Detection and neutralization of cobra venom using rabbit antiserum in experimental envenomated mice
Bibliographic record
Abstract
A sandwich enzyme-linked immunosorbent assay (ELISA) was developed to detect the venom of Indian cobra (Naja naja naja) in various tissues (brain, heart, lungs, liver, spleen, blood, kidneys, and tissue at the site of injection) of mice after cobra venom injected at different time intervals (0, 2, 4, 6, 8, and 12 h intervals up to 24 h). Whole venom antiserum or individual venom protein antiserum (14, 29, 65, 72, and 99 kDa) could recognize N. n. naja venom by Western blotting and ELISA, and antibody titer was also assayed by ELISA. Antiserum raised against cobra venom in rabbit significantly neutralized the toxicity of venom-injected mice at different time intervals after treatment. The assay could detect N. n. naja venom levels up to 2.5 ng/ml of tissue homogenate, and the venom was detected up to 24 h after venom injection. Venom was detected in brain, heart, lungs, liver, spleen, kidneys, tissue at the bite area, and blood. As observed in mice, tissue at the site of bite area showed the highest concentration of venom and the brain showed the least. Moderate amounts of venoms were found in liver, spleen, kidneys, heart, and lungs. Development of a simple, rapid, and species-specific diagnostic kit based on this ELISA technique useful to clinicians is discussed.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".