Thermal Stabilization of Viral Vaccines in Low-Cost Sugar Films
Bibliographic record
Abstract
Most currently available vaccines, particularly live vaccines, require the cold chain, as vaccine efficacy can be significantly hampered if they are not stored in a temperature range of 2-8 °C at all times. This necessity places a tremendous financial and logistical burden on vaccination programs, particularly in the developing world. The development of thermally stable vaccines can greatly alleviate this problem and, in turn, increase vaccine accessibility worldwide. In this paper, we detail a simple and cost-effective method for stabilizing live vaccines that uses FDA-approved materials. To this end, we dried enveloped DNA (Herpes Simplex Virus type 2) and RNA (Influenza A virus) viral vaccines in a pullulan and trehalose mixture. The results of these studies showed that the live-attenuated HSV-2 vaccine retained its efficacy for at least 2 months of storage at 40 °C, while the inactivated influenza vaccine was able to retain its immunogenicity for at least 3 months of storage at 40 °C. This work presents a simple approach that allows thermo-sensitive vaccines to be converted into thermo-stable vaccines that do not require refrigeration, thus contributing to the improvement of vaccine deployment throughout the world.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".