OUP accepted manuscript
Bibliographic record
Abstract
Necrotic enteritis is an economically important disease of poultry mainly caused by Clostridium perfringens The bacteria release multiple toxins of which NetB, alpha toxin and TpeL have been reported to play important roles in pathogenicity and/or severity of the disease. In this study, the sequence of clostridial toxins NetB, alpha toxin and TpeL were analyzed using bioinformatics tools to determine protein domains with high immunogenicity factor. Several chimeric trivalent proteins consisting of the immunogenic regions of the three toxins were designed and evaluated. The separate regions were fused together using rigid linkers. Based on a modeled tertiary structure, a proper combination was selected and expressed in a bacterial host (Escherichia coli) and successfully purified. The expression of the chimeric protein was further verified by western blotting. The ability of the immunized serum in recognizing each individual subunit of the chimeric protein was also examined. Circular dichroism was used to evaluate the predicted secondary structure of the chimeric protein. In vitro potency test demonstrated that the serum from a rabbit immunized with the chimeric protein is able to partially neutralize Alpha toxin, hence the construct can potentially be used as a vaccine against C. perfringens.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.777 | 0.690 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".