https://www.atjournal.ir/ Advanced Therapies Journal en daily 1 Mon, 30 Mar 2026 00:00:00 +0330 Mon, 30 Mar 2026 00:00:00 +0330 https://www.atjournal.ir/article_243076.html Breast cancer is one of the most common types of cancer among women. This disease poses serious clinical challenges due to its variable response to treatment and biological heterogeneity. Therefore, conventional therapies fail to complement the characteristics of specific tumors, limiting the effectiveness of treatment even in personalized therapies. In recent years, powerful tools such as artificial intelligence(AI), have emerged to advance personalized medicine, especially in the field of breast cancer, with the help of which complex biomedical data can be better analyzed.In this article, we aim to provide a comprehensive review of the impact of AI on early detection, prognosis and recurrence assessment, response prediction, biomarker discovery, and clinical decision-making in breast cancer. We will also explore how AI-based imaging analysis can help improve diagnostic accuracy, while integrated multi-omics models can enhance treatment decision-making and risk stratification. Emerging approaches such as explainable AI, radiogenomics, and AI-based multi-omics integration are also highlighted as key drivers in this field.Despite encouraging results, significant challenges remain, including data heterogeneity, limited external and prospective validation, algorithmic bias, interpretability concerns, and ethical and regulatory barriers. Addressing these limitations through standardized data protocols, transparent and explainable models, and multi-center validation studies is essential for safe and equitable implementation. Overall, AI holds substantial potential to transform breast cancer management toward a more predictive, preventive, and patient-centered paradigm, provided that technological innovation is aligned with robust clinical validation and interdisciplinary collaboration. https://www.atjournal.ir/article_243077.html Personalized antibiotic therapy is transforming infectious disease management by tailoring antimicrobial regimens to individual patient and pathogen characteristics. Unlike conventional one-size-fits-all approaches, personalized strategies integrate host genetic profiles, pharmacokinetics/pharmacodynamics (PK/PD), microbial susceptibility, and biomarker data to optimize therapeutic outcomes while minimizing adverse effects and antimicrobial resistance (AMR). Conventional antibiotic administration often relies on empirical prescribing and fixed dosing, resulting in suboptimal exposure, treatment failures, and selection of resistant strains. Advanced drug delivery systems, including nanocarriers, liposomes, polymeric micelles, and stimulus-responsive platforms, enhance site-specific targeting, controlled release, biofilm penetration, and intracellular delivery, improving antibiotic efficacy and safety. Biomarker-guided selection and PK/PD-informed adaptive dosing allow dynamic adjustments based on infection progression and individual patient responses. Clinical studies demonstrate that these approaches reduce hospital stays, lower treatment failures, and minimize systemic toxicity. Future directions focus on integrating smart delivery systems, biosensors, artificial intelligence, and genomic/microbiome analyses to guide individualized therapy, enabling rapid, precise, and responsive antibiotic administration. Gene-targeted strategies, such as CRISPR-based antimicrobial payloads, offer additional potential to directly disrupt resistance mechanisms. Collectively, these innovations represent a shift toward precision antimicrobial therapy, addressing the limitations of conventional regimens, improving patient-centered outcomes, and mitigating the global AMR crisis. https://www.atjournal.ir/article_243078.html Phenylketonuria (PKU) is an inherited metabolic disorder characterized by deficient activity of phenylalanine hydroxylase, leading to toxic accumulation of phenylalanine. Current therapies rely primarily on dietary restriction or enzyme substitution, but long-term compliance and systemic side effects remain challenges. Recent advances in synthetic biology and probiotic engineering have enabled the development of live biotherapeutic products (LBPs) capable of in situ metabolic correction. Lactococcus lactis, a Gram-positive, non-colonizing, and generally recognized as safe (GRAS) bacterium, has emerged as a promising chassis for plasmid-based delivery of therapeutic enzymes. This review explores the biological features of L. lactis, plasmid engineering strategies, mechanisms of gastrointestinal delivery, preclinical and clinical evidence supporting microbial therapeutics, biosafety and regulatory considerations, and future perspectives for PKU treatment. Emphasis is placed on plasmid-mediated expression of phenylalanine ammonia-lyase (PAL) and strategies to enhance luminal phenylalanine degradation while maintaining host safety. The review integrates recent findings and key studies over the past five years to highlight the translational potential of L. lactis in metabolic biotherapy. https://www.atjournal.ir/article_243079.html Multiple sclerosis (MS) represents a complex, immune-driven CNS condition marked by significant clinical and biological diversity, which complicates diagnostic and prognostic efforts. In recent years, the paradigm of MS research has evolved through rapid biomarker breakthroughs, transitioning from a reliance on traditional neuroimaging to holistic molecular and multi-modal profiling. Liquid-based indicators, such as glial fibrillary acidic protein (GFAP), neurofilament light chain (NfL), and chitinase-3-like protein 1 (CHI3L1), have emerged as robust correlates of neuroaxonal damage and astrocytic involvement. Parallel to this, novel imaging features notably paramagnetic rim lesions and the central vein sign have increased diagnostic precision. Furthermore, the integration of multi-omics including genomics and metabolomics allows for a more granular understanding of the immune and degenerative pathways in MS. By leveraging systems biology and machine learning, researchers can now identify synergistic biomarker signatures that surpass individual markers in forecasting disease activity and therapeutic outcomes. However, achieving precision neurology requires overcoming obstacles in assay harmonization and clinical validation. https://www.atjournal.ir/article_243080.html Next-generation sequencing (NGS) has revolutionized both clinical practice and biomedical research by providing rapid, highly accurate, and high-throughput genomic analysis. This review explores the technological progression of NGS, its contribution to mapping human genomic diversity, and its growing utility in areas such as precision medicine, oncology, pharmacogenomics, the diagnosis of rare disorders, and clinical decision support. By detecting single nucleotide variants, copy number changes, structural variations, and complex genomic rearrangements, NGS has deepened our understanding of disease heterogeneity and facilitated the creation of targeted treatment plans. In the field of oncology, the adoption of NGS has improved tumor classification, enabled therapies tailored to specific genetic profiles, and allowed for real-time monitoring via circulating tumor DNA analysis. In pharmacogenomics, NGS has improved drug response prediction by identifying both common and rare variants affecting drug metabolism. Additionally, its application in rare diseases has shortened diagnostic odysseys and accelerated novel gene discovery. Despite challenges related to data interpretation, ethical governance, regulatory oversight, and data management, continuous technological innovation and multi-omics integration are strengthening the clinical utility of NGS. Collectively, NGS serves as a foundational pillar of personalized medicine, shaping a more predictive, preventive, and precision-oriented healthcare paradigm. https://www.atjournal.ir/article_243083.html In various cancers, the KRAS pathway is central to governing cellular proliferation, differentiation, and survival. Despite the comparative rarity of KRAS mutations in breast malignancies, aberrant pathway activity significantly influences tumor progression, immune modulation, and clinical resistance. In the present review, we synthesize current knowledge on KRAS signaling in breast cancer, focusing on its prevalence and the molecular drivers behind its activation. Our discussion extends to how dysregulated cascades associated with KRAS, such as PI3K-AKT-mTOR and RAF-MEK-ERK, impact the biological landscape of the tumor beyond mere mutational status.Furthermore, the review explores the transformative impact of omics technologies and artificial intelligence (AI) in decoding KRAS-driven molecular networks. Recent progress in genomics, transcriptomics, proteomics, and especially multiomics data integration has enabled a more comprehensive understanding of KRAS pathway dynamics. At the same time, machine learning and deep learning approaches have significantly improved tumor classification, biomarker identification, and prediction of therapeutic outcomes. Emerging AIdriven multimodal frameworks that combine molecular profiles, histopathological features, and imaging data show great promise for enhancing prognostic assessment and developing personalized treatment strategies in breast cancer.Nevertheless, ongoing challenges such as data heterogeneity, limited interpretability of models, insufficient external validation, and ethical considerations remain to be addressed. Future efforts are oriented toward explainable AI, federated learning, and clinically validated predictive systems to establish a robust foundation for AIenabled precision oncology in breast cancer. Ultimately, integrating KRAS pathway biology with advanced AI analytics could accelerate the evolution of individualized diagnostic and therapeutic approaches in breast cancer management.