Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Hybridoma Technology

Hybridoma Technology

Hybridoma technology is used for the large-scale production of monoclonal antibodies. Monoclonal antibodies bind to only a single antigenic determinant or epitope. Such antibodies are used in research, diagnostics, and disease therapy. The hybridoma technology established in 1975 by Georges Köhler and Cesar Milstein was awarded the Nobel Prize in Medicine in 1984 for revolutionizing research and therapy.
Hybridoma Selection
Commonly used fusion techniques — electroporation,...

Diversity of Antigen Receptors

Diversity of Antigen Receptors

Antigen receptors are essential components of the immune system crucial in defending the body against foreign invaders. These receptors are present on the surface of B and T cells, enabling them to recognize antigens and mount an appropriate immune response.
Before encountering any antigen, lymphocytes express these receptors. On B cells, the antigen receptor is a membrane-bound antibody molecule called BCR; on T cells, it is a T cell receptor or TCR. B and T cell receptors are composed of two...

Cross-reactivity

Cross-reactivity

Antigens Involved in Adaptive Immunity

Antigens Involved in Adaptive Immunity

An antigen is any substance the immune system identifies as foreign and potentially harmful to the body, prompting an immune response. Antigens have two functional properties: immunogenicity and reactivity. Immunogenicity is the ability of an antigen to stimulate a specific immune response. At the same time, reactivity describes the antigen's ability to react with the cells and antibodies produced in response to it.
Complete Antigens
Complete antigens possess both immunogenicity and...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Ambient AI Scribes to Create Educational Feedback Notes for Medical Students: Randomized Trial.

JMIR medical education·2026

Same author

Red cell antibody and antigen patterns in patients with sickle cell disease: A multi-center analysis.

Transfusion·2026

Same author

Correction: Real-World Evidence Synthesis of Digital Scribes Using Ambient Listening and Generative Artificial Intelligence for Clinician Documentation Workflows: Rapid Review.

JMIR AI·2026

Same author

The absence of an electronic interface between blood bank information systems and immunohematology reference laboratories.

Transfusion·2026

Same author

Simulated evaluation of large language model stepwise diagnostic reasoning with real-world chest pain encounters and Bayesian networks.

BMC medical informatics and decision making·2026

Same author

AgentMD: Empowering language agents for risk prediction with large-scale clinical tool learning.

Nature communications·2025

Search research articles

Related Experiment Video

Updated: Jan 7, 2026

Personalized Peptide Arrays for Detection of HLA Alloantibodies in Organ Transplantation

Personalized Peptide Arrays for Detection of HLA Alloantibodies in Organ Transplantation

Published on: September 6, 2017

Using generative artificial intelligence to standardize unstructured antigen profiles for the alloantibody exchange.

Kent McCann^1,2, Donald S Wright^1,2,3, Eileen Alvarez⁴

¹Veterans Affairs Connecticut Healthcare, West Haven, Connecticut, USA.

|January 2, 2026

Summary

Large language models (LLMs) can standardize unstructured red cell antigen profiles, improving data for transfusion history services. Commercial LLMs achieved high accuracy, reducing manual curation needs.

More Related Videos

Generation of Human Alloantigen-specific T Cells from Peripheral Blood

Generation of Human Alloantigen-specific T Cells from Peripheral Blood

Published on: November 21, 2014

Induction of Alloantigen-specific Anergy in Human Peripheral Blood Mononuclear Cells by Alloantigen Stimulation with Co-stimulatory Signal Blockade

Induction of Alloantigen-specific Anergy in Human Peripheral Blood Mononuclear Cells by Alloantigen Stimulation with Co-stimulatory Signal Blockade

Published on: March 14, 2011

Related Experiment Videos

Last Updated: Jan 7, 2026

Personalized Peptide Arrays for Detection of HLA Alloantibodies in Organ Transplantation

Personalized Peptide Arrays for Detection of HLA Alloantibodies in Organ Transplantation

Published on: September 6, 2017

Generation of Human Alloantigen-specific T Cells from Peripheral Blood

Generation of Human Alloantigen-specific T Cells from Peripheral Blood

Published on: November 21, 2014

Induction of Alloantigen-specific Anergy in Human Peripheral Blood Mononuclear Cells by Alloantigen Stimulation with Co-stimulatory Signal Blockade

Induction of Alloantigen-specific Anergy in Human Peripheral Blood Mononuclear Cells by Alloantigen Stimulation with Co-stimulatory Signal Blockade

Published on: March 14, 2011

Area of Science:

Medical Informatics
Computational Biology
Bioinformatics

Background:

Standardized data is crucial for national transfusion history sharing services like the alloantibody exchange.
Red cell antigen profiles are often unstructured text, hindering data standardization.
Manual curation of antigen profiles is labor-intensive and limits scalability.

Purpose of the Study:

To evaluate the efficacy of large language models (LLMs) in standardizing unstructured free-text red cell antigen profiles.
To compare the performance of locally run open-weight LLMs versus commercially available LLMs accessed via the internet.
To assess the accuracy, scalability, and cost-effectiveness of LLM-based standardization for transfusion data.

Main Methods:

Utilized deidentified red cell antigen profiles from an academic center.
Employed schema-guided decoding and error-aware retries for LLM output.
Compared performance metrics including exact profile match, antigen-level agreement, error types, and processing costs.
Statistically analyzed differences using Cochran's Q and pairwise McNemar tests with Holm correction.

Main Results:

Commercial LLMs significantly outperformed open-source LLMs in standardizing antigen profiles.
The top-performing commercial LLM achieved 92.5% exact match for antigen profiles and 99.4% antigen-level agreement.
Open-source LLMs showed improved performance with a higher number of parameters.
Estimated API costs for commercial LLMs were modest for the evaluated workload.

Conclusions:

Commercial LLMs, when combined with schema-guided decoding and error-aware retries, can accurately standardize unstructured antigen profiles.
LLM-based standardization offers significant scalability and reduces manual curation efforts for transfusion history services.
LLMs represent a foundational technology for enabling efficient and accurate national transfusion history sharing.