General Workflow Research Articles

Overview of commercial treatment planning systems for targeted radionuclide therapy.

Targeted Radionuclide Therapy (TRT) is a branch of cancer medicine dealing with the therapeutic use of radioisotopes associated with biological vectors accumulating in the tumors/targets, indicated as Molecular Radiotherapy (MRT), or directly injected into the arteries that supply blood to liver tumour vasculature, indicated as Selective RT (SRT). The aim of this work is to offer a panoramic view on the increasing number of commercially-available TRT treatment planning systems (TPSs). A questionnaire was sent to manufacturers' representatives. Academic software were not considered. Questions were grouped as follows: general information, clinical workflow, calibration procedure, image processing/reconstruction, image registration and segmentation tools, time-activity curve (TAC) fitting and absorbed dose calculation. All software reported have CE-marking. TPSs were divided between SRT-dedicated software [4] and MRT [5] dosimetry software. In SRT, since no kinetic process is involved, absorbed dose calculation does not require TAC fitting, and image registration is not fully developed in all TPS. All software requires a radionuclide-specific calibration. In SRT, a relative image calibration can be obtained by scaling the counts to a known activity. Automated VOI contouring and rigid/deformable propagation between different acquisitions time-points is implemented in most TPSs, although DICOM export is rare. Different TAC fits are available depending on the number of time-points. Voxel S-value and Local deposition methods are the most frequent dosimetric approaches; dose-voxel kernel convolution and semi-Monte Carlo method are also available. Available TPSs allows performing personalized dosimetry in clinical practice. Individual variations in methodology/algorithms must be considered in the standardisation/harmonization processes.

Reimbursement Approaches for Radiopharmaceutical Dosimetry: Current Status and Future Opportunities.

Interest in performing dosimetry for clinical radiopharmaceutical therapy procedures has grown in recent years. Several approved therapies include dosimetry in the Food and Drug Administration-approved label instructions, and other therapies are best used under a patient-tailored paradigm. This paper, which is a product of the Society of Nuclear Medicine and Molecular Imaging Dosimetry Task Force, presents motivations and general workflows for radiopharmaceutical therapy dosimetry, as well as existing strategies for obtaining reimbursement for clinical activities related to dosimetry. Several specific patient examples are provided, including suggested codes for reimbursement. In addition to current reimbursement approaches, key dosimetry services that are not supported under the current coding structure are presented and suggested as areas of focus in the coming years.

A Digital Environment for Empowering the Main Actors of the Deep Energy Retrofitting Value Chain

The H2020 HAPPEN project has defined a new approach to tackle significant unsolved bottlenecks affecting the energy renovation markets of the residential built stock in the Mediterranean area: the MedZEB approach (Mediterranean way to nearly Zero Energy Building), which backbone is the HAPPEN ICT Platform. The paper describes firstly the MedZEB concept as a holistic, flexible and cost-optimal approach especially tailored for the Med area. It then presents the HAPPEN Platform in terms of general architecture, workflows, resources and tools aimed at fostering the spread of the MedZEB approach by integrating technical, financial, environmental and operational aspects. A critical assessment on the state-of-the-art highlights the novelty of the HAPPEN Platform as a comprehensive and effective framework for empowering the evolution of the Med renovation markets, as means for fostering the progressive uptake of deep energy retrofitting in the Med area.

AREHS: effects of changing boundary conditions on the development of hydrogeological systems: numerical long-term modelling considering thermal–hydraulic–mechanical(–chemical) coupled effects

Abstract. Within the framework of the “Gesetz zur Suche und Auswahl eines Standortes für ein Endlager für hochradioaktive Abfälle” (Repository Site Selection Act – StandAG), the geoscientific and planning requirements and criteria for the site selection for a repository for high-active nuclear waste are specified. This includes, among others, the modelling of hydrogeological scenarios such as how future cold and warm periods and associated glaciation events can change the (petro-)physical properties specified in the StandAG as well as the natural hydrogeological properties of the overall system through, for example, reactivation of faults or changes in hydraulic gradients and consequently flow directions. The main objective of the AREHS (Effects of Changing Boundary Conditions on the Development of Hydrogeological Systems) project, funded by BASE (Federal Office for the Safety of Nuclear Waste Management; FKZ 4719F10402), is to model the effects of changing external boundary conditions on the hydrogeologically relevant parameters and effects (e.g. hydraulic permeability, porosity, migration pathways, fluid availability, hydraulic gradients) of a generic geological repository in Germany in all three potential host rocks (clay, salt and crystalline rocks) and its surrounding hydrogeological setting (Table 1). Special attention is paid to the cyclic mechanical loading and unloading due to glaciation events and the resulting stress changes (M), as well as induced temperature effects (T) due to permafrost and warm periods. As such processes can cause changes in the coupled far-field regime with groundwater flow and groundwater supply (H), as well as fluid transport due to thermal (T) and chemical (C) gradients, and reactivate faults/fractures (M) and thus create new/additional pathways, they are particularly relevant to the integrity of a repository over a period of 1 million years and must be properly captured with coupled THM(C) modelling. Before a model is set up for the different host rocks, a detailed assessment of relevant processes has been conducted based on NEA-2019 FEP catalogue (NEA, 2019) for high-level waste repositories. The modelling is performed using generic 3D models of typical host rock formations satisfying the StandAG criteria. Although the models for salt and clay rock have been adapted from generic models from recent research projects, for crystalline rock a new generic model had to be developed (Fig. 1) considering discontinuities of different scales that have to be incorporated into the THM(C) models explicitly as DFN (Discrete Fracture Network) networks. This is done by coupling two numerical codes: DFN-lab and 3DEC. A central phase in the overall modelling process is the benchmarking of the models with data from existing models and with field-scale studies. This is done separately for all three host rocks. In addition to extending the modelling capacities for glaciation processes and verifying by corresponding benchmarking tests (analytical solutions and literature comparisons), automated workflows have been developed to generate OpenGeoSys models from GOCAD structure models. Script-based automated workflows improve software quality for site investigation, especially in a sense of modularization as well as reproducibility. The generic workflow concept is currently being tested for the literature-based benchmarks and will, therefore, support a persistent and sustainable benchmarking procedure in the future.

A practical guide for synthetic fNIRS data generation.

The use of a large and diversified ground-truth synthetic fNIRS dataset enables researchers to objectively validate and compare data analysis procedures. In this work, we describe each step of the synthetic data generation workflow and we provide tools to generate the dataset.

Automatic Taxonomy Classification by Pretrained Language Model

In recent years, automatic ontology generation has received significant attention in information science as a means of systemizing vast amounts of online data. As our initial attempt of ontology generation with a neural network, we proposed a recurrent neural network-based method. However, updating the architecture is possible because of the development in natural language processing (NLP). By contrast, the transfer learning of language models trained by a large, unlabeled corpus has yielded a breakthrough in NLP. Inspired by these achievements, we propose a novel workflow for ontology generation comprising two-stage learning. Our results showed that our best method improved accuracy by over 12.5%. As an application example, we applied our model to the Stanford Question Answering Dataset to show ontology generation in a real field. The results showed that our model can generate a good ontology, with some exceptions in the real field, indicating future research directions to improve the quality.

Cancer Cell Profiling Using Image Moments and Neural Networks with Model Agnostic Explainability: A Case Study of Breast Cancer Histopathological (BreakHis) Database

With the evolution of modern digital pathology, examining cancer cell tissues has paved the way to quantify subtle symptoms, for example, by means of image staining procedures using Eosin and Hematoxylin. Cancer tissues in the case of breast and lung cancer are quite challenging to examine by manual expert analysis of patients suffering from cancer. Merely relying on the observable characteristics by histopathologists for cell profiling may under-constrain the scale and diagnostic quality due to tedious repetition with constant concentration. Thus, automatic analysis of cancer cells has been proposed with algorithmic and soft-computing techniques to leverage speed and reliability. The paper’s novelty lies in the utility of Zernike image moments to extract complex features from cancer cell images and using simple neural networks for classification, followed by explainability on the test results using the Local Interpretable Model-Agnostic Explanations (LIME) technique and Explainable Artificial Intelligence (XAI). The general workflow of the proposed high throughput strategy involves acquiring the BreakHis public dataset, which consists of microscopic images, followed by the application of image processing and machine learning techniques. The recommended technique has been mathematically substantiated and compared with the state-of-the-art to justify the empirical basis in the pursuit of our algorithmic discovery. The proposed system is able to classify malignant and benign cancer cell images of 40× resolution with 100% recognition rate. XAI interprets and reasons the test results obtained from the machine learning model, making it reliable and transparent for analysis and parameter tuning.

Improving Blood Flow Visualization of Recirculation Regions at Carotid Bulb in 4D Flow MRI Using Semi-Automatic Segmentation with ITK-SNAP.

Assessment of carotid bulb hemodynamics using four-dimensional (4D) flow magnetic resonance imaging (MRI) requires accurate segmentation of recirculation regions that is frequently hampered by limited resolution. This study aims to improve the accuracy of 4D flow MRI carotid bulb segmentation and subsequent recirculation regions analysis. Time-of-flight (TOF) MRI and 4D flow MRI were performed on bilateral carotid artery bifurcations in seven healthy volunteers. TOF-MRI data was segmented into 3D geometry for computational fluid dynamics (CFD) simulations. ITK-SNAP segmentation software was included in the workflow for the semi-automatic generation of 4D flow MRI angiographic data. This study compared the velocities calculated at the carotid bifurcations and the 3D blood flow visualization at the carotid bulbs obtained by 4D flow MRI and CFD. By applying ITK-SNAP segmentation software, an obvious improvement in the 4D flow MRI visualization of the recirculation regions was observed. The 4D flow MRI images of the recirculation flow characteristics of the carotid artery bulbs coincided with the CFD. A reasonable agreement was found in terms of velocity calculated at the carotid bifurcation between CFD and 4D flow MRI. However, the dispersion of velocity data points relative to the local errors of measurement in 4D flow MRI remains. Our proposed strategy showed the feasibility of improving recirculation regions segmentation and the potential for reliable blood flow visualization in 4D flow MRI. However, quantitative analysis of recirculation regions in 4D flow MRI with ITK-SNAP should be enhanced for use in clinical situations.

Machine Learning: Applications and Advanced Progresses of Radiomics in Endocrine Neoplasms.

Endocrine neoplasms remain a great threat to human health. It is extremely important to make a clear diagnosis and timely treatment of endocrine tumors. Machine learning includes radiomics, which has long been utilized in clinical cancer research. Radiomics refers to the extraction of valuable information by analyzing a large amount of standard data with high-throughput medical images mainly including computed tomography, positron emission tomography, magnetic resonance imaging, and ultrasound. With the quantitative imaging analysis and model building, radiomics can reflect specific underlying characteristics of a disease that otherwise could not be evaluated visually. More and more promising results of radiomics in oncological practice have been seen in recent years. Radiomics may have the potential to supplement traditional imaging analysis and assist in providing precision medicine for patients. Radiomics had developed rapidly in endocrine neoplasms practice in the past decade. In this review, we would introduce the general workflow of radiomics and summarize the applications and developments of radiomics in endocrine neoplasms in recent years. The limitations of current radiomic research studies and future development directions would also be discussed.

Structure-Based Virtual Screening for Ligands of G Protein-Coupled Receptors: What Can Molecular Docking Do for You?

G protein-coupled receptors (GPCRs) constitute the largest family of membrane proteins in the human genome and are important therapeutic targets. During the last decade, the number of atomic-resolution structures of GPCRs has increased rapidly, providing insights into drug binding at the molecular level. These breakthroughs have created excitement regarding the potential of using structural information in ligand design and initiated a new era of rational drug discovery for GPCRs. The molecular docking method is now widely applied to model the three-dimensional structures of GPCR-ligand complexes and screen for chemical probes in large compound libraries. In this review article, we first summarize the current structural coverage of the GPCR superfamily and the understanding of receptor-ligand interactions at atomic resolution. We then present the general workflow of structure-based virtual screening and strategies to discover GPCR ligands in chemical libraries. We assess the state of the art of this research field by summarizing prospective applications of virtual screening based on experimental structures. Strategies to identify compounds with specific efficacy and selectivity profiles are discussed, illustrating the opportunities and limitations of the molecular docking method. Our overview shows that structure-based virtual screening can discover novel leads and will be essential in pursuing the next generation of GPCR drugs. SIGNIFICANCE STATEMENT: Extraordinary advances in the structural biology of G protein-coupled receptors have revealed the molecular details of ligand recognition by this large family of therapeutic targets, providing novel avenues for rational drug design. Structure-based docking is an efficient computational approach to identify novel chemical probes from large compound libraries, which has the potential to accelerate the development of drug candidates.

Deadline-Constrained Cost-Effective Load-Balanced Improved Genetic Algorithm for Workflow Scheduling

Resource scheduling in a cloud computing environment is noteworthy for scientific workflow execution under a cost-effective deadline constraint. Although various researchers have proposed to resolve this critical issue by applying various meta-heuristic and heuristic approaches, no one is able to meet the strict deadline conditions with load-balanced among machines. This article has proposed an improved genetic algorithm that initializes the population with a greedy strategy. Greedy strategy assigns the task to a virtual machine that is under loaded instead of assigning the tasks randomly to a machine. In general workflow scheduling, task dependency is tested after each crossover and mutation operators of genetic algorithm, but here the authors perform after the mutation operation only which yield better results. The proposed model also considered booting time and performance variation of virtual machines. The authors compared the algorithm with previously developed heuristics and metaheuristics both and found it increases hit rate and load balance. It also reduces execution time and cost.

A Model‐Based Workflow to Benchmark the Clinical Cholestasis Risk of Drugs

We present a generic workflow combining physiology-based computational modeling and in vitro data to assess the clinical cholestatic risk of different drugs systematically. Changes in expression levels of genes involved in the enterohepatic circulation of bile acids were obtained from an in vitro assay mimicking 14days of repeated drug administration for 10 marketed drugs. These changes in gene expression over time were contextualized in a physiology-based bile acid model of glycochenodeoxycholic acid. The simulated drug-induced response in bile acid concentrations was then scaled with the applied drug doses to calculate the cholestatic potential for each compound. A ranking of the cholestatic potential correlated very well with the clinical cholestasis risk obtained from medical literature. The proposed workflow allows benchmarking the cholestatic risk of novel drug candidates. We expect the application of our workflow to significantly contribute to the stratification of the cholestatic potential of new drugs and to support animal-free testing in future drug development.

A systematic bioinformatics approach for large-scale identification and characterization of host-pathogen shared sequences

BackgroundBiology has entered the era of big data with the advent of high-throughput omics technologies. Biological databases provide public access to petabytes of data and information facilitating knowledge discovery. Over the years, sequence data of pathogens has seen a large increase in the number of records, given the relatively small genome size and their important role as infectious and symbiotic agents. Humans are host to numerous pathogenic diseases, such as that by viruses, many of which are responsible for high mortality and morbidity. The interaction between pathogens and humans over the evolutionary history has resulted in sharing of sequences, with important biological and evolutionary implications.ResultsThis study describes a large-scale, systematic bioinformatics approach for identification and characterization of shared sequences between the host and pathogen. An application of the approach is demonstrated through identification and characterization of the Flaviviridae-human share-ome. A total of 2430 nonamers represented the Flaviviridae-human share-ome with 100% identity. Although the share-ome represented a small fraction of the repertoire of Flaviviridae (~ 0.12%) and human (~ 0.013%) non-redundant nonamers, the 2430 shared nonamers mapped to 16,946 Flaviviridae and 7506 human non-redundant protein sequences. The shared nonamer sequences mapped to 125 species of Flaviviridae, including several with unclassified genus. The majority (~ 68%) of the shared sequences mapped to Hepacivirus C species; West Nile, dengue and Zika viruses of the Flavivirus genus accounted for ~ 11%, ~ 7%, and ~ 3%, respectively, of the Flaviviridae protein sequences (16,946) mapped by the share-ome. Further characterization of the share-ome provided important structural-functional insights to Flaviviridae-human interactions.ConclusionMapping of the host-pathogen share-ome has important implications for the design of vaccines and drugs, diagnostics, disease surveillance and the discovery of unknown, potential host-pathogen interactions. The generic workflow presented herein is potentially applicable to a variety of pathogens, such as of viral, bacterial or parasitic origin.

Development of deep learning models for microglia analyses in brain tissue using DeePathology™ STUDIO

BackgroundInterest in artificial intelligence-driven analysis of medical images has seen a steep increase in recent years. Thus, our paper aims to promote and facilitate the use of this state-of-the-art technology to fellow researchers and clinicians. New methodWe present custom deep learning models generated in DeePathology™ STUDIO without the need for background knowledge in deep learning and computer science underlined by practical suggestions. ResultsWe describe the general workflow in this commercially available software and present three real-world examples how to detect microglia on IBA1-stained mouse brain sections including their differences, validation results and analysis of a sample slide. Comparison with existing methodsDeep-learning assisted analysis of histological images is faster than classical analysis methods, and offers a wide variety of detection possibilities that are not available using methods based on staining intensity. ConclusionsReduced researcher bias, increased speed and extended possibilities make deep-learning assisted analysis of histological images superior to traditional analysis methods for histological images.

A Workflow for Synthetic Data Generation and Predictive Maintenance for Vibration Data

Digital twins, virtual representations of real-life physical objects or processes, are becoming widely used in many different industrial sectors. One of the main uses of digital twins is predictive maintenance, and these technologies are being adapted to various new applications and datatypes in many industrial processes. The aim of this study was to propose a methodology to generate synthetic vibration data using a digital twin model and a predictive maintenance workflow, consisting of preprocessing, feature engineering, and classification model training, to classify faulty and healthy vibration data for state estimation. To assess the success of the proposed workflow, the mentioned steps were applied to a publicly available vibration dataset and the synthetic data from the digital twin, using five different state-of-the-art classification algorithms. For several of the classification algorithms, the accuracy result for the classification of healthy and faulty data achieved on the public dataset reached approximately 86%, and on the synthetic data, approximately 98%. These results showed the great potential for the proposed methodology, and future work in the area.

Structure-specific, accurate quantitation of plasmalogen glycerophosphoethanolamine

Changes in plasmalogen glycerophosphoethanolamine (PE-P) composition (structure and abundance) are a key indicator of altered lipid metabolism. Differential changes in the levels of PE-P have been reported in different disease states, including neurodegenerative diseases. Of particular interest, traumatic brain injury (TBI) has resulted in altered expression of glycerophospholipid profiles, including PE-P. To date, most analytical assays assessing PE-P have focused on general lipidomic workflows to evaluate the relative, semi-quantitative abundance of PE-P during disease progression. This approach provides a broad evaluation of PE-P, yet often lacks specificity and sensitivity for individual PE-P structures which is a necessity for robust quantitative data. The present study highlights the development of a targeted, quantitative method using a HILIC separation and selective reaction monitoring mass spectrometry for the confident identification and accurate quantitation of PE-P. Our innovative method incorporates both the sn-1 alkyl vinyl ether and sn-2 acyl chain as product ion transitions, for specific and sensitive quantitation of 100 PE-P structures. Our method also uniquely allowed for the unambiguous assignment and quantitation of di-unsaturated sn-1 PE-P structures, which to date have not been conclusively quantified. Application of this assay to a TBI mouse model resulted in distinct temporal profiles for plasma PE-P up to 28 days post injury. Plasma PE-P were significantly increased 24 h after induced TBI, followed by a gradual reduction to sham concentrations by day 28. Overall, we established a structure-specific, quantitative assay for identification and quantitation of a comprehensive set of PE-P structures with demonstrated relevance to brain injury.

User behavior pattern mining and reuse across similar Android apps

Nowadays, Android apps have penetrated all aspects of our lives. Despite their popularity, understanding their behaviors is still a challenging task. Considering that many Android apps are in the same category and share similar workflows, in this paper, we propose a user behavior pattern mining and reuse approach across similar Android apps, thereby reducing the cost of understanding new apps. Particularly, for a specific new app, to figure out its typical behaviors, the behavior patterns that refer to the frequently-occurring workflows can be obtained from another similar app and transferred to this app. Moreover, to reuse the behavior patterns on this app, a semantic-based event fuzzy matching strategy and continuous workflow generation strategy are raised to generate workflows for this app. To evaluate our approach’s effectiveness and rationality, we conduct a series of experiments on 25 Android apps in five categories. Furthermore, the experimental results show that 88.3% of behavior patterns can be completely reused on similar apps, and the generated workflows cover 89.1% of the top 20% of important states.

Mapping Canopy Heights in Dense Tropical Forests Using Low-Cost UAV-Derived Photogrammetric Point Clouds and Machine Learning Approaches

Tropical forests are a key component of the global carbon cycle and climate change mitigation. Field- or LiDAR-based approaches enable reliable measurements of the structure and above-ground biomass (AGB) of tropical forests. Data derived from digital aerial photogrammetry (DAP) on the unmanned aerial vehicle (UAV) platform offer several advantages over field- and LiDAR-based approaches in terms of scale and efficiency, and DAP has been presented as a viable and economical alternative in boreal or deciduous forests. However, detecting with DAP the ground in dense tropical forests, which is required for the estimation of canopy height, is currently considered highly challenging. To address this issue, we present a generally applicable method that is based on machine learning methods to identify the forest floor in DAP-derived point clouds of dense tropical forests. We capitalize on the DAP-derived high-resolution vertical forest structure to inform ground detection. We conducted UAV-DAP surveys combined with field inventories in the tropical forest of the Congo Basin. Using airborne LiDAR (ALS) for ground truthing, we present a canopy height model (CHM) generation workflow that constitutes the detection, classification and interpolation of ground points using a combination of local minima filters, supervised machine learning algorithms and TIN densification for classifying ground points using spectral and geometrical features from the UAV-based 3D data. We demonstrate that our DAP-based method provides estimates of tree heights that are identical to LiDAR-based approaches (conservatively estimated NSE = 0.88, RMSE = 1.6 m). An external validation shows that our method is capable of providing accurate and precise estimates of tree heights and AGB in dense tropical forests (DAP vs. field inventories of old forest: r2 = 0.913, RMSE = 31.93 Mg ha−1). Overall, this study demonstrates that the application of cheap and easily deployable UAV-DAP platforms can be deployed without expert knowledge to generate biophysical information and advance the study and monitoring of dense tropical forests.

Immersed boundary method for high-order flux reconstruction based on volume penalization

In the last decade, there has been a lot of interest in developing high-order methods as a viable option for unsteady scale-resolving-simulations which are increasingly important in the industrial design process. High-order methods offer the advantages of low numerical dissipation, high efficiency on modern architectures and quasi mesh-independence. Despite significant advance in high-order solution methods, the general CFD workflow (geometry, CAD preparation, meshing, solution, post-processing) has largely remained unchanged, with mesh generation being a significant bottleneck and often determining the overall quality of the solution. In this work, we aim to combine the numerical advantages of the high-order Flux Reconstruction (FR) method and the simplicity of the mesh generation (or lack thereof) of the Immersed Boundary Method (IBM) for steady and unsteady problems over moving geometries. The volume penalization (penalty-IBM) method is selected for its ease of implementation and robustness. Detailed discussions about numerical implementation, including the boundary representation, mask function, data reconstruction, and selection of the penalization parameter are given. Advantages of combining volume penalization in the high-order framework are shown by various numerical test cases. The approach is firstly demonstrated for the linear advection-diffusion equation by investigating the numerical convergence for the coupled FR-IBM approach. Thereafter, the accuracy of the approach is demonstrated for canonical (static) test cases in 2D and 3D when compared to a standard body-fitted unstructured simulation. Finally, the efficiency of the method to handle moving geometries is demonstrated for the flow around an airfoil with pitching and plunging motions.

Fluorescent Probes and Mass Spectrometry-Based Methods to Quantify Thiols in Biological Systems.

Significance: Fluorescent probes and mass spectrometry are the two most popular and complementary methods to quantify thiols in biological systems. In this review, we focus on the widely used and commercially available methods to detect and quantify thiols in living cells and the general approaches applied in mass spectrometry-based thiol quantification. We hope that this review can serve as a general guide for redox biologists who are interested in thiol species. Sulfur, one of the most important elements in living systems, contributes to every aspect of physiology and pathology. Thiols, including cysteine, homocysteine, glutathione, hydrogen sulfide, and hydropersulfides, are the main players in the redox biology system. Therefore, quantifying these thiol species in biological systems is one of the important steps to understand their roles in biology. Recent Advances: Fluorescent probes and mass spectrometry-based methods have been developed to detect and/or quantify thiols in biological systems. Mass spectrometry-based methods have been the gold standard for metabolite quantification in cells. Fluorescent probes can directly detect or quantify thiol species in living cells with spatial and temporal resolutions. Additionally, organelle-specific fluorescent probes have been widely developed. These two methods are complementary to each other. Critical Issues: Reliable quantification of thiol species using fluorescent probes remains challenging. Future Directions: When developing fluorescent probes, we suggest using both the fluorescent probes and mass spectrometry-based thiol quantification methods to cross-check the results. In addition, we call on chemical biologists to move beyond qualitative probes and focus on probes that can provide quantitative results in live cells. These quantitative measurements based on fluorescent probes should be validated with mass spectrometry-based methods. More importantly, chemical biologists should make their probes accessible to the biology end users. Regarding mass spectrometry-based methods, quantification of the derivatized thiol specifies should fit into the general metabolomics workflow. Antioxid. Redox Signal. 36, 354-365.