A Strategy for Efficient Preparation of Genus-Specific Diagnostic Antibodies for Snakebites
Bibliographic record
Abstract
As said by former United Nations Secretary-General Kofi Annan, “Snakebite is the most important tropical disease you’ve never heard of.” Listed as a priority neglected tropical disease by the World Health Organization, snakebite envenoming (SBE) kills in excess of 125,000 people per year. However, due to the complexity and overlap of snake venom compositions, few reliable venom diagnostic methods for genus-/species-specific identification, which is crucial for successful SBE therapy, are available. Here, we develop a strategy to select and prepare genus-specific snake venom antibodies, which allows rapid and efficient clinical diagnosis of snakebite. Multi-omics approaches are used to choose candidate antigens from snake venoms and identify genus-specific antigenic epitope peptide fragments (GSAEPs) with ideal immunogenicity, specificity, and spatial accessibility. Double-antibody sandwich ELISA kit was established by matching a polyclonal antibody against a natural antigen and a monoclonal antibody that was prepared by natural protein as antigen and can specifically target the GSAEPs. The kit shows the ability to accurately identify venoms from similar genera of Trimeresurus and Protobothrops with a detection limit of 6.25 ng/ml on the snake venoms and a little cross-reaction, thus proving high feasibility and applicability.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".