CRISPR Advancements for Human Health.

CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) has emerged as a powerful gene editing technology that is revolutionizing biomedical research and clinical medicine. The CRISPR system allows scientists to rewrite the genetic code in virtually any organism. This review provides a comprehensive overview of CRISPR and its clinical applications. We first introduce the CRISPR system and explain how it works as a gene editing tool. We then highlight current and potential clinical uses of CRISPR in areas such as genetic disorders, infectious diseases, cancer, and regenerative medicine. Challenges that need to be addressed for the successful translation of CRISPR to the clinic are also discussed. Overall, CRISPR holds great promise to advance precision medicine, but ongoing research is still required to optimize delivery, efficacy, and safety.

Innovations in Medicine: Exploring ChatGPT's Impact on Rare Disorder Management.

Artificial intelligence (AI) is rapidly transforming the field of medicine, announcing a new era of innovation and efficiency. Among AI programs designed for general use, ChatGPT holds a prominent position, using an innovative language model developed by OpenAI. Thanks to the use of deep learning techniques, ChatGPT stands out as an exceptionally viable tool, renowned for generating human-like responses to queries. Various medical specialties, including rheumatology, oncology, psychiatry, internal medicine, and ophthalmology, have been explored for ChatGPT integration, with pilot studies and trials revealing each field's potential benefits and challenges. However, the field of genetics and genetic counseling, as well as that of rare disorders, represents an area suitable for exploration, with its complex datasets and the need for personalized patient care. In this review, we synthesize the wide range of potential applications for ChatGPT in the medical field, highlighting its benefits and limitations. We pay special attention to rare and genetic disorders, aiming to shed light on the future roles of AI-driven chatbots in healthcare. Our goal is to pave the way for a healthcare system that is more knowledgeable, efficient, and centered around patient needs.

Theranostics and artificial intelligence: new frontiers in personalized medicine.

The field of theranostics is rapidly advancing, driven by the goals of enhancing patient care. Recent breakthroughs in artificial intelligence (AI) and its innovative theranostic applications have marked a critical step forward in nuclear medicine, leading to a significant paradigm shift in precision oncology. For instance, AI-assisted tumor characterization, including automated image interpretation, tumor segmentation, feature identification, and prediction of high-risk lesions, improves diagnostic processes, offering a precise and detailed evaluation. With a comprehensive assessment tailored to an individual's unique clinical profile, AI algorithms promise to enhance patient risk classification, thereby benefiting the alignment of patient needs with the most appropriate treatment plans. By uncovering potential factors unseeable to the human eye, such as intrinsic variations in tumor radiosensitivity or molecular profile, AI software has the potential to revolutionize the prediction of response heterogeneity. For accurate and efficient dosimetry calculations, AI technology offers significant advantages by providing customized phantoms and streamlining complex mathematical algorithms, making personalized dosimetry feasible and accessible in busy clinical settings. AI tools have the potential to be leveraged to predict and mitigate treatment-related adverse events, allowing early interventions. Additionally, generative AI can be utilized to find new targets for developing novel radiopharmaceuticals and facilitate drug discovery. However, while there is immense potential and notable interest in the role of AI in theranostics, these technologies do not lack limitations and challenges. There remains still much to be explored and understood. In this study, we investigate the current applications of AI in theranostics and seek to broaden the horizons for future research and innovation.

[Personalized medicine in oncology]. / Personalisierte Medizin in der Onkologie.

Due to the considerable technological progress in molecular and genetic diagnostics as well as increasing insights into the molecular pathogenesis of diseases, there has been a fundamental paradigm shift in the past two decades from a "one-size-fits-all approach" to personalized, molecularly informed treatment strategies. Personalized medicine or precision medicine focuses on the genetic, physiological, molecular, and biochemical differences between individuals and considers their effects on the development, prevention, and treatment of diseases. As a pioneer of personalized medicine, the field of oncology is particularly noteworthy, where personalized diagnostics and treatment have led to lasting change in the treatment of cancer patients in recent years. In this article, the significant change towards personalized treatment concepts, especially in the field of personalized oncology, will be discussed and examined in more detail.

Converging on a Cure: The Roads to Predictive Immunotherapy.

SUMMARY: Convergence science teams integrating clinical, biological, engineering, and computational expertise are inventing new forecast systems to monitor and predict evolutionary changes in tumor and immune interactions during early cancer progression and therapeutic response. The resulting methods should inform a new predictive medicine paradigm to select adaptive immunotherapeutic regimens personalized to patients' tumors at a given time during their cancer progression for durable patient response.

Emerging Personalized Opportunities for Enhancing Translational Readthrough in Rare Genetic Diseases and Beyond.

Nonsense mutations trigger premature translation termination and often give rise to prevalent and rare genetic diseases. Consequently, the pharmacological suppression of an unscheduled stop codon represents an attractive treatment option and is of high clinical relevance. At the molecular level, the ability of the ribosome to continue translation past a stop codon is designated stop codon readthrough (SCR). SCR of disease-causing premature termination codons (PTCs) is minimal but small molecule interventions, such as treatment with aminoglycoside antibiotics, can enhance its frequency. In this review, we summarize the current understanding of translation termination (both at PTCs and at cognate stop codons) and highlight recently discovered pathways that influence its fidelity. We describe the mechanisms involved in the recognition and readthrough of PTCs and report on SCR-inducing compounds currently explored in preclinical research and clinical trials. We conclude by reviewing the ongoing attempts of personalized nonsense suppression therapy in different disease contexts, including the genetic skin condition epidermolysis bullosa.

Estado actual y retos futuros en el tratamiento de las enfermedades reumáticas / Current status and future challenges in the treatment of rheumatic diseases

Las nuevas estrategias, que incluyen el diagnóstico y el tratamiento tempranos, el enfoque de tratamiento dirigido a un objetivo, la remisión como ese objetivo principal del tratamiento, la participación de los pacientes en las decisiones terapéuticas, junto con el desarrollo de nuevos tratamientos efectivos, han cambiado las expectativas de los reumatólogos y de los pacientes con enfermedades reumáticas. Todavía existen, sin embargo, importantes desafíos tales como la seguridad a largo plazo de los tratamientos actuales y poder escoger tratamientos más individualizados y eficaces, de forma tal de elegir el mejor tratamiento para cada paciente. El futuro, como en el resto de la medicina, probablemente sea la prevención del desarrollo de enfermedades reumáticas. Discutiremos estos temas en esta revisión. (AU)

New strategies, including early diagnosis and treatment, targeted therapy, remission as the main objective of treatment, patient involvement in therapeutic decision-making, and the development of new effective therapies, have changed the expectations of rheumatologists and patients with rheumatic diseases.There are still serious challenges, such as the long-term safety of current treatments and the ability to make more individualized and effective treatments to choose the best treatment for each patient. The future, as that of the whole of medical science, will probably lie in preventing the development of rheumatic diseases. We will discuss these issues in this review. (AU)

Laying the groundwork for the Biobank of Rare Malignant Neoplasms at the service of the Hellenic Network of Precision Medicine on Cancer.

Biobanks constitute an integral part of precision medicine. They provide a repository of biospecimens that may be used to elucidate the pathophysiology, support diagnoses, and guide the treatment of diseases. The pilot biobank of rare malignant neoplasms has been established in the context of the Hellenic Network of Precision Medicine on Cancer and aims to enhance future clinical and/or research studies in Greece by collecting, processing, and storing rare malignant neoplasm samples with associated data. The biobank currently comprises 553 samples; 384 samples of hematopoietic and lymphoid tissue malignancies, 72 samples of pediatric brain tumors and 97 samples of malignant skin neoplasms. In this article, sample collections and their individual significance in clinical research are described in detail along with computational methods developed specifically for this project. A concise review of the Greek biobanking landscape is also delineated, in addition to recommended technologies, methodologies and protocols that were integrated during the creation of the biobank. This project is expected to re­enforce current clinical and research studies, introduce advances in clinical and genetic research and potentially aid in future targeted drug discovery. It is our belief that the future of medical research is entwined with accessible, effective, and ethical biobanking and that our project will facilitate research planning in the '­omic' era by contributing high­quality samples along with their associated data.

[Moving towards a personalized oncology: The contribution of genomic techniques and artificial intelligence in the use of circulating tumor biomarkers]. / En marche vers une oncologie personnalisée : l'apport des techniques génomiques et de l'intelligence artificielle dans l'usage des biomarqueurs tumoraux circulants.

Technological advances, in particular the development of high-throughput sequencing, have led to the emergence of a new generation of molecular biomarkers for tumors. These new tools have profoundly changed therapeutic management in oncology, with increasingly precise molecular characterization of tumors leading to increasingly personalized therapeutic targeting. Detection of circulating tumor cells and/or circulating tumor DNA in blood samples -so-called 'liquid biopsies'- can now provide a genetic snapshot of the patient's tumor through an alternative and less invasive procedure than biopsy of the tumor tissue itself. This procedure for characterizing and monitoring the disease in real time facilitates the search for possible relapses, the emergence of resistance, or emergence of a new therapeutic target. In the long term, it might also provide a means of early detection of cancer. These new approaches require the treatment of ever-increasing amounts of clinical data, notably, with the goal of calculating composite clinical-biological predictive scores. The use of artificial intelligence will be unavoidable in this domain, but it raises ethical questions and implications for the health-care system that will have to be addressed.

The Future of Somatostatin Receptor Ligands in Acromegaly.

Currently, the first-generation somatostatin receptor ligands (fg-SRLs), octreotide LAR and lanreotide autogel, are the mainstays of acromegaly treatment and achieve biochemical control in approximately 40% of patients and tumor shrinkage in over 60% of patients. Pasireotide, a second-generation SRL, shows higher efficacy with respect to both biochemical control and tumor shrinkage but has a worse safety profile. In this review, we discuss the future perspectives of currently available SRLs, focusing on the use of biomarkers of response and precision medicine, new formulations of these SRLs and new drugs, which are under development. Precision medicine, which is based on biomarkers of response to treatment, will help guide the decision-making process by allowing physicians to choose the appropriate drug for each patient and improving response rates. New formulations of available SRLs, such as oral, subcutaneous depot, and nasal octreotide, may improve patients' adherence to treatment and quality of life since there will be more options available that better suit each patient. Finally, new drugs, such as paltusotine, somatropin, ONO-5788, and ONO-ST-468, may improve treatment adherence and present higher efficacy than currently available drugs.

Overview of radiomics in prostate imaging and future directions.

Recent advancements in imaging technology and analysis methods have led to an analytic framework known as radiomics. This framework extracts comprehensive high-dimensional features from imaging data and performs data mining to build analytical models for improved decision-support. Its features include many categories spanning texture and shape; thus, it can provide abundant information for precision medicine. Many studies of prostate radiomics have shown promising results in the assessment of pathological features, prediction of treatment response, and stratification of risk groups. Herein, we aimed to provide a general overview of radiomics procedures, discuss technical issues, explain various clinical applications, and suggest future research directions, especially for prostate imaging.

Personalized Cell Therapy for Patients with Peripheral Arterial Diseases in the Context of Genetic Alterations: Artificial Intelligence-Based Responder and Non-Responder Prediction.

Stem/progenitor cell transplantation is a potential novel therapeutic strategy to induce angiogenesis in ischemic tissue, which can prevent major amputation in patients with advanced peripheral artery disease (PAD). Thus, clinicians can use cell therapies worldwide to treat PAD. However, some cell therapy studies did not report beneficial outcomes. Clinical researchers have suggested that classical risk factors and comorbidities may adversely affect the efficacy of cell therapy. Some studies have indicated that the response to stem cell therapy varies among patients, even in those harboring limited risk factors. This suggests the role of undetermined risk factors, including genetic alterations, somatic mutations, and clonal hematopoiesis. Personalized stem cell-based therapy can be developed by analyzing individual risk factors. These approaches must consider several clinical biomarkers and perform studies (such as genome-wide association studies (GWAS)) on disease-related genetic traits and integrate the findings with those of transcriptome-wide association studies (TWAS) and whole-genome sequencing in PAD. Additional unbiased analyses with state-of-the-art computational methods, such as machine learning-based patient stratification, are suited for predictions in clinical investigations. The integration of these complex approaches into a unified analysis procedure for the identification of responders and non-responders before stem cell therapy, which can decrease treatment expenditure, is a major challenge for increasing the efficacy of therapies.

Germline Sequencing Improves Tumor-Only Sequencing Interpretation in a Precision Genomic Study of Patients With Pediatric Solid Tumor.

PURPOSE: Molecular tumor profiling is becoming a routine part of clinical cancer care, typically involving tumor-only panel testing without matched germline. We hypothesized that integrated germline sequencing could improve clinical interpretation and enhance the identification of germline variants with significant hereditary risks. MATERIALS AND METHODS: Tumors from pediatric patients with high-risk, extracranial solid malignancies were sequenced with a targeted panel of cancer-associated genes. Later, germline DNA was analyzed for a subset of these genes. We performed a post hoc analysis to identify how an integrated analysis of tumor and germline data would improve clinical interpretation. RESULTS: One hundred sixty participants with both tumor-only and germline sequencing reports were eligible for this analysis. Germline sequencing identified 38 pathogenic or likely pathogenic variants among 35 (22%) patients. Twenty-five (66%) of these were included in the tumor sequencing report. The remaining germline pathogenic or likely pathogenic variants were single-nucleotide variants filtered out of tumor-only analysis because of population frequency or copy-number variation masked by additional copy-number changes in the tumor. In tumor-only sequencing, 308 of 434 (71%) single-nucleotide variants reported were present in the germline, including 31% with suggested clinical utility. Finally, we provide further evidence that the variant allele fraction from tumor-only sequencing is insufficient to differentiate somatic from germline events. CONCLUSION: A paired approach to analyzing tumor and germline sequencing data would be expected to improve the efficiency and accuracy of distinguishing somatic mutations and germline variants, thereby facilitating the process of variant curation and therapeutic interpretation for somatic reports, as well as the identification of variants associated with germline cancer predisposition.