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BACKGROUND: The Korean Society for Cytopathology introduced a digital proficiency test (PT) in 2021. However, many doubtful opinions remain on whether digitally scanned images can satisfactorily present subtle differences in the nuclear features and chromatin patterns of cytological samples. METHODS: We prepared 30 whole-slide images (WSIs) from the conventional PT archive by a selection process for digital PT. Digital and conventional PT were performed in parallel for volunteer institutes, and the results were compared using feedback. To assess the quality of cytological assessment WSIs, 12 slides were collected and scanned using five different scanners, with four cytopathologists evaluating image quality through a questionnaire. RESULTS: Among the 215 institutes, 108 and 107 participated in glass and digital PT, respectively. No significant difference was noted in category C (major discordance), although the number of discordant cases was slightly higher in the digital PT group. Leica, 3DHistech Pannoramic 250 Flash, and Hamamatsu NanoZoomer 360 systems showed comparable results in terms of image quality, feature presentation, and error rates for most cytological samples. Overall satisfaction was observed with the general convenience and image quality of digital PT. CONCLUSIONS: As three-dimensional clusters are common and nuclear/chromatin features are critical for cytological interpretation, careful selection of scanners and optimal conditions are mandatory for the successful establishment of digital quality assurance programs in cytology.

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Digital pathology (DP) has transformative potential, especially for Alzheimer disease and related disorders. However, infrastructure barriers may limit adoption. To provide benchmarks and insights into implementation barriers, a survey was conducted in 2019 within National Institutes of Health's Alzheimer's Disease Centers (ADCs). Questions covered infrastructure, funding sources, and data management related to digital pathology. Of the 35 ADCs to which the survey was sent, 33 responded. Most respondents (81%) stated that their ADC had digital slide scanner access, with the most frequent brand being Aperio/Leica (62.9%). Approximately a third of respondents stated there were fees to utilize the scanner. For DP and machine learning (ML) resources, 41% of respondents stated none was supported by their ADC. For scanner purchasing and operations, 50% of respondents stated they received institutional support. Some were unsure of the file size of scanned digital images (37%) and total amount of storage space files occupied (50%). Most (76%) were aware of other departments at their institution working with ML; a similar (76%) percentage were unaware of multiuniversity or industry partnerships. These results demonstrate many ADCs have access to a digital slide scanner; additional investigations are needed to further understand hurdles to implement DP and ML workflows.

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Computational machine learning (ML)-based frameworks could be advantageous for scalable analyses in neuropathology. A recent deep learning (DL) framework has shown promise in automating the processes of visualizing and quantifying different types of amyloid-ß deposits as well as segmenting white matter (WM) from gray matter (GM) on digitized immunohistochemically stained slides. However, this framework has only been trained and evaluated on amyloid-ß-stained slides with minimal changes in preanalytic variables. In this study, we evaluated select preanalytical variables including magnification, compression rate, and storage format using three digital slides scanners (Zeiss Axioscan Z1, Leica Aperio AT2, and Leica Aperio GT 450), on over 60 whole slide images, in a cohort of 14 cases having a spectrum of amyloid-ß deposits. We conducted statistical comparisons of preanalytic variables with repeated measures analysis of variance evaluating the outputs of two DL frameworks for segmentation and object classification tasks. For both WM/GM segmentation and amyloid-ß plaque classification tasks, there were statistical differences with respect to scanner types (p < 0.05) and magnifications (p < 0.05). Although small numbers of cases were analyzed, this pilot study highlights the significance of preanalytic variables that may alter the performance of ML algorithms.

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As a mean to cope with their potential cytotoxicity for the host plant, secondary metabolisms are often sequestered within specific cell types. This spatial organization may reach complex sequential multicellular compartmentation. The most complex example so far characterized is the sequential multicellular biosynthesis of the anticancer monoterpene indole alkaloids in Catharanthus roseus. RNA in situ hybridization has proven a key technological approach to unravel this complex spatial organization. Pioneer work in 1999 discovered the involvement of epidermis and laticifer/idioblasts in the intermediate and late steps of the pathway, respectively. The localization of the early steps of the pathway to the internal phloem-associated parenchyma later came to complete the three-tissular block organization of the pathway. Since then, RNA in situ hybridization was routinely used to map the gene expression profile of most of the nearly 30 genes involved in this pathway. We introduce here a comparison of advantages and drawbacks of in situ hybridization and more popular promoter: GUS strategies. Two main advantages of in situ hybridization are the suitability to any plant species and the direct localization of transcripts rather than the localization of a promoter activity. We provide a step-by-step protocol describing every details allowing to reach a medium throughput including riboprobe synthesis, paraffin-embedded plant tissue array preparation, prehybridization, in situ hybridization, stringent washing and immunodetection of hybridized probes, and imaging steps. This should be helpful for new comers willing to domesticate the technique. This protocol has no species limitation and is particularly adapted to the increasingly studied model, nonmodel species, nonamenable to promoter::GUS transformation, such as C. roseus.

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For many parasitic diseases, the microscopic examination of clinical samples such as urine and stool still serves as the diagnostic reference standard, primarily because microscopes are accessible and cost-effective. However, conventional microscopy is laborious, requires highly skilled personnel, and is highly subjective. Requirements for skilled operators, coupled with the cost and maintenance needs of the microscopes, which is hardly done in endemic countries, presents grossly limited access to the diagnosis of parasitic diseases in resource-limited settings. The urgent requirement for the management of tropical diseases such as schistosomiasis, which is now focused on elimination, has underscored the critical need for the creation of access to easy-to-use diagnosis for case detection, community mapping, and surveillance. In this paper, we present a low-cost automated digital microscope-the Schistoscope-which is capable of automatic focusing and scanning regions of interest in prepared microscope slides, and automatic detection of Schistosoma haematobium eggs in captured images. The device was developed using widely accessible distributed manufacturing methods and off-the-shelf components to enable local manufacturability and ease of maintenance. For proof of principle, we created a Schistosoma haematobium egg dataset of over 5000 images captured from spiked and clinical urine samples from field settings and demonstrated the automatic detection of Schistosoma haematobium eggs using a trained deep neural network model. The experiments and results presented in this paper collectively illustrate the robustness, stability, and optical performance of the device, making it suitable for use in the monitoring and evaluation of schistosomiasis control programs in endemic settings.

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The artificial neural network (ANN) is a computer software design or model that simulates the biological neural network of the human brain. Instead of biological neurons, ANN is composed of many layers of nodes that carry the signal and process it to make the final decision. ANN is a modern technology that is widely used in different fields of science. The ANN is reshaping the medical system and the various areas of pathology. In this paper, the basic concept and applications of ANN in cytology have been discussed. In this paper, the various articles published on ANN in the field of cytology have been systemically reviewed. The ANN is relatively less used in cytology. After introducing convolutional neural network and whole slide scanners in the commercial market, it is now essential to have thorough knowledge in this field to start diagnostic application of ANN.

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Digital pathology (DP) has disrupted the practice of traditional pathology, including applications in education, research, and clinical practice. Contemporary whole slide imaging (WSI) devices include technological advances that help address some of the challenges facing modern pathology, such as increasing workloads with fewer subspecialized pathologists, expanding integrated delivery networks with global reach, and greater customization when working up cases for precision medicine. This review focuses on integral hardware components of 43 market available and soon-to-be released digital WSI devices utilized throughout the world. Components such as objective lens type and magnification, scanning camera, illumination, and slide capacity were evaluated with respect to scan time, throughput, accuracy of scanning, and image quality. This analysis of assorted modern WSI devices offers essential, valuable information for successfully selecting and implementing a digital WSI solution for any given pathology practice.

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We have witnessed successive stages since the Seventies in the advancements towards digital pathology. We agree with Dr Pallua et al on the tremendous changes that are taking place in pathology, all leading toward greater role of digitalization in the field of pathology, both in terms of consultation and teaching. In particular, distance teaching using digital pathology will grow into a mainstream mode of pathology teaching, something that has been reinforced by COVID-19.

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Picrosirius red (PSR) staining is generally used to evaluate liver fibrosis; however, PSR sometimes causes nonspecific nuclear staining. In this study, we evaluated the ability of phosphomolybdic acid (PMA) pretreatment to prevent nonspecific nuclear staining by PSR. In a manual evaluation of 27 non-tumor samples from patients with hepatocellular carcinoma, nonspecific nuclear staining was observed in 3.7% of PMA-treated specimens, compared with 85.2% of untreated specimens. Conversely, computer-assisted image analysis (CAIA) identified nonspecific nuclear staining in 0% of PMA-treated samples, vs 44.4% of untreated samples. Surprisingly, after mounting, PMA-treated specimens exhibited a blue tinge because of molybdenum blue (MB) production following sunlight exposure or virtual slide scanning. Using UV cut film, MB production induced by sunlight exposure was prevented; however, the film did not prevent MB production during virtual slide scanning. Moreover, only blue light-emitting diode exposure resulted in a blue tinge in PMA solution. Our data indicated that PMA pretreatment is effective for evaluating liver fibrosis using CAIA. Meanwhile, improvements in virtual slide scanning protocols would directly improve the quality of PMA-pretreated specimens subjected to CAIA.

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BACKGROUND: The establishment of whole-slide imaging (WSI) as a medical diagnostic device allows that pathologists may evaluate mitotic activity with this new technology. Furthermore, the image digitalization provides an opportunity to develop algorithms for automatic quantifications, ideally leading to improved reproducibility as compared to the naked eye examination by pathologists. In order to implement them effectively, accuracy of mitotic figure detection using WSI should be investigated. In this study, we aimed to measure pathologist performance in detecting mitotic figures (MFs) using multiple platforms (multiple scanners) and compare the results with those obtained using a brightfield microscope. METHODS: Four slides of canine oral melanoma were prepared and digitized using 4 WSI scanners. In these slides, 40 regions of interest (ROIs) were demarcated, and five observers identified the MFs using different viewing modes: microscopy and WSI. We evaluated the inter- and intra-observer agreements between modes with Cohen's Kappa and determined "true" MFs with a consensus panel. We then assessed the accuracy (agreement with truth) using the average of sensitivity and specificity. RESULTS: In the 40 ROIs, 155 candidate MFs were detected by five pathologists; 74 of them were determined to be true MFs. Inter- and intra-observer agreement was mostly "substantial" or greater (Kappa = 0.594-0.939). Accuracy was between 0.632 and 0.843 across all readers and modes. After averaging over readers for each modality, we found that mitosis detection accuracy for 3 of the 4 WSI scanners was significantly less than that of the microscope (p = 0.002, 0.012, and 0.001). CONCLUSIONS: This study is the first to compare WSIs and microscopy in detecting MFs at the level of individual cells. Our results suggest that WSI can be used for mitotic cell detection and offers similar reproducibility to the microscope, with slightly less accuracy.

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(m)RNA spatiotemporal pattern of distribution is of key importance to decipher gene function. In this post-genomic era, numerous transcriptomic studies are made publicly available, sometimes reaching a tissular resolution and even more rarely the cellular level. This "one tissue-numerous genes" information can be completed by the reverse "one gene-numerous tissues" picture through traditional RNA in situ hybridization (ISH). Here, we present a method including (1) principles of transcriptomic data mining to be performed prior and following ISH and (2) a detailed step-by-step medium-throughput ISH protocol performed on serial sections from tissue microarrays. In a recent work, we implemented this method for 39 selected genes studied by medium-throughput ISH complementing an existing tissue-specific transcriptomic dataset focused on the model plant Arabidopsis seed development kinetics (Francoz et al., Scientific Reports 6:24644, 2016). This full integration of ISH and transcriptomics demonstrated the complementarity of both techniques in terms of tissue/cell specificity, signal sensitivity, gene specificity, and spatiotemporal resolution.

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Advancements in technology and digitization have ushered in novel ways of enhancing tissue-based research via digital microscopy and image analysis. Whole slide imaging scanners enable digitization of histology slides to be stored in virtual slide repositories and to be viewed via computers instead of microscopes. Easier and faster sharing of histologic images for teaching and consultation, improved storage and preservation of quality of stained slides, and annotation of features of interest in the digital slides are just a few of the advantages of this technology. Combined with the development of software for digital image analysis, digital slides further pave the way for the development of tools that extract quantitative data from tissue-based studies. This review introduces digital microscopy and pathology, and addresses technical and scientific considerations in slide scanning, quantitative image analysis, and slide repositories. It also highlights the current state of the technology and factors that need to be taken into account to insure optimal utility, including preanalytical considerations and the importance of involving a pathologist in all major steps along the digital microscopy and pathology workflow.

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Using light microscopy to describe the microarchitecture of normal and diseased tissues has changed very little since the middle of the 19th century. While the premise of histologic analysis remains intact, our relationship with the microscope is changing dramatically. Digital pathology offers new forms of visualization, and delivery of images is facilitated in unprecedented ways. This new technology can untether us entirely from our light microscopes, with many pathologists already performing their jobs using virtual microscopy. Several veterinary colleges have integrated virtual microscopy in their curriculum, and some diagnostic histopathology labs are switching to virtual microscopy as their main tool for the assessment of histologic specimens. Considering recent technical advancements of slide scanner and viewing software, digital pathology should now be considered a serious alternative to traditional light microscopy. This review therefore intends to give an overview of the current digital pathology technologies and their potential in all fields of veterinary pathology (ie, research, diagnostic service, and education). A future integration of digital pathology in the veterinary pathologist's workflow seems to be inevitable, and therefore it is proposed that trainees should be taught in digital pathology to keep up with the unavoidable digitization of the profession.

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Fluorescence in situ hybridization is an indispensable technique used in routine pathology and for theranostic purposes. Because fluorescence in situ hybridization techniques require sophisticated microscopic workstations and long procedures of image acquisition with sometimes subjective and poorly reproducible results, we decided to test a whole-slide imaging system as an alternative approach. In this study, we used the latest generation of Pannoramic 250 Flash digital microscopes (P250 Flash digital microscopes; 3DHISTECH, Budapest, Hungary) to digitize fluorescence in situ hybridization slides of diffuse large B cells lymphoma cases for detecting MYC rearrangement. The P250 Flash digital microscope was found to be precise with better definition of split signals in cells containing MYC rearrangement with fewer truncated signals as compared to traditional fluorescence microscopy. This digital technique is easier thanks to the preview function, which allows almost immediate identification of the tumor area, and the panning and zooming functionalities as well as a shorter acquisition time. Moreover, fluorescence in situ hybridization analyses using the digital technique appeared to be more reproducible between pathologists. Finally, the digital technique also allowed prolonged conservation of photos. In conclusion, whole-slide imaging technologies represent rapid, robust, and highly sensitive methods for interpreting fluorescence in situ hybridization slides with break-apart probes. In addition, these techniques offer an easier way to interpret the signals and allow definitive storage of the images for pathology expert networks or e-learning databases.

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