Evaluating Plasmodium falciparum automatic detection and parasitemia estimation: A comparative study on thin blood smear images.

Malaria is a deadly disease that is transmitted through mosquito bites. Microscopists use a microscope to examine thin blood smears at high magnification (1000x) to identify parasites in red blood cells (RBCs). Estimating parasitemia is essential in determining the severity of the Plasmodium falciparum infection and guiding treatment. However, this process is time-consuming, labor-intensive, and subject to variation, which can directly affect patient outcomes. In this retrospective study, we compared three methods for measuring parasitemia from a collection of anonymized thin blood smears of patients with Plasmodium falciparum obtained from the Clinical Department of Parasitology-Mycology, National Reference Center (NRC) for Malaria in Paris, France. We first analyzed the impact of the number of field images on parasitemia count using our framework, MALARIS, which features a top-classifier convolutional neural network (CNN). Additionally, we studied the variation between different microscopists using two manual techniques to demonstrate the need for a reliable and reproducible automated system. Finally, we included thin blood smear images from an additional 102 patients to compare the performance and correlation of our system with manual microscopy and flow cytometry. Our results showed strong correlations between the three methods, with a coefficient of determination between 0.87 and 0.92.

Intestinal Candida albicans overgrowth in IgA deficiency.

BACKGROUND: Secretory IgA interacts with commensal bacteria, but its impact on human mycobiota ecology has not been widely explored. In particular, whether human IgA-deficiency is associated with gut fungal dysbiosis remains unknown. OBJECTIVES: Our goal was to study the impact of IgA on gut mycobiota ecology. METHODS: The Fungi-Flow method was used to characterize fecal, systemic, and maternal IgA, IgM, and IgG responses against 14 representative fungal strains (yeast/spores or hyphae forms) in healthy donors (HDs) (n = 34, 31, and 20, respectively) and to also compare gut mycobiota opsonization by secretory antibodies in HDs (n = 28) and patients with selective IgA deficiency (SIgAd) (n = 12). Stool mycobiota composition was determined by internal transcribed spacer gene sequencing in HDs (n = 23) and patients with SIgAd (n = 17). Circulating CD4+ T-cell cytokine secretion profiles were determined by intracellular staining. The impact of secretory IgA, purified from breast milk (n = 9), on Candidaalbicans growth and intestinal Caco-2 cell invasion was tested in vitro. RESULTS: Homeostatic IgA binds commensal fungi with a body fluid-selective pattern of recognition. In patients with SIgAd, fungal gut ecology is preserved by compensatory IgM binding to commensal fungi. Gut Calbicans overgrowth nevertheless occurs in this condition but only in clinically symptomatic patients with decreased TH17/TH22 T-cell responses. Indeed, secretory IgA can reduce in vitro budding and invasion of intestinal cells by Calbicans and therefore exert control on this pathobiont. CONCLUSION: IgA has a selective impact on Calbicans ecology to preserve fungal-host mutualism.

Identification of a clonal population of Aspergillus flavus by MALDI-TOF mass spectrometry using deep learning.

The spread of fungal clones is hard to detect in the daily routines in clinical laboratories, and there is a need for new tools that can facilitate clone detection within a set of strains. Currently, Matrix Assisted Laser Desorption-Ionization Time-of-Flight Mass Spectrometry is extensively used to identify microbial isolates at the species level. Since most of clinical laboratories are equipped with this technology, there is a question of whether this equipment can sort a particular clone from a population of various isolates of the same species. We performed an experiment in which 19 clonal isolates of Aspergillus flavus initially collected on contaminated surgical masks were included in a set of 55 A. flavus isolates of various origins. A simple convolutional neural network (CNN) was trained to detect the isolates belonging to the clone. In this experiment, the training and testing sets were totally independent, and different MALDI-TOF devices (Microflex) were used for the training and testing phases. The CNN was used to correctly sort a large portion of the isolates, with excellent (> 93%) accuracy for two of the three devices used and with less accuracy for the third device (69%), which was older and needed to have the laser replaced.