Leveraging Artificial Intelligence to Expedite Antibody Design and Enhance Antibody–Antigen Interactions

Bioengineering, Journal Year: 2024, Volume and Issue: 11(2), P. 185 - 185

Published: Feb. 15, 2024

This perspective sheds light on the transformative impact of recent computational advancements in field protein therapeutics, with a particular focus design and development antibodies. Cutting-edge methods have revolutionized our understanding protein-protein interactions (PPIs), enhancing efficacy therapeutics preclinical clinical settings. Central to these is application machine learning deep learning, which offers unprecedented insights into intricate mechanisms PPIs facilitates precise control over functions. Despite advancements, complex structural nuances antibodies pose ongoing challenges their optimization. Our review provides comprehensive exploration latest approaches, including language models diffusion techniques, role surmounting challenges. We also present critical analysis methods, offering drive further progress this rapidly evolving field. The paper includes practical recommendations for supplemented independent benchmark studies. These studies key performance metrics such as accuracy ease program execution, providing valuable resource researchers engaged antibody development. Through detailed perspective, we aim contribute advancement design, equipping tools knowledge navigate complexities

Language: Английский

---

Editorial: Infectious Disease Surveillance Using Artificial Intelligence (AI) and its Role in Epidemic and Pandemic Preparedness

Medical Science Monitor, Journal Year: 2023, Volume and Issue: 29

Published: June 1, 2023

Artificial intelligence (AI), or machine learning, is an ancient concept based on the assumption that human thought and reasoning can be mechanized. AI techniques have been used in diagnostic medicine for several decades, particularly image analysis clinical diagnosis. During COVID-19 pandemic, was critical genome sequencing, drug vaccine development, identifying disease outbreaks, monitoring spread, tracking viral variants. AI-driven approaches complement human-curated ones, including traditional public health surveillance. Preparation future pandemics will require combined efforts of collaborative surveillance networks, which currently include US Centers Disease Control Prevention (CDC) Center Forecasting Outbreak Analytics World Health Organization (WHO) Hub Pandemic Epidemic Intelligence, use with international cooperation to implement programs. This Editorial aims provide update uses limitations infectious pandemic preparedness.

Language: Английский

---

Revolutionizing Vaccine Development for COVID-19: A Review of AI-Based Approaches

Information, Journal Year: 2023, Volume and Issue: 14(12), P. 665 - 665

Published: Dec. 18, 2023

The evolvement of COVID-19 vaccines is rapidly being revolutionized using artificial intelligence-based technologies. Small compounds, peptides, and epitopes are collected to develop new therapeutics. These substances can also guide modeling, screening, or creation. Machine learning techniques used leverage pre-existing data for drug detection vaccine advancement, while models these purposes. Models based on intelligence evaluate recognize the best candidate targets future therapeutic development. Artificial strategies be address issues with safety efficacy candidates, as well manufacturing, storage, logistics. Because antigenic peptides effective at eliciting immune responses, algorithms assist in identifying most promising candidates. Following vaccination, first phase vaccine-induced response occurs when major histocompatibility complex (MHC) class II molecules (typically bind 12–25 amino acids) peptides. Therefore, AI-based identify candidates ensure responses. This study explores use approaches logistics, safety, effectiveness associated several Additionally, we will potential next-generation treatments examine role that play considering triggering aim this project gain insights into how could revolutionize development they leveraged challenges In work, highlight barriers solutions focus recent improvements produce drugs vaccines, prospects intelligent training treatment discovery.

Language: Английский

---

Privacy-Enhancing Technologies in Federated Learning for the Internet of Healthcare Things: A Survey

Electronics, Journal Year: 2023, Volume and Issue: 12(12), P. 2703 - 2703

Published: June 16, 2023

Advancements in wearable medical devices using the IoT technology are shaping modern healthcare system. With emergence of Internet Healthcare Things (IoHT), efficient services can be provided to patients. professionals have effectively used AI-based models analyze data collected from IoHT treat various diseases. Data must processed and analyzed while avoiding privacy breaches, compliance with legal rules regulations, such as HIPAA GDPR. Federated learning (FL) is a machine learning-based approach allowing multiple entities train an ML model collaboratively without sharing their data. It particularly beneficial healthcare, where security substantial concerns. Even though FL addresses some concerns, there still no formal proof guarantees for Privacy-enhancing technologies (PETs) tools techniques designed enhance online communications sharing. PETs provide range features that help protect users’ personal information sensitive unauthorized access tracking. This paper comprehensively reviews concerning scenario identifies several key challenges future research.

Language: Английский

---

AI Empowering Traditional Chinese Medicine?

Chemical Science, Journal Year: 2024, Volume and Issue: unknown

Published: Jan. 1, 2024

AI-powered analysis of TCM chemical data enhances component identification, drug discovery, personalized treatment, and pharmacological action elucidation, driving the modernization sustainable development TCM.

Language: Английский

---

Need of booster vaccine doses to counteract the emergence of SARS-CoV-2 variants in the context of the Omicron variant and increasing COVID-19 cases: An update

Human Vaccines & Immunotherapeutics, Journal Year: 2022, Volume and Issue: 18(5)

Published: May 20, 2022

The emergence of different variants SARS-CoV-2, including the Omicron (B.1.1.529) variant in November 2021, has resulted a continuous major health concern at global scale. Presently, spread very rapidly worldwide within short time period. As most mutated instilled serious uncertainties on effectiveness humoral adaptive immunity generated by COVID-19 vaccination or an active viral infection as well protection provided antibody-based immunotherapies. Amidst such high public concerns, need to carry out booster been emphasized. Current evidence reveals importance incorporating using several vaccine platforms, vector- and mRNA-based vaccines, other platforms that are under explorative investigations. Further research is being conducted assess durability against SARS-CoV-2 variants.

Language: Английский

---

The use of artificial intelligence for delivery of essential health services across WHO regions: a scoping review

Frontiers in Public Health, Journal Year: 2023, Volume and Issue: 11

Published: July 4, 2023

Background Artificial intelligence (AI) is a broad outlet of computer science aimed at constructing machines capable simulating and performing tasks usually done by human beings. The aim this scoping review to map existing evidence on the use AI in delivery medical care. Methods We searched PubMed Scopus March 2022, screened identified records for eligibility, assessed full texts potentially eligible publications, extracted data from included studies duplicate, resolving differences through discussion, arbitration, consensus. then conducted narrative synthesis data. Results Several methods have been used detect, diagnose, classify, manage, treat, monitor prognosis various health issues. These models conditions, including communicable diseases, non-communicable mental health. Conclusions Presently available shows that models, predominantly deep learning, machine can significantly advance care regarding detection, diagnosis, management, monitoring different illnesses.

Language: Английский

---

Antiviral Potential of Plants against COVID-19 during Outbreaks—An Update

International Journal of Molecular Sciences, Journal Year: 2022, Volume and Issue: 23(21), P. 13564 - 13564

Published: Nov. 5, 2022

Several human diseases are caused by viruses, including cancer, Type I diabetes, Alzheimer’s disease, and hepatocellular carcinoma. In the past, people have suffered greatly from viral such as polio, mumps, measles, dengue fever, SARS, MERS, AIDS, chikungunya encephalitis, influenza. Recently, COVID-19 has become a pandemic in most parts of world. Although vaccines available to fight infection, their safety clinical trial data still questionable. Social distancing, isolation, use sanitizer, personal productive strategies been implemented prevent spread virus. Moreover, search for potential therapeutic molecule is ongoing. Based on experiences with outbreaks SARS many research studies reveal medicinal herbs/plants or chemical compounds extracted them counteract effects these diseases. COVID-19′s current status includes decrease infection rates result large-scale vaccination program implementation several countries. But it very close needs boost people’s natural immunity cost-effective way through phytomedicines because underdeveloped countries do not own facilities. this article, plant plant-derived metabolites that can affect entry virus its infectiousness inside hosts described. Finally, concluded plants must be analyzed evaluated entirely control cases uncontrollable infection.

Language: Английский

---

Research and development of Chinese anti-COVID-19 drugs

Acta Pharmaceutica Sinica B, Journal Year: 2022, Volume and Issue: 12(12), P. 4271 - 4286

Published: Sept. 13, 2022

Language: Английский

---

Non-contrast CT synthesis using patch-based cycle-consistent generative adversarial network (Cycle-GAN) for radiomics and deep learning in the era of COVID-19

Scientific Reports, Journal Year: 2023, Volume and Issue: 13(1)

Published: June 29, 2023

Abstract Handcrafted and deep learning (DL) radiomics are popular techniques used to develop computed tomography (CT) imaging-based artificial intelligence models for COVID-19 research. However, contrast heterogeneity from real-world datasets may impair model performance. Contrast-homogenous present a potential solution. We developed 3D patch-based cycle-consistent generative adversarial network (cycle-GAN) synthesize non-contrast images CTs, as data homogenization tool. multi-centre dataset of 2078 scans 1,650 patients with COVID-19. Few studies have previously evaluated GAN-generated handcrafted radiomics, DL human assessment tasks. the performance our cycle-GAN these three approaches. In modified Turing-test, experts identified synthetic vs acquired images, false positive rate 67% Fleiss’ Kappa 0.06, attesting photorealism images. on testing machine classifiers radiomic features, decreased use Marked percentage difference was noted in feature values between pre- post-GAN With classification, deterioration observed Our results show that whilst GANs can produce sufficient pass assessment, caution is advised before GAN-synthesized medical imaging applications.

Language: Английский

---