Automated parasitological diagnosis in clinical microbiology laboratories

Although there is a low prevalence of parasitological infections in Europe, the diagnosis of intestinal parasites is still difficult and laborious for microbiology laboratories. Currently, antigen detection assays and molecular biology allow a more accurate diagnosis, but these techniques have limitations as they cannot detect all the possible parasites present in the samples. The objective of the study was to evaluate the accuracy and the usefulness of automated microscopy SediMAX2 (77 Elektronika, Budapest, Hungary) in the detection of parasitic infections from feces. A total of 197 formol-fixed stool samples were processed in parallel by wet mount examination and by SediMAX2. Sensitivities, specificities and predictive values were analyzed, reaching a sensitivity of 89.51% and a specificity of 98.15% and a very good positive predictive value (99.22%). SediMAX2 is a good tool for a reliable diagnosis of intestinal parasitic infections. The rapid processing and the flexibilty of storage of images analyzed make its incorporation into the day to day laboratory routine recommendable

Estimating Gene Signals From Noisy Microarray Images

In oligonucleotide microarray experiments, noise is a challenging problem, as biologists now are studying their organisms not in isolation but in the context of a natural environment. In low photomultiplier tube (PMT) voltage images, weak gene signals and their interactions with the background fluorescence noise are most problematic. In addition, nonspecific sequences bind to array spots intermittently causing inaccurate measurements. Conventional techniques cannot precisely separate the foreground and the background signals. In this paper, we propose analytically based estimation technique. We assume a priori spot-shape information using a circular outer periphery with an elliptical center hole. We assume Gaussian statistics for modeling both the foreground and background signals. The mean of the foreground signal quantifies the weak gene signal corresponding to the spot, and the variance gives the measure of the undesired binding that causes fluctuation in the measurement. We propose a foreground-signal and shapeestimation algorithm using the Gibbs sampling method. We compare our developed algorithm with the existing Mann–Whitney (MW)- and expectation maximization (EM)/iterated conditional modes (ICM)-based methods. Our method outperforms the existing methods with considerably smaller mean-square error (MSE) for all signal-to-noise ratios (SNRs) in computer-generated images and gives better qualitative results in low-SNR real-data images. Our method is computationally relatively slow because of its inherent sampling operation and hence only applicable to very noisy-spot images. In a realistic example using our method, we show that the gene-signal fluctuations on the estimated foreground are better observed for the input noisy images with relatively higher undesired bindings

Endoplasmic Reticulum Stress-Sensing Mechanism Is Activated in Entamoeba histolytica upon Treatment with Nitric Oxide

The Endoplasmic Reticulum stores calcium and is a site of protein synthesis and modification. Changes in ER homeostasis lead to stress responses with an activation of the unfolded protein response (UPR). The Entamoeba histolytica endomembrane system is simple compared to those of higher eukaryotes, as a canonical ER is not observed. During amoebiasis, an infection of the human intestine and liver by E. histolytica, nitric oxide (NO) triggers an apoptotic-like event preceded by an impairment of energy production and a loss of important parasite pathogenic features. We address the question of how this ancient eukaryote responds to stress induced by immune components (i.e. NO) and whether stress leads to ER changes and subsequently to an UPR. Gene expression analysis suggested that NO triggers stress responses marked by (i) dramatic up-regulation of hsp genes although a bona fide UPR is absent; (ii) induction of DNA repair and redox gene expression and iii) up-regulation of glycolysis-related gene expression. Enzymology approaches demonstrate that NO directly inhibits glycolysis and enhance cysteine synthase activity. Using live imaging and confocal microscopy we found that NO dramatically provokes extensive ER fragmentation. ER fission in E. histolytica appears as a protective response against stress, as it has been recently proposed for neuron self-defense during neurologic disorders. Chronic ER stress is also involved in metabolic diseases including diabetes, where NO production reduces ER calcium levels and activates cell death. Our data highlighted unique cellular responses of interest to understand the mechanisms of parasite death during amoebiasis

A crowdsourcing semi-automatic image segmentation platform for cell biology

State-of-the-art computer-vision algorithms rely on big and accurately annotated data, which are expensive, laborious and time-consuming to generate. This task is even more challenging when it comes to microbiological images, because they require specialized expertise for accurate annotation. Previous studies show that crowdsourcing and assistive-annotation tools are two potential solutions to address this challenge. In this work, we have developed a web-based platform to enable crowdsourcing annotation of image data; the platform is powered by a semi-automated assistive tool to support non-expert annotators to improve the annotation efficiency. The behavior of annotators with and without the assistive tool is analyzed, using biological images of different complexity. More specifically, non-experts have been asked to use the platform to annotate microbiological images of gut parasites, which are compared with annotations by experts. A quantitative evaluation is carried out on the results, confirming that the assistive tools can noticeably decrease the non-expert annotation�s cost (time, click, interaction, etc.) while preserving or even improving the annotation�s quality. The annotation quality of non-experts has been investigated using IOU (intersection of union), precision and recall; based on this analysis we propose some ideas on how to better design similar crowdsourcing and assistive platforms

Rapid Diagnosis by Microfluidic Techniques

Pathogenic bacteria in an aqueous or airborne environments usually cause infectious diseases in hospital or among the general public. One critical step in the successful treatment of the pathogen-caused infections is rapid diagnosis by identifying the causative microorganisms, which helps to provide early warning of the diseases. However, current standard identification based on cell culture and traditional molecular biotechniques often depends on costly or time-consuming detection methods and equipments, which are not suitable for point-of-care tests. Microfluidic-based technique has recently drawn lots of attention, due to the advantage that it has the potential of providing a faster, more sensitive, and higher-throughput identification of causative pathogens in an automatic manner by integrating micropumps and valves to control the liquid accurately inside the chips. In this chapter, microfluidic techniques for serodiagnosis of amebiasis, allergy, and rapid analysis of airborne bacteria are described. The microfluidic chips that integrate microcolumns, protein microarray, or a staggered herringbone mixer structure with sample to answer capability have been introduced and shown to be powerful in rapid diagnosis especially in medical fields

SAFE Software and FED Database to Uncover Protein-Protein Interactions using Gene Fusion Analysis

Domain Fusion Analysis takes advantage of the fact that certain proteins in a given proteome A, are found to have statistically significant similarity with two separate proteins in another proteome B. In other words, the result of a fusion event between two separate proteins in proteome B is a specific full-length protein in proteome A. In such a case, it can be safely concluded that the protein pair has a common biological function or even interacts physically. In this paper, we present the Fusion Events Database (FED), a database for the maintenance and retrieval of fusion data both in prokaryotic and eukaryotic organisms and the Software for the Analysis of Fusion Events (SAFE), a computational platform implemented for the automated detection, filtering and visualization of fusion events (both available at: http://www.bioacademy.gr/bioinformatics/projects/ProteinFusion/index.htm). Finally, we analyze the proteomes of three microorganisms using these tools in order to demonstrate their functionality

Deep-learning assisted detection and quantification of (oo)cysts of Giardia and Cryptosporidium on smartphone microscopy images

The consumption of microbial-contaminated food and water is responsible for the deaths of millions of people annually. Smartphone-based microscopy systems are portable, low-cost, and more accessible alternatives for the detection of Giardia and Cryptosporidium than traditional brightfield microscopes. However, the images from smartphone microscopes are noisier and require manual cyst identification by trained technicians, usually unavailable in resource-limited settings. Automatic detection of (oo)cysts using deep-learning-based object detection could offer a solution for this limitation. We evaluate the performance of three state-of-the-art object detectors to detect (oo)cysts of Giardia and Cryptosporidium on a custom dataset that includes both smartphone and brightfield microscopic images from vegetable samples. Faster RCNN, RetinaNet, and you only look once (YOLOv8s) deep-learning models were employed to explore their efficacy and limitations. Our results show that while the deep-learning models perform better with the brightfield microscopy image dataset than the smartphone microscopy image dataset, the smartphone microscopy predictions are still comparable to the prediction performance of non-experts.Comment: 18 pages (including supplementary information), 4 figures, 7 tables, submitting to Journal of Machine Learning for Biomedical Imagin

Human ascariasis: diagnostics update

Soil-transmitted helminths (STHs) infect over one billion people worldwide. Ascariasis may mimic a number of conditions, and individual clinical diagnosis often requires a thorough work-up. Kato-Katz thick smears are the standard detection method for Ascaris and, despite low sensitivity, are often used for mapping and monitoring and evaluation of national control programmes. Although increased sampling (number of stools) and diagnostic (number of examinations per stool) efforts can improve sensitivity, Kato-Katz is less sensitive than other microscopy methods such as FLOTAC®. Antibody-based diagnostics may be a sensitive diagnostic tool; however, their usefulness is limited to assessing transmission in areas aiming for elimination. Molecular diagnostics are highly sensitive and specific, but high costs limit their use to individual diagnosis, drug - efficacy studies and identification of Ascaris suum. Increased investments in research on Ascaris and other STHs are urgently required for the development of diagnostic assays to support efforts to reduce human suffering caused by these infections

Development of a DNA-based microarray for the detection of zoonotic pathogens in rodent species

The demand for diagnostic tools that allow simultaneous screening of samples for multiple pathogens is increasing because they overcome the limitations of other methods, which can only screen for a single or a few pathogens at a time. Microarrays offer the advantages of being capable to test a large number of samples simultaneously, screening for multiple pathogen types per sample and having comparable sensitivity to existing methods such as PCR. Array design is often considered the most important process in any microarray experiment and can be the deciding factor in the success of a study. There are currently no microarrays for simultaneous detection of rodent-borne pathogens. The aim of this report is to explicate the design, development and evaluation of a microarray platform for use as a screening tool that combines ease of use and rapid identification of a number of rodent-borne pathogens of zoonotic importance. Nucleic acid was amplified by multiplex biotinylation PCR prior to hybridisation onto microarrays. The array sensitivity was comparable to standard PCR, though less sensitive than real-time PCR. The array presented here is a prototype microarray identification system for zoonotic pathogens that can infect rodent species