Era Of Precision Medicine Research Articles

The Role of microRNA Expression and DNA Methylation in HPV-Related Cervical Cancer: A Systematic Review

Human papillomavirus (HPV) infection is a major etiologic factor in cervical cancer, a major cause of cancer-related morbidity and mortality among women worldwide. The role of microRNA (miRNA) dysregulation in cervical carcinogenesis is still largely unknown, but epigenetic changes, including DNA methylation and miRNA regulation, are crucial factors. The integration of HPV DNA into the host genome can lead to alterations in DNA methylation patterns and miRNA expression, contributing to the progression from normal epithelium to cervical intraepithelial neoplasia and, ultimately, to cervical cancer. This review aimed to examine the relationship between epigenetic changes in the development and progression of HPV associated with cervical cancer. A systematic literature search was conducted in major databases using predefined inclusion and exclusion criteria. Studies that investigated the expression, function, and clinical significance of miRNAs, DNA methylation, and the expression of oncoproteins in HPV-related cervical cancer were included. Data extraction, quality assessment, and synthesis were performed to provide a comprehensive overview of the current state of knowledge. We provide an overview of the studies investigating miRNA expression in relation to cervical cancer progression, highlighting their common outcomes and their weaknesses/strengths. To achieve this, we systematically searched the Pubmed database for all articles published between January 2018 and December 2023. Our systematic review revealed a substantial body of evidence supporting the pivotal role of miRNA dysregulation in the pathogenesis of HPV-related cervical cancer and related oncoproteins. From the 28 studies retrieved, miR-124, FAM194/miR-124-2, and DNA methylation are the most frequently down- or up-regulated in CC progression. Notably, FAM194/miR-124-2 and DNA methylation emerged as a promising molecular marker for distinguishing between cases requiring immediate surgical intervention and those amenable to a more conservative wait-and-see approach. This systematic review underscores the critical involvement of microRNA in the context of HPV-related cervical cancer and sheds light on the potential clinical utility of FAM194/miR-124-2 and DNA methylation as a discriminatory tool for guiding treatment decisions. The identification of patients who may benefit from early surgical intervention versus those suitable for observation has important implications for personalized and targeted management strategies in the era of precision medicine.

Predictive and prognostic biomarkers in cancer: towards the precision medicine era

Predictive and prognostic biomarkers in cancer: towards the precision medicine era

Beyond NT-proBNP: Circulating Protein Biomarkers for Atrial Fibrillation in Heart Failure and Cardiomyopathy

Atrial fibrillation (AF) is largely co-morbid with heart failure (HF), worsening prognosis in patients with ventricular dysfunction. Treating AF through ablation can improve outcomes and reduce the transition to end-stage HF, highlighting the importance of early recognition in patients with ventricular dysfunction. Circulating NT-proBNP is a well-known biomarker for AF in patients with ventricular dysfunction, however, large individual variability limits its predictive power and therefore utility. With the rise of high-throughput proteomics in the era of precision medicine, novel and stable biomarkers may be identified with the potential to improve risk stratification, detection, and management.

Estimating the Number of Polygenic Diseases Among Six Mutually Exclusive Entities of Non-Tumors and Cancer.

In the era of precision medicine with increasing amounts of sequenced cancer and non-cancer genomes of different ancestries, we here enumerate the resulting polygenic disease entities. Based on the cell number status, we first identified six fundamental types of polygenic illnesses, five of which are non-cancerous. Like complex, non-tumor disorders, neoplasms normally carry alterations in multiple genes, including in 'Drivers' and 'Passengers'. However, tumors also lack certain genetic alterations/epigenetic changes, recently named 'Goners', which are toxic for the neoplasm and potentially constitute therapeutic targets. Drivers are considered essential for malignant transformation, whereas environmental influences vary considerably among both types of polygenic diseases. For each form, hyper-rare disorders, defined as affecting <1/108 individuals, likely represent the largest number of disease entities. Loss of redundant tumor-suppressor genes exemplifies such a profoundly rare mutational event. For non-tumor, polygenic diseases, pathway-centered taxonomies seem preferable. This classification is not readily feasible in cancer, but the inclusion of Drivers and possibly also of epigenetic changes to the existing nomenclature might serve as initial steps in this direction. Based on the detailed genetic alterations, the number of polygenic diseases is essentially countless, but different forms of nosologies may be used to restrict the number.

Enhancing IVF Outcomes with Artificial Intelligence: Current Advances and Future Possibilities

The integration of Artificial Intelligence (AI) into In Vitro Fertilization (IVF) practices has marked a revolutionary shift in reproductive medicine, offering enhanced precision, efficiency, and personalized treatment plans. The rapid advancement of Artificial Intelligence (AI) has led to significant innovations in the field of reproductive medicine, particularly in In Vitro Fertilization (IVF). Traditional IVF procedures, while effective, often face challenges such as variable success rates, high costs, and the emotional burden on patients due to multiple treatment cycles. AI offers a promising solution to these issues by enhancing accuracy, personalization, and efficiency throughout the IVF process. AI algorithms have shown remarkable capabilities in diagnosing infertility by analyzing complex datasets from hormone profiles, genetic testing, and medical imaging, enabling early identification of conditions like polycystic ovary syndrome (PCOS) and endometriosis. Moreover, one of the most promising applications of AI in IVF is embryo grading. However, AI systems have been developed to objectively evaluate embryos based on time-lapse imaging, morphology, and other parameters, improving the selection process. Additionally, AI has been instrumental in optimizing ovarian stimulation protocols by analyzing patient data to determine the appropriate medication dosage, minimizing the risk of ovarian hyperstimulation syndrome (OHSS). This review discusses the current state of AI integration in fertility treatments, successful case studies, and ongoing research to develop more sophisticated AI models. Overall, AI holds immense promise in making IVF more accessible, affordable, and successful for patients worldwide, ushering in a new era of precision medicine in reproductive health.

Cardiovascular Imaging in the Era of Precision Medicine: Insights from Advanced Technologies - A Narrative Review.

Cardiovascular diseases are responsible for a high mortality rate globally. Precision medicine has emerged as an essential tool for improving cardiovascular disease outcomes. In this context, using advanced imaging exams is fundamental in cardiovascular precision medicine, enabling more accurate diagnoses and customized treatments. This review aims to provide a concise review on how advanced cardiovascular imaging supports precision medicine, highlighting its benefits, challenges, and future directions. A literature review was carried out using the Pubmed and Google Scholar databases, using search strategies that combined terms such as precision medicine, cardiovascular diseases, and imaging tests. More advanced analysis aimed at diagnosing and describing cardiovascular diseases in greater detail is made possible by tests such as cardiac computed tomography, cardiac magnetic resonance imaging, and cardiac positron emission tomography. In addition, the aggregation of imaging data with other omics data allows for more personalized treatment and a better description of patient profiles. The use of advanced imaging tests is essential in cardiovascular precision medicine. Although there are still technical and ethical obstacles, it is essential that there is collaboration between health professionals, as well as investments in technology and education to better disseminate cardiovascular precision medicine and consequently promote improved patient outcomes.

The diagnostic value of endomyocardial biopsy in the era of precision medicine: data from over 8,000 patients with comprehensive biopsy diagnostics

Abstract Background Endomyocardial biopsy (EMB) is an important diagnostic tool for evaluating various cardiac diseases such as myocarditis and storage disorders as well as for the monitoring heart transplant patients. Despite advances in non-invasive imaging techniques to characterize myocardial tissue, EMB remains the gold standard for definitive diagnosis and initiation of specific therapy. Here we report EMB laboratory results from over 8,000 patients and demonstrate the diagnostic utility of EMB. Methods and results We retrospectively reviewed EMB analyses performed at our institution. EMBs for monitoring graft rejection were not included in this study. Clinical data, suspected clinical diagnoses and EMB-based laboratory results were collected. Histopathological, immunohistochemical and molecular biology biopsy findings were recorded and reviewed for degree of concordance with clinical diagnosis. In total, EMBs from N=8,085 consecutive patients with unexplained heart failure were analyzed. The comprehensive EMB analysis revealed that N=616 (7.6%) patients had cardiac storage disease, most of whom had cardiac amyloidosis (N=606). N=357 (4.4%) patients were diagnosed with acute forms of myocarditis, including N=95 cases with active myocarditis, N=148 cases with giant cell myocarditis, N=95 cases with sarcoidosis and N=19 cases with eosinophilic myocarditis. A total of N=3,392 (42%) patients were found to have inflammatory heart disease such as inflammatory cardiomyopathy and borderline myocarditis, whereby a differentiation of the inflammatory infiltrates revealed considerable differences in the quality of inflammation. Viral genomes were found in the myocardium of N=6,072 (75%) patients, with treatment-relevant viral infections detected in N=2,015 (25%) cases. The remaining N=1,705 (21%) patients were diagnosed with other cardiomyopathies, including dilated cardiomyopathy (N=1,252), heart failure with preserved ejection fraction (N=325), hypertrophic cardiomyopathy (N=117) and arrhythmogenic right ventricular cardiomyopathy (N=11). Conclusions This retrospective study of over 8,000 patients with heart failure clearly shows that a definitive diagnosis is only possible with a comprehensive EMB diagnosis and leads to specific therapy in 79% of the cases. In the remaining 21% of patients, infectious and inflammatory causes could be excluded in order to initiate standard heart failure therapy. It is important to emphasize here that the quality of the inflammation and therapy-relevant viral infections cannot be detected with imaging techniques, which underlines the clinical-therapeutic importance of endomyocardial biopsy.

A APLICAÇÃO DAS FERRAMENTAS IN SILICO NO DESENVOLVIMENTO DE QUIMIOTERÁPICOS

This article explores the use of in silico tools in the development of chemotherapeutics, highlighting their ability to accelerate the discovery and optimization of new cancer treatments. Through techniques such as molecular docking, molecular dynamics, QSAR, and machine learning, researchers can simulate molecular interactions, enabling the identification and refinement of selective kinase inhibitors and other molecules with therapeutic potential. The studies cited demonstrate how these approaches contribute to more effective and personalized treatments, reducing both the costs and the time of drug development. Moreover, the integration of these techniques promises to revolutionize anticancer therapies, moving the field of oncology into an era of precision medicine.

The role of artificial intelligence in the management of liver diseases.

Universal neonatal hepatitis B virus (HBV) vaccination and the advent of direct-acting antivirals (DAA) against hepatitis C virus (HCV) have reshaped the epidemiology of chronic liver diseases. However, some aspects of the management of chronic liver diseases remain unresolved. Nucleotide analogs can achieve sustained HBV DNA suppression but rarely lead to a functional cure. Despite the high efficacy of DAAs, successful antiviral therapy does not eliminate the risk of hepatocellular carcinoma (HCC), highlighted the need for cost-effective identification of high-risk populations for HCC surveillance and tailored HCC treatment strategies for these populations. The accessibility of high-throughput genomic data has accelerated the development of precision medicine, and the emergence of artificial intelligence (AI) has led to a new era of precision medicine. AI can learn from complex, non-linear data and identify hidden patterns within real-world datasets. The combination of AI and multi-omics approaches can facilitate disease diagnosis, biomarker discovery, and the prediction of treatment efficacy and prognosis. AI algorithms have been implemented in various aspects, including non-invasive tests, predictive models, image diagnosis, and the interpretation of histopathology findings. AI can support clinicians in decision-making, alleviate clinical burdens, and curtail healthcare expenses. In this review, we introduce the fundamental concepts of machine learning and review the role of AI in the management of chronic liver diseases.

Revisiting ecological fallacy: are single-case experimental study designs even more relevant in the era of precision medicine?

Revisiting ecological fallacy: are single-case experimental study designs even more relevant in the era of precision medicine?

Knee Arthroscopy in the Era of Precision Medicine: A Comprehensive Review of Tailored Approaches and Emerging Technologies.

Knee arthroscopy, a minimally invasive procedure, has transformed the treatment of knee pathologies by enabling direct visualization and management with minimal tissue disruption. Recent advances in precision medicine have introduced a new dimension to this field, allowing for highly individualized surgical approaches considering each patient's unique genetic, environmental, and biomechanical characteristics. This review explores the integration of precision medicine into knee arthroscopy, focusing on tailored approaches and emerging technologies. Key innovations such as robotic-assisted surgery, advanced imaging, and patient-specific instrumentation have enhanced surgical accuracy and patient outcomes, reduced recovery times, and minimized postoperative complications. The review also examines the role of biomarkers in guiding personalized treatment strategies, including ligament reconstructions, meniscal repairs, and cartilage restoration, which are now being refined to cater to the specific needs of individual patients. While the benefits of these innovations are clear, there are challenges to widespread adoption, including cost, resource allocation, and the need for further research to validate the efficacy of precision-driven approaches in knee arthroscopy. Moreover, the ethical considerations surrounding personalized medicine, such as patient privacy and genetic data usage, must also be addressed. Despite these barriers, the future of knee arthroscopy in the era of precision medicine holds great promise, with ongoing developments in artificial intelligence, genomics, and biomarker discovery poised to further refine patient-centered care. This comprehensive review provides valuable insights into how precision medicine reshapes knee arthroscopy, offering a glimpse into the future of more targeted and effective orthopedic interventions. By embracing these advancements, surgeons and healthcare providers can ensure optimal outcomes for patients undergoing knee arthroscopy.

Early detection of pancreatic cancer in the era of precision medicine.

Pancreatic ductal adenocarcinoma (PDAC) is the third leading cause of cancer-related mortality and it is often diagnosed at advanced stages due to non-specific clinical presentation. Disease detection at localized disease stage followed by surgical resection remains the only potentially curative treatment. In this era of precision medicine, a multifaceted approach to early detection of PDAC includes targeted screening in high-risk populations, serum biomarkers and "liquid biopsies", and artificial intelligence augmented tumor detection from radiologic examinations. In this review, we will review these emerging techniques in the early detection of PDAC.

A brief clinical genetics review: stepwise diagnostic processes of a monogenic disorder-hypertriglyceridemia.

The completion of the Human Genome Project and tremendous advances in automated high-throughput genetic analysis technologies have enabled explosive progress in the field of genetics, which resulted in countless discoveries of novel genes and pathways. Many phenotype- or disease-associated single nucleotide polymorphisms (SNPs) with a high statistical significance have been identified through numerous genome-wide association studies (GWAS), and various polygenic risk scoring (PRS) schemes have been proposed to identify individuals with a high risk for a certain trait or disorder. Meanwhile, medical education in genetics has lagged far behind, leaving many physicians and healthcare providers unprepared in the genomic era. Thus, there is an urgent need to educate physicians and healthcare providers with basic knowledge and skills in genetics. To facilitate this, some basic terminologies and concepts are discussed in this review. In addition, some important considerations in delineating and incorporating clinical genetic testing in the diagnosis and management of a monogenic disorder are illustrated in a stepwise fashion. Furthermore, the effects of disease-associated SNPs represented by a PRS scheme clearly demonstrated that even the phenotypes of a monogenic disorder due to the same pathogenic variant in family members are modulated by the polygenic background. In human genetics, despite these explosive advancements, we are still far from clearly deciphering the interplay of gene variants to effect unique characteristics in an individual. In addition, sophisticated genome or gene directed therapies are being investigated for numerous disorders. Therefore, evolution in the field of genetics is likely to continue into the foreseeable future. In the meantime, much emphasis should be placed on educating physicians and healthcare professionals to be well-versed and skillful in the clinical use of genetics so that they can fully embrace the new era of precision medicine.

Genomics Vision In Nephrology and Transplantation.

Current nephrology practice starts by patient-doctor interactions and can lead to investigations. Genetic testing has had a limited role in these investigations. In the future era of precision medicine, adding genomic sequencing early in the management paradigm could modify treatment plans. Genomic approaches followed by physician planning for targeted treatment of renal disease and transplant are precision medicine tools that can lead to better prognosis and medical outcomes.

One Step Ahead in Realizing Pharmacogenetics in Low- and Middle-Income Countries: What Should We Do?

Pharmacogenetics is a promising approach in future personalized medicine. This field holds excellent prospects for healthcare quality acceleration. It promotes the transition to the precision medicine era, whereby a health treatment is driven by a deeper understanding of individual characteristics by interpreting the underlying genomic variation. Pharmacogenetics has been developing rapidly since the human genome project. Many pharmacogenetics studies have shown the association between genetic variants and therapy outcomes. Several pharmacogenetics working groups have recommended guidelines for the clinical application of pharmacogenetics. However, the development of pharmacogenetics in low- and middle-income countries (LMICs) is still retarded behind. The problems mainly include clinical evidence, technology, policy and regulation, and human resources. Currently, available genome and drug effect data in LMICs are scarce. Pharmacogenetics development should be escalated with evidence proof through research collaboration across countries. The challenges of pharmacogenetics implementation are discussed comprehensively in this article, along with the prospect of pharmacogenetics-guided personalized medicine in developed countries. Stepwise is expected to help the researchers and stakeholders define the problem that hindered the pharmacogenetics application.

Leveraging Generative AI for Personalized Healthcare: Revolutionizing Treatment Plans

This comprehensive article explores the transformative potential of generative artificial intelligence (AI) in healthcare, examining its applications in data analysis, disease outcome prediction, and personalized treatment planning. The article discusses how generative AI systems can process vast amounts of multi-modal patient data to uncover subtle patterns and correlations, leading to more accurate diagnoses and tailored treatment strategies. It highlights successful implementations across various medical specialties, including oncology, chronic disease management, and rare disease diagnosis. The article also addresses the challenges and ethical considerations associated with the deployment of generative AI in healthcare, emphasizing the need for data privacy, algorithmic transparency, and regulatory compliance. By presenting both the opportunities and obstacles, this work provides a balanced view of the role generative AI can play in ushering in a new era of precision medicine and patient-centered care

Using random forest and biomarkers for differentiating COVID-19 and Mycoplasma pneumoniaeinfections.

The COVID-19 pandemic has underscored the critical need for precise diagnostic methods to distinguish between similar respiratory infections, such as COVID-19 and Mycoplasma pneumoniae (MP). Identifying key biomarkers and utilizing machine learning techniques, such as random forest analysis, can significantly improve diagnostic accuracy. We conducted a retrospective analysis of clinical and laboratory data from 214 patients with acute respiratory infections, collected between October 2022 and October 2023 at the Second Hospital of Nanping. The study population was categorized into three groups: COVID-19 positive (n = 52), MP positive (n = 140), and co-infected (n = 22). Key biomarkers, including C-reactive protein (CRP), procalcitonin (PCT), interleukin- 6 (IL-6), and white blood cell (WBC) counts, were evaluated. Correlation analyses were conducted to assess relationships between biomarkers within each group. The random forest analysis was applied to evaluate the discriminative power of these biomarkers. The random forest model demonstrated high classification performance, with area under the ROC curve (AUC) scores of 0.86 (95% CI: 0.70-0.97) for COVID-19, 0.79 (95% CI: 0.64-0.92) for MP, 0.69 (95% CI: 0.50-0.87) for co-infections, and 0.90 (95% CI: 0.83-0.95) for the micro-average ROC. Additionally, the precision-recall curve for the random forest classifier showed a micro-average AUC of 0.80 (95% CI: 0.69-0.91). Confusion matrices highlighted the model's accuracy (0.77) and biomarker relationships. The SHAP feature importance analysis indicated that age (0.27), CRP (0.25), IL6 (0.14), and PCT (0.14) were the most significant predictors. The integration of computational methods, particularly random forest analysis, in evaluating clinical and biomarker data presents a promising approach for enhancing diagnostic processes for infectious diseases. Our findings support the use of specific biomarkers in differentiating between COVID-19 and MP, potentially leading to more targeted and effective diagnostic strategies. This study underscores the potential of machine learning techniques in improving disease classification in the era of precision medicine.

Data Commons Platforms: Addressing Challenges and Opportunities in Large-Scale Biomedical Data Integration and Analysis

Abstract: Data Commons Platforms are emerging as transformative tools in the landscape of medical research, offering a centralized, accessible, and scalable infrastructure for managing, analyzing, and sharing vast amounts of biomedical data. This article provides a comprehensive examination of these platforms, exploring their core functionalities, benefits, and challenges in the context of advancing medical discovery. We discuss how Data Commons Platforms accelerate research by facilitating datadriven hypothesis generation, enhancing collaboration among researchers, improving data accessibility, and ensuring data interoperability. The article also presents a reference architecture for these platforms, detailing the key components that enable their powerful capabilities. Furthermore, we address critical challenges such as data privacy, quality standardization, and ethical considerations, while also highlighting opportunities for future development. Through case studies and an analysis of emerging trends, this work demonstrates the potential of Data Commons Platforms to revolutionize biomedical research, paving the way for more efficient, collaborative, and impactful scientific discoveries in the era of big data and precision medicine.

MammOnc-DB, an integrative breast cancer data analysis platform for target discovery.

Breast cancer (BCa) is one of the most common malignancies among women worldwide. It is a complex disease that is characterized by morphological and molecular heterogeneity. In the early stages of the disease, most BCa cases are treatable, particularly hormone receptor-positive and HER2-positive tumors. Unfortunately, triple-negative BCa and metastases to distant organs are largely untreatable with current medical interventions. Recent advances in sequencing and proteomic technologies have improved our understanding of the molecular changes that occur during breast cancer initiation and progression. In this era of precision medicine, researchers and clinicians aim to identify subclass-specific BCa biomarkers and develop new targets and drugs to guide treatment. Although vast amounts of omics data including single cell sequencing data, can be accessed through public repositories, there is a lack of user-friendly platforms that integrate information from multiple studies. Thus, to meet the need for a simple yet effective and integrative BCa tool for multi-omics data analysis and visualization, we developed a comprehensive BCa data analysis platform called MammOnc-DB (http://resource.path.uab.edu/MammOnc-Home.html), comprising data from more than 20,000 BCa samples. MammOnc-DB was developed to provide a unique resource for hypothesis generation and testing, as well as for the discovery of biomarkers and therapeutic targets. The platform also provides pre- and post-treatment data, which can help users identify treatment resistance markers and patient groups that may benefit from combination therapy.

Targeted Therapies in Pancreatic Cancer: A New Era of Precision Medicine.

Pancreatic ductal adenocarcinoma (PDAC), a leading cause of cancer mortality in the United States, presents significant treatment challenges due to its late diagnosis and poor prognosis. Despite advances, the five-year survival rates remain dismally low, with only a fraction of patients eligible for potentially curative surgical interventions. This review aims to comprehensively examine the current landscape of targeted therapies in PDAC, focusing on recent developments in precision medicine approaches. We explore various molecular targets, including KRAS mutations, DNA damage repair deficiencies, mismatch repair pathway alterations, and rare genetic fusions. The review discusses emerging therapies, such as PARP inhibitors, immune checkpoint inhibitors, and novel targeted agents, like RET and NTRK inhibitors. We analyze the results of key clinical trials and highlight the potential of these targeted approaches in specific patient subgroups. Recent developments in PDAC research have emphasized precision oncology, facilitated by next-generation sequencing and the identification of genetic and epigenetic alterations. This approach tailors treatments to individual genetic profiles, improving outcomes and reducing side effects. Significant strides have been made in classifying PDAC into various subtypes, enhancing therapeutic precision. The identification of specific mutations in genes like KRAS, along with advancements in targeted therapies, including small molecule inhibitors, offers new hope. Furthermore, emerging therapies targeting DNA repair pathways and immunotherapeutic strategies also show promising results. As research evolves, integrating these targeted therapies with conventional treatments might improve survival rates and quality of life for PDAC patients, underscoring the shift towards a more personalized treatment paradigm.