The aim of this study was to describe how the detection of protozoan and helminth parasites has been affected by the introduction of polymerase chain reaction (PCR) and changes in test algorithms. We extracted data about faecal samples tested for parasites (n = 114839) at five Norwegian clinical microbiology laboratories. Samples were classified into prePCR or postPCR depending on whether they were submitted before or after the introduction of PCR, and into diagnostic episodes (n = 99320). The number of diagnostic episodes increased 3.7-fold from prePCR to postPCR. Giardia positive episodes doubled, the positivity rate decreased from 2.0% to 1.3%. Cryptosporidium was hardly detected prePCR and increased to a positivity rate of 1.2%. Entamoeba histolytica was rarely found. Episodes examined for helminths decreased 51%, the number of positive episodes decreased 34%. Samples from immigrants were more likely to be positive for Giardia, E. histolytica, or helminths and less likely to be Cryptosporidium positive. During the COVID-19 pandemic, the number of Giardia and helminth-positive episodes decreased. Cryptosporidium-positive episodes remained unchanged. The implementation of multiplex PCR for protozoa led to a doubling of Giardia cases and a better test for Cryptosporidium. Fewer microscopy examinations raise concerns that helminth infections may be overlooked.

Peat is formed by the accumulation of organic material in water-saturated soils. Drainage of peatlands and peat extraction contribute to carbon emissions and biodiversity loss. Most peat extracted for commercial purposes is used for energy production or as a growing substrate. Many countries aim to reduce peat usage but this requires tools to detect its presence in substrates. We propose a decision support system based on deep learning to detect peat-specific testate amoeba in microscopy images. We identified six taxa that are peat-specific and frequent in European peatlands. The shells of two taxa (Archerella sp. and Amphitrema sp.) were well preserved in commercial substrate and can serve as indicators of peat presence. Images from surface and commercial samples were combined into a training set. A separate test set exclusively from commercial substrates was also defined. Both datasets were annotated and YOLOv8 models were trained to detect the shells. An ensemble of eight models was included in the decision support system. Test set performance (average precision) reached values above 0.8 for Archerella sp. and above 0.7 for Amphitrema sp. The system processes thousands of images within minutes and returns a concise list of crops of the most relevant shells. This allows a human operator to quickly make a final decision regarding peat presence. Our method enables the monitoring of peat presence in commercial substrates. It could be extended by including more species for applications in restoration ecology and paleoecology.

Schistosomiasis is a neglected tropical disease with significant health implications, particularly among children. A cross-sectional study was conducted among school-aged children (SAC) in Mwanga district, Tanzania, a region known to be co-endemic for S. haematobium and S. mansoni infection and where annual mass drug administration (MDA) has been conducted for 20 years. In total, 576 SAC from 5 schools provided a urine sample for the detection of Schistosoma circulating anodic antigen using the upconverting particle-based lateral flow (UCP-LF CAA) test. Additionally, the potential of the point-of-care circulating cathodic antigen (POC-CCA) and microhaematuria dipstick test as field-applicable diagnostic alternatives for schistosomiasis were assessed and the prevalence outcome compared to UCP-LF CAA. Risk factors associated with schistosomiasis was assessed based on UCP-LF CAA. The UCP-LF CAA test revealed an overall schistosomiasis prevalence of 20.3%, compared to 65.3% based on a combination of POC-CCA and microhaematuria dipstick. No agreement was observed between the combined POC tests and UCP-LF CAA. Factors associated with schistosomiasis included age (5–10 years), involvement in fishing, farming, swimming activities and attending 2 of the 5 primary schools. Our findings suggest a significant progress in infection control in Mwanga district due to annual MDA, although not enough to interrupt transmission. Accurate diagnostics play a crucial role in monitoring intervention measures to effectively combat schistosomiasis.

Supervised deep learning approaches can artificially increase the resolution of microscopy images by learning a mapping between two image resolutions or modalities. However, such methods often require a large set of hard-to-get low-res/high-res image pairs and produce synthetic images with a moderate increase in resolution. Conversely, recent methods based on generative adversarial network (GAN) latent search offered a drastic increase in resolution without the need of paired images. However, they offer limited reconstruction of the high-resolution (HR) image interpretable features. Here, we propose a robust super-resolution (SR) method based on regularized latent search (RLS) that offers an actionable balance between fidelity to the ground truth (GT) and realism of the recovered image given a distribution prior. The latter allows to split the analysis of a low-resolution (LR) image into a computational SR task performed by deep learning followed by a quantification task performed by a handcrafted algorithm based on interpretable biological features. This two-step process holds potential for various applications such as diagnostics on mobile devices, where the main aim is not to recover the HR details of a specific sample but rather to obtain HR images that preserve explainable and quantifiable differences between conditions.

Although viral protein structure and replication mechanisms have been explored extensively with X-ray crystallography, cryo-electron microscopy, and population imaging studies, these methods are often not able to distinguish dynamic conformational changes in real time. Single-molecule fluorescence resonance energy transfer (smFRET) offers unique insights into interactions and states that may be missed in ensemble studies, such as nucleic acid or protein structure, and conformational transitions during folding, receptor–ligand interactions, and fusion. We discuss the application of smFRET to the study of viral protein conformational dynamics, with a particular focus on viral glycoprotein dynamics, viral helicases, proteins involved in HIV reverse transcription, and the influenza RNA polymerase. smFRET experiments have played a crucial role in deciphering conformational changes in these processes, emphasising the importance of smFRET as a tool to help elucidate the life cycle of viral pathogens and identify key anti-viral targets.

Infection with the parasitic nematode Strongyloides stercoralis is characteristic for tropical and subtropical regions of the world, but autochthonous cases have been reported in European countries as well. Here we present the first nation-wide survey of S. stercoralis seroprevalence in Croatian individuals presenting with eosinophilia, and evaluate the fraction of positive microscopy rates in stool specimens of seropositive individuals. In our sample of 1407 patients tested between 2018 and 2021, the overall prevalence of strongyloidiasis was 9.31%, with significantly higher rates in those older than 60 years of age (P = 0.005). Of those, one-quarter (25.95%) were also positive following microscopy examination of faeces after using the merthiolate–iodine–formaldehyde concentration method. Our findings reinforce the notion of endemic strongyloidiasis transmission in Croatia, particularly in older individuals, and highlight the need to consider the presence of S. stercoralis in patients with eosinophilia.

Cryo-soft-X-ray tomography is being increasingly used in biological research to study the morphology of cellular compartments and how they change in response to different stimuli, such as viral infections. Segmentation of these compartments is limited by time-consuming manual tools or machine learning algorithms that require extensive time and effort to train. Here we describe Contour, a new, easy-to-use, highly automated segmentation tool that enables accelerated segmentation of tomograms to delineate distinct cellular compartments. Using Contour, cellular structures can be segmented based on their projection intensity and geometrical width by applying a threshold range to the image and excluding noise smaller in width than the cellular compartments of interest. This method is less laborious and less prone to errors from human judgement than current tools that require features to be manually traced, and it does not require training datasets as would machine-learning driven segmentation. We show that high-contrast compartments such as mitochondria, lipid droplets, and features at the cell surface can be easily segmented with this technique in the context of investigating herpes simplex virus 1 infection. Contour can extract geometric measurements from 3D segmented volumes, providing a new method to quantitate cryo-soft-X-ray tomography data. Contour can be freely downloaded at github.com/kamallouisnahas/Contour.

The mechanics underlying ice–skate friction remain uncertain despite over a century of study. In the 1930s, the theory of self-lubrication from frictional heat supplanted an earlier hypothesis that pressure melting governed skate friction. More recently, researchers have suggested that a layer of abraded wear particles or the presence of quasi-liquid molecular layers on the surface of ice could account for its slipperiness. Here, we assess the dominant hypotheses proposed to govern ice–skate friction and describe experiments conducted in an indoor skating rink aimed to provide observations to test these hypotheses. Our results indicate that the brittle failure of ice under rapid compression plays a strong role. Our observations did not confirm the presence of full-contact water films and are more consistent with the presence of lubricating ice-rich slurries at discontinuous high-pressure zones (HPZs). The presence of ice-rich slurries supporting skates through HPZs merges pressure-melting, abrasion and lubricating films as a unified hypothesis for why skates are so slippery across broad ranges of speeds, temperatures and normal loads. We suggest tribometer experiments to overcome the difficulties of investigating these processes during actual skating trials.

Rich in carbohydrates, underground storage organs played a key role in human history. This project aims to establish a systematic methodology for their multi-proxy study, creating an online reference collection, using different microscopy techniques for identification and establishing a reference guide to use-wear patterns on experimental archaeology tools used for the processing of underground storage organs.