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Abstrakt

Detection of Intestinal Parasites in Stool Samples by Microscopy and Real-Time PCR in Children with Vulnerable Living Conditions in Dakar, Senegal

Souleye Lelo*, Fatimata Ly, Aminata Lam, Cheikh Binetou Fall, Issac Manga, Fassiatou Tairou, Khadim Sylla, Magatte Ndiaye, Doudou Sow, Roger Tine, Babacar Faye

Background: Intestinal Parasitic Infections (IPIs) are considered a serious public health problem and widely distributed worldwide, mainly in urban and rural environments of tropical and subtropical countries. Globally, soil- transmitted helminths and protozoa are the most common intestinal parasites. Decreasing the prevalence of IPIs is one of the main aims of health services in these countries. This study was designed to determine the current status of IPIs in children with vulnerable living conditions by microscopy and PCR.

Methodology/main findings: A cross-sectional population-based survey was conducted. One stool sample per participant (n=253) was examined by direct smear, Formal-Ether Concentration (FEC), and real-time PCR. It was found that 17.39% harboured at least one helminth while 12.64% harboured two helminths or more. Among the microscopic techniques, FEC was able to detect the broadest spectrum of parasite species. However, FEC also missed a considerable number of infections, notably S. stercoralis and G. intestinalis. PCR outperformed microscopy in terms of sensitivity and range of parasite species detected.

Conclusion: It was shown that intestinal parasites, especially helminths were omnipresent in our population studies. Classical techniques such as FEC are useful for the detection of some intestinal helminth species, but they lack sensitivity for other parasite species. PCR can detect intestinal parasites more accurately but is generally not feasible in resource-poor settings, at least not in peripheral labs. Hence, there is a need for a more field-friendly, sensitive approach for on-the-spot diagnosis of parasitic infections.

Haftungsausschluss: Dieser Abstract wurde mit Hilfe von Künstlicher Intelligenz übersetzt und wurde noch nicht überprüft oder verifiziert.