Journal of Pharmaceutical Innovation, Journal Year: 2025, Volume and Issue: 20(2)
Published: March 6, 2025
Language: Английский
Critical Reviews in Oncology/Hematology, Journal Year: 2025, Volume and Issue: unknown, P. 104653 - 104653
Published: Feb. 1, 2025
Language: Английский
Citations
2
Molecular Aspects of Medicine, Journal Year: 2025, Volume and Issue: 101, P. 101335 - 101335
Published: Jan. 1, 2025
Renal cell carcinoma (RCC) is a malignant tumor with highly heterogeneous and complex molecular mechanisms. Through systematic analysis of TCGA, COSMIC other databases, 24 mutated genes closely related to RCC were screened, including VHL, PBRM1, BAP1 SETD2, which play key roles in signaling pathway transduction, chromatin remodeling DNA repair. The PI3K/AKT/mTOR particularly important the pathogenesis RCC. Mutations such as PIK3CA, MTOR PTEN are associated metabolic abnormalities proliferation. Clinically, mTOR inhibitors VEGF-targeted drugs have shown significant efficacy personalized therapy. Abnormal regulation reprogramming, especially glycolysis glutamine pathways, provides cells continuous energy supply survival advantages, GLS1 promising results preclinical studies. This paper also explores potential immune checkpoint combination targeted drugs, well application nanotechnology drug delivery In addition, unique mechanisms revealed individualized therapeutic strategies explored for specific subtypes TFE3, TFEB rearrangement type SDHB mutant type. review summarizes common gene mutations their mechanisms, emphasizes diagnosis, treatment prognosis, looks forward prospects multi-pathway therapy, immunotherapy treatment, providing theoretical support clinical guidance new development.
Language: Английский
Citations
1
Information, Journal Year: 2025, Volume and Issue: 16(2), P. 131 - 131
Published: Feb. 11, 2025
Integrating artificial intelligence (AI) into pharmacy operations and drug discovery represents a groundbreaking milestone in healthcare, offering unparalleled opportunities to revolutionize medication management, accelerate development, deliver truly personalized patient care. This review examines the pivotal impact of AI critical domains, including repurposing, clinical trials, pharmaceutical productivity enhancement. By significantly reducing human workload, improving precision, shortening timelines, empowers industry achieve ambitious objectives efficiently. study delves tools methodologies enabling implementation, addressing ongoing challenges such as data privacy, algorithmic transparency, ethical considerations while proposing actionable strategies overcome these barriers. Furthermore, it offers insights future pharmacy, highlighting its potential foster innovation, enhance efficiency, improve outcomes. research is grounded rigorous methodology, employing advanced collection techniques. A comprehensive literature was conducted using platforms PubMed, Semantic Scholar, multidisciplinary databases, with AI-driven algorithms refining retrieval relevant up-to-date studies. Systematic scoping incorporated diverse perspectives from medical, pharmaceutical, computer science leveraging natural language processing for trend analysis thematic content coding identify patterns, challenges, emerging applications. Modern visualization synthesized findings explicit graphical representations, view key role shaping healthcare.
Language: Английский
Citations
1
Pharmaceutics, Journal Year: 2025, Volume and Issue: 17(3), P. 375 - 375
Published: March 15, 2025
Recent progress in material science has led to the development of new drug delivery systems that go beyond conventional approaches and offer greater accuracy convenience application therapeutic agents. This review discusses evolutionary role nanocarriers, hydrogels, bioresponsive polymers enhanced release, target accuracy, bioavailability. Oncology, chronic disease management, vaccine are some applications explored this paper show how these materials improve results, counteract multidrug resistance, allow for sustained localized treatments. The also translational barriers bringing advanced into clinical setting, which include issues biocompatibility, scalability, regulatory approval. Methods overcome challenges surface modifications reduce immunogenicity, scalable production methods such as microfluidics, harmonization systems. In addition, convergence artificial intelligence (AI) machine learning (ML) is opening frontiers personalized medicine. These technologies predictive modeling real-time adjustments optimize needs individual patients. use can be applied rare underserved diseases; thus, strategies gene therapy, orphan drugs development, global distribution may hopes millions
Language: Английский
Citations
1
Biomedical & Pharmacology Journal, Journal Year: 2025, Volume and Issue: 18(December Spl Edition), P. 283 - 294
Published: Jan. 20, 2025
Artificial intelligence (AI) has emerged as a transformative force in personalized healthcare and precision medicine over the past decade. AI techniques like machine learning, deep natural language processing make possible study of huge quantities heterogeneous patient records from electronic health records, genomic profiles, wearable devices, clinical trials. This allows for more accurate disease prediction, treatment planning, tailored drug discovery. Key areas impact include AI-driven biomarker discovery, virtual screening, de novo design, pharmacogenomics. The integration is revolutionizing multiple aspects medicine, identifying novel therapeutic targets to optimizing trial design dosing. algorithms can detect subtle patterns complex biological data, predict drug-target interactions, simulate molecular behaviour accelerate typically costly time-consuming development process. However, challenges remain around data quality, privacy, algorithmic bias, equitable implementation. Ethical considerations regarding genetic discrimination informed consent also need be carefully addressed. review examines current applications, challenges, future directions advancing patient-specific therapies development.
Language: Английский
Citations
1
Biomolecules and Biomedicine, Journal Year: 2025, Volume and Issue: unknown
Published: Jan. 14, 2025
Artificial intelligence (AI) has become a powerful tool in biochemistry, greatly enhancing research capabilities by enabling the analysis of complex datasets, predicting molecular interactions, and accelerating drug discovery. As AI continues to evolve, its applications biochemistry are poised expand, revolutionizing both theoretical applied research. This review explores current potential with focus on data analysis, modeling, enzyme engineering, metabolic pathway studies. Key techniques—such as machine learning algorithms, natural language processing, AI-based modeling—are discussed. The also highlights emerging areas benefiting from AI, including personalized medicine synthetic biology. methodology involves an extensive existing literature, particularly peer-reviewed studies biochemistry. AI-driven tools like AlphaFold, which have significantly advanced protein structure prediction, evaluated alongside AI’s role expediting addresses challenges such quality, model interpretability, ethical considerations. Results indicate that expanded scope biochemical facilitating large-scale simulations, opening new avenues inquiry. However, remain, handling concerns. In conclusion, is transforming driving innovation expanding possibilities. Future advancements interdisciplinary collaboration, integration automated techniques will be crucial fully unlocking advancing
Language: Английский
Citations
0
Journal of Drug Delivery Science and Technology, Journal Year: 2025, Volume and Issue: unknown, P. 106745 - 106745
Published: Feb. 1, 2025
Language: Английский
Citations
0
Advanced Intelligent Systems, Journal Year: 2025, Volume and Issue: unknown
Published: March 2, 2025
Materials science has traditionally relied on a combination of experimental techniques and theoretical modeling to discover develop new materials with desired properties. However, these processes can be time‐consuming, resource‐intensive, often limited by the complexity material systems. The advent artificial intelligence (AI), particularly machine learning, revolutionized offering powerful tools accelerate discovery, design, characterization novel materials. AI not only enhances predictive properties but also streamlines data analysis in like X‐Ray diffraction, Raman spectroscopy, scanning probe microscopy, electron microscopy. By leveraging large datasets, algorithms identify patterns, reduce noise, predict behavior unprecedented accuracy. In this review, recent advancements applications across various domains science, including synchrotron studies, microscopies, metamaterials, atomistic modeling, molecular drug are highlighted. It is discussed how AI‐driven methods reshaping field, making discovery more efficient, paving way for breakthroughs design real‐time analysis.
Language: Английский
Citations
0
Applied Materials Today, Journal Year: 2025, Volume and Issue: 43, P. 102672 - 102672
Published: March 13, 2025
Language: Английский
Citations
0
Journal of Clinical Medicine, Journal Year: 2025, Volume and Issue: 14(7), P. 2515 - 2515
Published: April 7, 2025
Background/Objectives: Bipolar disorder (BD) is a complex and chronic mental health condition that poses significant challenges for both patients healthcare providers. Traditional treatment methods, including medication therapy, remain vital, but there increasing interest in the application of artificial intelligence (AI) to enhance BD management. AI has potential improve mood episode prediction, personalize plans, provide real-time support, offering new opportunities managing more effectively. Our primary objective was explore role transforming management BD, specifically tracking, personalized regimens. Methods: To management, we conducted review recent literature using key search terms. We included studies discussed applications personalization. The were selected based on their relevance AI's with attention PICO criteria: Population-individuals diagnosed BD; Intervention-AI tools personalization, support; Comparison-traditional methods (when available); Outcome-measures effectiveness, improvements patient care. Results: findings from research reveal promising developments use Studies suggest AI-powered can enable proactive care, improving outcomes reducing burden professionals. ability analyze data wearable devices, smartphones, even social media platforms provides valuable insights early detection dynamic adjustments. Conclusions: While still its stages, it presents transformative However, further development are crucial fully realize supporting optimizing efficacy.
Language: Английский
Citations
0