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Impact of Envenomation With Snake Venoms on Rabbit Carcass Decomposition and Differential Adult Dipteran Succession Patterns.

Abdelwahab KhalilMahmoud M M ZidanReem A AlajmiAshraf Mohamed Ahmed
Published in: Journal of medical entomology (2022)
The current study investigates the postmortem successional patterns of necrophagous dipteran insects and the rabbit carcass decomposition rate upon envenomation with snake venom. In total, 15 rabbits, Oryctolagus cuniculus domesticus L. (Lagomorpha, Leporidae), were divided into 3 groups (5 rabbits each; n = 5); the first and second groups were injected with lethal doses of venoms from the Egyptian cobra, Naja haje L. (Squamata, Elapidae), and the horned viper, Cerastes cerastes L. (Squamata, viperidae), respectively. The third group (control) was injected with 0.85% physiological saline and euthanized with CO2. The carcass decomposition stages: fresh, bloating, decay, and dry were recorded and monitored. Data revealed that envenomation shortened the decomposition process by 3 d, 20% shorter than the control. The overall succession pattern of fly species revealed a lower abundance during the fresh stage, which peaked during the decay stage, and declined to the minimum number in the dry stage at the end of the 15-d experimental duration. A total of 2,488 individual flies, belonging to 21 species of 10 families, were collected from all experimental carcasses. The Calliphoridae, Muscidae, and Sarcophagidae were the most abundant and diverse families, whereas the other seven families were rare and least abundant. Although C. cerastes venom was significantly less lethal than N. haje, it showed a faster carcass decomposition process and a higher impact on fly abundance. These data showed that envenomation impacts insect succession and carcass decomposition, which should be taken into account when using insects in forensic investigations since envenomation with snake venoms is one of the leading causes of death worldwide.
Keyphrases
  • microbial community
  • electronic health record
  • single cell
  • antibiotic resistance genes
  • big data
  • zika virus
  • machine learning
  • wastewater treatment
  • young adults
  • deep learning
  • genetic diversity