The impact of rattlesnake venom on mice cerebellum proteomics points to synaptic inhibition and tissue damage
Bibliographic record
Abstract
Snake envenomation is responsible for more than 130,000 deaths worldwide. In Brazil, the Crotalus rattlesnake is responsible for the second largest number of accidental snake bites in the country. Although there are many descriptions of the clinical and biochemical effects of Crotalus envenoming, there are few works describing the molecular events in the central nervous system of an organism due to envenomation. In this study, we analyzed the proteomic effect of Crotalus durissus terrificus snake venom on mice cerebellums. To monitor the envenomation over time, changes in the protein abundance were evaluated at 1 h, 6 h, 12 h and 24 h after venom injection by mass spectrometry. The analysis of the variation of over 4600 identified proteins over time showed a reduction in components of inhibitory synapse signaling, oxidative stress, and maintenance of neuronal cells, which paralleled increasing tissue damage and apoptosis factors. These analyses revealed the potential protein targets of the C. d. terrificus venom on the murine cerebellum, showing new aspects of the snake envenomation effect. These data may contribute to new therapeutic approaches (i.e., approaches directed at protein targets affected by the envenomation) on the treatment of envenomation by the neurotoxic C. d. terrificus snake venom. SIGNIFICANCE: Snakebites are a neglected global health problem that affects mostly rural and tropical areas of developing countries. It is estimated that over 5.4 million people are bitten by snakes each year, from which 2.7 million people are bitten by venomous snakes, resulting in disabilities such as amputations and in some cases leading to death. The C. d. terrificus snake is the most lethal snake in Brazil. Studying the molecular changes upon envenomation in a specific tissue may lead to a better understanding of the envenomation process by C. d. terrificus snakebites.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".