Applications of Immunochemistry in Human Health: Advances in Vaccinology and Antibody Design
Bibliographic record
Abstract
This report discusses the history and mechanisms of vaccination of humans as well as the engineering of therapeutic antibodies. Deeper understanding of the molecular interactions involved in both acquired and innate immunity is allowing sophistication in design of modified and even synthetic vaccines. Recombinant DNA technologies are facilitating development of DNA-based vaccines, for example, with the recognition that unmethylated CpG sequences in plasmid DNA will target Toll-like receptors on antigen-presenting cells. Formulations of DNA vaccines with increased immunogenicity include engineering into plasmids with “genetic adjuvant” capability, incorporation into polymeric or magnetic nanoparticles, and formulation with cationic polymers and other polymeric and non-polymeric coatings. Newer methods of delivery, such as particle bombardment, DNA tattooing, electroporation, and magnetic delivery, are also improving the effectiveness of DNA vaccines. RNA-based vaccines and reverse vaccinology based on gene sequencing and bioinformatic approaches are also considered. Structural vaccinology is an approach in which the detailed molecular structure of viral epitopes is used to design synthetic antigenic peptides. Virus-like particles are being designed for vaccine deliveries that are based on structures of viral capsid proteins and other synthetic lipopeptide building blocks. A new generation of adjuvants is being developed to further enhance immunogenicity, based on squalene and other oil–water emulsions, saponins, muramyl dipeptide, immunostimulatory oligonucleotides, Toll-like receptor ligands, and lymphotoxins. Finally, current trends in engineering of therapeutic antibodies including improvements of antigen-binding properties, pharmacokinetic and pharmaceutical properties, and reduction of immunogenicity are discussed. Taken together, understanding the chemistry of vaccine design, delivery and immunostimulation, and knowledge of the techniques of antibody design are allowing targeted development for the treatment of chronic disorders characterized by continuing activation of the immune system, such as autoimmune disorders, cancer, or allergies that have long been refractory to conventional approaches.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".