CURRENT ISSUE

ABSTRACT: Background: CRISPR-Cas9 has revolutionized genome editing by providing an efficient, versatile, and comparatively accessible platform for targeted genetic modification. Its rapid progression from laboratory innovation to clinical application has generated substantial interest across multiple therapeutic domains, while simultaneously raising significant safety, ethical, and regulatory concerns.
Objective: This scoping review aimed to comprehensively map current evidence regarding the therapeutic applications, safety challenges, and ethical frameworks associated with CRISPR-Cas9 in human health.
Methods: A scoping review methodology guided by the Arksey and O’Malley framework and PRISMA-ScR guidelines was employed. Comprehensive searches were conducted across PubMed, Dimensions, and Embase for peer-reviewed English-language studies published between 2015 and 2026. Eligible studies focused on CRISPR-Cas9 applications in human health, including therapeutic interventions, safety evaluations, ethical analyses, and regulatory considerations. A total of 32 studies met the inclusion criteria and underwent thematic synthesis.
Results: CRISPR-Cas9 demonstrated substantial therapeutic promise across hematologic disorders, metabolic diseases, ophthalmologic conditions, oncology, immunotherapy, and rare genetic disorders. The most clinically advanced applications were observed in sickle cell disease, β-thalassemia, hereditary transthyretin amyloidosis, and hereditary angioedema, where clinical trials showed durable and potentially curative outcomes. However, major barriers remain, including off-target effects, genomic instability, delivery inefficiencies, immunogenicity, and limited long-term safety data. Ethical and regulatory concerns were prominent, particularly regarding germline editing, health equity, global governance, and accessibility.
Conclusion: CRISPR-Cas9 has demonstrated considerable clinical potential across a range of therapeutic applications, particularly for selected monogenic disorders in which the strongest clinical evidence is currently available. Although important advances have been achieved, broader clinical implementation will require continued improvements in editing precision, long-term safety evaluation, delivery technologies, ethical oversight, equitable access, and harmonized regulatory frameworks.
 

ABSTRACT: The human microbiome functions as a metabolically active organ whose biochemical output is continuously integrated with host physiology. Conventional microbiome surveys, built largely on taxonomic profiling, capture community composition and diversity but resolve neither the functional capacity of these communities nor the bidirectional metabolic exchange that links them to the host. A central limitation is that taxonomy is a poor proxy for function: phylogenetically distinct organisms can perform equivalent reactions, and closely related taxa can diverge metabolically. Resolving host microbiome interactions, therefore, requires integration across heterogeneous, high-dimensional molecular layers, such as metagenomics, metatranscriptomics, proteomics, metabolomics, and host genomic and phenotypic data at a scale and complexity that exceeds classical analytical pipelines. Artificial intelligence (AI) has emerged as a complementary framework for this problem. Machine learning, deep learning, and graph-based models can integrate multi-omics data, infer latent metabolic structure, predict microbial functional potential, and model microbe-metabolite-host relationships as connected networks rather than isolated parts. These approaches have sharpened the discovery of disease-associated microbial and metabolic signatures and candidate therapeutic targets, and they underpin emerging precision medicine applications, including individualized risk stratification, biomarker discovery, and treatment response prediction. Substantial barriers remain, however, including incomplete and non-standardized reference data, limited model interpretability, vulnerability to bias and overfitting, and a shortage of prospective clinical validation. Continued progress in foundation models, real-time microbiome monitoring, and patient-specific metabolic modelling is expected to move the field from descriptive association toward predictive, preventive, and personalized clinical application.