Advances in mRNA LNP-Based Cancer Vaccines: Mechanisms, Formulation Aspects, Challenges, and Future Directions DOI Open Access
Eslam Ramadan, Ali Ahmed, Youssef W. Naguib

и другие.

Journal of Personalized Medicine, Год журнала: 2024, Номер 14(11), С. 1092 - 1092

Опубликована: Ноя. 4, 2024

After the COVID-19 pandemic, mRNA-based vaccines have emerged as a revolutionary technology in immunization and vaccination. These shown remarkable efficacy against virus opened up avenues for their possible application other diseases. This has renewed interest investment mRNA vaccine research development, attracting scientific community to explore all its applications beyond infectious Recently, researchers focused on possibility of adapting this vaccination approach cancer immunotherapy. While there is huge potential, challenges still remain design optimization synthetic molecules lipid nanoparticle delivery system required ensure adequate elicitation immune response successful eradication tumors. review points out basic mechanisms mRNA-LNP immunotherapy recent approaches design. displays current modifications components how these factors affect efficacy. Furthermore, discusses future directions clinical treatment.

Язык: Английский

Improving generalizability for MHC-I binding peptide predictions through geometric deep learning DOI Creative Commons
Dario F. Marzella, Giulia Crocioni, Tadija Radusinović

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2023, Номер unknown

Опубликована: Дек. 5, 2023

The interaction between peptides and major histocompatibility complex (MHC) molecules is pivotal in autoimmunity, pathogen recognition tumor immunity. Recent advances cancer immunotherapies demand for more accurate computational prediction of MHC-bound peptides. We address the generalizability challenge peptide predictions, revealing limitations current sequence-based approaches. Our structure-based methods leveraging geometric deep learning (GDL) demonstrated promising improvement across unseen MHC alleles. Further, we tackle data efficiency by introducing a self-supervised approach on structures (3D-SSL). Without being exposed to any binding affinity data, our 3D-SSL outperforms trained ~90 times datapoints. Finally, demonstrate resilience GDL biases an Hepatitis B virus vaccine immunopeptidomics case study. This proof-of-concept study highlights methods' potential enhance efficiency, with important implications data-intensive fields like T-cell receptor specificity paving way enhanced comprehension manipulation immune responses.

Язык: Английский

Процитировано

4

Harnessing Artificial Intelligence for the Detection and Management of Colorectal Cancer Treatment DOI
Michael Jacob, Ruhananhad P. Reddy,

Ricardo Isaiah Garcia

и другие.

Cancer Prevention Research, Год журнала: 2024, Номер 17(11), С. 499 - 515

Опубликована: Июль 30, 2024

Abstract Currently, eight million people in the United States suffer from cancer and it is a major global health concern. Early detection interventions are urgently needed for all cancers, including colorectal cancer. Colorectal third most common type of worldwide. Based on diagnostic efforts to general awareness lifestyle choices, understandable why so prevalent today. There notable lack concerning impact this its connection elements, as well sometimes mistaking symptoms different gastrointestinal condition. Artificial intelligence (AI) may assist early The usage AI has exponentially grown healthcare through extensive research, since clinical implementation, succeeded improving patient lifestyles, modernizing processes, innovating current treatment strategies. Numerous challenges arise patients with oncologists alike during treatment. For initial screening phases, conventional methods often result misdiagnosis. Moreover, after detection, determining course which can contribute delays. This article touches recent advancements application while shedding light disease

Язык: Английский

Процитировано

1

DeepNeoAG: Neoantigen epitope prediction from melanoma antigens using a synergistic deep learning model combining protein language models and multi-window scanning convolutional neural networks DOI

Cheng-Che Chuang,

Yuchen Liu, Yu‐Yen Ou

и другие.

International Journal of Biological Macromolecules, Год журнала: 2024, Номер unknown, С. 136252 - 136252

Опубликована: Окт. 1, 2024

Язык: Английский

Процитировано

1

Prospects and challenges of neoantigen applications in oncology DOI
Ranran Shi,

Ling Ran,

Yuan Tian

и другие.

International Immunopharmacology, Год журнала: 2024, Номер 143, С. 113329 - 113329

Опубликована: Окт. 14, 2024

Язык: Английский

Процитировано

1

Advances in mRNA LNP-Based Cancer Vaccines: Mechanisms, Formulation Aspects, Challenges, and Future Directions DOI Open Access
Eslam Ramadan, Ali Ahmed, Youssef W. Naguib

и другие.

Journal of Personalized Medicine, Год журнала: 2024, Номер 14(11), С. 1092 - 1092

Опубликована: Ноя. 4, 2024

After the COVID-19 pandemic, mRNA-based vaccines have emerged as a revolutionary technology in immunization and vaccination. These shown remarkable efficacy against virus opened up avenues for their possible application other diseases. This has renewed interest investment mRNA vaccine research development, attracting scientific community to explore all its applications beyond infectious Recently, researchers focused on possibility of adapting this vaccination approach cancer immunotherapy. While there is huge potential, challenges still remain design optimization synthetic molecules lipid nanoparticle delivery system required ensure adequate elicitation immune response successful eradication tumors. review points out basic mechanisms mRNA-LNP immunotherapy recent approaches design. displays current modifications components how these factors affect efficacy. Furthermore, discusses future directions clinical treatment.

Язык: Английский

Процитировано

1