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Improvements in digital microscopy are critical for the development of a malaria diagnosis method that is accurate at the cellular level and exhibits satisfactory clinical performance. Digital microscopy can be enhanced by improving deep learning algorithms and achieving consistent staining results. In this study, a novel miLab™ device incorporating the solid hydrogel staining method was proposed for consistent blood film preparation, eliminating the use of complex equipment and liquid reagent maintenance. The miLab™ ensures consistent, high-quality, and reproducible blood films across various hematocrits by leveraging deformable staining patches. Embedded-deep-learning-enabled miLab™ was utilized to detect and classify malarial parasites from autofocused images of stained blood cells using an internal optical system. The results of this method were consistent with manual microscopy images. This method not only minimizes human error but also facilitates remote assistance and review by experts through digital image transmission. This method can set a new paradigm for on-site malaria diagnosis. The miLab™ algorithm for malaria detection achieved a total accuracy of 98.86% for infected red blood cell (RBC) classification. Clinical validation performed in Malawi demonstrated an overall percent agreement of 92.21%. Based on these results, miLab™ can become a reliable and efficient tool for decentralized malaria diagnosis.

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Exploring the tumour microenvironment provides insight into the unique interaction between the host and tumour. Ultimately, its study improves understanding of how an individual mounts and achieves an anti-tumour immune response. In the context of colorectal cancer, immune biomarkers within the tumour microenvironment outperform traditional histopathological staging in predicting disease recurrence. Multiplex immunofluorescence enables simultaneous assessment of multiple markers to provide a highly accurate classification of immune cells and their spatial characterisation relative to tumour tissue. Further, automated slide staining provides staining consistency and reduces labour costs. Image acquisition using a non-spectral scanner allows more researchers to utilise multiplexed immunofluorescence for translational research. Herein we describe the optimisation process of conducting automated staining using a five-colour, tyramide signal amplification-based multiplex immunofluorescence panel. Using antibodies against CD3, CD8, CD103 and cytokeratin, the panel characterises T cell populations within human colorectal adenocarcinoma tissue. We provide an overview of primary antibody titration and the development of tyramide signal amplification immunofluorescence monoplex assays. We detail the processes of antibody stripping and the role of exogenous horseradish peroxidase inhibition to facilitate multiplexing. An account of determining the staining sequence and fluorophore assignment is provided. We describe image acquisition using a standard fluorescence microscope slide scanner and the management of spectral crosstalk using this system. Finally, we briefly document the digital image analysis required to characterise cells and determine their spatial distribution within the colorectal tumour microenvironment.

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As immuno-oncology (I/O) emerges as an effective approach in the fight against cancer, multispectral imaging of multiplex immunofluorescence (mIF) is maturing as an analytical platform. The timing is fortuitous. Due to health economic considerations surrounding the use of I/O, there is an urgent need for tests that accurately predict response to the growing list of available therapies. Multispectral mIF provides several advantages over other biomarker modalities by enabling deeper interrogation of the intricate biology within the tumor microenvironment, including detection of cell-to-cell spatial interactions that correlate with clinical outcomes. It also provides a practical path for generating reliable and reproducible results in a clinically suitable, high-throughput workflow. In this article, we (1) describe the principles behind multispectral mIF; (2) provide advice and recommendations on assay development and optimization and highlight characteristics of a well-performing assay; and (3) discuss the requirements for translating this approach into clinical practice.

RESUMEN

microRNAs are an important class of noncoding regulatory RNAs with functional roles in development, physiology, and disease. Visualization of microRNA expression at a single-cell level has contributed to a better understanding of their biological function in animal models and their etiological contribution to human diseases. In addition, several microRNAs have been highlighted as potential biomarkers carrying diagnostic and prognostic information. Co-detection of microRNA expression with that of cell-type-specific proteins can enhance the interpretative power of expression changes during development or altered expression in pathological conditions. Here, we describe an automated fluorescence-based five-color multiplex assay for co-detection of microRNA (e.g., miR-10b, miR-21, miR-205), noncoding RNA (e.g., snRNA U6, 18S rRNA), and protein expression (e.g., cytokeratin 19, vimentin, collagen I) in paraffin-embedded formalin-fixed tissue slides on a Leica Bond Rx staining station. While this protocol uses mainly mouse tissues to demonstrate multiplex detection, it can be generally applied to single-cell expression analysis of other animal models and clinical specimens.

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