Piximi - An Images to Discovery web tool for bioimages and beyond.

Deep learning has greatly accelerated research in biological image analysis yet it often requires programming skills and specialized tool installation. Here we present Piximi, a modern, no-programming image analysis tool leveraging deep learning. Implemented as a web application at Piximi.app, Piximi requires no installation and can be accessed by any modern web browser. Its client-only architecture preserves the security of researcher data by running all computation locally. Piximi offers four core modules: a deep learning classifier, an image annotator, measurement modules, and pre-trained deep learning segmentation modules. Piximi is interoperable with existing tools and workflows by supporting import and export of common data and model formats. The intuitive researcher interface and easy access to Piximi allows biological researchers to obtain insights into images within just a few minutes. Piximi aims to bring deep learning-powered image analysis to a broader community by eliminating barriers to entry.

Multi-round recycling of green waste for the production of iron nanoparticles: synthesis, characterization, and prospects in remediation.

Due to the widespread applications of metal nanoparticles (NPs), green synthesis strategies have recently advanced, e.g., methods that utilize extracts made from different plant wastes. A particularly innovative approach to reducing large amounts of available household/agricultural green wastes is their application in nanoparticle generation. Regarding this, the aim of our work was to examine the possibility of upgrading green nanoparticle syntheses from an innovative economic and environmental point of view, namely by investigating the multiple recyclabilities of green tea (GT), coffee arabica (CA), and Virginia creeper (Parthenocissus quinquefolia) (VC) waste residues for iron nanoparticle (FeNPs) synthesis. The plant extracts obtained by each extraction round were analyzed individually to determine the amount of main components anticipated to be involved in NPs synthesis. The synthesized FeNPs were characterized by X-ray powder diffraction and transmission electron microscopy. The activity of the generated FeNPs in degrading chlorinated volatile organic compounds (VOC) and thus their future applicability for remediation purposes were also assessed. We have found that VC and especially GT residues could be reutilized in multiple extraction rounds; however, only the first extract of CA was suitable for FeNPs' generation. All of the obtained FeNPs could degrade VOC with efficiencies GT1-Fe 91.0%, GT2-Fe 83.2%, GT3-Fe 68.5%; CA1-Fe 76.2%; VC1-Fe 88.2%, VC2-Fe 79.7%, respectively, where the number (as in GT3) marked the extraction round. These results indicate that the adequately selected green waste material can be reutilized in multiple rounds for nanoparticle synthesis, thus offering a clean, sustainable, straightforward alternative to chemical methods.

Gene loss and compensatory evolution promotes the emergence of morphological novelties in budding yeast.

Deleterious mutations are generally considered to be irrelevant for morphological evolution. However, they could be compensated by conditionally beneficial mutations, thereby providing access to new adaptive paths. Here we use high-dimensional phenotyping of laboratory-evolved budding yeast lineages to demonstrate that new cellular morphologies emerge exceptionally rapidly as a by-product of gene loss and subsequent compensatory evolution. Unexpectedly, the capacities for invasive growth, multicellular aggregation and biofilm formation also spontaneously evolve in response to gene loss. These multicellular phenotypes can be achieved by diverse mutational routes and without reactivating the canonical regulatory pathways. These ecologically and clinically relevant traits originate as pleiotropic side effects of compensatory evolution and have no obvious utility in the laboratory environment. The extent of morphological diversity in the evolved lineages is comparable to that of natural yeast isolates with diverse genetic backgrounds and lifestyles. Finally, we show that both the initial gene loss and subsequent compensatory mutations contribute to new morphologies, with their synergistic effects underlying specific morphological changes. We conclude that compensatory evolution is a previously unrecognized source of morphological diversity and phenotypic novelties.

Genome-wide RNAi screen identifies novel players in human 60S subunit biogenesis including key enzymes of polyamine metabolism.

Ribosome assembly is an essential process that is linked to human congenital diseases and tumorigenesis. While great progress has been made in deciphering mechanisms governing ribosome biogenesis in eukaryotes, an inventory of factors that support ribosome synthesis in human cells is still missing, in particular regarding the maturation of the large 60S subunit. Here, we performed a genome-wide RNAi screen using an imaging-based, single cell assay to unravel the cellular machinery promoting 60S subunit assembly in human cells. Our screen identified a group of 310 high confidence factors. These highlight the conservation of the process across eukaryotes and reveal the intricate connectivity of 60S subunit maturation with other key cellular processes, including splicing, translation, protein degradation, chromatin organization and transcription. Intriguingly, we also identified a cluster of hits comprising metabolic enzymes of the polyamine synthesis pathway. We demonstrate that polyamines, which have long been used as buffer additives to support ribosome assembly in vitro, are required for 60S maturation in living cells. Perturbation of polyamine metabolism results in early defects in 60S but not 40S subunit maturation. Collectively, our data reveal a novel function for polyamines in living cells and provide a rich source for future studies on ribosome synthesis.

Case Report: Medullary Thyroid Cancer Workup Initiated by Unexpectedly High Procalcitonin Level-Endocrine Training Saves Life in the COVID-19 Unit.

Background: Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is a novel coronavirus that has caused a worldwide pandemic. The majority of medullary thyroid cancers present as a thyroid nodule. At the time of diagnosis, cervical lymph nodes and distant metastases are frequently detected. Case Report: Here, we present a case of a 46-year-old man with coronavirus disease (COVID) pneumonia, who had persistently high serum procalcitonin levels despite normal C-reactive protein levels. The attending infectologist happened to be a colleague who spent some time, as part of her internal medicine rotation, in the Endocrine Ward and recalled that medullary thyroid cancer might be the cause. This led to the timely workup and treatment of the medullary cancer.

Regression plane concept for analysing continuous cellular processes with machine learning.

Biological processes are inherently continuous, and the chance of phenotypic discovery is significantly restricted by discretising them. Using multi-parametric active regression we introduce the Regression Plane (RP), a user-friendly discovery tool enabling class-free phenotypic supervised machine learning, to describe and explore biological data in a continuous manner. First, we compare traditional classification with regression in a simulated experimental setup. Second, we use our framework to identify genes involved in regulating triglyceride levels in human cells. Subsequently, we analyse a time-lapse dataset on mitosis to demonstrate that the proposed methodology is capable of modelling complex processes at infinite resolution. Finally, we show that hemocyte differentiation in Drosophila melanogaster has continuous characteristics.

Green Silver and Gold Nanoparticles: Biological Synthesis Approaches and Potentials for Biomedical Applications.

The nanomaterial industry generates gigantic quantities of metal-based nanomaterials for various technological and biomedical applications; however, concomitantly, it places a massive burden on the environment by utilizing toxic chemicals for the production process and leaving hazardous waste materials behind. Moreover, the employed, often unpleasant chemicals can affect the biocompatibility of the generated particles and severely restrict their application possibilities. On these grounds, green synthetic approaches have emerged, offering eco-friendly, sustainable, nature-derived alternative production methods, thus attenuating the ecological footprint of the nanomaterial industry. In the last decade, a plethora of biological materials has been tested to probe their suitability for nanomaterial synthesis. Although most of these approaches were successful, a large body of evidence indicates that the green material or entity used for the production would substantially define the physical and chemical properties and as a consequence, the biological activities of the obtained nanomaterials. The present review provides a comprehensive collection of the most recent green methodologies, surveys the major nanoparticle characterization techniques and screens the effects triggered by the obtained nanomaterials in various living systems to give an impression on the biomedical potential of green synthesized silver and gold nanoparticles.

High Genetic Diversity and No Evidence of Clonal Relation in Synchronous Thyroid Carcinomas Associated with Hashimoto's Thyroiditis: A Next-Generation Sequencing Analysis.

The close association between pre-existing Hashimoto's thyroiditis and thyroid cancer is well established. The simultaneous occurrence of multiple neoplastic foci within the same organ suggests a common genotoxic effect potentially contributing to carcinogenesis, the nature of which is still not clear. Next-generation sequencing (NGS) provides a potent tool to demonstrate and compare the mutational profile of the independent neoplastic foci. Our collection of 47 cases with thyroid carcinoma and Hashimoto's thyroiditis included 14 with at least two tumorous foci. Detailed histological analysis highlighted differences in histomorphology, immunoprofile, and biological characteristics. Further, a 67-gene NGS panel was applied to demonstrate the mutational diversity of the synchronic tumors. Significant differences could be detected with a wide spectrum of pathogenic gene variants involved (ranging between 5 and 18, cutoff >5.0 variant allele frequencies (VAF)). Identical gene variants represented in both synchronous tumors of the same thyroid gland were found in only two cases (BRAF and JAK3 genes). An additional set of major driver mutations was identified at variable allele frequencies in a highly individual setup suggesting a clear clonal independence. The different BRAF statuses in coincident thyroid carcinoma foci within the same organ outline a special challenge for molecular follow-up and therapeutic decision-making.

nucleAIzer: A Parameter-free Deep Learning Framework for Nucleus Segmentation Using Image Style Transfer.

Single-cell segmentation is typically a crucial task of image-based cellular analysis. We present nucleAIzer, a deep-learning approach aiming toward a truly general method for localizing 2D cell nuclei across a diverse range of assays and light microscopy modalities. We outperform the 739 methods submitted to the 2018 Data Science Bowl on images representing a variety of realistic conditions, some of which were not represented in the training data. The key to our approach is that during training nucleAIzer automatically adapts its nucleus-style model to unseen and unlabeled data using image style transfer to automatically generate augmented training samples. This allows the model to recognize nuclei in new and different experiments efficiently without requiring expert annotations, making deep learning for nucleus segmentation fairly simple and labor free for most biological light microscopy experiments. It can also be used online, integrated into CellProfiler and freely downloaded at www.nucleaizer.org. A record of this paper's transparent peer review process is included in the Supplemental Information.

Evaluation of Deep Learning Strategies for Nucleus Segmentation in Fluorescence Images.

Identifying nuclei is often a critical first step in analyzing microscopy images of cells and classical image processing algorithms are most commonly used for this task. Recent developments in deep learning can yield superior accuracy, but typical evaluation metrics for nucleus segmentation do not satisfactorily capture error modes that are relevant in cellular images. We present an evaluation framework to measure accuracy, types of errors, and computational efficiency; and use it to compare deep learning strategies and classical approaches. We publicly release a set of 23,165 manually annotated nuclei and source code to reproduce experiments and run the proposed evaluation methodology. Our evaluation framework shows that deep learning improves accuracy and can reduce the number of biologically relevant errors by half. © 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

Thyroid Carcinoma Coexisting with Hashimoto's Thyreoiditis: Clinicopathological and Molecular Characteristics Clue up Pathogenesis.

Thyroid cancer (TC) coexisting with Hashimoto's thyroiditis (HT) presents with several characteristic features including multifocality and lower clinical stages compared to de novo carcinomas but its exact biology is still not understood. We reexamined clinico-pathological and molecular correlations between Hashimoto's thyroditis and papillary thyroid cancer. A total of 262 patients with TC was evaluated who underwent thyroidectomy at the Surgical Department of the University of Debrecen. Clinical data, histology and molecular data were evaluated. Our cohort included 43 patients (16.4%) with (5 male, 38 female) and 219 (83.6%) patients without coexisting HT (48 male, 171 female). Hashimoto's thyroiditis related thyroid cancer presented predominantly (93.0% of the cases) with the papillary histological type. Multifocality was observed more frequently with coexisting HT (16/40; 40.0%) compared to cases uninvolved (45/190; 23.7%)(p = 0.034). In contrast, lymphatic metastasis (pN1) with a significantly reduced frequency in patients with HT (4/11; 36.4%) then without HT (34/41 pN1; 82.9%)(p = 0.002). BRAF V600E mutation could be demonstrated at significantly lower rates in cases of PTC + HT (32.1 vs 60.7%, p < 0.005). High incidence, multifocality and papillary morphology strongly support a causal relation between TC and preexisting Hashimoto's thyroiditis, the latter to be considered as a preneoplastic condition promoting thyroid carcinogenesis.

A deep convolutional neural network approach for astrocyte detection.

Astrocytes are involved in various brain pathologies including trauma, stroke, neurodegenerative disorders such as Alzheimer's and Parkinson's diseases, or chronic pain. Determining cell density in a complex tissue environment in microscopy images and elucidating the temporal characteristics of morphological and biochemical changes is essential to understand the role of astrocytes in physiological and pathological conditions. Nowadays, manual stereological cell counting or semi-automatic segmentation techniques are widely used for the quantitative analysis of microscopy images. Detecting astrocytes automatically is a highly challenging computational task, for which we currently lack efficient image analysis tools. We have developed a fast and fully automated software that assesses the number of astrocytes using Deep Convolutional Neural Networks (DCNN). The method highly outperforms state-of-the-art image analysis and machine learning methods and provides precision comparable to those of human experts. Additionally, the runtime of cell detection is significantly less than that of other three computational methods analysed, and it is faster than human observers by orders of magnitude. We applied our DCNN-based method to examine the number of astrocytes in different brain regions of rats with opioid-induced hyperalgesia/tolerance (OIH/OIT), as morphine tolerance is believed to activate glia. We have demonstrated a strong positive correlation between manual and DCNN-based quantification of astrocytes in rat brain.

Environmental properties of cells improve machine learning-based phenotype recognition accuracy.

To answer major questions of cell biology, it is often essential to understand the complex phenotypic composition of cellular systems precisely. Modern automated microscopes produce vast amounts of images routinely, making manual analysis nearly impossible. Due to their efficiency, machine learning-based analysis software have become essential tools to perform single-cell-level phenotypic analysis of large imaging datasets. However, an important limitation of such methods is that they do not use the information gained from the cellular micro- and macroenvironment: the algorithmic decision is based solely on the local properties of the cell of interest. Here, we present how various features from the surrounding environment contribute to identifying a cell and how such additional information can improve single-cell-level phenotypic image analysis. The proposed methodology was tested for different sizes of Euclidean and nearest neighbour-based cellular environments both on tissue sections and cell cultures. Our experimental data verify that the surrounding area of a cell largely determines its entity. This effect was found to be especially strong for established tissues, while it was somewhat weaker in the case of cell cultures. Our analysis shows that combining local cellular features with the properties of the cell's neighbourhood significantly improves the accuracy of machine learning-based phenotyping.

Intelligent image-based in situ single-cell isolation.

Quantifying heterogeneities within cell populations is important for many fields including cancer research and neurobiology; however, techniques to isolate individual cells are limited. Here, we describe a high-throughput, non-disruptive, and cost-effective isolation method that is capable of capturing individually targeted cells using widely available techniques. Using high-resolution microscopy, laser microcapture microscopy, image analysis, and machine learning, our technology enables scalable molecular genetic analysis of single cells, targetable by morphology or location within the sample.

Exophthalmos in a young woman with no graves' disease - a case report of IgG4-related orbitopathy.

BACKGROUND: Immunoglobulin G4-related disease (IgG4-rd) is characterized by lymphoplasmacytic infiltration and tissue fibrosis. Orbital manifestations of IgG4-rd may include unilateral or bilateral proptosis, cicatricial extraocular muscle myopathy, orbital inflammation and pain which may mimic ophthalmic Graves' disease. CASE PRESENTATION: A 25-year-old woman has been referred to the endocrinology clinic, 4 months after delivery, with suspected Graves' orbitopathy. She has had bronchial asthma and recurrent skin rashes of unknown aetiology for the last 10 years and was treated for dacryoadenitis with steroid containing eye drops 5 years ago. During pregnancy she developed eyelid swelling. After delivery, eyelid redness and retrobulbar pain evolved. Proptosis was demonstrated by Hertel's exophthalmometry. Orbital magnetic resonance imaging showed enlarged lateral and superior rectus muscles in both orbits. Thyroid function tests were in the normal range and no thyroid stimulating hormone (TSH) receptor autoantibodies were present. The eye muscle involvement pattern raised suspicion, and the high IgG4 level with positive histology of the lacrimal gland confirmed the diagnosis of immunoglobulin G4-related orbitopathy. Rapid improvement was observed following oral methylprednisolone. CONCLUSIONS: IgG4-related orbitopathy may mimic Graves' orbitopathy. Euthyroid patients with no TSH receptor autoantibodies should be evaluated for immunoglobulin G4-related orbitopathy. Once IgG4-related orbitopathy is proven, other manifestations of IgG4-related disease have to be searched for; lifelong follow-up is warranted.

Hsp70-associated chaperones have a critical role in buffering protein production costs.

Proteins are necessary for cellular growth. Concurrently, however, protein production has high energetic demands associated with transcription and translation. Here, we propose that activity of molecular chaperones shape protein burden, that is the fitness costs associated with expression of unneeded proteins. To test this hypothesis, we performed a genome-wide genetic interaction screen in baker's yeast. Impairment of transcription, translation, and protein folding rendered cells hypersensitive to protein burden. Specifically, deletion of specific regulators of the Hsp70-associated chaperone network increased protein burden. In agreement with expectation, temperature stress, increased mistranslation and a chemical misfolding agent all substantially enhanced protein burden. Finally, unneeded protein perturbed interactions between key components of the Hsp70-Hsp90 network involved in folding of native proteins. We conclude that specific chaperones contribute to protein burden. Our work indicates that by minimizing the damaging impact of gratuitous protein overproduction, chaperones enable tolerance to massive changes in genomic expression.

Data-analysis strategies for image-based cell profiling.

Image-based cell profiling is a high-throughput strategy for the quantification of phenotypic differences among a variety of cell populations. It paves the way to studying biological systems on a large scale by using chemical and genetic perturbations. The general workflow for this technology involves image acquisition with high-throughput microscopy systems and subsequent image processing and analysis. Here, we introduce the steps required to create high-quality image-based (i.e., morphological) profiles from a collection of microscopy images. We recommend techniques that have proven useful in each stage of the data analysis process, on the basis of the experience of 20 laboratories worldwide that are refining their image-based cell-profiling methodologies in pursuit of biological discovery. The recommended techniques cover alternatives that may suit various biological goals, experimental designs, and laboratories' preferences.

Advanced Cell Classifier: User-Friendly Machine-Learning-Based Software for Discovering Phenotypes in High-Content Imaging Data.

High-content, imaging-based screens now routinely generate data on a scale that precludes manual verification and interrogation. Software applying machine learning has become an essential tool to automate analysis, but these methods require annotated examples to learn from. Efficiently exploring large datasets to find relevant examples remains a challenging bottleneck. Here, we present Advanced Cell Classifier (ACC), a graphical software package for phenotypic analysis that addresses these difficulties. ACC applies machine-learning and image-analysis methods to high-content data generated by large-scale, cell-based experiments. It features methods to mine microscopic image data, discover new phenotypes, and improve recognition performance. We demonstrate that these features substantially expedite the training process, successfully uncover rare phenotypes, and improve the accuracy of the analysis. ACC is extensively documented, designed to be user-friendly for researchers without machine-learning expertise, and distributed as a free open-source tool at www.cellclassifier.org.

[Clinico-pathological features of papillary thyroid cancer coexistent with Hashimoto's thyroiditis]. / A Hashimoto-thyreoiditisben kialakuló papillaris pajzsmirigy-carcinoma klinikopatológiai jellegzetességei.

INTRODUCTION AND AIM: Former studies suggest the frequent coexistence of Hashimoto's thyreoditis with papillary thyroid cancer, frequently featured by multifocal carcinogenesis but lower clinical stages compared to thyroid cancers lacking thyroiditis. We examined the clinico-pathological correlations between Hashimoto's thyroditis and papillary thyroid cancer in our region in the North-Eastern part of Hungary. PATIENTS AND METHOD: We included a total of 230 patients with papillary thyroid cancer who underwent thyroid surgery at the Surgical Department of the University of Debrecen. Patients' sex, age, multifocality of thyroid cancer and clinical stage were evaluated. RESULTS: Cases included 40 patients (17.4%) with (4 male, 36 female) and 190 (82.6%) patients without HT (44 male, 146 female). Hashimoto's thyroiditis related thyroid cancer was almost exclusively associated with the papillary histological type. Multifocality of papillary cancer was significantly more frequent with coexisting Hashimoto's thyroiditis (16/40; 40.0%) compared to cases uninvolved (45/190; 23.7%; p = 0.034). In contrast, lymph node metastasis was significantly less frequent among patients with Hashimoto's thyroiditis (4 pN1 [36.4%]; 7 pN0 [63.6%]) then without it (34 pN1 [82.9%]; 7 pN0 [17.1%]; p = 0.002). CONCLUSION: Higher frequency and multifocality of papillary thyroid cancer might be the consequence of preexisting Hashimoto's thyroiditis to be considered as a preneoplastic stimulus supporting carcinogenesis, though the exact pathomechanism of this correlation is not clear yet. Orv. Hetil., 2017, 158(5), 178-182.