Bibliographic record
Abstract
The threat from the use of biowarfare (BW)/bioterrorism (BT) agents is now more likely than ever. Antibodies, which are naturally produced molecules with high specificity and affinity, play an important role in immune defence by recognizing and eliminating invading microbial pathogens or neutralizing toxins. Passive antibody administration is an effective means of conferring immediate immunity to a susceptible host for post-exposure prophylaxis or therapy of BW/BT agent-mediated diseases, but the immunity would not last long and antibody production is a lengthy, labor intensive, and expensive process. An alternative approach is to take advantage of the body's natural ability to express transgenes to produce passive antibodies. This approach can be achieved by the in vivo delivery of genes encoding BW/BT agent-specific antibodies for biodefence applications. It is also possible to design antibody fragments to be expressed inside a cell via antibody gene delivery for combating intracellular BW/BT agents and toxins, which natural antibodies cannot reach. Animal studies have shown that the expressed antibodies can be detected as early as day 3, reaches peak levels at day 7, and maintains therapeutic levels in serum for more than seven months after a single administration via antibody gene delivery. Therefore, antibody gene delivery in vivo might be a new approach for post-exposure prophylaxis or therapy and for pre-exposure prophylaxis (vaccination) of BW/BT agent-mediated diseases although there are still some problems to be overcome before this new approach can actually be used in humans.
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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".