Advancement of abrin toxin bioforensics capabilities
Bibliographic record
Abstract
Abrin toxin, located in the seeds of Abrus precatorius, is a potent Type II Ribosome-Inactivating Protein (RIP) A-B subunit toxin that has acquired a heightened biothreat profile over the past decade. Significant research efforts must be made in order to develop abrin bioforensics diagnostic response capabilities to the level which is currently in place for ricin, another Type II RIP toxin with similar molecular structure and mechanism of toxicity. To serve this endeavor, three interrelated investigations have been carried out. The first study addresses the need to expand the supply of anti-abrin monoclonal antibody (mAb) reagents. Monoclonal antibodies were developed from hybridomas immunized with synthetic peptides derived from in silico prediction of abrin B-cell epitopes. Two epitopes produced nine abrin-reactive mAbs. Secondly, an attenuated, antigenically-faithful recombinant proabrin holotoxoid was expressed and purified using the baculovirus system for purposes as both an immunogen in future hybridoma experiments, as well as a non-toxic ‘safe antigen’ diagnostic reagent standard. The reduction in toxicity of proabrin relative to wild-type abrin was 108-fold in cell-free translation assay, and 291- to 302-fold in cytotoxicity assays. Finally, the third study combined shotgun proteomics with multivariate analysis differentiation methods in order to retrospectively identify six toxin extraction protocols with further sub-variation of reagent source. Differentiation was based solely on forensic proteomic signatures of carryover seed proteins. A 5-way hierarchical sPLS-DA model correctly classified samples into 8 extraction categories with 100 percent accuracy. Continued assessment of FASP LC-MS/MS and sPLS-DA modelling for attribution of plant toxin extraction methods may lead to establishment of a novel bioforensics diagnostic platform. Together, these three studies serve to advance Canadian bioforensics capabilities and to provide a foundation for future work.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".