Amidst the current health crisis and social distancing, telemedicine has become an important part of mainstream of healthcare, and building and deploying computational tools to support screening more efficiently is an increasing medical priority. The early identification of cervical cancer precursor lesions by Pap smear test can identify candidates for subsequent treatment. However, one of the main challenges is the accuracy of the conventional method, often subject to high rates of false negative. While machine learning has been highlighted to reduce the limitations of the test, the absence of high-quality curated datasets has prevented strategies development to improve cervical cancer screening. The Center for Recognition and Inspection of Cells (CRIC) platform enables the creation of CRIC Cervix collection, currently with 400 images (1,376 × 1,020 pixels) curated from conventional Pap smears, with manual classification of 11,534 cells. This collection has the potential to advance current efforts in training and testing machine learning algorithms for the automation of tasks as part of the cytopathological analysis in the routine work of laboratories.

Whipworms are large metazoan parasites that inhabit multi-intracellular epithelial tunnels in the large intestine of their hosts, causing chronic disease in humans and other mammals. How first-stage larvae invade host epithelia and establish infection remains unclear. Here we investigate early infection events using both Trichuris muris infections of mice and murine caecaloids, the first in-vitro system for whipworm infection and organoid model for live helminths. We show that larvae degrade mucus layers to access epithelial cells. In early syncytial tunnels, larvae are completely intracellular, woven through multiple live dividing cells. Using single-cell RNA sequencing of infected mouse caecum, we reveal that progression of infection results in cell damage and an expansion of enterocytes expressing of Isg15, potentially instigating the host immune response to the whipworm and tissue repair. Our results unravel intestinal epithelium invasion by whipworms and reveal specific host-parasite interactions that allow the whipworm to establish its multi-intracellular niche.

Specific chemotherapy using benznidazole (BNZ) for Chagas disease during the chronic stage is controversial due to its limited efficacy and toxic effects. Although BNZ has been used to treat Chagas disease since the 1970s, few studies about the biodistribution of this drug exist. In this study, BNZ tissue biodistribution in a murine model and its pharmacokinetic profile in plasma were monitored. A bioanalytical high-performance liquid chromatography method with a UV detector (HPLC-UV) was developed and validated according to the European Medicines Agency for quantification of BNZ in organs and plasma samples prepared by liquid-liquid extraction using ethyl acetate. The developed method was linear in the BNZ concentration, which ranged from 0.1 to 100.0 μg/ml for plasma, spleen, brain, colon, heart, lung, and kidney and from 0.2 to 100.0 μg/ml for liver. Validation assays demonstrated good stability for BNZ under all conditions evaluated. Pharmacokinetic parameters confirmed rapid, but low, absorption of BNZ after oral administration. Biodistribution assays demonstrated different maximum concentrations in organs and similar times to maximum concentration and mean residence times, with means of 40 min and 2.5 h, respectively. Therefore, the biodistribution of BNZ is extensive, reaching organs such as the heart and colon, which are the most relevant organs affected by Trypanosoma cruzi infection, and also the spleen, brain, liver, lungs, and kidneys. Simultaneous analyses of tissues and plasma indicated high BNZ metabolism in the liver. Our results suggest that low bioavailability, instead of inadequate biodistribution, could be responsible for therapeutic failure during the chronic phase of Chagas disease.

In recent years, deep learning methods have outperformed previous state-of-the-art machine learning techniques for several problems, including image classification. Classifying cells in Pap smear images is very challenging, and it is still of paramount importance for cytopathologists. The Pap test is a cervical cancer prevention test that tracks preneoplastic changes in cervical epithelial cells. Carrying out this exam is important in that early detection. It is directly related to a greater chance of curing or reducing the number of deaths caused by the disease. The analysis of Pap smears is exhaustive and repetitive, as it is performed manually by cytopathologists. Therefore, a tool that assists cytopathologists is needed. This work considers 10 deep convolutional neural networks and proposes an ensemble of the three best architectures to classify cervical cancer upon cell nuclei and reduce the professionals’ workload. The dataset used in the experiments is available in the Center for Recognition and Inspection of Cells (CRIC) Searchable Image Database. Considering the metrics of precision, recall, F1-score, accuracy, and sensitivity, the proposed ensemble improves previous methods shown in the literature for two- and three-class classification. We also introduce the six-class classification outcome.