novel approaches, such as glucagon-like peptide-1 receptor (GLP-1R) agonists, insulin sensitizer drugs, neuroinflammation-targeted therapies, and precision medicine approaches. The review concludes by emphasizing the need for further research, including large-scale clinical trials, to validate the efficacy and safety of antidiabetic agents in treating Alzheimer's and Parkinson's disease. The collaboration between researchers, clinicians, and pharmaceutical companies is essential in advancing the field and effectively treating patients affected by these debilitating neurodegenerative disorders.

Genetical Signature-An Example of a Personalized Skin Aging Investigation with Possible Implementation in Clinical Practice

We conducted a research study to create the groundwork for personalized solutions within a skin aging segment. This test utilizes genetic and general laboratory data to predict individual susceptibility to weak skin characteristics, leveraging the research on genetic polymorphisms related to skin functional properties. A cross-sectional study was conducted in a collaboration between the Private Clinic Medicina Practica Laboratory (Vilnius, Lithuania) and the Public Institution Lithuanian University of Health Sciences (Kaunas, Lithuania). A total of 370 participants agreed to participate in the project. The median age of the respondents was 40, with a range of 19 to 74 years. After the literature search, we selected 15 polymorphisms of the genes related to skin aging, which were subsequently categorized in terms of different skin functions: SOD2 (rs4880), GPX1 (rs1050450), NQO1 (rs1800566), CAT (rs1001179), TYR (rs1126809), SLC45A2 (rs26722), SLC45A2 (rs16891982), MMP1 (rs1799750), ELN (rs7787362), COL1A1 (rs1800012), AHR (rs2066853), IL6 (rs1800795), IL1Beta (rs1143634), TNF-α (rs1800629), and AQP3 (rs17553719). RT genotyping, blood count, and immunochemistry results were analyzed using statistical methods. The obtained results show significant associations between genotyping models and routine blood screens. These findings demonstrate the personalized medicine approach for the aging segment and further add to the growing literature. Further investigation is warranted to fully understand the complex interplay between genetic factors, environmental influences, and skin aging.

Epigenetics in Neurological and Psychiatric Disorders: A Comprehensive Review of Current Understanding and Future Perspectives

The burgeoning field of epigenetics offers transformative insights into the complex landscape of neurological and psychiatric disorders. By unraveling the intricate interplay between genetic, epigenetic, environmental, and lifestyle factors, this comprehensive review highlights the multifaceted nature of mental health. The exploration reveals the potential of epigenetic modifications to revolutionize our understanding, diagnosis, treatment, and prevention of these disorders. Emphasizing the importance of multidisciplinary collaborations, large-scale studies, technological advancements, and ethical considerations, the review asserts the promise of epigenetics as a vital tool for personalized medicine, early intervention, and public health strategies. While acknowledging the challenges in a still-emerging field, the review paints an optimistic picture of epigenetics as a groundbreaking approach that can reshape mental healthcare, offering hope for those affected by neurological and psychiatric conditions. The future trajectory of the field relies on interdisciplinary efforts, ethical diligence, innovative technologies, and translating scientific insights into real-world applications, thereby unlocking the vast potential of epigenetics in mental health.

Advancing Precision Medicine in South Tyrol, Italy: A Public Health Development Proposal for a Bilingual, Autonomous Province

This paper presents a comprehensive development plan for advancing precision medicine in the autonomous province of South Tyrol, Italy, a region characterized by its bilingual population and unique healthcare challenges. This study highlights the need to address the shortage of healthcare professionals proficient in language for person-centered medicine, the lag in healthcare sector digitalization, and the absence of a local medical university, all within the context of an initiated pharmacogenomics program and a population-based precision medicine study known as the "Cooperative Health Research in South Tyrol" (CHRIS) study. The key strategies for addressing these challenges and integrating CHRIS study findings into a broader precision medicine development plan are discussed, including workforce development and training, investment in digital infrastructure, enhanced data management and analytic capabilities, collaboration with external academic and research institutions, education and capacity building, securing funding and resources, and promoting a patient-centered approach. This study emphasizes the potential benefits of implementing such a comprehensive development plan, including improved early detection, personal ized treatment, and prevention of chronic diseases, ultimately leading to better healthcare outcomes and overall well-being in the South Tyrolean population.

Human Pangenomics: Promises and Challenges of a Distributed Genomic Reference

A pangenome is a collection of the common and unique genomes that are present in a given species. It combines the genetic information of all the genomes sampled, resulting in a large and diverse range of genetic material. Pangenomic analysis offers several advantages compared to traditional genomic research. For example, a pangenome is not bound by the physical constraints of a single genome, so it can capture more genetic variability. Thanks to the introduction of the concept of pangenome, it is possible to use exceedingly detailed sequence data to study the evolutionary history of two different species, or how populations within a species differ genetically. In the wake of the Human Pangenome Project, this review aims at discussing the advantages of the pangenome around human genetic variation, which are then framed around how pangenomic data can inform population genetics, phylogenetics, and public health policy by providing insights into the genetic basis of diseases or determining personalized treatments, targeting the specific genetic profile of an individual. Moreover, technical limitations, ethical concerns, and legal considerations are discussed.

World-Class Innovation, but at What Cost? A Brief Examination of the American Healthcare System

The American healthcare system, often counted among the world's best, offers expedient access to a highly subspecialized network of physicians who work at the forefront of developing and utilizing novel, cutting-edge procedures, and medications. Patients typically have access to large numbers of qualified physicians in every metropolitan area and thus are afforded the luxury of individual choice in hospital, physician, and experience. Unfortunately, the costs of maintaining such a system are quite profound, and the higher investments do not pay dividends in health outcomes. Here, we elaborate on the greatest achievement and worst flaw in the American healthcare system.

Epigenetics Approaches toward Precision Medicine for Idiopathic Pulmonary Fibrosis: Focus on DNA Methylation

Genetic information is not transmitted solely by DNA but by the epigenetics process. Epigenetics describes molecular missing link pathways that could bridge the gap between the genetic background and environmental risk factors that contribute to the pathogenesis of pulmonary fibrosis. Specific epigenetic patterns, especially DNA methylation, histone modifications, long non-coding, and microRNA (miRNAs), affect the endophenotypes underlying the development of idiopathic pulmonary fibrosis (IPF). Among all the epigenetic marks, DNA methylation modifications have been the most widely studied in IPF. This review summarizes the current knowledge concerning DNA methylation changes in pulmonary fibrosis and demonstrates a promising novel epigenetics-based precision medicine.

Trends and opportunities in computable clinical phenotyping: A scoping review

Identifying patient cohorts meeting the criteria of specific phenotypes is essential in biomedicine and particularly timely in precision medicine. Many research groups deliver pipelines that automatically retrieve and analyze data elements from one or more sources to automate this task and deliver high-performing computable phenotypes. We applied a systematic approach based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines to conduct a thorough scoping review on computable clinical phenotyping. Five databases were searched using a query that combined the concepts of automation, clinical context, and phenotyping. Subsequently, four reviewers screened 7960 records (after removing over 4000 duplicates) and selected 139 that satisfied the inclusion criteria. This dataset was analyzed to extract information on target use cases, data-related topics, phenotyping methodologies, evaluation strategies, and portability of developed solutions. Most studies supported patient cohort selection without discussing the application to specific use cases, such as precision medicine. Electronic Health Records were the primary source in 87.1 % (N = 121) of all studies, and International Classification of Diseases codes were heavily used in 55.4 % (N = 77) of all studies, however, only 25.9 % (N = 36) of the records described compliance with a common data model. In terms of the presented methods, traditional Machine Learning (ML) was the dominant method, often combined with natural language processing and other approaches, while external validation and portability of computable phenotypes were pursued in many cases. These findings revealed that defining target use cases precisely, moving away from sole ML strategies, and evaluating the proposed solutions in the real setting are essential opportunities for future work. There is also momentum and an emerging need for computable phenotyping to support clinical and epidemiological research and precision medicine.

Integrating oculomics with genomics reveals imaging biomarkers for preventive and personalized prediction of arterial aneurysms

Objective

Arterial aneurysms are life-threatening but usually asymptomatic before requiring hospitalization. Oculomics of retinal vascular features (RVFs) extracted from retinal fundus images can reflect systemic vascular properties and therefore were hypothesized to provide valuable information on detecting the risk of aneurysms. By integrating oculomics with genomics, this study aimed to (i) identify predictive RVFs as imaging biomarkers for aneurysms and (ii) evaluate the value of these RVFs in supporting early detection of aneurysms in the context of predictive, preventive and personalized medicine (PPPM).

Methods

This study involved 51,597 UK Biobank participants who had retinal images available to extract oculomics of RVFs. Phenome-wide association analyses (PheWASs) were conducted to identify RVFs associated with the genetic risks of the main types of aneurysms, including abdominal aortic aneurysm (AAA), thoracic aneurysm (TAA), intracranial aneurysm (ICA) and Marfan syndrome (MFS). An aneurysm-RVF model was then developed to predict future aneurysms. The performance of the model was assessed in both derivation and validation cohorts and was compared with other models employing clinical risk factors. An RVF risk score was derived from our aneurysm-RVF model to identify patients with an increased risk of aneurysms.

Results

PheWAS identified a total of 32 RVFs that were significantly associated with the genetic risks of aneurysms. Of these, the number of vessels in the optic disc ('ntreeA') was associated with both AAA (β = -0.36, P = 6.75e-10) and ICA (β = -0.11, P = 5.51e-06). In addition, the mean angles between each artery branch ('curveangle_mean_a') were commonly associated with 4 MFS genes (FBN1: β = -0.10, P = 1.63e-12; COL16A1: β = -0.07, P = 3.14e-09; LOC105373592: β = -0.06, P = 1.89e-05; C8orf81/LOC441376: β = 0.07, P = 1.02e-05). The developed aneurysm-RVF model showed good discrimination ability in predicting the risks of aneurysms. In the derivation cohort, the C-index of the aneurysm-RVF model was 0.809 [95% CI: 0.780-0.838], which was similar to the clinical risk model (0.806 [0.778-0.834]) but higher than the baseline model (0.739 [0.733-0.746]). Similar performance was observed in the validation cohort, with a C-index of 0.798 (0.727-0.869) for the aneurysm-RVF model, 0.795 (0.718-0.871) for the clinical risk model and 0.719 (0.620-0.816) for the baseline model. An aneurysm risk score was derived from the aneurysm-RVF model for each study participant. The individuals in the upper tertile of the aneurysm risk score had a significantly higher risk of aneurysm compared to those in the lower tertile (hazard ratio = 17.8 [6.5-48.8], P = 1.02e-05).

Conclusion

We identified a significant association between certain RVFs and the risk of aneurysms and revealed the impressive capability of using RVFs to predict the future risk of aneurysms by a PPPM approach. Our finds have great potential to support not only the predictive diagnosis of aneurysms but also a preventive and more personalized screening plan which may benefit both patients and the healthcare system.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-023-00315-7.

Optimizing and Accelerating the Development of Precision Pain Treatments for Chronic Pain: IMMPACT Review and Recommendations

Large variability in the individual response to even the most-efficacious pain treatments is observed clinically, which has led to calls for a more personalized, tailored approach to treating patients with pain (ie, "precision pain medicine"). Precision pain medicine, currently an aspirational goal, would consist of empirically based algorithms that determine the optimal treatments, or treatment combinations, for specific patients (ie, targeting the right treatment, in the right dose, to the right patient, at the right time). Answering this question of "what works for whom" will certainly improve the clinical care of patients with pain. It may also support the success of novel drug development in pain, making it easier to identify novel treatments that work for certain patients and more accurately identify the magnitude of the treatment effect for those subgroups. Significant preliminary work has been done in this area, and analgesic trials are beginning to utilize precision pain medicine approaches such as stratified allocation on the basis of prespecified patient phenotypes using assessment methodologies such as quantitative sensory testing. Current major challenges within the field include: 1) identifying optimal measurement approaches to assessing patient characteristics that are most robustly and consistently predictive of inter-patient variation in specific analgesic treatment outcomes, 2) designing clinical trials that can identify treatment-by-phenotype interactions, and 3) selecting the most promising therapeutics to be tested in this way. This review surveys the current state of precision pain medicine, with a focus on drug treatments (which have been most-studied in a precision pain medicine context). It further presents a set of evidence-based recommendations for accelerating the application of precision pain methods in chronic pain research. PERSPECTIVE: Given the considerable variability in treatment outcomes for chronic pain, progress in precision pain treatment is critical for the field. An array of phenotypes and mechanisms contribute to chronic pain; this review summarizes current knowledge regarding which treatments are most effective for patients with specific biopsychosocial characteristics.

Components, prospects and challenges of personalized prevention

Effective preventive strategies are urgently needed to address the rising burden of non-communicable diseases such as cardiovascular disease and cancer. To date, most prevention efforts to reduce disease incidence have primarily targeted populations using "one size fits all" public health recommendations and strategies. However, the risk for complex heterogeneous diseases is based on a multitude of clinical, genetic, and environmental factors, which translate into individual sets of component causes for every person. Recent advances in genetics and multi-omics enable the use of new technologies to stratify disease risks at an individual level fostering personalized prevention. In this article, we review the main components of personalized prevention, provide examples, and discuss both emerging opportunities and remaining challenges for its implementation. We encourage physicians, health policy makers, and public health professionals to consider and apply the key elements and examples of personalized prevention laid out in this article while overcoming challenges and potential barriers to their implementation.

Chronic non-communicable diseases and health awareness of patients: An observational study analysing the health adaptive behaviours through self-care skills

The incidence of chronic non-communicable diseases (CNCDs) increases with age, thus placing a burden on individuals and healthcare systems; furthermore, CNCDs are associated with lower quality of life (QoL) in the elderly. This study aimed to evaluate the behavioural management of CNCDs by comparing different diseases under its ambit exhibiting various side effects, as well as vascular, gastrointestinal, and other chronic diseases, simultaneously analysing self-care efficacy and emotional dimensions (i.e. anxiety, stress, and depression). A total of 122 outpatients aged >18 years (mean age, 50.0 years; standard deviation, ±13.8) participated in the study, and 59% of the participants were women. The psychological battery was composed of three standardised self-assessments measuring the relevant emotional dimensions, QoL, and self-care indices. Analysis of the emotional dimensions of depression, anxiety, and stress indices resulted in significant differences. Furthermore, post-hoc analyses (Tukey test) showed that patients with gastrointestinal disease (GD) experienced depression more than patients with vascular disease (VD) (p = 0.002). In addition, there were no differences between the VD and Oth and between the GD and Oth groups; however, the Oth group appeared to experience more anxiety than the VD group (p = 0.04); there were no differences between the GD and VD groups, as well the Oth group. The ANCOVA test compared the chronic disease groups, the disease stage (early and long-term), self-care indices, and sex. They were covariated by age, showing a significant effect on the management index only in chronic disease groups (p = 0.007). Our study suggests that the following specific factors need to be considered in health decision-making processes to enhance the compliance of patients to the clinically expected results: (1) tailored clinical therapies; (2) side effects of disease; (3) comorbidity; (4) mental health. The harmonisation of these internal and external factors could offer a powered perspective for personalized medicine.

Knowledge, attitudes and practices of Egyptian healthcare professionals toward therapeutic drug monitoring service as a principal component of personalized medicine

Aim: To assess pharmacists' and physicians' knowledge, attitudes and practices toward therapeutic drug monitoring (TDM) service at the Children's Cancer Hospital Egypt 57357. Materials & methods: This was a single-site cross-sectional study where all practicing pharmacists and physicians were eligible to participate. Results: A statistically significant difference in the knowledge scores between pharmacists and physicians (p = 0.022) was found. In general, attitudes toward TDM among pharmacists and physicians were positive. Regarding practices, pharmacists were more likely than physicians to agree or strongly agree that they have studied some scientific references on TDM (p = 0.034), but more physicians recommend the TDM service (p = 0.046). Conclusion: A multidisciplinary educational program in Egypt for TDM for both medicine and pharmacy staff will improve interprofessional collaboration in the clinical setting, leading to better personalized medication management.

Multidisciplinary Effort to Drive Precision-Medicine for the Future

In the past one or two decades, countries across the world have successively implemented different precision medicine (PM) programs, and also cooperated to implement international PM programs. We are now in the era of PM. Singapore's National Precision Medicine (NPM) program, initiated in 2017, is now entering its second phase to generate a large genomic database for Asians. The National University of Singapore (NUS) also launched its own PM translational research program (TRP) in 2021, aimed at consolidating multidisciplinary expertise within the Yong Loo Lin School of Medicine to develop collaborative projects that can help to identify and validate novel therapeutic targets for the realization of PM. To achieve this, appropriate data collection, data processing, and results interpretation must be taken into consideration. There may be some difficulties during these processes, but with the improvement of relevant rules and the continuous development of omics-based technologies, we will be able to solve these problems, eventually achieving precise prediction, diagnosis, treatment, or even prevention of diseases.

Multi-omics strategies for personalized and predictive medicine: past, current, and future translational opportunities

Precision medicine is driven by the paradigm shift of empowering clinicians to predict the most appropriate course of action for patients with complex diseases and improve routine medical and public health practice. It promotes integrating collective and individualized clinical data with patient specific multi-omics data to develop therapeutic strategies, and knowledgebase for predictive and personalized medicine in diverse populations. This study is based on the hypothesis that understanding patient's metabolomics and genetic make-up in conjunction with clinical data will significantly lead to determining predisposition, diagnostic, prognostic and predictive biomarkers and optimal paths providing personalized care for diverse and targeted chronic, acute, and infectious diseases. This study briefs emerging significant, and recently reported multi-omics and translational approaches aimed to facilitate implementation of precision medicine. Furthermore, it discusses current grand challenges, and the future need of Findable, Accessible, Intelligent, and Reproducible (FAIR) approach to accelerate diagnostic and preventive care delivery strategies beyond traditional symptom-driven, disease-causal medical practice.

Identification of macrophage related gene in colorectal cancer patients and their functional roles

BACKGROUND

Recent scientific research has enabled the identification of macrophages related-genes (MaRG), which play a key role in the control of the immune microenvironment in many human cancers. However, the functional role of MaRGs in human tumors is ill-defined. Herein, we aimed at bioinformatically exploring the molecular signatures of MaRGs in colorectal cancer.

METHODS

A list of MaRGs was generated and their differential expression was analyzed across multiple datasets downloaded from the publicly available functional genomics database Gene Expression Omnibus. The weighted gene co-expression network analysis (WGCNA) was also applied to identify the partner genes of these MaRGs in colorectal cancer.

RESULTS

After integration of the results from analyses of different datasets, we found that 29 differentially expressed MaRGs (DE-MaRGs) could be considered as CRC-related genes as obtained from the WGCNA analysis. These genes were functionally involved in positive regulation of DNA biosynthetic process and glutathione metabolism. Protein-protein interaction network analysis indicated that PDIA6, PSMA1, PRC1, RRM2, HSP90AB1, CDK4, MCM7, RFC4, and CCT5 were the hub MaRGs. The LASSO approach was used for validating the 29 MaRGs in TCGA-COAD and TCGA-READ data and the results showed that ten among the 29 genes could be considered as MaRGs significantly involved in CRC. The maftools analysis showed that MaRGs were mutated at varying degrees. The nomogram analysis indicated the correlation of these MaRGs with diverse clinical features of CRC patients.

CONCLUSIONS

Conclusively, the present disclosed a signature of MaRGs as potential key regulators involved in CRC pathogenesis and progression. These findings contribute not only to the understanding of the molecular mechanism of CRC pathogenesis but also to the development of adequate immunotherapies for CRC patients.

Artificial intelligence in early drug discovery enabling precision medicine

Introduction: Precision medicine is the concept of treating diseases based on environmental factors, lifestyles, and molecular profiles of patients. This approach has been found to increase success rates of clinical trials and accelerate drug approvals. However, current precision medicine applications in early drug discovery use only a handful of molecular biomarkers to make decisions, whilst clinics gear up to capture the full molecular landscape of patients in the near future. This deep multi-omics characterization demands new analysis strategies to identify appropriate treatment regimens, which we envision will be pioneered by artificial intelligence.Areas covered: In this review, the authors discuss the current state of drug discovery in precision medicine and present our vision of how artificial intelligence will impact biomarker discovery and drug design.Expert opinion: Precision medicine is expected to revolutionize modern medicine; however, its traditional form is focusing on a few biomarkers, thus not equipped to leverage the full power of molecular landscapes. For learning how the development of drugs can be tailored to the heterogeneity of patients across their molecular profiles, artificial intelligence algorithms are the next frontier in precision medicine and will enable a fully personalized approach in drug design, and thus ultimately impacting clinical practice.

Angiotensin System Polymorphisms' in SARS-CoV-2 Positive Patients: Assessment Between Symptomatic and Asymptomatic Patients: A Pilot Study

Introduction

The renin-angiotensin-aldosterone system (RAAS), a metabolic cascade regulating pressure and circulating blood volume, has been considered the main system involved in the pathogenesis of severe lung injury and organs decline in COVID-19 patients. The angiotensin I-converting enzyme (ACE1), angiotensin-converting enzyme 2 (ACE2), angiotensinogen (AGT) and receptors angiotensin II receptor type 1 (AGTR1) are key factors for SARS-CoV-2 entering in the cells, sodium and water retention with an increase blood pressure, promotion of fibrotic and inflammatory phenomena resulting in a cytokine storm.

Methods

In this pilot study, the frequencies of six polymorphisms in the ACE1, ACE2, AGT and AGTR1 genes were analysed in symptomatic patients affected by COVID-19 and compared with the results obtained from asymptomatic subjects.

Results

Thus, we have identified that rs2074192 (ACE2), rs1799752 (ACE1) and rs699 (AGT) SNPs could potentially be a valuable tool for predicting the clinical outcome of SARS-CoV-2 infected patients. A genetic predisposition may be prospected for severe internal organ damages and poor prognosis in patients with COVID-19 disease, as observed in symptomatic vs asymptomatic.

Conclusion

This study provides evidence that analysis of RAAS polymorphisms could be considered the key point in understanding and predicting the SARS-CoV-2 course infection.

Precision Medicine and Public Health: New Challenges for Effective and Sustainable Health

The development of high-throughput omics technologies represents an unmissable opportunity for evidence-based prevention of adverse effects on human health. However, the applicability and access to multi-omics tests are limited. In Italy, this is due to the rapid increase of knowledge and the high levels of skill and economic investment initially necessary. The fields of human genetics and public health have highlighted the relevance of an implementation strategy at a national level in Italy, including integration in sanitary regulations and governance instruments. In this review, the emerging field of public health genomics is discussed, including the polygenic scores approach, epigenetic modulation, nutrigenomics, and microbiomes implications. Moreover, the Italian state of implementation is presented. The omics sciences have important implications for the prevention of both communicable and noncommunicable diseases, especially because they can be used to assess the health status during the whole course of life. An effective population health gain is possible if omics tools are implemented for each person after a preliminary assessment of effectiveness in the medium to long term.