Back

Improving the detection of A. cantonensis in brain tissues of mammalian hosts

Arango-Colonna, M. A.; Delgado-Serra, S.; Haines, L.; Paredes-Esquivel, C.

2022-12-11 zoology
10.1101/2022.12.07.518866 bioRxiv
Show abstract

Angiostrongylus cantonensis is an invasive nematode parasite that can cause eosinophilic meningitis in many vertebrate hosts, including humans. This parasite is spreading rapidly through the six continents, with Europe being the final frontier. Sentinel surveillance may be a cost-effective surveillance strategy to monitor the arrival of this pathogen to new geographical regions as this can be easily expanded to incorporate symptomatic wildlife. Necropsy and tissue digestion techniques are often used to recover helminth parasites from vertebrate host tissues, however their application to detect brain parasites is poorly utilized. Here we show that employing these techniques in combination can 1) help resolve false positive and negative animals, 2) provide accurate parasitic load values and 3) establish an accurate prevalence of A. cantonensis. Our adapted tissue digestion technique can be easily performed, especially in wildlife hospitals where animal infections often precede human cases. Early detection of the parasite increases the efficacy of prevention, treatment, and disease control strategies not only in humans, but also in susceptible animal populations. Key FindingsO_LIOptimized brain digestion techniques can detect parasitic helminths (A. cantonensis) in mammalian hosts. C_LIO_LIAccuracy identifying neurotropic parasitic infections can be increased if a standard digestion protocol is applied. C_LIO_LIThe technique presented here can be easily implemented to detect brain nematodes in wildlife hospitals. C_LI

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.