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.

The raw materials of “licorice root” in the commerce consist of roots and/or rhizomes (stolons) of different species of Glycyrrhiza. Licorice products and raw materials are frequently mislabeled and often have mixed, misidentified, or unidentified species and parts. This paper provides a detailed comparative analysis of the morpho-anatomies of the rhizomes and roots of five species of Glycyrrhiza, namely G. glabra, G. uralensis, G. echinata, G. inflata, and G. lepidota, by bright-field light microscopy and scanning electron microscopy. The studied species showed some similarities in their basic anatomical features due to the fact that they are phylogenetically closely related and belong to the same genus. However, differences in microscopic features such as the thickness of cork and medullary rays, pore frequency, and size of the vessels were observed. The rhizomes can readily be distinguished by the presence of a distinct pith. The roots lack a well-defined pith and instead have primary xylem in the center.

Many universities, commercial companies, and science organizations who use microscopy have outreach programs. The members of such generally love to share their enthusiasm and passion with the next generation. Content and confidence on providing such a program often comes from sharing ideas with like-minded scientists. These programs are typically delegated with the acronym STEM or STEAM: science, technology, engineering, art, and math. One of MSA's outreach programs is ProjectMICRO. Included are some STEAM ideas to share.

This study aimed to reveal the anatomical features of the bovine eye by scanning electron and light microscopic methods. For this purpose, a total of 40 eyes were evaluated. Gross and microscopic characteristics of the cornea, sclera, ciliary body, choroid, iris, and lens were determined. Bowman's and Descemet's membranes of the cornea were quite dense and prominent. Collagen lamellae of the cornea were wavy in the periphery and more parallel to the basal and metachromatic fibroblasts were noted. Three to four ciliary plicae merged to form ciliary processes. The presence of prominent intermediate bands connecting the ciliary plicae was determined. The zonular fibrils merged and attached to the lens in the form of thick zonular bands. A dense corpora nigra was present at the rectangular pupillary border of the iris. Tapetum fibrosum, consisting of polygonal tapetal cells, was in blue-yellow-green color and covered most of the choroid. A complex drainage system consisting of trabecular meshwork, angular aqueous plexus, ciliary sinus, and scleral venous vessels localized in a fairly wide iridocorneal angle was identified. Identifying structural features of the bovine eye is very important and useful for pathological evaluations, understanding species-specific physiological mechanisms and for operative interventions of ruminant species.

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.

Advancements in high content image analysis have led to an increase in the adoption of these techniques in basic science and clinical research. High-throughput approaches to imaging and image analysis require minimal user interventions, circumventing the often prohibitively time-consuming and unreliable standard manual analysis. In this study, we demonstrate how high content imaging (HCI) techniques, in combination with high content analysis (HCA), can be paired with more traditional manual analysis to quantify both micro- and macro-level features of biopsied tissue sections. High-resolution, full-color images of stained tissue were acquired and stitched together to reconstruct the entire tissue section, which enabled analyses that required accurate identification of a given region's location within the tissue section. A custom region of interest grid was generated that followed the curvature of the tissue. The composite images were used in two separate analyses: tissue layer thickness as a macro-level approach, and nuclei density as a micro-level approach. Ultimately, the flexibility of the HCI and HCA methodologies used in this study allowed for complex analysis of tissue that would not have been otherwise feasible.

The morphology and composition of the corrosion products of archaeological arsenical copper alloys buried in a specific environment for a long time were investigated using optical microscopy (OM), scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDS), micro X-ray fluorescence (μ-XRF), and X-ray diffraction (XRD). The analyses demonstrated that the alloy composition of the artifacts was copper-arsenic (Cu-AS) with significant amounts of lead in some samples. Cuprite, malachite, and copper (II) hydroxychlorides were observed on a completely mineralized matrix. The surface of the objects was covered by two main corrosion layers formed on the internal cuprous oxide. However, several successive layers of corrosion products were recognized on the artifacts in some cases. Furthermore, phosphatic corrosion products including, mimetite [Pb5(AsO4)3Cl], and pyromorphite [Pb5(PO4)3Cl] were identified. Conichalcite [CaCu(AsO4)](OH), which grew in the form of Liesegang rings, was also identified as a corrosion product of one of the objects. As and Pb exhibited some enrichment in the corrosion crusts of the artifacts. Environmental changes in burial conditions, particularly due to seasonal streams and consequent changes in soil corrosivity, vicinity of objects to bone material, along with the migration of alloy elements, especially lead and arsenic, can explain the morphological features of these archaeological objects.

Gray-level co-occurrence matrix (GLCM) analysis is a contemporary and innovative computational method for the assessment of textural patterns, applicable in almost any area of microscopy. The aim of our research was to perform the GLCM analysis of cell nuclei in Saccharomyces cerevisiae yeast cells after the induction of sublethal cell damage with ethyl alcohol, and to evaluate the performance of various machine learning (ML) models regarding their ability to separate damaged from intact cells. For each cell nucleus, five GLCM parameters were calculated: angular second moment, inverse difference moment, GLCM contrast, GLCM correlation, and textural variance. Based on the obtained GLCM data, we applied three ML approaches: neural network, random trees, and binomial logistic regression. Statistically significant differences in GLCM features were observed between treated and untreated cells. The multilayer perceptron neural network had the highest classification accuracy. The model also showed a relatively high level of sensitivity and specificity, as well as an excellent discriminatory power in the separation of treated from untreated cells. To the best of our knowledge, this is the first study to demonstrate that it is possible to create a relatively sensitive GLCM-based ML model for the detection of alcohol-induced damage in Saccharomyces cerevisiae cell nuclei.

In response to the requirements imposed by the COVID-19 pandemic in 2020, we developed a remote learning undergraduate workshop for 44 students at the University of Newcastle by embedding scanning electron microscope (SEM) images of Maratus (Peacock) spiders into the MyScope Explore environment. The workshop session had two main components: 1) to use the online MyScope Explore tool to virtually image scales with structural color and pigmented color on Maratus spiders; 2) to join a live SEM session via Zoom to image an actual Maratus spider. In previous years, the undergraduate university students attending this annual workshop would enter the Microscopy Facility at the University of Newcastle to image specimens with SEM; however, in 2020 the Microscopy Facility was closed to student visitors, and this virtual activity was developed in order to proceed with the educational event. The program was highly successful and constitutes a platform that can be used in the future by universities for teaching microscopy remotely.