Tropical Parasitology最新文献

Cysticercosis is one of the most common parasitic infections seen in the human population which most commonly affects the nervous system. Isolated cysticercosis of the liver is rare, and only less than five cases have been reported in the literature. Here, we describe an isolated cysticercosis of the liver. The cyst appeared as a firm nodule on the surface of the liver during a prepyloric perforation repair. The biopsy of the nodule revealed a cyst wall, and a contrast-enhanced computed tomography postoperative showed cysts in the liver. The patient was further evaluated with whole-body imaging which did not reveal any other cysts elsewhere.

Cryptosporidium species cause watery diarrhea in several vertebrate hosts, including humans. Most apparently, immunocompetent-infected individuals remain asymptomatic, whereas immunocompromised may develop severe or chronic cryptosporidiosis. We report here the case of a 6-year-old girl undergoing chemotherapy for Burkitt lymphoma who experienced multiple episodes of watery diarrhea during her hospital stay. Microscopic examination of her stool sample revealed oocysts of Cryptosporidium species. The rapid immunochromatographic test was also positive for Cryptosporidium species. She was treated with nitazoxanide for 3 weeks, which failed to provide both clinical improvement and parasitological clearance. This case highlights the importance of treatment failure in human cryptosporidiosis.

Purpose: As we edge closer to the eradication of malaria, several methods for detecting Plasmodium species have been developed, including peripheral blood smear examination (PBS), rapid diagnostic tests (RDTs), serological evaluations, fluorescent microscopy, polymerase chain reactions (PCRs), fluorescent in situ hybridization, and flow cytometry. The suitability of these tools for routine diagnosis requires evaluation, considering both their diagnostic accuracy and cost-effectiveness.

Materials and methods: Our study compared four diagnostic techniques for malaria: PBS, quantitative buffy coat (QBC), RDT, and PCR. We used PCR as the benchmark standard and statistically assessed the performance of PBS, QBC, and RDT against PCR in detecting malaria. Adopting a prospective observational approach, we collected blood samples from 117 patients exhibiting the symptoms suggestive of malaria.

Results: The findings from our study showed that PBS had a positivity rate of 93.4%, with a 95% confidence interval (CI) of 0.881-0.987, indicating reliable results for a similar population. The QBC assay demonstrated an elevated positivity rate of 96.7% with a solid 95% CI of 0.930-1.000. Although the RDT had a slightly lower rate of 92.4%, it still delivered dependable results, presenting a significant 95% CI of 0.868-0.980, ensuring a robust diagnostic performance compared to PCR.

Conclusion: PCR is a reliable test when the identification of the specific species is inconclusive. Conversely, the commonly used PBS occasionally overlooks positive malaria cases due to the specialized skills needed for accurate reading. The cost-effective RDT is feasible for field operations without the need for expert knowledge. However, it fails to differentiate between old and new infections. Meanwhile, the QBC test, known for its sensitivity and speed, can be consistently employed for malaria diagnosis in a tertiary care settings.

Background: Giardia intestinalis is an intestinal protozoan which commonly causes parasitic gastroenteritis globally. It is a species complex consisting of at least eight assemblages (genotypes). In India, Giardia is mostly underreported and missed in asymptomatic cases.

Aim: The aim of this study was to genotype the G. intestinalis isolates from stool samples of patients at a tertiary care center in Rajasthan, India, and to clinically correlate it.

Methods: This prospective pilot cross-sectional study was conducted from 2019 to 2021 in a tertiary care center in western India. Patients who were microscopically positive for giardiasis were enrolled. DNA was extracted from their stool samples and amplified by polymerase chain reaction (PCR) using 4E1-HP as the target sequence. Anthropometric measurements and analysis were done for children by using Anthrocal application.

Results: A total of 50 patients were enrolled. Diarrhea was present in 18 patients (36%). Among these, 6 were immunocompromised and had different comorbidities. Among the children <12 years of age, 55.17% (n = 16/29) were stunted (<-2 S.D.), and among <5 years, 44.4% (n = 4/9) showed wasting (<-2 S.D.). A PCR product corresponding to assemblage B of G. intestinalis was amplified in 47 stool specimens. Only three stool samples were negative for both assemblages A and B and posed an interesting enigma.

Conclusion: In this study, a predominance of assemblage B of G. intestinalis was detected in 94% of the isolates. Furthermore, the possibility of zoonotic transmission could not be ruled out.

Parasitic diseases, including malaria, leishmaniasis, and trypanosomiasis, continue to plague populations worldwide, particularly in resource-limited settings and disproportionately affecting vulnerable populations. It has limited the use of conventional health-care delivery and disease control approaches and necessitated exploring innovative strategies. In this direction, artificial intelligence (AI) has emerged as a transformative tool with immense promise in parasitic disease control, offering the potential for enhanced diagnostics, precision drug discovery, predictive modeling, and personalized treatment. Predictive AI algorithms have assisted in understanding parasite transmission patterns and outbreaks by analyzing vast amounts of epidemiological data, environmental factors, and population demographics. This has strengthened public health interventions, resource allocation, and outbreak preparedness strategies, enabling proactive measures to mitigate disease spread. In diagnostics, AI-enabled accurate and rapid identification of parasites by analyzing microscopic images. This capability is particularly valuable in remote regions with limited access to diagnostic facilities. AI-driven computational methods have also assisted in drug discovery for parasitic diseases by identifying novel drug targets and predicting the efficacy and safety of potential drug candidates. This approach has streamlined drug development, leading to more effective and targeted therapies. This article reviews these current developments and their transformative impacts on the health-care sector. It also assessed the hurdles that require attention before these transformations can be realized in real-life scenarios.