Continuing Discoveries in Immunogenetics and Computational Immunology: An Update DOI
Giulia Russo, Elena Crispino, Esther M. Lafuente

et al.

Elsevier eBooks, Journal Year: 2024, Volume and Issue: unknown

Published: Jan. 1, 2024

Language: Английский

Leveraging artificial intelligence in vaccine development: A narrative review DOI Creative Commons
David B. Olawade,

Jennifer Teke,

Oluwaseun Fapohunda

et al.

Journal of Microbiological Methods, Journal Year: 2024, Volume and Issue: 224, P. 106998 - 106998

Published: July 15, 2024

Vaccine development stands as a cornerstone of public health efforts, pivotal in curbing infectious diseases and reducing global morbidity mortality. However, traditional vaccine methods are often time-consuming, costly, inefficient. The advent artificial intelligence (AI) has ushered new era design, offering unprecedented opportunities to expedite the process. This narrative review explores role AI development, focusing on antigen selection, epitope prediction, adjuvant identification, optimization strategies. algorithms, including machine learning deep learning, leverage genomic data, protein structures, immune system interactions predict antigenic epitopes, assess immunogenicity, prioritize antigens for experimentation. Furthermore, AI-driven approaches facilitate rational design immunogens identification novel candidates with optimal safety efficacy profiles. Challenges such data heterogeneity, model interpretability, regulatory considerations must be addressed realize full potential development. Integrating emerging technologies, single-cell omics synthetic biology, promises enhance precision scalability. underscores transformative impact highlights need interdisciplinary collaborations harmonization accelerate delivery safe effective vaccines against diseases.

Language: Английский

Citations

18

Vaccine design and development: Exploring the interface with computational biology and AI DOI

Ananya Ananya,

Darshan C. Panchariya, Anandakrishnan Karthic

et al.

International Reviews of Immunology, Journal Year: 2024, Volume and Issue: 43(6), P. 361 - 380

Published: July 10, 2024

Computational biology involves applying computer science and informatics techniques in to understand complex biological data. It allows us collect, connect, analyze data at a large scale build predictive models. In the twenty first century, computational resources along with Artificial Intelligence (AI) have been widely used various fields of sciences such as biochemistry, structural biology, immunology, microbiology, genomics handle massive for decision-making, including applications drug design vaccine development, one major areas focus human animal welfare. The knowledge available AI-enabled tools development can improve our ability conduct cutting-edge research. Therefore, this review article aims summarize important AI-based tools. Further, discusses limitations AI development.

Language: Английский

Citations

6

AI in autoimmune diseases: Transforming diagnosis and treatment DOI

Dipali Vikas Mane,

A Deshmukh,

Rohit Hanumant Ambare

et al.

Journal of Pharmaceutical and Biological Sciences, Journal Year: 2025, Volume and Issue: 12(2), P. 109 - 118

Published: Jan. 9, 2025

Because of their diverse clinical manifestations and intricate pathophysiology, autoimmune diseases which are defined by the immune system wrongly attacking healthy tissues present serious difficulties. Artificial intelligence (AI) has shown revolutionary promise in this field, especially improving diagnostic precision, facilitating tailored treatment plans, offering real-time illness tracking. This paper highlights AI's role assessing various datasets pertaining to function pathology while critically examining applications AI therapy diseases. In order find new biomarkers enable early accurate detection disorders, advanced approaches such as machine learning deep have proven essential. AI-powered predictive models demonstrated predicting periods remission disease flares, allowing for prompt focused modifications. Furthermore, accelerating identification promising therapeutic candidates lowering related costs, is transforming drug discovery repurposing. However, issues including data heterogeneity, algorithmic transparency, patient confidence AI-driven suggestions limit full potential need ethical frameworks interdisciplinary collaboration these limits suggesting solutions. shows transform diagnosis, treatment, management disorders combining recent developments future applications. will pave way a where healthcare solutions proactive, accurate, individualized.

Language: Английский

Citations

0

A Systemic Immunoinformatics Approach to Design Combinatorial Multiepitope Vaccine Candidates against Vector-borne Bacterial Infections Exploiting the Proteomes of the Causative Agent and Vector for Scrub typhus DOI Creative Commons

Swarna Shaw,

Arka Bagchi,

Debyani Ruj

et al.

The Microbe, Journal Year: 2025, Volume and Issue: unknown, P. 100324 - 100324

Published: April 1, 2025

Language: Английский

Citations

0

AI in the development of vaccines for emerging and re-emerging diseases DOI
Rita Elizabeth Velastegui-Hernández, Verónica Gabriela Salinas Velasteguí, Diana Catalina Velasteguí Hernández

et al.

Salud Ciencia y Tecnología, Journal Year: 2025, Volume and Issue: 4

Published: Jan. 15, 2025

Introduction: The integration of artificial intelligence (AI) into vaccine development has revolutionized traditional methodologies, significantly enhancing the speed, precision, and scalability immunological research. Emerging re-emerging infectious diseases, driven by zoonotic spillovers, antimicrobial resistance, global environmental changes, pose substantial challenges. Addressing these requires innovative approaches, with AI playing a pivotal role in advancing solutions.Development: applications vaccinology include antigen detection, adjuvant optimization, immune response simulation. Deep learning algorithms streamline identification immunogenic targets conserved antigens, enabling for highly mutable pathogens such as SARS-CoV-2, HIV, influenza. Case studies demonstrate AI's transformative impact, including its rapid creation mRNA vaccines COVID-19, promising antigens malaria, enhanced efficacy influenza through predictive modeling. However, challenges unequal access to technology, biases data models, ethical concerns regarding genomic privacy persist. Recommendations address barriers increasing diversity, strengthening frameworks, investing infrastructure democratize AI-driven innovations.Conclusions: ability reduce time cost, improve enable personalized immunization strategies positions it cornerstone modern vaccinology. With continued advancements equitable implementation, holds potential reshape development, pandemic preparedness, longstanding public health disparities globally.

Language: Английский

Citations

0

Vaccine Strategies Against RNA Viruses: Current Advances and Future Directions DOI Creative Commons
Kuei-Ching Hsiung, Huan-Jung Chiang,

Sebastian Reinig

et al.

Vaccines, Journal Year: 2024, Volume and Issue: 12(12), P. 1345 - 1345

Published: Nov. 28, 2024

The development of vaccines against RNA viruses has undergone a rapid evolution in recent years, particularly driven by the COVID-19 pandemic. This review examines key roles that viruses, with their high mutation rates and zoonotic potential, play fostering vaccine innovation. We also discuss both traditional modern platforms impact new technologies, such as artificial intelligence, on optimizing immunization strategies. evaluates various platforms, ranging from approaches (inactivated live-attenuated vaccines) to technologies (subunit vaccines, viral bacterial vectors, nucleic acid mRNA DNA, phage-like particle vaccines). To illustrate these platforms’ practical applications, we present case studies developed for SARS-CoV-2, influenza, Zika, dengue. Additionally, assess role intelligence predicting mutations enhancing design. underscore successful application RNA-based fight COVID-19, which saved millions lives. Current clinical trials dengue continue show promise, highlighting growing efficacy adaptability platforms. Furthermore, is driving improvements candidate optimization providing predictive models evolution, our ability respond future outbreaks. Advances technology, success highlight potential combating viruses. Ongoing demonstrate platform adaptability, while enhances design mutations. Integrating innovations One Health approach, unites human, animal, environmental health, essential strengthening global preparedness virus threats.

Language: Английский

Citations

2

Artificial Intelligence and Machine Learning in Spine Research: A New Frontier DOI Creative Commons
Min Cheol Chang

Bioengineering, Journal Year: 2024, Volume and Issue: 11(9), P. 915 - 915

Published: Sept. 13, 2024

Artificial Intelligence (AI) refers to the creation of computer systems capable performing tasks typically requiring human intelligence [...].

Language: Английский

Citations

1

Advancements in Human Vaccine Development: From Traditional to Modern Approaches DOI
Mourad Aribi

IntechOpen eBooks, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 19, 2024

Vaccinology, the scientific discipline focused on vaccines, has evolved from combating infectious diseases to addressing a wide array of broad spectrum health concerns, including autoimmune disorders, neurodegenerative diseases, and allergies, with promising therapeutic vaccines for cancer utilizing tumor-infiltrating lymphocytes (TILs) adoptive cell therapy, like chimeric antigen receptor T-cell (CAR-T-cells), CAR-natural killer cells (CAR-NK cells), CAR-macrophages (CAR-M), as well necrotic necroptotic cells. Additionally, ongoing research endeavors aim develop anti-addiction vaccines. This chapter offers comprehensive exploration vaccinology, encompassing fundamental immunity concepts, role adjuvants, various vaccine types. It traces evolution development traditional methods modern innovations messenger ribonucleic acid (mRNA) exemplified by those developed coronavirus disease 2019 (COVID-19), which offer rapid adaptability emerging variants. The significance measuring neutralizing antibodies in assessing efficacy effectiveness, is crucial guiding epidemic responses, underscored. By delving into historical contemporary developments, current challenges, envisioning future directions, this fosters deeper understanding vaccinology encourages critical reflection innovative solutions global challenges.

Language: Английский

Citations

1

Systems Biology Resources DOI

Anandhu Presannan,

Gautham Manoj,

Pramod P Nair

et al.

Elsevier eBooks, Journal Year: 2024, Volume and Issue: unknown

Published: Jan. 1, 2024

Language: Английский

Citations

0

Analytical Techniques for Characterizing Tumor-Targeted Antibody-Functionalized Nanoparticles DOI Creative Commons
Ana Camila Marques, Paulo Costa, Sérgia Velho

et al.

Life, Journal Year: 2024, Volume and Issue: 14(4), P. 489 - 489

Published: April 10, 2024

The specific interaction between cell surface receptors and corresponding antibodies has driven opportunities for developing targeted cancer therapies using nanoparticle systems. It is challenging to design develop such nanomedicines antibody ligands, as the final nanoconjugate's specificity hinges on cohesive functioning of its components. multicomponent nature antibody-conjugated nanoparticles also complicates characterization process. Regardless type nanoparticle, it essential perform physicochemical establish a solid foundation knowledge suitable preclinical studies. A meaningful evaluation should include determining quantity orientation antibodies, confirming antibodies' integrity following attachment, assessing immunoreactivity obtained nanoconjugates. In this review, authors describe various techniques (electrophoresis, spectroscopy, colorimetric assays, immunoassays, etc.) used analyze properties functionalized with discuss main results.

Language: Английский

Citations

0