Broad and Durable Humoral Responses Following Single Hydrogel Immunization of SARS‐CoV‐2 Subunit Vaccine
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Most vaccines require several immunizations to induce robust immunity, and indeed, most SARS-CoV-2 vaccines require an initial two-shot regimen followed by several boosters to maintain efficacy. Such a complex series of immunizations unfortunately increases the cost and complexity of populations-scale vaccination and reduces overall compliance and vaccination rate. In a rapidly evolving pandemic affected by the spread of immune-escaping variants, there is an urgent need to develop vaccines capable of providing robust and durable immunity. In this work, a single immunization SARS-CoV-2 subunit vaccine is developed that can rapidly generate potent, broad, and durable humoral immunity. Injectable polymer-nanoparticle (PNP) hydrogels are leveraged as a depot technology for the sustained delivery of a nanoparticle antigen (RND-NP) displaying multiple copies of the SARS-CoV-2 receptor-binding domain (RBD) and potent adjuvants including CpG and 3M-052. Compared to a clinically relevant prime-boost regimen with soluble vaccines formulated with CpG/alum or 3M-052/alum adjuvants, PNP hydrogel vaccines more rapidly generated higher, broader, and more durable antibody responses. Additionally, these single-immunization hydrogel-based vaccines elicit potent and consistent neutralizing responses. Overall, it is shown that PNP hydrogels elicit improved anti-COVID immune responses with only a single administration, demonstrating their potential as critical technologies to enhance overall pandemic readiness.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it