Open-source Image Analysis Software Research Articles

A novel automated method for comprehensive renal cast quantification from rat kidney sections using QuPath.

The presence of tubular casts within the kidney serves as an important feature when assessing the degree of renal injury. Quantification of renal tubular casts has been historically difficult due to varying cast morphologies, protein composition, and stain uptake properties, even within the same kidney. Color thresholding remains one of the most common methods of quantification in the laboratory when assessing the percentage of renal casting; however, this method is unable to account for tubule casts stained a variety of colors. We have developed a novel method of automated cast quantification using the machine learning pixel classification tool within QuPath, an open-source software designed for digital pathology. We demonstrated the usability of this method in male and female Dahl salt-sensitive rats fed either low or high salt for 2 wk and male Sprague-Dawley rats treated with podotoxin puromycin aminonucleoside (PAN). Briefly, the pixel classifier was trained to identify kidney tissue, various cast color types, and slide backgrounds. Following the development of the pixel classifier, we applied it to the sample population and compared the results with those of other methods of cast quantification, including color thresholding and manual quantification. We found that the automated pixel classifier designed in QuPath was able to comprehensively quantify metachromatic tubular casts compared with color thresholding. This novel method of cast quantification provides researchers with the ability to reliably automate cast quantification that is both comprehensive and efficient.NEW & NOTEWORTHY We developed a method of automated renal tubule cast quantification using a machine learning-based pixel classifier within QuPath, an open-source image analysis software. The advantages of this approach are demonstrated by rigorous comparison of quantification methods on a set of Masson's trichrome-stained kidney sections from high- and low-salt fed salt-sensitive Dahl rats. Researchers are provided with step-by-step instructions for creating and training a pixel classifier in QuPath for application to image analysis.

POLCAM: instant molecular orientation microscopy for the life sciences

Current methods for single-molecule orientation localization microscopy (SMOLM) require optical setups and algorithms that can be prohibitively slow and complex, limiting widespread adoption for biological applications. We present POLCAM, a simplified SMOLM method based on polarized detection using a polarization camera, which can be easily implemented on any wide-field fluorescence microscope. To make polarization cameras compatible with single-molecule detection, we developed theory to minimize field-of-view errors, used simulations to optimize experimental design and developed a fast algorithm based on Stokes parameter estimation that can operate over 1,000-fold faster than the state of the art, enabling near-instant determination of molecular anisotropy. To aid in the adoption of POLCAM, we developed open-source image analysis software and a website detailing hardware installation and software use. To illustrate the potential of POLCAM in the life sciences, we applied our method to study α-synuclein fibrils, the actin cytoskeleton of mammalian cells, fibroblast-like cells and the plasma membrane of live human T cells.

SynBot is an open-source image analysis software for automated quantification of synapses

The formation of precise numbers of neuronal connections, known as synapses, is crucial for brain function. Therefore, synaptogenesis mechanisms have been one of the main focuses of neuroscience. Immunohistochemistry is a common tool for visualizing synapses. Thus, quantifying the numbers of synapses from light microscopy images enables screening the impacts of experimental manipulations on synapse development. Despite its utility, this approach is paired with low-throughput analysis methods that are challenging to learn, and the results are variable between experimenters, especially when analyzing noisy images of brain tissue. We developed an open-source ImageJ-based software, SynBot, to address these technical bottlenecks by automating the analysis. SynBot incorporates the advanced algorithms ilastik and SynQuant for accurate thresholding for synaptic puncta identification, and the code can easily be modified by users. The use of this software will allow for rapid and reproducible screening of synaptic phenotypes in healthy and diseased nervous systems.

Quantification of Cytokine-Induced Cell Death in Human Colonic Organoids Using Live Fluorescence Microscopy.

Intestinal epithelial cell (IEC) death is increased in patients with inflammatory bowel diseases (IBD) such as ulcerative colitis (UC) and Crohn's disease (CD). This can contribute to defects in intestinal barrier function, exacerbation of inflammation, and disease immunopathogenesis. Cytokines and death receptor ligands are partially responsible for this increase in IEC death. IBD-relevant cytokines, such as TNF-α and IFN-γ, are cytotoxic to IECs both independently and in combination. This protocol describes a simple and practical assay to quantify cytokine-induced cytotoxicity in CD patient-derived colonic organoids using a fluorescent cell death dye (SYTOX Green Nucleic Acid Stain), live fluorescence microscopy, and open-source image analysis software. We also demonstrate how to use the Bliss independence mathematical model to calculate a coefficient of perturbagen interaction (CPI) based on organoid cytotoxicity. The CPI can be used to determine if interactions between cytokine combinations or other types of perturbagens are antagonistic, additive, or synergistic. This protocol can be implemented to investigate the cytotoxic activity of cytokines and other perturbagens using patient-derived colonic organoids.

3D-printed neck phantoms with detailed anatomy for ultrasound-guided procedure and device testing.

With rapid advances in ultrasound-guided procedures, there is an unmet need for echogenic phantoms with sufficient anatomical details for artificial intelligence and ultrasound-guided device testing. We developed a method for creating neck phantoms for novel otolaryngology-related device testing. To achieve accurate representation of the anatomy, we utilized CT scans and 3D printing technology to create customized agar molds, thus providing high-fidelity yet cost-effective tools. Based on previous studies, the key components in our neck phantom include the cervical vertebrae, trachea, common carotid arteries, internal jugular veins, thyroid gland, and surrounding soft tissue. Open-source image analysis software were employed to process CT data to generate high fidelity 3D models of the target structures. Resin molds were 3D printed and filled with various agar mixtures to mimic anatomical echogenicity. Following the method proposed, we successfully assembled the neck phantom which provided a detailed representation of the target structures. To evaluate the results, ultrasound data was collected on the phantom and living tissue and analyzed with ImageJ. We were able to demonstrate echogenicity comparable to that of living tissue. The proposed method for building neck phantoms with detailed anatomical features offers a valuable, detailed, low-cost tool for medical training and device testing in otolaryngology, particularly for novel devices that involve artificial intelligence (AI) guidance and robotic-based needle insertion. Additional anatomical refinements and validation studies could further enhance the consistency and accuracy, thus paving the way for future advancements in ultrasound training and research, and ultimately benefiting patient care and safety.

SynBot: An open-source image analysis software for automated quantification of synapses.

The formation of precise numbers of neuronal connections, known as synapses, is crucial for brain function. Therefore, synaptogenesis mechanisms have been one of the main focuses of neuroscience. Immunohistochemistry is a common tool for visualizing synapses. Thus, quantifying the numbers of synapses from light microscopy images enables screening the impacts of experimental manipulations on synapse development. Despite its utility, this approach is paired with low throughput analysis methods that are challenging to learn and results are variable between experimenters, especially when analyzing noisy images of brain tissue. We developed an open-source ImageJ-based software, SynBot, to address these technical bottlenecks by automating the analysis. SynBot incorporates the advanced algorithms ilastik and SynQuant for accurate thresholding for synaptic puncta identification, and the code can easily be modified by users. The use of this software will allow for rapid and reproducible screening of synaptic phenotypes in healthy and diseased nervous systems.

Noninvasive quantitative ultrasound fatty liver evaluation of hepato-renal index in pediatric patients using 3D-slicer

AimHepato-renal index (HRI) calculated by ultrasound images has been shown to be an effective, noninvasive tool to screen patients with steatosis. The aim of this study was to non-invasively explore a new method for the calculation, directly on DICOM images, of HRI in pediatric patients using 3D Slicer, a free and open-source software for medical image analysis, especially used for artificial intelligence data annotation. Previous studies in literature were based on non-medical image format (such as png and tiff) and analyzed by using ImageJ, a popular image analysis software.Materials and methodsDICOM images were collected retrospectively between November 2022 and December 2023 at a tertiary institution on any patient under 18 years referred to US assessment of suspected steatosis. The HRI was measured on sagittal images with a clear visualization of both the liver and the kidney by 3D Slicer. The HRI was calculated as the ratio of average pixel intensity values between the two ROIs. Also, we correlated the index with the qualitative operator assessment grade of steatosis made by two expert radiologists with more than 15 years of experience.Results49 patients (59% male, age 11.6 ± 2.6 year) were recruited. Of these, 29 (59%) had qualitative ultrasound confirmation of the diagnosis. The Bland–Altman plots showed a good agreement between the HRI indexes calculated with the standard tool ImageJ and 3D Slicer. Furthermore, the quantitative HRI calculated by the two software showed a correlation (Spearman’s coefficient = 0.52, P < 0.0001) with the qualitative operator measurement.ConclusionsFor the first time, an estimation of HRI directly on DICOM images was executed by using 3D Slicer, obtained quantitative information directly from clinically approved image format.

Quantification of isoniazid in tablets for tuberculosis treatment by thin layer chromatography with smartphone image capture and ImageJ analysis

Isoniazid (INH) oral dosage tablets are an antibacterial medication commonly used to treat tuberculosis (TB). While this medication can be highly effective in managing and curing TB, manufacturing of poor-quality INH products can lead to the proliferation of drug resistant TB strains – particularly in low- and middle-income countries (LMICs). It is therefore imperative that reliable methods of quality screening of INH tablets are available for areas that have limited access to traditional testing resources. In this study, a thin layer chromatography (TLC) procedure for INH identification is validated in conjunction with smartphone image capture and open-source image analysis software to provide an alternative quantitative quality screening method for INH tablets, primarily intended for applications in LMICs. The method assessed two individual tablet formulations: 100 mg and 300 mg INH, each from different manufacturers. The modified TLC procedure used for the study was based on a validated high-performance thin layer chromatography method for the determination of rifampicin, INH, and pyrazinamide in a fixed dosage tablet. Quantitation was performed by visualizing and photographing TLC plates using a UV lamp, 3D-printed light box, and Apple iPhone SE 2nd Generation (2020) and Google Pixel 4a (5G) smartphones, and images were then analyzed using ImageJ Fiji software. The processed images’ pixel data were used to create a calibration curve from the INH standard spots to determine the concentration of an INH sample spot. Linearity, range, accuracy, repeatability and intermediate precision, specificity, and robustness were evaluated. The method was linear in the range of 0.150–0.450 mg mL−1, with coefficients of determination ≥0.98. The overall accuracy of the method was 99.2 % with an RSD of 2.4 % for image sets captured using the Apple iPhone, and 99.7 % with an RSD of 3.3 % for image sets captured using the Google Pixel. The pooled standard deviations for repeatability and intermediate precision were 2.27 % and 3.25 %, respectively, for the iPhone and 2.62 % and 3.74 %, respectively, for the Pixel. The method demonstrated sufficient specificity and robustness. The results of this method validation suggest that this procedure may provide a feasible alternative to traditional testing as a low-cost means of quality screening in LMICs.

Abstract 5504: Integrating ultrahigh-plex spatial phenotyping: From discovery to clinical applications

Abstract Background The journey from discovering predictive spatial biomarkers (known as spatial signatures) for immunotherapy responses to their clinical application necessitates a cohesive approach bridging ultrahigh-plex discovery experiments with high-throughput translational studies. This study focuses on integrating Akoya Biosciences spatial multiplexed imaging technologies with advanced data analysis techniques for comprehensive spatial phenotyping across the discovery-to-clinical continuum. Methods Human formalin-fixed, paraffin-embedded (FFPE) cancer tissues were profiled using ultrahigh-plex PhenoCode™ Discovery panels to examine cell lineage, immune activation, and checkpoint markers via the PhenoCycler®-Fusion spatial biology platform. The subsequent use of PhenoCode™ Signature panels targeted specific biomarkers of immune profile, immune contexture, tumor-infiltrating lymphocytes (TIL), macrophage polarization, and T cell status via the PhenoImager® HT platform. Open-source whole slide image analysis software QuPath was employed for precise image analysis, including ROI segmentation, cell detection, classification, exploration of spatial interactions, and identification of distinct spatial signatures. Results Our analysis revealed distinct spatial relationships within various tumor types, quantifying immune cell distributions and their interactions. The ultrahigh-plex data correlated with high-throughput signature panel analyses, thus paving a new simplified way for targeted identification and development of predictive spatial signatures for immunotherapy outcomes. Conclusions The combined use of ultrahigh-plex discovery panels, high-throughput signature panels, and deep-learning image analysis provides a comprehensive understanding of cellular interactions in the tumor microenvironment. This integrated approach, utilizing Akoya's end-to-end workflows, accelerates the identification of predictive spatial biomarker signatures across various human tissue samples. Citation Format: Aditya Pratapa, Ning Ma, Niyati Jhaveri. Integrating ultrahigh-plex spatial phenotyping: From discovery to clinical applications [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5504.

Abstract 1525: Integration of high-plex tumor-Immune phenotyping and checkpoint interactions for deeper spatial characterization of human cancer tissues

Abstract Background: Immune evasion is a key cancer hallmark, and one of the primary mechanisms is the PD-1/PD-L1 checkpoint axis. While immunotherapies that target these interactions have reshaped cancer treatment, the variable efficacy in patients is not fully understood. Due to the weak correlation between PD-1/PD-L1 expression measured by single-plex assays and observed clinical outcomes, there is a need to understand protein interactions at the cellular and subcellular level. Proximity Ligation Assays, detecting spatial interactions between ligand-receptor targets, provide insights into the activation of signaling pathways. Coupled with high-plex spatial phenotyping with tumor and immune profiling biomarkers, the integration of cellular and functional information will be beneficial for a deeper characterization of the tumor microenvironment (TME), understanding immune responses, and identifying spatial signatures for patient stratification and targeted treatment strategies. Methods: In this study, we phenotyped biopsies from head and neck cancer patients enrolled in immune checkpoint inhibitor therapies. We integrated Naveni® PD-1/PD-L1 proximity ligation assays and a customizable high-plex PhenoCode™ Signature Panel (CD3ε/CD8/CD20/CD68/PanCK) for high-throughput profiling of the TME utilizing the PhenoImager® HT 2.0 platform. Comprehensive bioinformatic analyses were conducted for whole-slide segmentation, identifying cellular phenotypes, spatial neighborhoods, functional interactions, and distinct spatial signatures via the open-source image analysis software QuPath. Results: Our analyses revealed key differences in the localization of the PD-1/PD-L1 interactions within the TME of head and neck cancers. Areas of PD-1/PD-L1 interactions are associated with immune cell types on the periphery of the tumor and some within the tumor-infiltrating lymphocytes. The PhenoCode Signature panels helped resolve the localization of PD-1/PD-L1 interactions to immune cell types within the TME and provided a dual workflow for spatial co-localization of interacting receptor/ligand interactions and cell phenotypes within the TME. Conclusions: By combining spatial immune profiling with Akoya’s PhenoCode Signature panels and protein-protein interaction data from Navinci’s Naveni® PD-1/PD-L1 assay, deep correlative insights of the TME can be applied to improve predictions of clinical outcomes. This innovative approach enriches our comprehension of underlying mechanisms and stands as a promising tool for refining patient selection and optimizing treatment outcomes in immunotherapy. Citation Format: Sara Bodbin, Ning Ma, Aditya Pratapa, Nadya Nikulina, James Monkman, Niyati Jhaveri, Hampus Elofsson, Subham Basu, Agata Zieba-Wicher, Arutha Kulasinghe. Integration of high-plex tumor-Immune phenotyping and checkpoint interactions for deeper spatial characterization of human cancer tissues [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1525.

Using the traditional microscope for mineral grain orientation determination: A prototype image analysis pipeline for optic-axis mapping (POAM).

This paper reports on the development of an open-source image analysis software 'pipeline' dedicated to petrographic microscopy. Using conventional rock thin sections and images from a standard polarising microscope, the pipeline can classify minerals and subgrains into objects and obtain information about optic-axis orientation. Five metamorphic rocks were chosen to test and illustrate the method. Thin sections were imaged using reflected and cross- and plane-polarised transmitted light. Images were taken at different angles of the polariser and analyser (360° with 10° steps), both with and without the full-lambda plate. The resulting image stacks were analysed with a modular pipeline for optic-axis mapping (POAM). POAM consists of external and internal software packages that register, segment, classify, and interpret the visible light spectra using object-based image analysis (OBIAS). The mapped fields-of-view and grain orientation stereonets of interest are presented in the context of whole-slide images. Two innovations are reported. First, we used hierarchical tree region merging on blended multimodal images to classify individual grains of rock-forming minerals into objects. Second, we assembled a new optical mineralogy algorithm chain that identifies the mineral slow axis orientation. The c-axis orientation results were verified with scanning electron microscopy electron backscattered diffraction (SEM-EBSD) data. For quartz (uniaxial) in a granite mylonite the test yielded excellent correspondence of c-axis azimuth and good agreement for inclination. For orthorhombic orthopyroxene in a deformed garnet harzburgite, POAM produced acceptable results for slow axis azimuth. In addition, the method identified slight anisotropy in garnet that would not be appreciated by traditional microscopy. We propose that our method is ideally suited for two commonly performed tasks in mineralogy. First, for mineral grain classification of entire thin sections scans on blended images to provide automated modal abundance estimates and grain size distribution. Second, for prospective fields of view of interest, POAM can rapidly generate slow axis crystal orientation maps from multiangle image stacks on conventionally prepared thin sections for targeting detailed SEM-EBSD studies.

A case study of medical image software evolution and its impact in the medical imaging community

Objective: We present the evolution of medical imaging software and its impact on the medical imaging community through the study of four open-source image analysis software platforms: 3D Slicer, FreeSurfer, FSL, and SPM. Materials and methods: We have studied the impact of these software tools over time, measured by the number of scientific citations. Additionally, we have also studied the source code evolution by measuring the lines of code and the tarball size of the stable releases and the changes in programming languages. Results and discussion: The rising number of related scientific publications confirms the popularity of these software tools in the research community, albeit some differences can be observed in the popularity of the tools. Moreover, we demonstrate that source code has evolved to modernize and optimize, at least partially thanks to the collaboration and code sharing with the user community. Furthermore, this evolution reveals an increased use of higher-level programming languages and meta-languages. Conclusions: The study of four open-source packages has revealed certain patterns in the evolution of medical imaging software and their impact on the medical image community. Further analyses and complementary metrics are suggested.

Morphometric differences between basal cell carcinomas & melanomas of the head & neck versus other sites and their influence on neural networks

BackgroundDermoscopic appearance of basal cell carcinomas (BCC) and melanomas guide diagnosis and may be influenced by anatomic location. ObjectiveWe aimed to investigate the morphometric differences between anatomic sites of the head and neck(H&N) versus other body sites in basal cell carcinomas and melanomas and how these differences may impact neural network model accuracy. MethodsMorphometric image analysis of the BCCs(n=422) and melanomas(n=868) from the publicly available HAM10000 dataset was performed using an open-source image analysis software. Univariate and multivariate statistical analysis was done to identify differences between H&N and other anatomic sites. The multifactorial data was further interrogated with dimensionality reduction techniques. Randomly generated neural networks with different anatomic proportions of training data were used to assess how these features influence diagnostic accuracy. ResultsFifty-three univariate and 11 multivariate features were found to be statistically significant in the BCC group(P<0.05). Thirteen univariate and 8 multivariate features were statistically significant in the melanoma group(P<0.05). Dimensionality reduction via linear discriminant analysis of the BCC groups revealed modest separation of the data by anatomical site. Melanomas appeared to be more homogenous across anatomic sites. Morphometric features significantly influenced neural network accuracies in classifying BCCs, with a mix of H&N and other site training data resulting in the best models(P<0.05). ConclusionMorphometric features and the anatomic composition of BCC training data may influence the accuracy of AI models. Model developers should be aware of the anatomic distribution of their training datasets and report site-specific validation metrics.

Thin-layer chromatography quantification of ibuprofen using digital imaging

Ibuprofen is a pharmaceutical that is commonly used to reduce inflammation and to treat headaches. A thin-layer chromatographic method combined with simple and inexpensive digital imaging is proposed for the determination and quantification of ibuprofen. The recommendations of the standard pharmacopoeia were followed with the addition of a minimal spotting method as a local calibration curve (n = 3) and comparison with a spotted sample. Images were acquired using a smartphone and then converted to greyscale using Fiji, an open-source image analysis software. The peak area and density of each spot was then calculated using a densitometric principle. To analyse the present image data, several images were taken with different filters. Using a calibration curve ranging in the range of 3 to 5 mg/mL, linearity could be determined across the subjected images (R2 ranging from 0.984 to 0.996), with a mean accuracy of 99.9 ± 2.20% and RSD of 2.20%. The method studied, using smartphone imaging and open-source software (Fiji), offered a simple and inexpensive application for the quantification of ibuprofen in pharmaceuticals, applicable to preliminary studies and curricula.Graphical abstractMethod proposal for ibuprofen quantification using digital image processing software (Fiji)

In-situ evaluation of zirconium-bearing minerals for geochronology using micro X-ray fluorescence

In this contribution we present a method for pre-screening geological materials for zircon prior to submitting samples for heavy mineral separation. The proposed workflow utilizes micro X-ray fluorescence to identify zirconium-bearing pixels in slabbed rock samples. The open-source image analysis software ImageJ™ is applied to the micro X-ray fluorescence elemental map to determine the abundance and spatial distribution of zirconium-bearing pixels in the scanned surface area. This method allows for the prediction of zircon abundance and estimation of grain size within a sample which can be used to prioritize samples for geochronology as well as inform crushing and grinding metrics for heavy mineral separation. This information can ultimately lead to improved recovery of zircon and other mineral geochronometers for geochronological studies. Advantages of the proposed workflow include:•Minimal sample preparation and rapid results;•Analytical method is non-destructive; and•In-situ grain size estimation and abundance predictions prior to initiating time-consuming and costly heavy mineral separation methods.

RGBradford: Protein Quantitation with a Smartphone Camera.

Protein quantitation is an essential procedure in life sciences research. Amongst several other methods, the Bradford assay is one of the most used. Because of its widespread, the limitations and advantages of the Bradford assay have been exhaustively reported, including several modifications of the original method to improve its performance. One of the alterations of the original method is the use of a smartphone camera as an analytical instrument. Taking advantage of the three forms of the Coomassie Brilliant Blue dye that exist in the conditions of the Bradford assay, this paper describes how to accurately quantify protein in samples using color data extracted from a single picture of a microplate. After performing the assay in a microplate, a picture is taken using a smartphone camera, and RGB color data is extracted from the picture using a free and open-source image analysis software application. Then, the ratio of blue to green intensity (in the RGB scale) of samples with unknown concentrations of protein is used to calculate the protein content based on a standard curve. No significant difference is observed between values calculated using RGB color data and those calculated using conventional absorbance data.

Automated Cell Counting of Macrophages In Situ.

Using the open-source image analysis software CellProfiler to automatically quantify antibody-stained or fluorescently labeled macrophages in situ provides accurate and reproducible cell counts. It is a vastly enhanced alternative method to both manual cell counting and estimation of cell marker expression based on fluorescence intensity. Quantification of tissue-resident macrophages acquired on widefield or confocal microscopes can be batch processed using our pipeline to produce data within minutes.

Exploring the immune microenvironment in small bowel adenocarcinoma using digital image analysis.

Small bowel adenocarcinoma (SBA) is a rare malignancy of the small intestine associated with late stage diagnosis and poor survival outcome. High expression of immune cells and immune checkpoint biomarkers especially programmed cell death ligand-1 (PD-L1) have been shown to significantly impact disease progression. We have analysed the expression of a subset of immune cell and immune checkpoint biomarkers in a cohort of SBA patients and assessed their impact on progression-free survival (PFS) and overall survival (OS). 25 patient samples in the form of formalin fixed, paraffin embedded (FFPE) tissue were obtained in tissue microarray (TMAs) format. Automated immunohistochemistry (IHC) staining was performed using validated antibodies for CD3, CD4, CD8, CD68, PD-L1, ICOS, IDO1 and LAG3. Slides were scanned digitally and assessed in QuPath, an open source image analysis software, for biomarker density and percentage positivity. Survival analyses were carried out using the Kaplan Meier method. Varying expressions of biomarkers were recorded. High expressions of CD3, CD4 and IDO1 were significant for PFS (p = 0.043, 0.020 and 0.018 respectively). High expression of ICOS was significant for both PFS (p = 0.040) and OS (p = 0.041), while high PD-L1 expression in tumour cells was significant for OS (p = 0.033). High correlation was observed between PD-L1 and IDO1 expressions (Pearson correlation co-efficient = 1) and subsequently high IDO1 expression in tumour cells was found to be significant for PFS (p = 0.006) and OS (p = 0.034). High levels of immune cells and immune checkpoint proteins have a significant impact on patient survival in SBA. These data could provide an insight into the immunotherapeutic management of patients with SBA.

User-friendly analysis of droplet array images

Water-in-oil droplets allow performing massive experimental parallelization and high-throughput studies, such as single-cell experiments. However, analyzing such vast arrays of droplets usually requires advanced expertise and sophisticated workflow tools, which limits accessibility for a wider user base in the fields of chemistry and biology. Thus, there is a need for more user-friendly tools for droplet analysis. In this article, we deliver a set of analytical pipelines for user-friendly analysis of typical scenarios in droplet experiments. We built pipelines that combine various open-source image-analysis software with a custom-developed data processing tool called “EasyFlow”. Our pipelines are applicable to the typical experimental scenarios that users encounter when working with droplets: i) mono- and polydisperse droplets, ii) brightfield and fluorescent images, iii) droplet and object detection, iv) signal profile of droplets and objects (e.g., fluorescence).

Sustainable and smart HPTLC determination of silodosin and solifenacin using a constructed two illumination source chamber with a smartphone camera as a detector: Comparative study with conventional densitometric scanner

The tremendous revolution in the built-in digital camera of smartphones has opened new horizons for its applicability as a portable, easily accessible, and user-friendly detector in different analytical techniques. In this article, we proposed and developed a new and inexpensive detector for simultaneous analysis of silodosin and solifenacin. The innovation makes use of a smartphone camera coupled with high-performance thin layer chromatographic (HPTLC) approach. Firstly, chromatographic separation was attained using HPTLC plates with an ecofriendly mobile phase composed of ethyl acetate, ethanol & 25% w/w ammonia (3.0: 7.0: 0.3, by volume). The developed plates were then visualized using Dragendorff's reagent and then photographed via smartphone's rear-facing camera fixed on a fabricated two-illumination source chamber. The intensities of drug spots were quantified using an open-source image analysis software, ImageJ, over concentration ranges of 2.0–30.0 μg/band. Moreover, the study was extended to compare the obtained results with a benchtop densitometric method at 215.0 nm where the densitometric one gave linear response ranging from 0.1 to 7.0 μg/band for silodosin and 0.1–6.0 μg/band for solifenacin. The fast, simple, reliable and green merits of the proposed HPTLC/smartphone method promted it as an excellent platform for assaying marketed combined tablets and assuring their content uniformity. Moreover, the high sensitivity of the densitometric method was exploited, for the first time, for determining the residual content of the cited drugs on manufacturing equipment surfaces during cleaning validation. Finally, the environmental impact of the methods was appraised via three state-of-the-art greenness metrics.